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docs/05-dataviz2_files/libs/quarto-html/tippy.css create mode 100644 docs/05-dataviz2_files/libs/quarto-html/tippy.umd.min.js diff --git a/.quarto/_freeze/04-dataviz/execute-results/html.json b/.quarto/_freeze/04-dataviz/execute-results/html.json index e8dac19..e353daa 100644 --- a/.quarto/_freeze/04-dataviz/execute-results/html.json +++ b/.quarto/_freeze/04-dataviz/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "a0658998945d62ceacfc2d301080a2be", + "hash": "47f954a1418e3731174a328af3ddce00", "result": { - "markdown": "# Data viz I {#sec-dataviz}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter, you should be able to:\n\n- explain the layered grammar of graphics\n- choose an appropriate plot for categorical variables\n- create a basic version of an appropriate plot\n- apply additional layers to modify the appearance of the plot\n\n\nIt is time to think about selecting the most appropriate plot for your data. Different types of variables call for different kinds of plots, which depends on how many variables you’re aiming to plot and what their data types are. In this chapter, we will focus on **plots for categorical data**. Next week, we will explore plots for continuous variables and learn which plots work best when combining continuous and categorical data.\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n## Building plots\n\nWe are using the package `ggplot2` to create data visualisations. It's part of the tidyverse package. Actually, most people call th package `ggplot` but it's official name is `ggplot2`.\n\nWe’ll be using the `ggplot2` package to create data visualisations. It’s part of the `tidyverse` suite of packages. Although many people refer to it simply as `ggplot`, its official name is `ggplot2`.\n\n::: grid\n::: g-col-6\n**ggplot2** uses a layered grammar of graphics, where plots are constructed through a series of layers. You start with a base layer (by calling `ggplot`), then add **data** and **aesthetics**, followed by selecting the appropriate **geometries** for the plot.\n\nThese first 3 layers will give you the most simple version of a complete plot. However, you can enhance the plot’s clarity and appearance by adding additional layers such as **scales**, **facets**, **coordinates**, **labels** and **themes**.\n\n:::\n\n::: g-col-6\n![gg layers [(Presentation by Ryan Safner)](https://metricsf20.classes.ryansafner.com/slides/1.3-slides#20){target=\"_blank\"}](images/gglayers.png){width=\"90%\"}\n:::\n:::\n\nTo give you a brief overview of the layering system, we will use the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}). This dataset contains information about penguins, including bill length and depth, flipper length, body mass, and more.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(penguins)\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | bill_length_mm| bill_depth_mm| flipper_length_mm| body_mass_g|sex | year|\n|:-------|:---------|--------------:|-------------:|-----------------:|-----------:|:------|----:|\n|Adelie |Torgersen | 39.1| 18.7| 181| 3750|male | 2007|\n|Adelie |Torgersen | 39.5| 17.4| 186| 3800|female | 2007|\n|Adelie |Torgersen | 40.3| 18.0| 195| 3250|female | 2007|\n|Adelie |Torgersen | NA| NA| NA| NA|NA | 2007|\n|Adelie |Torgersen | 36.7| 19.3| 193| 3450|female | 2007|\n|Adelie |Torgersen | 39.3| 20.6| 190| 3650|male | 2007|\n\n
\n:::\n:::\n\n\nLet's build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will customise plots further later on in the individual plots. This is just a quick overview of the different layers.\n\nLet’s build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will further customise the plots in subsequent sections, but for now, this will provide a quick overview of the different layers.\n\n* **Layer 1** creates the base plot that we build upon.\n* **Layer 2** adds the `data` and some `aesthetics`:\n * The data is passed as the first argument.\n * Aesthetics are added via the mapping argument, where you define your variables (e.g., x or both x and y). This also allows you to specify general properties, like the color for grouping variables, etc.\n* **Layer 3** adds geometries, or `geom_?` for short. This tells ggplot how to display the data points. Remember to add these layers with a `+`, rather than using a pipe (`%>%`). You can also add multiple geoms if needed, for example, combining a violin plot with a boxplot.\n* **Layer 4** includes `scale_?` functions, which let you customise aesthetics like color. You can do much more with scales, but we'll explore later.\n* **Layer 5** introduces facets, such as `facet_wrap()`, allowing you to add an extra dimension to your plot by showing the relationship you are interested in for each level of a categorical variable.\n* **Layer 6** involves coordinates, where `coord_cartesian()` controls the limits for the x- and y-axes (xlim and ylim), enabling you to zoom in or out of the plot.\n* **Layer 7** helps you modify axis labels.\n* **Layer 8** controls the overall style of the plot, including background color, text size, and borders. R provides several predefined themes, such as `theme_classic`, `theme_bw`, `theme_minimal`, and `theme_light`.\n\nClick on the tabs below to see how each layer contributes to refining the plot.\n\n::: {.panel-tabset}\n## Layer 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-3-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThere’s not much to see at this stage - this is basically an empty plot layer.\n\n## Layer 2\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-4-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou won’t see any data points yet because we haven’t specified how to display them. However, we have mapped the aesthetics, indicating that we want to plot `body_mass` on the x-axis and `flipper_length` on the y-axis. This also sets the axis titles, as well as the axis values and breakpoints.\n\n::: callout-tip\nYou won't need to add `data =` or `mapping =` if you keep those arguments in exactly that order. Likewise, the first column name you enter within the `aes()` function will always be interpreted as x, and the second as y, so you could omit them if you wish.\n\nYou don’t need to include `data =` or `mapping =` if you keep those arguments in the default order. Similarly, the first column name you enter in the `aes()` function will automatically be interpreted as the x variable, and the second as y, so you can omit specifying `x` and `y` if you prefer.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(body_mass_g, flipper_length_mm))\n```\n:::\n\n\nwill give you the same output as the code above.\n:::\n\n## Layer 3\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-6-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we are telling `ggplot` to add a scatterplot. You may notice a warning indicating that some rows were removed due to missing values.\n\nThe `colour` argument adds colour to the points based on a grouping variable (in this case, `sex`). If you want all the points to be black — representing only two dimensions rather than three — simply omit the `colour` argument.\n\n## Layer 4\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point() +\n # changes colour palette\n scale_colour_brewer(palette = \"Dark2\") + \n # add breaks from 2500 to 6500 in increasing steps of 500\n scale_x_continuous(breaks = seq(from = 2500, to = 6500, by = 500)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `scale_?` functions allow us to modify the color palette of the plot, adjust axis breaks, and more. You could change the axis labels within `scale_x_continuous()` as well or leave it for Layer 7.\n\n## Layer 5\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n # split main plot up into different subplots by species \n facet_wrap(~ species) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nIn this step, we’re using faceting to split the plot by species.\n\n## Layer 6\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n # limits the range of the y axis\n coord_cartesian(ylim = c(0, 250)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we adjust the limits of the y-axis to zoom out of the plot. If you want to zoom in or out of the x-axis, you can add the `xlim` argument to the `coord_cartesian()` function.\n\n## Layer 7\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", # labels the x axis\n y = \"Flipper length (in mm)\", # labels the y axis\n colour = \"Sex\") # labels the grouping variable in the legend\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-10-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou can change the axis labels using the `labs()` function, or you can modify them when adjusting the scales (e.g., within the `scale_x_continuous()` function).\n\n## Layer 8\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", \n y = \"Flipper length (in mm)\",\n colour = \"Sex\") +\n # add a theme\n theme_classic()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `theme_classic()` function is applied to change the overall appearance of the plot.\n\n:::\n\n::: callout-important\n\nYou need to stick to the first three layers to create your base plot. Everything else is optional, meaning you don’t need to use all eight layers. Additionally, layers 4-8 can be added in any order (more or less), whereas layers 1-3 must follow a fixed sequence.\n\n:::\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data from Pownall et al. (2023), so **open your project**.\n* However, let’s start with a fresh R Markdown file: **Create a new `.Rmd` file** and save it in your project folder. Give it a meaningful name (e.g., \"chapter_04.Rmd\" or \"04_data_viz.Rmd\"). If you need guidance, refer to @sec-rmd. Delete everything below line 12, but keep the setup code chunk.\n* We previously aggregated the data in @sec-wrangling and @sec-wrangling2. If you want a fresh copy, download the data here: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\"). Make sure to place the csv file in the project folder.\n* If you need a reminder about the data and variables, check the codebook or refer back to @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we will be using the `tidyverse` package and the dataset `data_prp_for_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- read_csv(???)\n```\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nAs mentioned in @sec-familiarise, it is always a good idea to take a glimpse at the data to see how many variables and observations are in the dataset, as well as the data types.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## glimpse output\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(data_prp_viz)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 89\nColumns: 28\n$ Code \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age 22, 20, 22, 26, 22, 20, 21, 21, …\n$ Ethnicity \"White European\", \"White British…\n$ Secondyeargrade 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT \"Research methods in first year\"…\n$ Research_exp 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ Pre_reg_group 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ SATS28_Affect_Time1_mean 4.000000, 3.833333, 5.000000, 5.…\n$ SATS28_CognitiveCompetence_Time1_mean 5.166667, 5.166667, 5.666667, 4.…\n$ SATS28_Value_Time1_mean 6.000000, 6.666667, 5.222222, 5.…\n$ SATS28_Difficulty_Time1_mean 3.571429, 2.428571, 3.571429, 3.…\n$ QRPs_Acceptance_Time1_mean 5.909091, 6.090909, 6.545455, 5.…\n$ Time1_Understanding_OS 5.500000, 3.166667, 4.500000, 3.…\n$ Other_OS_behav_2 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS 5.583333, 3.333333, 5.416667, 4.…\n$ Mean_Supervisor_Support 5.230769, 6.285714, 6.857143, 2.…\n```\n:::\n:::\n\n\n:::\n\n\nWe can see that some of the categorical data in `data_prp_viz` was read in as numeric variables which makes them continuous. This will haunt us big time when building the plots. We would be better off addressing these changes in the dataset before we start plotting (and potentially getting frustrated with R and data viz in general).\n\nLet’s convert some of the categorical variables into factors. We’ll use the `factor()` function, which requires the `variable` to convert, the `levels` (where we can re-order them as needed), and the corresponding `labels`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n\n## Activity 3: Barchart (`geom_bar()`)\n\nA bar chart is the best choice when you want to plot a single categorical variable.\n\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a **barplot**. This is done with `geom_bar()` in the third layer. Since the counting is done automatically in the background, the `aes()` function only requires an x value (i.e., the name of your variable).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n```\n\n::: {.cell-output-display}\n![Default barchart](04-dataviz_files/figure-html/fig-bc-base-1.png){#fig-bc-base fig-align='center' width=100%}\n:::\n:::\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the **labels** could be clearer, or you might want to add a splash **colour**. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own `.Rmd` file.\n\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` argument. If you have specific colours in mind, you would use `scale_fill_manual()`, or if you prefer to stick with pre-defined options like viridis, you can use `scale_fill_viridis_d()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels & margins\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the `scale_x_discrete()` function and the `labels =` argument. Just make sure to order the labels according to the order in the dataframe.\n\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the `expansion()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_minimal()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n## Activity 4: Column plot (`geom_col()`)\n\nIf the counts had already been summarised for you, `geom_bar()` would not work. Instead, you’d need to use `geom_col()` to display the pre-aggregated data.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngender_count <- data_prp_viz %>% \n count(Gender)\n\ngender_count\n```\n\n::: {.cell-output-display}\n
\n\n|Gender | n|\n|:----------|--:|\n|females | 69|\n|males | 17|\n|non-binary | 3|\n\n
\n:::\n:::\n\n\nThe mapping for `geom_col()` requires both **x** and **y** aesthetics. In this example, **x** would represent the categorical variable (e.g., `Gender`), while **y** would refer to the column storing the summarised values (e.g., `n`). Notice how the axis title now reflects `n` instead of `count` in the base version.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col()\n```\n\n::: {.cell-output-display}\n![Column plot with different coloured bars](04-dataviz_files/figure-html/fig-col-1.png){#fig-col fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"}\n\n\n## Your Turn: Make the column plot pretty\n\nThe other layers to change the colour scheme, axes labels and margins, removing the legend and altering the theme require exactly the same functions as with the boxplot above. Test yourself to see if you can...\n\n* [ ] change the colour scheme (e.g., viridis or [any other colour palettes](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/){target=\"_blank\"})\n* [ ] remove the legend\n* [ ] change the titles of the x and y axes\n* [ ] make the bars start directly on the x-axis\n* [ ] add a theme of your liking\n\n\n\n::: {.callout-tip collapse=\"true\"}\n\n## Possible solution code for the column plot (with a different colour palette and a different theme)\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col() +\n # replaced vidiris with the brewer palette\n scale_fill_brewer(\n palette = \"Set1\", # try \"Set2\" or \"Dark2\" for some variety\n guide = \"none\") + # legend removed\n # labels of the categories changed\n scale_x_discrete(labels = c(\"Male\", \"Female\", \"Non-Binary\")) + \n scale_y_continuous(\n # change y axis label\n name = \"Count\",\n # starts bars on x axis without any gaps but leaves some space at the top (this time 10%)\n expand = expansion(mult = c(0, 0.1)) \n ) +\n # different theme\n theme_light()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n\n## Activity 5: Stacked, Percent Stacked, and Grouped Barchart\n\nWhen dealing with **two categorical variables**, you have three options for displaying stacked barcharts: the \"normal\" **Stacked Barchart** (the default option), a **Percent Stacked Barchart**, or a **Grouped Barchart**.\n\nFor this activity, we will explore the variable `Plan_prereg`, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and `Pre_reg_group`, which tracks whether they actually followed through with a pre-registration for their dissertation.\n\nOne way to display this data is by creating either a **Stacked Barchart** (the default) or a **Percent Stacked Barchart**. In both cases, the subgroups are displayed on top of each other. To make comparison easier, we will place the two plots side by side and move the legend to the bottom of the chart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar() + # no position argument added\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Percent stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Stacked barchart (left), and Percent stacked barchart (right)](04-dataviz_files/figure-html/fig-barcharts_stacked-1.png){#fig-barcharts_stacked fig-align='center' width=100%}\n:::\n:::\n\n\n\nIn the **stacked barchart** (@fig-barcharts_stacked, left plot), you can display participant numbers. From this, we can see that the highest number of students were unsure whether they wanted to pre-register their dissertation, followed closely by those who answered \"yes.\" We also see that the number of students who did not end up with a pre-registered dissertation (blue category) is the same for both those who had planned to pre-register and those who did not want to. However, since the \"No\" category has significantly fewer participants than the other two, it’s difficult to tell if the ratio remains consistent across all three groups.\n\nIf we want to highlight this ratio, a **Percent Stacked Barchart** (@fig-barcharts_stacked, right plot) would be more appropriate. This plot shows that approximately 80% of the students who had planned to pre-register their dissertations, 50% of the students who were initially unsure, and only 33% of the students who had no plan to pre-register ended up with a pre-registered dissertation. BUT! We would lose the information about the raw values in the sample.\n\n**It’s all a trade-off, and the plot you choose depends on the \"story\" you want the data to tell.**\n\n\n::: callout-note\n\nThe position argument `position = \"stack\"` is the default. Adding this argument to the code for the left plot in @fig-barcharts_stacked would produce the same plot as leaving the argument out.\n\n\n:::\n\n\nThe other option is a **Grouped Barchart**, which displays the bars next to each other. You can achieve this by changing the `position` argument to `\"dodge\"`. You can see the default version of the plot in @fig-barchart_grouped on the left, and one with additional layers on the right.\n\nInstead of using a pre-existing colour palette, we manually changed the colours using hex codes. These are some of the colours Gaby used in her PhD thesis, but you can:\n\n* create your own colour hex codes by using [this website](https://www.hexcolortool.com/){target=\"_blank\"}, OR\n* use pre-defined colour names like \"green\" or \"purple\" instead. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\nFeel free to explore.\n\nSince the legend title for the second plot is a bit long, we displayed the legend content across two rows by adding the layer `guides(fill = guide_legend(nrow = 2))` at the end.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Default grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Prettier grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n # changing labels for x, y, and fill category - alternative method\n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registered dissertation\") +\n # manual colour change for values\n scale_fill_manual(values = c('#648FFF', '#DC267F'),\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(\n # remove the space below the bars, but keep a tiny bit (5%) above\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_classic() + \n # need to move this following line to the end otherwise the `theme_*` overrides it\n theme(legend.position = \"bottom\") + \n # display across 2 rows\n guides(fill = guide_legend(nrow = 2))\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right)](04-dataviz_files/figure-html/fig-barchart_grouped-1.png){#fig-barchart_grouped fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Categorical variables with missing values\n\nIf we had chosen a different categorical variable that contains missing values, such as `Closely_follow`, our plots would have included those missing values by default. To change the colour of the missing value bars, you would need to specify this using the `na.value =` argument within the `scale_fill()` function. Here’s an example of a grouped barchart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n## Prettier grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right) for a variable with missing values](04-dataviz_files/figure-html/fig-barchart_grouped_na-1.png){#fig-barchart_grouped_na fig-align='center' width=100%}\n:::\n:::\n\n\n\nIf you don’t want the missing values to appear in the plot, you will need to do some data wrangling to remove them first. The function for this is `drop_na()`. Here we applied `drop_na()` to `Closely_follow` only.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# remove NA\nprereg_plan_follow <- data_prp_viz %>% \n select(Code, Plan_prereg, Closely_follow) %>% \n drop_na(Closely_follow)\n```\n:::\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## check NAs have been removed\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# check NA have been removed\nprereg_plan_follow %>% \n distinct(Plan_prereg, Closely_follow) %>% \n arrange(Plan_prereg, Closely_follow)\n```\n\n::: {.cell-output-display}\n
\n\n|Plan_prereg |Closely_follow |\n|:-----------|:--------------------|\n|Yes |Followed it somewhat |\n|Yes |Followed it exactly |\n|Unsure |Followed it somewhat |\n|Unsure |Followed it exactly |\n|No |Followed it somewhat |\n|No |Followed it exactly |\n\n
\n:::\n:::\n\n\n:::\n\nBut keep in mind that it could misrepresent the data, e.g., giving a wrong impression about proportions. As a comparison...\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# with NA\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n# without NA\nggplot(prereg_plan_follow, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Percent stacked barchart with (left) and without missing values (right)](04-dataviz_files/figure-html/fig-barchart_na_no_na-1.png){#fig-barchart_na_no_na fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n## Activity 6: Save your plots\n\nYou can save your figures using the `ggsave()` function, which will save them to your project folder.\n\nThere are two ways to use `ggsave()`. If you don’t specify which plot to save, by **default** it will **save the last plot you created**. In our case, the last plot was the one without `NA` from the special case scenario (@fig-barchart_na_no_na). However, if you did not follow along with the special case scenario, your last plot will be the grouped bar chart on the right from @fig-barchart_grouped.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"last_plot.png\")\n```\n:::\n\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our last plot saved\n\n![](images/last_plot.png)\n:::\n\n\nThe second option is to save the plot as an object and refer to the object within `ggsave()`. As an example, let's save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\nThe second option is to save the plot as an object and then refer to that object within `ggsave()`. For example, let’s save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngrouped_bar <- ggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n\nThen, you can run the following line:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar.png\", \n plot = grouped_bar)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nSaving 7 x 5 in image\n```\n:::\n:::\n\n\nThe `filename` is the name you want your PNG file to have, and `plot` refers to the name of the plot object.\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our saved `grouped_bar.png` would look like this:\n\n![](images/grouped_bar.png)\n:::\n\nThis is the plot saved with the default settings. If you like it, feel free to keep it as is. However, if it seems a bit \"off\", you can adjust the width, height, and units (e.g., \"cm\", \"mm\", \"in\", \"px\"). You might need to experiment with the dimensions until it feels about right.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar2.png\", \n plot = grouped_bar, \n width = 16, height = 9, units = \"cm\")\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## `grouped_bar.png` with different dimensions\n\n![](images/grouped_bar2.png)\n:::\n\n\n\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the datafile we should read in is `dog_data_clean_wide.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Create an appropriate plot {.unnumbered}\n\nPick **any single or two categorical variables** from the Binfet dataset and **choose one of the appropriate plot choices**. Things to think about: \n\n* [ ] Select your categorical variable(s): `GroupAssignment`, `Year_of_Study`, `Live_Pets`, and/or `Consumer_BARK`\n* [ ] Decide on the plot you want to display: barchart, stacked barchart, percent stacked barchart, or grouped barchart\n* [ ] You may need to convert your variables into factors\n* [ ] Think about what you want to do with missing data\n* [ ] Pick a colour scheme (manual or pre-defined colour palette)\n* [ ] Tidy the axes labels\n* [ ] Decide whether you need a legend or not, and if so, where you would want to place it\n* [ ] Remove the gap between the bottom of the chart and the bars\n* [ ] Pick a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 1 categorical variable\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = Year_of_Study, fill = Year_of_Study)) +\n geom_bar() + \n scale_fill_brewer(\n palette = \"Dark2\",\n guide = \"none\") + \n scale_x_discrete(name = \"Year of Study\") + \n scale_y_continuous(name = \"Count\",\n expand = expansion(mult = c(0, 0.05))) + \n theme_classic()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-37-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 2 categorical variables\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = GroupAssignment , fill = Live_Pets)) +\n geom_bar(position = \"fill\") + \n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Pets at Home\") +\n scale_fill_manual(values = c('deeppink', 'springgreen2'), na.value = 'orangered',\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n theme_classic() + \n theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-39-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nLet's go back to the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}), and assume you have the following data available:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n\npenguin_selection <- penguins %>% \n group_by(species, island) %>% \n summarise(penguin_count = n())\n\npenguin_selection\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | penguin_count|\n|:---------|:---------|-------------:|\n|Adelie |Biscoe | 44|\n|Adelie |Dream | 56|\n|Adelie |Torgersen | 52|\n|Chinstrap |Dream | 68|\n|Gentoo |Biscoe | 124|\n\n
\n:::\n:::\n\n\n\n\n### Knowledge check {.unnumbered}\n\n#### Question 1 {.unnumbered}\n\nWhat `geom` would you use to plot penguin count for each species? \n\n\n#### Question 2 {.unnumbered}\n\nWhat mapping would you use to display penguin count across species?
\n\n\n\n#### Question 3 {.unnumbered}\n\nWhat `geom` would you use to count the number of species on each island? \n\n\n#### Question 4 {.unnumbered}\n\nWhat mapping would you use to display the count of species per island?
\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain these answers\n\n**Question 1**: `geom_col()` is the appropriate choice for bar charts with predefined y-values, such as `penguin_count`.\n\n**Question 2**: The correct aesthetic mapping places the categorical variable (`species`) on the x-axis and the numeric variable (number of observed penguins) on the y-axis. Using `aes(x = penguin_count, y = species)` would flip the axes, placing the number of penguins on the x-axis and species on the y-axis, which doesn’t match the conventional structure of a bar chart.\n\n**Question 3**: `geom_bar()` is the appropriate choice when you want to automatically count the number of observations within each category, such as counting the number of penguin species on each island.\n\n**Question 4**: For a simple count of species per island, you only need to map the categorical variable (`island`) to the x-axis. The y-axis will automatically represent counts when using `geom_bar()`.\n\n:::\n\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) +\n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `check_aesthetics()`:\n! Aesthetics must be either length 1 or the same as the data (344).\n✖ Fix the following mappings: `x`.\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe error message consists of 2 parts. Part 1 is perhaps a bit trickier to interpret, but part 2 gives some useful hints:\n\n* *\"Aesthetics must be either length 1 or the same as the data (344)\"*: This means that the variable mapped to `x` should either be a constant (like a single value) or a column that has 344 entries (matching the number of rows in the penguins dataset).\n* *\"Fix the following mappings: `x`\"*: The issue is specifically with the `x` aesthetic, meaning `islands` is either misspelled or doesn’t exist in the dataset.\n\nTo check the `penguins` data, you can use `glimpse()`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\n\nTo fix the error, you need to correct the column name. The correct column in the `penguins` dataset is called `island` (without the \"s\" at the end). The `island` column has 344 entries, just like the rest of the dataset, so the mapping now works properly.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nNext, we want to create a grouped bar chart displaying species per island, using the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in scale_fill_viridis(): could not find function \"scale_fill_viridis\"\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe function `scale_fill_viridis()` is incorrect; the correct function is called `scale_fill_viridis_d()`.\n\nFIX: correct the function name to display the grouped bar chart with the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-45-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n#### Question 7 {.unnumbered}\n\nWe want to create a grouped bar chart showing the number of penguins on each island, broken down by year.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = year)) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The following aesthetics were dropped during statistical transformation: fill.\nℹ This can happen when ggplot fails to infer the correct grouping structure in\n the data.\nℹ Did you forget to specify a `group` aesthetic or to convert a numerical\n variable into a factor?\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-46-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but certainly not the one we intended. The warning message mentions something about the grouping structure and gives some additional hints.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe grouping variable needs to be a factor. R helpfully asks if we’ve forgotten to convert a numerical variable into a factor!!! Oh, let's check that in the `penguins` data using the `glimpse()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\nIndeed, `year` is currently stored as a numeric (integer) variable. To fix this, we need to convert `year` to a factor. We can do this directly within the `ggplot()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = as.factor(year))) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-48-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n#### Question 8 {.unnumbered}\n\nWe want to create a percent stacked bar chart that displays the ratio of penguins' sex on each island, using a manual color palette. Female penguins should be displayed in blue, males in green, and `NA` values in red.\n\n**Note**: This task is trickier than it looks. Although the code runs and produces a plot, there are three mistakes to identify and fix.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"dodge\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-49-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 1\n\nThe task was to create a **percent stacked barchart**, but the current plot is displaying a grouped barchart. You will need to adjust the argument that defines the type of plot to achieve the correct visualisation.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-50-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 2\n\nYou may have also noticed that the colours are mapped to the islands, not to the penguins' sex, which needs to be corrected.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 2\n\nWe need to switch the columns mapped to the `x` and `fill` aesthetics.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-51-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 3\n\nNow that the correct variables are mapped to the x-axis and the fill argument, the colour scheme no longer matches the instructions. According to the guidelines, missing values should be displayed in red.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 3\n\nChanging the colour of missing values is a special case that requires the argument `na.value =`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\"), na.value = \"red\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-52-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n:::\n", + "markdown": "# Data viz I {#sec-dataviz}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter, you should be able to:\n\n- explain the layered grammar of graphics\n- choose an appropriate plot for categorical variables\n- create a basic version of an appropriate plot\n- apply additional layers to modify the appearance of the plot\n\n\nIt is time to think about selecting the most appropriate plot for your data. Different types of variables call for different kinds of plots, which depends on how many variables you’re aiming to plot and what their data types are. In this chapter, we will focus on **plots for categorical data**. Next week, we will explore plots for continuous variables and learn which plots work best when combining continuous and categorical data.\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n## Building plots\n\nWe are using the package `ggplot2` to create data visualisations. It's part of the tidyverse package. Actually, most people call th package `ggplot` but it's official name is `ggplot2`.\n\nWe’ll be using the `ggplot2` package to create data visualisations. It’s part of the `tidyverse` suite of packages. Although many people refer to it simply as `ggplot`, its official name is `ggplot2`.\n\n::: grid\n::: g-col-6\n**ggplot2** uses a layered grammar of graphics, where plots are constructed through a series of layers. You start with a base layer (by calling `ggplot`), then add **data** and **aesthetics**, followed by selecting the appropriate **geometries** for the plot.\n\nThese first 3 layers will give you the most simple version of a complete plot. However, you can enhance the plot’s clarity and appearance by adding additional layers such as **scales**, **facets**, **coordinates**, **labels** and **themes**.\n\n:::\n\n::: g-col-6\n![gg layers [(Presentation by Ryan Safner)](https://metricsf20.classes.ryansafner.com/slides/1.3-slides#20){target=\"_blank\"}](images/gglayers.png){width=\"90%\"}\n:::\n:::\n\nTo give you a brief overview of the layering system, we will use the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}). This dataset contains information about penguins, including bill length and depth, flipper length, body mass, and more.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(penguins)\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | bill_length_mm| bill_depth_mm| flipper_length_mm| body_mass_g|sex | year|\n|:-------|:---------|--------------:|-------------:|-----------------:|-----------:|:------|----:|\n|Adelie |Torgersen | 39.1| 18.7| 181| 3750|male | 2007|\n|Adelie |Torgersen | 39.5| 17.4| 186| 3800|female | 2007|\n|Adelie |Torgersen | 40.3| 18.0| 195| 3250|female | 2007|\n|Adelie |Torgersen | NA| NA| NA| NA|NA | 2007|\n|Adelie |Torgersen | 36.7| 19.3| 193| 3450|female | 2007|\n|Adelie |Torgersen | 39.3| 20.6| 190| 3650|male | 2007|\n\n
\n:::\n:::\n\n\nLet's build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will customise plots further later on in the individual plots. This is just a quick overview of the different layers.\n\nLet’s build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will further customise the plots in subsequent sections, but for now, this will provide a quick overview of the different layers.\n\n* **Layer 1** creates the base plot that we build upon.\n* **Layer 2** adds the `data` and some `aesthetics`:\n * The data is passed as the first argument.\n * Aesthetics are added via the mapping argument, where you define your variables (e.g., x or both x and y). This also allows you to specify general properties, like the color for grouping variables, etc.\n* **Layer 3** adds geometries, or `geom_?` for short. This tells ggplot how to display the data points. Remember to add these layers with a `+`, rather than using a pipe (`%>%`). You can also add multiple geoms if needed, for example, combining a violin plot with a boxplot.\n* **Layer 4** includes `scale_?` functions, which let you customise aesthetics like color. You can do much more with scales, but we'll explore later.\n* **Layer 5** introduces facets, such as `facet_wrap()`, allowing you to add an extra dimension to your plot by showing the relationship you are interested in for each level of a categorical variable.\n* **Layer 6** involves coordinates, where `coord_cartesian()` controls the limits for the x- and y-axes (xlim and ylim), enabling you to zoom in or out of the plot.\n* **Layer 7** helps you modify axis labels.\n* **Layer 8** controls the overall style of the plot, including background color, text size, and borders. R provides several predefined themes, such as `theme_classic`, `theme_bw`, `theme_minimal`, and `theme_light`.\n\nClick on the tabs below to see how each layer contributes to refining the plot.\n\n::: {.panel-tabset}\n## Layer 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-3-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThere’s not much to see at this stage - this is basically an empty plot layer.\n\n## Layer 2\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-4-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou won’t see any data points yet because we haven’t specified how to display them. However, we have mapped the aesthetics, indicating that we want to plot `body_mass` on the x-axis and `flipper_length` on the y-axis. This also sets the axis titles, as well as the axis values and breakpoints.\n\n::: callout-tip\nYou won't need to add `data =` or `mapping =` if you keep those arguments in exactly that order. Likewise, the first column name you enter within the `aes()` function will always be interpreted as x, and the second as y, so you could omit them if you wish.\n\nYou don’t need to include `data =` or `mapping =` if you keep those arguments in the default order. Similarly, the first column name you enter in the `aes()` function will automatically be interpreted as the x variable, and the second as y, so you can omit specifying `x` and `y` if you prefer.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(body_mass_g, flipper_length_mm))\n```\n:::\n\n\nwill give you the same output as the code above.\n:::\n\n## Layer 3\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-6-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we are telling `ggplot` to add a scatterplot. You may notice a warning indicating that some rows were removed due to missing values.\n\nThe `colour` argument adds colour to the points based on a grouping variable (in this case, `sex`). If you want all the points to be black — representing only two dimensions rather than three — simply omit the `colour` argument.\n\n## Layer 4\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point() +\n # changes colour palette\n scale_colour_brewer(palette = \"Dark2\") + \n # add breaks from 2500 to 6500 in increasing steps of 500\n scale_x_continuous(breaks = seq(from = 2500, to = 6500, by = 500)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `scale_?` functions allow us to modify the color palette of the plot, adjust axis breaks, and more. You could change the axis labels within `scale_x_continuous()` as well or leave it for Layer 7.\n\n## Layer 5\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n # split main plot up into different subplots by species \n facet_wrap(~ species) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nIn this step, we’re using faceting to split the plot by species.\n\n## Layer 6\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n # limits the range of the y axis\n coord_cartesian(ylim = c(0, 250)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we adjust the limits of the y-axis to zoom out of the plot. If you want to zoom in or out of the x-axis, you can add the `xlim` argument to the `coord_cartesian()` function.\n\n## Layer 7\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", # labels the x axis\n y = \"Flipper length (in mm)\", # labels the y axis\n colour = \"Sex\") # labels the grouping variable in the legend\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-10-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou can change the axis labels using the `labs()` function, or you can modify them when adjusting the scales (e.g., within the `scale_x_continuous()` function).\n\n## Layer 8\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", \n y = \"Flipper length (in mm)\",\n colour = \"Sex\") +\n # add a theme\n theme_classic()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `theme_classic()` function is applied to change the overall appearance of the plot.\n\n:::\n\n::: callout-important\n\nYou need to stick to the first three layers to create your base plot. Everything else is optional, meaning you don’t need to use all eight layers. Additionally, layers 4-8 can be added in any order (more or less), whereas layers 1-3 must follow a fixed sequence.\n\n:::\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data from Pownall et al. (2023), so **open your project**.\n* However, let’s start with a fresh R Markdown file: **Create a new `.Rmd` file** and save it in your project folder. Give it a meaningful name (e.g., \"chapter_04.Rmd\" or \"04_data_viz.Rmd\"). If you need guidance, refer to @sec-rmd. Delete everything below line 12, but keep the setup code chunk.\n* We previously aggregated the data in @sec-wrangling and @sec-wrangling2. If you want a fresh copy, download the data here: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\"). Make sure to place the csv file in the project folder.\n* If you need a reminder about the data and variables, check the codebook or refer back to @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we will be using the `tidyverse` package and the dataset `data_prp_for_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- read_csv(???)\n```\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nAs mentioned in @sec-familiarise, it is always a good idea to take a glimpse at the data to see how many variables and observations are in the dataset, as well as the data types.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## glimpse output\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(data_prp_viz)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 89\nColumns: 28\n$ Code \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age 22, 20, 22, 26, 22, 20, 21, 21, …\n$ Ethnicity \"White European\", \"White British…\n$ Secondyeargrade 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT \"Research methods in first year\"…\n$ Research_exp 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ Pre_reg_group 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ SATS28_Affect_Time1_mean 4.000000, 3.833333, 5.000000, 5.…\n$ SATS28_CognitiveCompetence_Time1_mean 5.166667, 5.166667, 5.666667, 4.…\n$ SATS28_Value_Time1_mean 6.000000, 6.666667, 5.222222, 5.…\n$ SATS28_Difficulty_Time1_mean 3.571429, 2.428571, 3.571429, 3.…\n$ QRPs_Acceptance_Time1_mean 5.909091, 6.090909, 6.545455, 5.…\n$ Time1_Understanding_OS 5.500000, 3.166667, 4.500000, 3.…\n$ Other_OS_behav_2 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS 5.583333, 3.333333, 5.416667, 4.…\n$ Mean_Supervisor_Support 5.230769, 6.285714, 6.857143, 2.…\n```\n:::\n:::\n\n\n:::\n\n\nWe can see that some of the categorical data in `data_prp_viz` was read in as numeric variables which makes them continuous. This will haunt us big time when building the plots. We would be better off addressing these changes in the dataset before we start plotting (and potentially getting frustrated with R and data viz in general).\n\nLet’s convert some of the categorical variables into factors. We’ll use the `factor()` function, which requires the `variable` to convert, the `levels` (where we can re-order them as needed), and the corresponding `labels`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n\n## Activity 3: Barchart (`geom_bar()`)\n\nA barchart is the best choice when you want to plot a single categorical variable.\n\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a **barplot**. This is done with `geom_bar()` in the third layer. Since the counting is done automatically in the background, the `aes()` function only requires an x value (i.e., the name of your variable).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n```\n\n::: {.cell-output-display}\n![Default barchart](04-dataviz_files/figure-html/fig-bc-base-1.png){#fig-bc-base fig-align='center' width=100%}\n:::\n:::\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the **labels** could be clearer, or you might want to add a splash **colour**. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own `.Rmd` file.\n\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` argument. If you have specific colours in mind, you would use `scale_fill_manual()`, or if you prefer to stick with pre-defined options like viridis, you can use `scale_fill_viridis_d()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels & margins\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the `scale_x_discrete()` function and the `labels =` argument. Just make sure to order the labels according to the order in the dataframe.\n\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the `expansion()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_minimal()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n## Activity 4: Column plot (`geom_col()`)\n\nIf the counts had already been summarised for you, `geom_bar()` would not work. Instead, you’d need to use `geom_col()` to display the pre-aggregated data.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngender_count <- data_prp_viz %>% \n count(Gender)\n\ngender_count\n```\n\n::: {.cell-output-display}\n
\n\n|Gender | n|\n|:----------|--:|\n|females | 69|\n|males | 17|\n|non-binary | 3|\n\n
\n:::\n:::\n\n\nThe mapping for `geom_col()` requires both **x** and **y** aesthetics. In this example, **x** would represent the categorical variable (e.g., `Gender`), while **y** would refer to the column storing the summarised values (e.g., `n`). Notice how the axis title now reflects `n` instead of `count` in the base version.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col()\n```\n\n::: {.cell-output-display}\n![Column plot with different coloured bars](04-dataviz_files/figure-html/fig-col-1.png){#fig-col fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"}\n\n\n## Your Turn: Make the column plot pretty\n\nThe other layers to change the colour scheme, axes labels and margins, removing the legend and altering the theme require exactly the same functions as with the boxplot above. Test yourself to see if you can...\n\n* [ ] change the colour scheme (e.g., viridis or [any other colour palettes](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/){target=\"_blank\"})\n* [ ] remove the legend\n* [ ] change the titles of the x and y axes\n* [ ] make the bars start directly on the x-axis\n* [ ] add a theme of your liking\n\n\n\n::: {.callout-tip collapse=\"true\"}\n\n## Possible solution code for the column plot (with a different colour palette and a different theme)\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col() +\n # replaced vidiris with the brewer palette\n scale_fill_brewer(\n palette = \"Set1\", # try \"Set2\" or \"Dark2\" for some variety\n guide = \"none\") + # legend removed\n # labels of the categories changed\n scale_x_discrete(labels = c(\"Male\", \"Female\", \"Non-Binary\")) + \n scale_y_continuous(\n # change y axis label\n name = \"Count\",\n # starts bars on x axis without any gaps but leaves some space at the top (this time 10%)\n expand = expansion(mult = c(0, 0.1)) \n ) +\n # different theme\n theme_light()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n\n## Activity 5: Stacked, Percent Stacked, and Grouped Barchart {#sec-adv_bar}\n\nWhen dealing with **two categorical variables**, you have three options for displaying stacked barcharts: the \"normal\" **Stacked Barchart** (the default option), a **Percent Stacked Barchart**, or a **Grouped Barchart**.\n\nFor this activity, we will explore the variable `Plan_prereg`, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and `Pre_reg_group`, which tracks whether they actually followed through with a pre-registration for their dissertation.\n\nOne way to display this data is by creating either a **Stacked Barchart** (the default) or a **Percent Stacked Barchart**. In both cases, the subgroups are displayed on top of each other. To make comparison easier, we will place the two plots side by side and move the legend to the bottom of the chart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar() + # no position argument added\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Percent stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Stacked barchart (left), and Percent stacked barchart (right)](04-dataviz_files/figure-html/fig-barcharts_stacked-1.png){#fig-barcharts_stacked fig-align='center' width=100%}\n:::\n:::\n\n\n\nIn the **stacked barchart** (@fig-barcharts_stacked, left plot), you can display participant numbers. From this, we can see that the highest number of students were unsure whether they wanted to pre-register their dissertation, followed closely by those who answered \"yes.\" We also see that the number of students who did not end up with a pre-registered dissertation (blue category) is the same for both those who had planned to pre-register and those who did not want to. However, since the \"No\" category has significantly fewer participants than the other two, it’s difficult to tell if the ratio remains consistent across all three groups.\n\nIf we want to highlight this ratio, a **Percent Stacked Barchart** (@fig-barcharts_stacked, right plot) would be more appropriate. This plot shows that approximately 80% of the students who had planned to pre-register their dissertations, 50% of the students who were initially unsure, and only 33% of the students who had no plan to pre-register ended up with a pre-registered dissertation. BUT! We would lose the information about the raw values in the sample.\n\n**It’s all a trade-off, and the plot you choose depends on the \"story\" you want the data to tell.**\n\n\n::: callout-note\n\nThe position argument `position = \"stack\"` is the default. Adding this argument to the code for the left plot in @fig-barcharts_stacked would produce the same plot as leaving the argument out.\n\n\n:::\n\n\nThe other option is a **Grouped Barchart**, which displays the bars next to each other. You can achieve this by changing the `position` argument to `\"dodge\"`. You can see the default version of the plot in @fig-barchart_grouped on the left, and one with additional layers on the right.\n\nInstead of using a pre-existing colour palette, we manually changed the colours using hex codes. These are some of the colours Gaby used in her PhD thesis, but you can:\n\n* create your own colour hex codes by using [this website](https://www.hexcolortool.com/){target=\"_blank\"}, OR\n* use pre-defined colour names like \"green\" or \"purple\" instead. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\nFeel free to explore.\n\nSince the legend title for the second plot is a bit long, we displayed the legend content across two rows by adding the layer `guides(fill = guide_legend(nrow = 2))` at the end.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Default grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Prettier grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n # changing labels for x, y, and fill category - alternative method\n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registered dissertation\") +\n # manual colour change for values\n scale_fill_manual(values = c('#648FFF', '#DC267F'),\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(\n # remove the space below the bars, but keep a tiny bit (5%) above\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_classic() + \n # need to move this following line to the end otherwise the `theme_*` overrides it\n theme(legend.position = \"bottom\") + \n # display across 2 rows\n guides(fill = guide_legend(nrow = 2))\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right)](04-dataviz_files/figure-html/fig-barchart_grouped-1.png){#fig-barchart_grouped fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Categorical variables with missing values\n\nIf we had chosen a different categorical variable that contains missing values, such as `Closely_follow`, our plots would have included those missing values by default. To change the colour of the missing value bars, you would need to specify this using the `na.value =` argument within the `scale_fill()` function. Here’s an example of a grouped barchart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n## Prettier grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right) for a variable with missing values](04-dataviz_files/figure-html/fig-barchart_grouped_na-1.png){#fig-barchart_grouped_na fig-align='center' width=100%}\n:::\n:::\n\n\n\nIf you don’t want the missing values to appear in the plot, you will need to do some data wrangling to remove them first. The function for this is `drop_na()`. Here we applied `drop_na()` to `Closely_follow` only.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# remove NA\nprereg_plan_follow <- data_prp_viz %>% \n select(Code, Plan_prereg, Closely_follow) %>% \n drop_na(Closely_follow)\n```\n:::\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## check NAs have been removed\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# check NA have been removed\nprereg_plan_follow %>% \n distinct(Plan_prereg, Closely_follow) %>% \n arrange(Plan_prereg, Closely_follow)\n```\n\n::: {.cell-output-display}\n
\n\n|Plan_prereg |Closely_follow |\n|:-----------|:--------------------|\n|Yes |Followed it somewhat |\n|Yes |Followed it exactly |\n|Unsure |Followed it somewhat |\n|Unsure |Followed it exactly |\n|No |Followed it somewhat |\n|No |Followed it exactly |\n\n
\n:::\n:::\n\n\n:::\n\nBut keep in mind that it could misrepresent the data, e.g., giving a wrong impression about proportions. As a comparison...\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# with NA\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n# without NA\nggplot(prereg_plan_follow, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Percent stacked barchart with (left) and without missing values (right)](04-dataviz_files/figure-html/fig-barchart_na_no_na-1.png){#fig-barchart_na_no_na fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n## Activity 6: Save your plots\n\nYou can save your figures using the `ggsave()` function, which will save them to your project folder.\n\nThere are two ways to use `ggsave()`. If you don’t specify which plot to save, by **default** it will **save the last plot you created**. In our case, the last plot was the one without `NA` from the special case scenario (@fig-barchart_na_no_na). However, if you did not follow along with the special case scenario, your last plot will be the grouped bar chart on the right from @fig-barchart_grouped.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"last_plot.png\")\n```\n:::\n\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our last plot saved\n\n![](images/last_plot.png)\n:::\n\n\nThe second option is to save the plot as an object and refer to the object within `ggsave()`. As an example, let's save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\nThe second option is to save the plot as an object and then refer to that object within `ggsave()`. For example, let’s save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngrouped_bar <- ggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n\nThen, you can run the following line:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar.png\", \n plot = grouped_bar)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nSaving 7 x 5 in image\n```\n:::\n:::\n\n\nThe `filename` is the name you want your PNG file to have, and `plot` refers to the name of the plot object.\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our saved `grouped_bar.png` would look like this:\n\n![](images/grouped_bar.png)\n:::\n\nThis is the plot saved with the default settings. If you like it, feel free to keep it as is. However, if it seems a bit \"off\", you can adjust the width, height, and units (e.g., \"cm\", \"mm\", \"in\", \"px\"). You might need to experiment with the dimensions until it feels about right.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar2.png\", \n plot = grouped_bar, \n width = 16, height = 9, units = \"cm\")\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## `grouped_bar.png` with different dimensions\n\n![](images/grouped_bar2.png)\n:::\n\n\n\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the datafile we should read in is `dog_data_clean_wide.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Create an appropriate plot {.unnumbered}\n\nPick **any single or two categorical variables** from the Binfet dataset and **choose one of the appropriate plot choices**. Things to think about: \n\n* [ ] Select your categorical variable(s): `GroupAssignment`, `Year_of_Study`, `Live_Pets`, and/or `Consumer_BARK`\n* [ ] Decide on the plot you want to display: barchart, stacked barchart, percent stacked barchart, or grouped barchart\n* [ ] You may need to convert your variables into factors\n* [ ] Think about what you want to do with missing data\n* [ ] Pick a colour scheme (manual or pre-defined colour palette)\n* [ ] Tidy the axes labels\n* [ ] Decide whether you need a legend or not, and if so, where you would want to place it\n* [ ] Remove the gap between the bottom of the chart and the bars\n* [ ] Pick a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 1 categorical variable\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = Year_of_Study, fill = Year_of_Study)) +\n geom_bar() + \n scale_fill_brewer(\n palette = \"Dark2\",\n guide = \"none\") + \n scale_x_discrete(name = \"Year of Study\") + \n scale_y_continuous(name = \"Count\",\n expand = expansion(mult = c(0, 0.05))) + \n theme_classic()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-37-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 2 categorical variables\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = GroupAssignment , fill = Live_Pets)) +\n geom_bar(position = \"fill\") + \n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Pets at Home\") +\n scale_fill_manual(values = c('deeppink', 'springgreen2'), na.value = 'orangered',\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n theme_classic() + \n theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-39-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nLet's go back to the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}), and assume you have the following data available:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n\npenguin_selection <- penguins %>% \n group_by(species, island) %>% \n summarise(penguin_count = n())\n\npenguin_selection\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | penguin_count|\n|:---------|:---------|-------------:|\n|Adelie |Biscoe | 44|\n|Adelie |Dream | 56|\n|Adelie |Torgersen | 52|\n|Chinstrap |Dream | 68|\n|Gentoo |Biscoe | 124|\n\n
\n:::\n:::\n\n\n\n\n### Knowledge check {.unnumbered}\n\n#### Question 1 {.unnumbered}\n\nWhat `geom` would you use to plot penguin count for each species? \n\n\n#### Question 2 {.unnumbered}\n\nWhat mapping would you use to display penguin count across species?
\n\n\n\n#### Question 3 {.unnumbered}\n\nWhat `geom` would you use to count the number of species on each island? \n\n\n#### Question 4 {.unnumbered}\n\nWhat mapping would you use to display the count of species per island?
\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain these answers\n\n**Question 1**: `geom_col()` is the appropriate choice for bar charts with predefined y-values, such as `penguin_count`.\n\n**Question 2**: The correct aesthetic mapping places the categorical variable (`species`) on the x-axis and the numeric variable (number of observed penguins) on the y-axis. Using `aes(x = penguin_count, y = species)` would flip the axes, placing the number of penguins on the x-axis and species on the y-axis, which doesn’t match the conventional structure of a bar chart.\n\n**Question 3**: `geom_bar()` is the appropriate choice when you want to automatically count the number of observations within each category, such as counting the number of penguin species on each island.\n\n**Question 4**: For a simple count of species per island, you only need to map the categorical variable (`island`) to the x-axis. The y-axis will automatically represent counts when using `geom_bar()`.\n\n:::\n\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) +\n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `check_aesthetics()`:\n! Aesthetics must be either length 1 or the same as the data (344).\n✖ Fix the following mappings: `x`.\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe error message consists of 2 parts. Part 1 is perhaps a bit trickier to interpret, but part 2 gives some useful hints:\n\n* *\"Aesthetics must be either length 1 or the same as the data (344)\"*: This means that the variable mapped to `x` should either be a constant (like a single value) or a column that has 344 entries (matching the number of rows in the penguins dataset).\n* *\"Fix the following mappings: `x`\"*: The issue is specifically with the `x` aesthetic, meaning `islands` is either misspelled or doesn’t exist in the dataset.\n\nTo check the `penguins` data, you can use `glimpse()`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\n\nTo fix the error, you need to correct the column name. The correct column in the `penguins` dataset is called `island` (without the \"s\" at the end). The `island` column has 344 entries, just like the rest of the dataset, so the mapping now works properly.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nNext, we want to create a grouped bar chart displaying species per island, using the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in scale_fill_viridis(): could not find function \"scale_fill_viridis\"\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe function `scale_fill_viridis()` is incorrect; the correct function is called `scale_fill_viridis_d()`.\n\nFIX: correct the function name to display the grouped bar chart with the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-45-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n#### Question 7 {.unnumbered}\n\nWe want to create a grouped bar chart showing the number of penguins on each island, broken down by year.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = year)) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The following aesthetics were dropped during statistical transformation: fill.\nℹ This can happen when ggplot fails to infer the correct grouping structure in\n the data.\nℹ Did you forget to specify a `group` aesthetic or to convert a numerical\n variable into a factor?\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-46-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but certainly not the one we intended. The warning message mentions something about the grouping structure and gives some additional hints.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe grouping variable needs to be a factor. R helpfully asks if we’ve forgotten to convert a numerical variable into a factor!!! Oh, let's check that in the `penguins` data using the `glimpse()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\nIndeed, `year` is currently stored as a numeric (integer) variable. To fix this, we need to convert `year` to a factor. We can do this directly within the `ggplot()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = as.factor(year))) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-48-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n#### Question 8 {.unnumbered}\n\nWe want to create a percent stacked bar chart that displays the ratio of penguins' sex on each island, using a manual color palette. Female penguins should be displayed in blue, males in green, and `NA` values in red.\n\n**Note**: This task is trickier than it looks. Although the code runs and produces a plot, there are three mistakes to identify and fix.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"dodge\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-49-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 1\n\nThe task was to create a **percent stacked barchart**, but the current plot is displaying a grouped barchart. You will need to adjust the argument that defines the type of plot to achieve the correct visualisation.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-50-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 2\n\nYou may have also noticed that the colours are mapped to the islands, not to the penguins' sex, which needs to be corrected.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 2\n\nWe need to switch the columns mapped to the `x` and `fill` aesthetics.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-51-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 3\n\nNow that the correct variables are mapped to the x-axis and the fill argument, the colour scheme no longer matches the instructions. According to the guidelines, missing values should be displayed in red.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 3\n\nChanging the colour of missing values is a special case that requires the argument `na.value =`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\"), na.value = \"red\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-52-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n:::\n", "supporting": [ "04-dataviz_files" ], diff --git a/.quarto/_freeze/05-dataviz2/execute-results/html.json b/.quarto/_freeze/05-dataviz2/execute-results/html.json index 83521fd..c9544c2 100644 --- a/.quarto/_freeze/05-dataviz2/execute-results/html.json +++ b/.quarto/_freeze/05-dataviz2/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "95f73437e1db40d9de50e88a434c53bd", + "hash": "b6258efe5544313db086c348b4efd5b1", "result": { - "markdown": "# Data viz II {#sec-dataviz2}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter you should be able to:\n\n- choose an appropriate plot for continuous variables \n- choose an appropriate plot when you've got a mix of continuous/categorical variables \n- create a basic version of an appropriate plot\n- apply extra layers to change the appearance of the plot\n\nIn this chapter, we are continuing our journey of appropriate plots. Last week, we looked at which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**.\n\n\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n## Activity 1: Set-up and data for today\n\n- We are still working with the data by Pownall et al. (2023). **Open the project**.\n- You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you.\n- The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download it again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\").\n- If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nWe need the package `tidyverse` today, and the data `data_prp_ch3.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- ???\n```\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nThis is the same as last week. We need to turn our categorical variables into factors to make plotting easier.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n::: {.callout-tip}\n\nIf you are working within the same `.Rmd` file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week's `.Rmd` file to load tidyverse into the library, read in the data, and convert some of the variables into factors.\n\n:::\n\n\n## Activity 3: Histogram (`geom_histogram()`)\n\nIf you wanted to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting \"in the background\".\n\nA histogram splits the data into “bins” (i.e., groupings displayed in a single bar). These values are plotted along the x-axis and shows the count of how many observations are in each bin along the y-axis. It's basically a bar chart for continuous variables.\n\nLet's have a look at the age distribution in our dataset.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram() \n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Default histogram](05-dataviz2_files/figure-html/fig-hist-1.png){#fig-hist fig-align='center' width=100%}\n:::\n:::\n\n\n\nThe default **bin number** is 30 (as displayed in @fig-hist above). Changing the number of bins (argument `bins`) can help to show more or less fine tuning in the data. Bigger numbers of bins means more finetuning.\n\nPerhaps it's more intuitive to modify the **width of each bin** instead via the argument (`binwidth`). So for example `binwidth = 1` for category age would mean 1 year of age per \"age group\"; `binwidth = 5` would mean 5 years of age span per \"age group\", etc. The plots below show modifications for both `bin` and `binwidth`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n#less finetuning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(bins = 10) \n\n# more fineturning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1) \n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).\nRemoved 2 rows containing non-finite outside the scale range (`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Bins vs binwidth arguments](05-dataviz2_files/figure-html/fig-bins-1.png){#fig-bins fig-align='center' width=100%}\n:::\n:::\n\n\nThe **warning message** telling us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `hp_data` to see if you can decipher the warning message.\n\nThey were removed because .\n\nColours are getting manipulated slightly differently to the barchart. Click through each tab to see how you can alter colour, axes labels, margins and breaks, and add a different theme.\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument manipulates the colour of the bars, and the `colour` argument changes the outline of the bars. Pay attention that they are **added directly to the `geom_histogram` arguments**, not the overall `aes()` like we did with the boxplot.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: callout-tip\n\nYou could use \n\n* hex codes for fill and color, like we used here, `geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")`. If you want create your own colours, check out [this website](https://www.hexcolortool.com/){target=\"_blank\"}. OR\n* a pre-defined colour name `geom_histogram(binwidth = 1, fill = \"purple\", colour = \"green\")`. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"} OR\n\n\n:::\n\n## Axes labels, margins, and breaks\n\nHere we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y axis is now displayed in increasing steps of 5 (rather than 10), and the x axis has 1-year increments rather than 5. \n\nNotice how the `breaks =` argument manipulates the labels of the break ticks but not the limit of the scale. You can manipulate the limits of the scale with the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values for 15 to 19, and 44 to 50 do not have a label. You would need to adjust that in `breaks =` argument.\n\nThe `expansion()` function removes the gap between x axis and bars. It is exactly the same code as we used in @sec-dataviz .\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 20, to = 43, by = 1),\n # Experimenting with\n limits = c(15, 50)\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_bw()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 19, to = 44, by = 1)\n ) +\n # pick a theme\n theme_bw()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n\n## Activity 4: Scatterplot (`geom_point()`)\n\nScatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and 2. The default scatterplot would be created with `geom_point()`.\n\nWe could also **add a trendline** by adding `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = lm` into `geom_smooth()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth()\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth(method = lm)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![Default Scatterplot with added trendline - loess (left) and linear (right)](05-dataviz2_files/figure-html/fig-scatter-1.png){#fig-scatter fig-align='center' width=100%}\n:::\n:::\n\n\n\n\nCustomising the colour of plot is slightly different to the other plots we've encountered so far. Technically, the point is not a \"filled-in black area\", but an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument, but have to switch to the `colour` argument (like we did for the outline of the histogram). See the tabs below how to change the colour for all points or if you want to change the colour according to groupings.\n\n\n::: {.panel-tabset}\n\n## Colour for all points\n\nIf we want to **change the colour of all the points**, we would add the `colour` argument to the `geom_point()` function. Likewise, changing the **colour of the trendline** would also require a `colour` argument. Here we went with pre-defined colour names, but HEX codes would work too\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# colour of all points and the trendline\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point(colour = 'magenta') +\n geom_smooth(method = lm, colour = 'turquoise')\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n## Colour with grouping\n\nIf we wanted the points to **change colour due to another grouping variable**, the colour argument would go into the `aes()` If you don't want to define the colours manually, use a colour palette like brewer (`scale_colour_brewer()`) or viridis (`scale_colour_viridis_d()`).\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## adding grouping variable Pre_reg_group and changing the colour values manually\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-12-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n## Legend title and labels\n\nYou can tidy the legend title and group labels via the `scale_colour_?` function\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\"))\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-13-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAll other layers would be exactly the same as in other plots. Try to add layers to the make the plot above prettier: \n\n* [ ] 1. relabel axes x and y\n* [ ] 2. set the x and y axis range from 1 to 7\n* [ ] 3. move the legend to a different position (either top, left, or bottom)\n* [ ] 4. add a theme\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## Hints\n\n1. can be done in 2 different ways - labs() or scale_x_?\n2. we did that for the histogram\n3. We did that for the bar charts\n4. pick a theme you like\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\")) +\n labs (x = \"Acceptance of Questionable Research Practices (Time 1)\", \n y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_light() + # place before moving the legend position\n theme(legend.position = \"top\") # move legend to the top\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-14-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n\n\n\n\n\n## Activity 5: Boxplot (`geom_boxplot()`)\n\nA boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to investigate whether their understanding of open science depends on whether or not students had research experience. Our default boxplot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-15-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nTada. As usual, we can make the plot pretty by adding various layers. Click on the tabs to see how\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a fill argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` layer If you have specific colours in mind, you would use `scale_fill_manual()` or if you want to stick with pre-defined ones, like brewer `scale_fill_brewer()`.\n\nBtw, this is exactly the same code we used for the barcharts.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n # customise colour\n scale_fill_brewer(palette = \"Dark2\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-16-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels\n\nWe need to relabel the axes labels for x and y with `scale_x_discrete()` and `scale_y_continuous()`. We can also tidy the labels for the groups and the breaks on the y-axis (in steps of 1 rather than 2) within the same functions\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n scale_x_discrete(\n # changing the label of x\n name = \"Research Experience\",\n # changing the group labels of the 2 groups\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Confidence in Understanding Open Science (Time 1)\",\n # changing break labels\n breaks = c(seq(from = 1, to = 7, by = 1))\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend and Theme\n\nThe legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = \"none\"` into the `scale_fill` function.\n\nLet's pick a theme we haven't used yet: `theme_dark()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\",\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(\n name = \"Research Experience\",\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n name = \"Confidence in Understanding Open Science (Time 1)\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_dark()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## Activity 6: Violin plot (`geom_violin()`)\n\nAn alternative to display a continuous variable with categorical grouping variable is a violin plot. Here, we want to create a violin plot to investigate whether the perception of supervisor support depended on planning to pre-register the dissertation. Our default violin plot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +\n geom_violin()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAdjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to \n\n* [ ] change the colours either manually or using a pre-defined colour palette \n* [ ] tidy the axes labels and group names\n* [ ] in case a legend appears, take it off\n* [ ] add a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +\n geom_violin() +\n scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(name = \"Plan to pre-register the dissertation\") + \n scale_y_continuous(\n name = \"Perceived Supervisory Support\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 7: Violin-boxplots\n\nSo far, we've only added one `geom_` to our plots. Due to the layered system, we could add multiple geoms, for example, when creating a violin-boxplot. \n\nRemember, the order of the layers makes a difference sometimes. We've seen already, with themes we added at the very end that could override the argument for a legend position earlier. Here, `ggplot + violinplot + boxplot` looks different to `ggplot + boxplot + violinplot`.\n\nLet's take the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot()\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_boxplot() +\n geom_violin()\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default violin-boxplot: Order of the layer matters](05-dataviz2_files/figure-html/fig-vb_bv-1.png){#fig-vb_bv fig-align='center' width=100%}\n:::\n:::\n\n\n\nCustomising a few elements such as width of the boxes and the colour.\n\n\n::: {.panel-tabset}\n\n## Width of the boxes\n\nIf we want to get any information from the boxplot, we need to use order 1. But still, the boxplot is pretty wide and covers up important information from the violin. We could **adjust the width of the boxes** to make information more visible. This takes a bit of trial and error which width looks appropriate.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot(width = 0.2)\n```\n\n::: {.cell-output-display}\n![Default violin-boxplot: adjusting width of the box](05-dataviz2_files/figure-html/fig-vb_width-1.png){#fig-vb_width fig-align='center' width=100%}\n:::\n:::\n\n\n## Colour\n\nAdding colour should be pretty straight forward by now. This code is no different to the one we used in the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, and a `scale_fill_?`layer.\n\nHowever, we can modify this further by adding an opacity argument `alpha` to the violin plot geom.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) + # alpha for opacity\n geom_boxplot(width = 0.2) + # change width of the boxes\n scale_fill_brewer(palette = \"RdPu\") # customise colour\n```\n\n::: {.cell-output-display}\n![Violin-boxplot with a different colour palette](05-dataviz2_files/figure-html/fig-vb_colour-1.png){#fig-vb_colour fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\n* [ ] Change the x- and y-axis labels\n* [ ] Remove the legend\n* [ ] add a theme\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + # removes the legend\n # change labels of x and y\n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 8: Faceting - adding another grouping variable\n\nFaceting is really useful when you have subsets in the data. We will use this here on the violin-boxplot from above, but you could add this to pretty much any plot. The function to split up the plots into facets is called `facet_wrap()`. \n\nLet's **add another grouping variable**, `Pre_reg_group`, so we can see separate plots for the yes and no groups. \n\nSince the group labels on the x-axis are quite long, we need to adjust them. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + \n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic() +\n facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no\n scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows\n```\n\n::: {.cell-output-display}\n![Pretty violin-boxplot split into pre-registration groups (yes and no)](05-dataviz2_files/figure-html/fig-vb_pretty_facet-1.png){#fig-vb_pretty_facet fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-tip}\n\nThe labels of `Pre_reg_group` are displayed as numbers 1 and 2. If this bugs you, fix the labels in the dataset. This would be less hassle than trying to fix it in the plot.\n\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Variables with subscales\n\nFor example, we want to show the relationship between SATs scores at timepoints 1 and 2, separately for all 4 subscales of the SATs questionnaires, we would need to wrangle the data first. The dataframe we need should look like this:\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(data_facet, n=5)\n```\n\n::: {.cell-output-display}\n
\n\n|Code |Subscale | Time1| Time2|\n|:----|:-------------------|--------:|--------:|\n|Tr10 |Affect | 4.000000| 3.500000|\n|Tr10 |CognitiveCompetence | 5.166667| 4.166667|\n|Tr10 |Value | 6.000000| 3.000000|\n|Tr10 |Difficulty | 3.571429| 2.857143|\n|Bi07 |Affect | 3.833333| 3.166667|\n\n
\n:::\n:::\n\n\nTry wrangling the data so that it looks like `data_facet` above\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hints\n\n* step 1: select variables of interest\n* step 2: pivot\n* step 3: try to access information on subscales and timepoints from the variable names\n* step 4: pivot in the other direction\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_facet <- data_prp_viz %>% \n select(Code, starts_with(\"SATS\")) %>% \n pivot_longer(cols = starts_with(\"SATS\"), names_to = \"Variable\", values_to = \"Mean_Scores\") %>% \n separate(Variable, into = c(NA, \"Subscale\", \"Timepoint\", NA), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Mean_Scores)\n```\n:::\n\n\n:::\n\n:::\n\nThen we can build a scatterplot with facets for the subscales\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_facet, aes(x = Time1, y = Time2)) +\n geom_point() +\n facet_wrap(~Subscale)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-26-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nProvide a barchart, a violin-boxplot, and a scatterplot from the loneliness data and the students have to try and recreate one of those in the lab. If they are overly fast, they can do the other 2.\n\n\n\n\n\n\n\n## [Test your knowledge on Chapters 3 and 4]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nWhich plot would you choose for\n\n### Activity 5: Your Turn\n\nPick **any single or two categorical variables** from the dataset and **choose one of the appropriate plot choices**. Start with a base plot and add other layers if you please. \n\nSave your plot and share it with us on Teams.\n\n\n", + "markdown": "# Data viz II {#sec-dataviz2}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter you should be able to:\n\n- choose an appropriate plot for continuous variables \n- choose an appropriate plot for a mix of continuous/categorical variables \n- create a basic version of an appropriate plot\n- apply extra layers to change the appearance of the plot\n\nIn this chapter, we continue our journey of appropriate plots. Last week, we examined which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**.\n\n\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data by Pownall et al. (2023). **Open the project**.\n* You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you.\n* The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download the csv again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\").\n* If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we need to load the package `tidyverse`, and read in the data `data_prp_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- ???\n```\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nThis is the same code as last week. We need to turn our categorical variables into factors to make plotting easier.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n::: {.callout-tip}\n\nIf you are working within the same `.Rmd` file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week's `.Rmd` file to load tidyverse into the library, read in the data, and convert some of the variables into factors.\n\n:::\n\n\n## Activity 3: Histogram (`geom_histogram()`) {#sec-hist}\n\nIf you want to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting \"in the background\".\n\nA histogram divides the data into \"bins\" (i.e., groupings displayed in a single bar). These bins are plotted along the x-axis, with the y-axis showing the count of observations in each bin. It's basically a barchart for continuous variables.\n\nLet's have a look at the age distribution in our dataset.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram() \n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Default histogram](05-dataviz2_files/figure-html/fig-hist-1.png){#fig-hist fig-align='center' width=100%}\n:::\n:::\n\n\n\nThe default **number of bins** is 30 (as shown in @fig-hist above). Changing the number of bins (argument `bins`) allows for more or less fine-tuning of the data. A higher number of bins results in more detailed granularity.\n\nPerhaps it’s more intuitive to modify the **width of each bin** using the `binwidth` argument. For example, `binwidth = 1` for the age category would mean each \"age group\" represents 1 year, while `binwidth = 5` would group ages into 5-year spans. The plots below show modifications for both `bins` and `binwidth`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n#less finetuning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(bins = 10) \n\n# more fineturning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1) \n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).\nRemoved 2 rows containing non-finite outside the scale range (`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Bins vs binwidth arguments](05-dataviz2_files/figure-html/fig-bins-1.png){#fig-bins fig-align='center' width=100%}\n:::\n:::\n\n\nThe **warning message** tells us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `data_prp_viz` to see if you can decipher the warning message.\n\nThe rows were removed because .\n\nColours are manipulated slightly differently than in the barchart. Click through each tab to see how you can modify colours, axis labels, margins, and breaks, and apply a different theme.\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument changes the colour of the bars, while the `colour` argument modifies the outline of the bars. Note that these arguments are **added directly to the `geom_histogram()`**, rather than within the overall `aes()`, as we did with the barchart.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: callout-tip\n\nYou could use:\n\n* Hex codes for `fill` and `colour`, as we did here: `geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")`. If you want to create your own colours, check out [this website](https://www.hexcolortool.com/){target=\"_blank\"}.\n\n* Pre-defined colour names: `geom_histogram(binwidth = 1, fill = \"purple\", colour = \"green\")`. See the full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\n:::\n\n\n## Axes labels, margins, and breaks\n\nHere we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y-axis is now displayed in increasing steps of 5 (rather than 10), and the x-axis has 1-year increments instead of 5. \n\nNotice how the `breaks =` argument changes the labels of the break ticks but not the scale limits. You can adjust the limits of the scale using the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values from 15 to 19, and 44 to 50 do not have labels? You would need to adjust that using the `breaks =` argument.\n\n\nThe `expansion()` function removes the gap between the x-axis and the bars. It is exactly the same code we used in @sec-dataviz.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 20, to = 43, by = 1),\n # Experimenting with\n limits = c(15, 50)\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_bw()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 19, to = 44, by = 1)\n ) +\n # pick a theme\n theme_bw()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n\n## Activity 4: Scatterplot (`geom_point()`) {#sec-scatter}\n\nScatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and Time point 2. The default scatterplot can be created with `geom_point()`.\n\nWe can also **add a trendline** by using `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = \"lm\"` inside the `geom_smooth()` function.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth()\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth(method = lm)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default Scatterplot with added trendline - loess (left) and linear (right)](05-dataviz2_files/figure-html/fig-scatter-1.png){#fig-scatter fig-align='center' width=100%}\n:::\n:::\n\n\n\n\nCustomising the colour of a scatterplot is slightly different from the other plots we’ve encountered so far. Technically, the point is not a \"filled-in black area\" but rather an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument and instead need to use the `colour` argument, similar to how we customised the outline of the histogram. \n\nSee the tabs below to learn how to change the colour for all points or how to adjust the colour based on groupings.\n\n\n::: {.panel-tabset}\n\n\n## Colour for all points\n\nIf we want to **change the colour of all the points**, we can add the `colour` argument to the `geom_point()` function. Likewise, to change the **colour of the trendline**, we would also use the `colour` argument. Here, we used pre-defined colour names, but HEX codes would work just as well.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# colour of all points and the trendline\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point(colour = 'magenta') +\n geom_smooth(method = lm, colour = 'turquoise')\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n## Colour with grouping\n\nIf we want the points to **change colour based on another grouping variable**, the `colour` argument should go inside the `aes()`. If you don’t want to define the colours manually, you can use a colour palette like Brewer (`scale_colour_brewer()`) or Viridis (`scale_colour_viridis_d()`).\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## adding grouping variable Pre_reg_group and changing the colour values manually\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-12-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n## Legend title and labels\n\nYou can tidy the legend title and group labels using the `scale_colour_?` function, depending on the palette you’re using (e.g., `scale_colour_manual()`, `scale_colour_brewer` and many more).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\"))\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-13-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAll other layers remain exactly the same as in other plots. Try adding layers to make the plot above prettier:\n\n* [ ] 1. relabel axes x and y\n* [ ] 2. set the x and y axis range from 1 to 7\n* [ ] 3. move the legend to a different position (either top, left, or bottom)\n* [ ] 4. add a theme\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## Hints\n\n1. can be done in 2 different ways - labs() or scale_x_?\n2. we did that for the histogram\n3. We did that for the barcharts\n4. pick a theme you like\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## More hints\n\nIf you are experiencing issues with the legend position and theme, try modifying the order of the layers.\n\n:::\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\")) +\n labs (x = \"Acceptance of Questionable Research Practices (Time 1)\", \n y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_light() + # place before moving the legend position\n theme(legend.position = \"top\") # move legend to the top\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-14-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n\n\n\n\n\n## Activity 5: Boxplot (`geom_boxplot()`)\n\nA boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to explore whether students' understanding of open science varies based on whether or not they have research experience. Our default boxplot would look like this:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-15-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\nTada! As usual, we can enhance the plot by adding various layers. Click on each tab below to see how.\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument inside the `aes()`. To customise the colours further, we can add a `scale_fill_?` layer. If you have specific colours in mind, use `scale_fill_manual()`. If you prefer pre-defined palettes, such as Brewer, you can use `scale_fill_brewer()`.\n\nBtw, this is exactly the same code we used for the barcharts.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n # customise colour\n scale_fill_brewer(palette = \"Dark2\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-16-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels\n\nWe need to relabel the axes. The function to use depends on the variable type. Here, we need `scale_x_discrete()` for the x-axis and `scale_y_continuous()` for the y-axis. We can also tidy up the group labels and adjust the breaks on the y-axis (e.g., in steps of 1 instead of 2) within these same functions.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n scale_x_discrete(\n # changing the label of x\n name = \"Research Experience\",\n # changing the group labels of the 2 groups\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Confidence in Understanding Open Science (Time 1)\",\n # changing break labels\n breaks = c(seq(from = 1, to = 7, by = 1))\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend and Theme\n\nThe legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill_?` function.\n\nLet's pick a theme we haven't used yet: `theme_dark()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\",\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(\n name = \"Research Experience\",\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n name = \"Confidence in Understanding Open Science (Time 1)\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_dark()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## Activity 6: Violin plot (`geom_violin()`)\n\nAn alternative way to display a **continuous variable with a categorical grouping variable** is a violin plot. Here, we want to create a violin plot to explore whether the perception of supervisor support depends on whether students plan to pre-register their dissertation. Our default violin plot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +\n geom_violin()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAdjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to\n\n* [ ] change the colours either manually or using a pre-defined colour palette \n* [ ] tidy the axes labels and group names\n* [ ] in case a legend appears, take it off\n* [ ] add a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +\n geom_violin() +\n scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(name = \"Plan to pre-register the dissertation\") + \n scale_y_continuous(\n name = \"Perceived Supervisory Support\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 7: Violin-boxplots\n\nSo far, we’ve only added one `geom_?` layer to our plots. However, thanks to ggplot's layered system, we can add multiple geoms, for example, when creating a violin-boxplot.\n\nRemember, the order of the layers can sometimes make a difference. We've seen this already - adding a theme at the end can override earlier arguments like the legend position. Similarly, `ggplot + violinplot + boxplot` will look different from `ggplot + boxplot + violinplot`.\n\nLet's use the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot()\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_boxplot() +\n geom_violin()\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default violin-boxplot: Order of the layer matters](05-dataviz2_files/figure-html/fig-vb_bv-1.png){#fig-vb_bv fig-align='center' width=100%}\n:::\n:::\n\n\n\nSee the tabs below to learn how to customise various elements, such as the width of the boxes, and the colour or opacity.\n\n\n::: {.panel-tabset}\n\n## Width of the boxes\n\nIf we want to get any information from the boxplot, we need to place it \"on top of\" the violin plot. But still, the boxplot is pretty wide and covers important details from the violin plot. To make the information more visible, we can **adjust the width of the boxes**. Finding an appropriate width might take some trial and error.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot(width = 0.2)\n```\n\n::: {.cell-output-display}\n![Default violin-boxplot: adjusting width of the box](05-dataviz2_files/figure-html/fig-vb_width-1.png){#fig-vb_width fig-align='center' width=100%}\n:::\n:::\n\n\n## Colour\n\nAdding colour should be pretty straightforward by now. The code is no different from what we used for the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, along with a `scale_fill_?` layer.\n\nHowever, we can further customise the plot by adding an **opacity argument** using `alpha` to the violin plot geom.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) + # alpha for opacity\n geom_boxplot(width = 0.2) + # change width of the boxes\n scale_fill_brewer(palette = \"RdPu\") # customise colour\n```\n\n::: {.cell-output-display}\n![Violin-boxplot with a different colour palette](05-dataviz2_files/figure-html/fig-vb_colour-1.png){#fig-vb_colour fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\n* [ ] Change the x- and y-axis labels\n* [ ] Remove the legend\n* [ ] add a theme\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + # removes the legend\n # change labels of x and y\n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 8: Faceting - adding another grouping variable\n\nFaceting is really useful when you have subsets in the data. We will apply it to the violin-boxplot from above, but you could add this layer to pretty much any plot. The function to split the plots into facets is called `facet_wrap()`.\n\nLet's **add another grouping variable**, `Pre_reg_group`, to create separate plots for the yes and no groups.\n\nSince the group labels on the x-axis are quite long, we will need to adjust them for better readability. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + \n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic() +\n facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no\n scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows\n```\n\n::: {.cell-output-display}\n![Pretty violin-boxplot split into pre-registration groups (yes and no)](05-dataviz2_files/figure-html/fig-vb_pretty_facet-1.png){#fig-vb_pretty_facet fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-tip}\n\nYou may have noticed that the labels of `Pre_reg_group` are displayed as numbers (1 and 2). If this bugs you, fix the labels in the data object. This would be less hassle than trying to adjust the facet headings in the plot.\n\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Variables with subscales\n\nFor example, if we want to show the relationship between SATs scores at Timepoints 1 and 2, separately for all four subscales of the SATs questionnaire, we need to wrangle the data so that the scores for Time 1 and Time 2 are in separate columns, but each participant has four rows (one for each subscale). The dataframe should look like this:\n\n\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(data_facet, n=5)\n```\n\n::: {.cell-output-display}\n
\n\n|Code |Subscale | Time1| Time2|\n|:----|:-------------------|--------:|--------:|\n|Tr10 |Affect | 4.000000| 3.500000|\n|Tr10 |CognitiveCompetence | 5.166667| 4.166667|\n|Tr10 |Value | 6.000000| 3.000000|\n|Tr10 |Difficulty | 3.571429| 2.857143|\n|Bi07 |Affect | 3.833333| 3.166667|\n\n
\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nCreate a new data object `data_facet` and wrangle the data from `data_prp_viz` so that it looks like the table above.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Broad hints\n\n* **Step 1**: select variables of interest from `data_prp_viz`\n* **Step 2**: pivot the data\n* **Step 3**: try to access information on subscales and timepoints from the variable names\n* **Step 4**: pivot in the other direction\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## More specific hints\n\n* **Step 1**: The variables of interest are the participants' ID and all columns that start with SATS.\n* **Step 2**: Pivot all columns, except the participant ID, from wide to long format.\n* **Step 3**: Get some inspiration from Activity 4 in {#sec-wrangling2}.\n* **Step 4**: At this point, subscales and timepoints should be in long format. However, only the subscale information should remain in long format. Pivot the data from long to wide format so that Time 1 and Time 2 are in separate columns.\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_facet <- data_prp_viz %>% \n select(Code, starts_with(\"SATS\")) %>% \n pivot_longer(cols = starts_with(\"SATS\"), names_to = \"Variable\", values_to = \"Mean_Scores\") %>% \n separate(Variable, into = c(NA, \"Subscale\", \"Timepoint\", NA), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Mean_Scores)\n```\n:::\n\n\n:::\n\n:::\n\nNow we can build a scatterplot with facets for the subscales.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_facet, aes(x = Time1, y = Time2)) +\n geom_point() +\n facet_wrap(~Subscale)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-26-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the data file we need to read in is `dog_data_clean_wide.csv`. I've named my data object `dog_data_wide` to shorten the name but feel free to use whatever object name sounds intuitive to you.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Re-create one of the 3 plots below {.unnumbered}\n\nRe-create one of the 3 plot below:\n\n* grouped barchart (easy)\n* violin-boxplot (medium)\n* scatterplot (hard)\n\n#### Difficulty level: easy {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-27-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n## Hints \n\n* I've created a new data object `data_bar` to select the relevant variables but you could also work straight from `dog_data_wide`.\n* Consider turning the 2 categorical variables into factors before plotting\n* Plotting should be relatively straightforward - these are default colours and you would only need to change the axes labels/ legend title. \n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n## More hints \n\nWe can change all of the 3 labels in one go. Check out the *## Prettier grouped barchart* in @sec-adv_bar, where we did exactly that.\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_bar`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_bar <- dog_data_wide %>% \n select(RID, GroupAssignment, Year_of_Study) %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")),\n Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the barchart\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) +\n geom_bar(position = \"dodge\") +\n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Year of Study\")\n```\n:::\n\n:::\n\n\n:::\n\n#### Difficulty level: medium {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-30-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-warning collapse=\"true\" icon=\"false\"}\n## Hints \n\n* I've created a new data object `data_vb` to select the relevant variables but you could also work straight from `dog_data_wide`.\n* Consider turning the categorical variable into a factor before plotting\n* Plotting tips:\n * the colour scale is one of the viridis options \n * it's a bit of trial and error for the opacity of the violin and the box width of the boxes (it is totally fine if it looks approximately right)\n * the tricky part might be adjusting the y-axis ticks. Take inspiration from the histogram in @sec-hist (Tab *Axes labels, margins, and breaks*)\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_vb`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_vb <- dog_data_wide %>% \n select(RID, Year_of_Study, Loneliness_post) %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n::: \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the violin-boxplot\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.25) +\n scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)),\n limits = c(1, 4)) +\n scale_fill_viridis_d(option = \"magma\",\n guide = \"none\") +\n labs(x = \"Year of Study\", y = \"Loneliness scores post intervention\") +\n theme_classic()\n```\n:::\n\n\n::: \n\n:::\n\n#### Difficulty level: hard {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-33-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-important collapse=\"true\" icon=\"false\"}\n## Hints \n\n* **Data wrangling**: Even though we cleaned the data, it may not be in the shape for the task at hand. Have a look what the data object `dog_data_wide` looks like and think about how you'd need to restructure it to be able to plot the scatterplot. As always, I would suggest creating a new data object for the scatterplot (e.g., `data_scatter`).\n* Once you have the data in the right shape, start **plotting**. Start with a basic scatterplot and then add various layers and change elements you notice. \n* Remember, some finetuning might need to be done in `data_scatter` rather than plot itself.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Data structure you have\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n
\n\n| RID| PANAS_PA_pre| PANAS_PA_post| PANAS_NA_pre| PANAS_NA_post|\n|---:|------------:|-------------:|------------:|-------------:|\n| 1| 3.2| 3.8| 1.0| 1.2|\n| 2| 3.0| 3.2| 1.8| 1.0|\n| 3| 2.8| 3.0| 1.6| 1.6|\n| 4| 4.2| 3.8| 1.8| 1.6|\n| 5| 3.4| 4.0| 2.2| 1.6|\n\n
\n:::\n:::\n\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Data structure you need\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n
\n\n| RID|Subscale | pre| post|\n|---:|:---------------|---:|----:|\n| 1|Positive Affect | 3.2| 3.8|\n| 1|Negative Affect | 1.0| 1.2|\n| 2|Positive Affect | 3.0| 3.2|\n| 2|Negative Affect | 1.8| 1.0|\n| 3|Positive Affect | 2.8| 3.0|\n\n
\n:::\n:::\n\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Hints for `data_scatter`\n\n* **Step 1**: select the variables you need from `dog_data_wide`.\n* **Step 2**: pivot all columns (bar the Participant ID) into long format\n* **Step 3**: think about how to separate information of the subscales and timepoints\n* **Step 4**: pivot from long into wide format. Take some inspiration from the `Special case: Variables with subscales` scenario above.\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Hints for the plot\n\n* The colour scheme is Dark2 from the colour palette brewer\n* The colour of the trendline is #7570b3\n* Think about how to make the Negative and Positive Affect points different colours. The solution is in @sec-scatter\n* Renaming the different facets is one of those things that should be fixed in the data object instead\n\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_scatter`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_scatter <- dog_data_wide %>% \n select(RID, starts_with(\"PANAS\")) %>% \n pivot_longer(cols = -RID, names_to = \"Q\", values_to = \"Values\") %>% \n separate(Q, into = c(NA, \"Subscale\", \"Timepoint\"), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Values) %>% \n mutate(Subscale = case_match(Subscale,\n \"NA\" ~ \"Negative Affect\",\n \"PA\" ~ \"Positive Affect\"),\n Subscale = factor(Subscale)) %>% \n drop_na()\n```\n:::\n\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the scatterplot\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) +\n geom_point() +\n geom_smooth(method = lm, colour = \"#7570b3\") +\n facet_wrap(~Subscale) +\n labs(x = \"Pre-Intervention (Timepoint 1)\",\n y = \"Post-Intervention (Timepoint 2)\") +\n scale_colour_brewer(palette = \"Dark2\",\n guide = \"none\") +\n theme_bw()\n```\n:::\n\n\n\n:::\n\n:::\n\nIf you are extremely fast, challenge yourself and re-create one of the other plots.\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n### Knowledge check {.unnumbered}\n\n\n#### Question 1 {.unnumbered}\n\nWhy would this line of code not create a barplot, assuming you already loaded all data and libraries and you spelt the data and column names correctly?\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(summarydata, aes(x = sex)) +\n geom_barplot()\n```\n:::\n\n\n
\n\n\n\n\n#### Question 2 {.unnumbered}\n\nIf I wanted precisely 5 bars in my histogram, what argument would I use? \n\n
\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain this answer\n\n* `ggplot() + geom_histogram(bins = 5)`. This is the **correct** answer as you are asking ggplot2 to give you the plot organised into 5 bins. \n* `ggplot() + geom_histogram(bars = 5)`. This is incorrect as you bars is not the right argument name. You want 5 bars, but the argument is bins. \n* `ggplot() + geom_histogram(binwidth = 5)`. This is incorrect as binwidth controls the x-axis range to include per bar, rather than the number of bars. \n* `ggplot() + geom_histogram()`. This is incorrect as you did not control the number of bins, so it will default to 30. \n\n:::\n\n\n#### Question 3 {.unnumbered}\n\nYou want to create a scatterplot to show the correlation between two continuous variables, which geom would you use? \n\n
\n\n\n\n#### Question 4 {.unnumbered}\n\nTrue or False? To showcase different groups in a scatterplot, you could specify a grouping variable using the `fill` argument to change the colour of the points. \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Explain this answer\n\nHave a look in the scatterplot section again. There, we explained that we cannot \"colour in\" the area of the points using `fill`, but need to change the outline colour of the points with `colour` instead.\n:::\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\nLet's go back to the Palmer penguins for this part.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n```\n:::\n\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) %>% \n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! `mapping` must be created by `aes()`.\nℹ Did you use `%>%` or `|>` instead of `+`?\n```\n:::\n:::\n\n\nThe error message is incredibly useful. So that should be an easy fix!\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nTo fix the error, all we need to do is turn the pipe `%>%` into a plus `+`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-41-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nWe want to create a violin-boxplot showing the bill length across species, separately for male and female penguins.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npenguins_no_na <- penguins %>% \n drop_na(island, flipper_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = flipper_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-42-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but is it the one we aimed for? \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThis was a tricky one. The code runs, so we think everything is fine. Despite having re-labelled the y-axis \"Bill length in mm\", it's really showing the flipper length. Did you spot this???\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npenguins_no_na <- penguins %>% \n drop_na(island, bill_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = bill_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n#### Question 7 {.unnumbered}\n\nThis time, we want to show the relationship between flipper length and body mass of the penguins. The following code runs, but the x- and y-axes are misbehaving somehow. Not what we wanted. Any idea why?\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_discrete(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_discrete(name = \"Flipper length in mm\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in scale_y_discrete(name = \"Body mass in g\", breaks = seq(2500, : Continuous limits supplied to discrete scale.\nℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-44-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nIn this example, we used the wrong function for continuous variables. We used `scale_y_discrete` and `scale_y_discrete`, instead of `scale_x_continuous` and `scale_y_continuous`. We must honour the variable type when we customise the plot, so think about what type of variable is on each axis and which function lets you edit it. \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_continuous(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_continuous(name = \"Flipper length in mm\")\n```\n:::\n\n:::\n\n", "supporting": [ "05-dataviz2_files" ], diff --git a/.quarto/cites/index.json b/.quarto/cites/index.json index af2cfae..2966493 100644 --- a/.quarto/cites/index.json +++ b/.quarto/cites/index.json @@ -1 +1 @@ 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+{"options":{"chapters-alpha":true,"chapters-appendix":true,"chapter-id":"symbols","chapters":true},"headings":["symbols"],"entries":[]} \ No newline at end of file diff --git a/.quarto/xref/b49efd57 b/.quarto/xref/b49efd57 index ff5f063..874ba09 100644 --- a/.quarto/xref/b49efd57 +++ b/.quarto/xref/b49efd57 @@ -1 +1 @@ -{"entries":[{"key":"sec-dataviz2","order":{"number":1,"section":[5,0,0,0,0,0,0]}},{"key":"fig-scatter","caption":"Default Scatterplot with added trendline - loess (left) and linear (right)","order":{"number":3,"section":[5,4,0,0,0,0,0]}},{"key":"fig-bins","caption":"Bins vs binwidth arguments","order":{"number":2,"section":[5,3,0,0,0,0,0]}},{"key":"fig-vb_bv","caption":"Default violin-boxplot: Order of the layer matters","order":{"number":4,"section":[5,7,0,0,0,0,0]}},{"key":"fig-vb_width","caption":"Default violin-boxplot: adjusting width of the box","order":{"number":5,"section":[5,7,0,0,0,0,0]}},{"key":"fig-hist","caption":"Default 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b/.quarto/xref/e3ca897e @@ -1 +1 @@ -{"headings":["overview","how-to-use-this-book","statement-on-use-of-ai"],"entries":[],"options":{"chapters":true}} \ No newline at end of file +{"options":{"chapters":true},"entries":[],"headings":["overview","how-to-use-this-book","statement-on-use-of-ai"]} \ No newline at end of file diff --git a/04-dataviz.qmd b/04-dataviz.qmd index a3c1f16..db8f31a 100644 --- a/04-dataviz.qmd +++ b/04-dataviz.qmd @@ -267,7 +267,7 @@ data_prp_viz <- data_prp_viz %>% ## Activity 3: Barchart (`geom_bar()`) -A bar chart is the best choice when you want to plot a single categorical variable. +A barchart is the best choice when you want to plot a single categorical variable. For example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a **barplot**. This is done with `geom_bar()` in the third layer. Since the counting is done automatically in the background, the `aes()` function only requires an x value (i.e., the name of your variable). @@ -422,7 +422,7 @@ ggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) + ::: -## Activity 5: Stacked, Percent Stacked, and Grouped Barchart +## Activity 5: Stacked, Percent Stacked, and Grouped Barchart {#sec-adv_bar} When dealing with **two categorical variables**, you have three options for displaying stacked barcharts: the "normal" **Stacked Barchart** (the default option), a **Percent Stacked Barchart**, or a **Grouped Barchart**. diff --git a/05-dataviz2.qmd b/05-dataviz2.qmd index 7cd2bed..5ae57f5 100644 --- a/05-dataviz2.qmd +++ b/05-dataviz2.qmd @@ -14,11 +14,11 @@ library(wesanderson) By the end of this chapter you should be able to: - choose an appropriate plot for continuous variables -- choose an appropriate plot when you've got a mix of continuous/categorical variables +- choose an appropriate plot for a mix of continuous/categorical variables - create a basic version of an appropriate plot - apply extra layers to change the appearance of the plot -In this chapter, we are continuing our journey of appropriate plots. Last week, we looked at which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**. +In this chapter, we continue our journey of appropriate plots. Last week, we examined which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**. @@ -27,16 +27,16 @@ In this chapter, we are continuing our journey of appropriate plots. Last week, ## Activity 1: Set-up and data for today -- We are still working with the data by Pownall et al. (2023). **Open the project**. -- You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you. -- The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download it again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv "download"). -- If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1. +* We are still working with the data by Pownall et al. (2023). **Open the project**. +* You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you. +* The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download the csv again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv "download"). +* If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1. ## Activity 2: Load in libraries, read in data, and adjust data types -We need the package `tidyverse` today, and the data `data_prp_ch3.csv`. +Today, we need to load the package `tidyverse`, and read in the data `data_prp_ch4.csv`. ```{r eval=FALSE} ## packages @@ -68,7 +68,7 @@ data_prp_viz <- read_csv("data/data_prp_for_ch4.csv") ``` -This is the same as last week. We need to turn our categorical variables into factors to make plotting easier. +This is the same code as last week. We need to turn our categorical variables into factors to make plotting easier. ```{r} data_prp_viz <- data_prp_viz %>% @@ -97,11 +97,11 @@ If you are working within the same `.Rmd` file as last week, you can skip these ::: -## Activity 3: Histogram (`geom_histogram()`) +## Activity 3: Histogram (`geom_histogram()`) {#sec-hist} -If you wanted to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting "in the background". +If you want to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting "in the background". -A histogram splits the data into “bins” (i.e., groupings displayed in a single bar). These values are plotted along the x-axis and shows the count of how many observations are in each bin along the y-axis. It's basically a bar chart for continuous variables. +A histogram divides the data into "bins" (i.e., groupings displayed in a single bar). These bins are plotted along the x-axis, with the y-axis showing the count of observations in each bin. It's basically a barchart for continuous variables. Let's have a look at the age distribution in our dataset. @@ -111,9 +111,10 @@ ggplot(data_prp_viz, aes(x = Age)) + ``` -The default **bin number** is 30 (as displayed in @fig-hist above). Changing the number of bins (argument `bins`) can help to show more or less fine tuning in the data. Bigger numbers of bins means more finetuning. +The default **number of bins** is 30 (as shown in @fig-hist above). Changing the number of bins (argument `bins`) allows for more or less fine-tuning of the data. A higher number of bins results in more detailed granularity. + +Perhaps it’s more intuitive to modify the **width of each bin** using the `binwidth` argument. For example, `binwidth = 1` for the age category would mean each "age group" represents 1 year, while `binwidth = 5` would group ages into 5-year spans. The plots below show modifications for both `bins` and `binwidth`. -Perhaps it's more intuitive to modify the **width of each bin** instead via the argument (`binwidth`). So for example `binwidth = 1` for category age would mean 1 year of age per "age group"; `binwidth = 5` would mean 5 years of age span per "age group", etc. The plots below show modifications for both `bin` and `binwidth`. ```{r eval = FALSE, message = FALSE} #less finetuning @@ -139,17 +140,19 @@ binwidth_manipulation <- bins_manipulation + binwidth_manipulation + plot_layout(nrow = 1) ``` -The **warning message** telling us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `hp_data` to see if you can decipher the warning message. +The **warning message** tells us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `data_prp_viz` to see if you can decipher the warning message. + +The rows were removed because `r mcq(c(x = "they fall outside of the plot range", answer = "they contain missing values"))`. -They were removed because `r mcq(c(x = "they fall outside of the plot range", answer = "they contain missing values"))`. +Colours are manipulated slightly differently than in the barchart. Click through each tab to see how you can modify colours, axis labels, margins, and breaks, and apply a different theme. -Colours are getting manipulated slightly differently to the barchart. Click through each tab to see how you can alter colour, axes labels, margins and breaks, and add a different theme. ::: {.panel-tabset} ## Colour -We can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument manipulates the colour of the bars, and the `colour` argument changes the outline of the bars. Pay attention that they are **added directly to the `geom_histogram` arguments**, not the overall `aes()` like we did with the boxplot. +We can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument changes the colour of the bars, while the `colour` argument modifies the outline of the bars. Note that these arguments are **added directly to the `geom_histogram()`**, rather than within the overall `aes()`, as we did with the barchart. + ```{r warning=FALSE} ggplot(data_prp_viz, aes(x = Age)) + @@ -158,21 +161,24 @@ ggplot(data_prp_viz, aes(x = Age)) + ::: callout-tip -You could use +You could use: -* hex codes for fill and color, like we used here, `geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE")`. If you want create your own colours, check out [this website](https://www.hexcolortool.com/){target="_blank"}. OR -* a pre-defined colour name `geom_histogram(binwidth = 1, fill = "purple", colour = "green")`. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target="_blank"} OR +* Hex codes for `fill` and `colour`, as we did here: `geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE")`. If you want to create your own colours, check out [this website](https://www.hexcolortool.com/){target="_blank"}. +* Pre-defined colour names: `geom_histogram(binwidth = 1, fill = "purple", colour = "green")`. See the full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target="_blank"}. ::: + ## Axes labels, margins, and breaks -Here we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y axis is now displayed in increasing steps of 5 (rather than 10), and the x axis has 1-year increments rather than 5. +Here we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y-axis is now displayed in increasing steps of 5 (rather than 10), and the x-axis has 1-year increments instead of 5. -Notice how the `breaks =` argument manipulates the labels of the break ticks but not the limit of the scale. You can manipulate the limits of the scale with the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values for 15 to 19, and 44 to 50 do not have a label. You would need to adjust that in `breaks =` argument. +Notice how the `breaks =` argument changes the labels of the break ticks but not the scale limits. You can adjust the limits of the scale using the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values from 15 to 19, and 44 to 50 do not have labels? You would need to adjust that using the `breaks =` argument. + + +The `expansion()` function removes the gap between the x-axis and the bars. It is exactly the same code we used in @sec-dataviz. -The `expansion()` function removes the gap between x axis and bars. It is exactly the same code as we used in @sec-dataviz . ```{r warning=FALSE} ggplot(data_prp_viz, aes(x = Age)) + @@ -220,11 +226,13 @@ ggplot(data_prp_viz, aes(x = Age)) + ::: -## Activity 4: Scatterplot (`geom_point()`) +## Activity 4: Scatterplot (`geom_point()`) {#sec-scatter} + +Scatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and Time point 2. The default scatterplot can be created with `geom_point()`. + +We can also **add a trendline** by using `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = "lm"` inside the `geom_smooth()` function. -Scatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and 2. The default scatterplot would be created with `geom_point()`. -We could also **add a trendline** by adding `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = lm` into `geom_smooth()` function. ```{r eval=FALSE} ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) + @@ -237,7 +245,7 @@ ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Tim ``` -```{r fig-scatter, echo=FALSE, fig.cap="Default Scatterplot with added trendline - loess (left) and linear (right)"} +```{r fig-scatter, echo=FALSE, message=FALSE, fig.cap="Default Scatterplot with added trendline - loess (left) and linear (right)"} Scat_1 <- ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) + geom_point() + geom_smooth() @@ -251,16 +259,21 @@ Scat_1 + scat_2 + plot_layout(nrow = 1) -Customising the colour of plot is slightly different to the other plots we've encountered so far. Technically, the point is not a "filled-in black area", but an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument, but have to switch to the `colour` argument (like we did for the outline of the histogram). See the tabs below how to change the colour for all points or if you want to change the colour according to groupings. +Customising the colour of a scatterplot is slightly different from the other plots we’ve encountered so far. Technically, the point is not a "filled-in black area" but rather an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument and instead need to use the `colour` argument, similar to how we customised the outline of the histogram. + +See the tabs below to learn how to change the colour for all points or how to adjust the colour based on groupings. ::: {.panel-tabset} + ## Colour for all points -If we want to **change the colour of all the points**, we would add the `colour` argument to the `geom_point()` function. Likewise, changing the **colour of the trendline** would also require a `colour` argument. Here we went with pre-defined colour names, but HEX codes would work too +If we want to **change the colour of all the points**, we can add the `colour` argument to the `geom_point()` function. Likewise, to change the **colour of the trendline**, we would also use the `colour` argument. Here, we used pre-defined colour names, but HEX codes would work just as well. -```{r} + + +```{r message=FALSE} # colour of all points and the trendline ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) + geom_point(colour = 'magenta') + @@ -268,11 +281,12 @@ ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Tim ``` + ## Colour with grouping -If we wanted the points to **change colour due to another grouping variable**, the colour argument would go into the `aes()` If you don't want to define the colours manually, use a colour palette like brewer (`scale_colour_brewer()`) or viridis (`scale_colour_viridis_d()`). +If we want the points to **change colour based on another grouping variable**, the `colour` argument should go inside the `aes()`. If you don’t want to define the colours manually, you can use a colour palette like Brewer (`scale_colour_brewer()`) or Viridis (`scale_colour_viridis_d()`). -```{r} +```{r message=FALSE} ## adding grouping variable Pre_reg_group and changing the colour values manually ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) + geom_point() + @@ -281,12 +295,13 @@ ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Tim ``` + ## Legend title and labels -You can tidy the legend title and group labels via the `scale_colour_?` function +You can tidy the legend title and group labels using the `scale_colour_?` function, depending on the palette you’re using (e.g., `scale_colour_manual()`, `scale_colour_brewer` and many more). -```{r} +```{r message=FALSE} ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) + geom_point() + geom_smooth(method = lm) + @@ -301,7 +316,7 @@ ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Tim ## Your Turn -All other layers would be exactly the same as in other plots. Try to add layers to the make the plot above prettier: +All other layers remain exactly the same as in other plots. Try adding layers to make the plot above prettier: * [ ] 1. relabel axes x and y * [ ] 2. set the x and y axis range from 1 to 7 @@ -315,18 +330,25 @@ All other layers would be exactly the same as in other plots. Try to add layers 1. can be done in 2 different ways - labs() or scale_x_? 2. we did that for the histogram -3. We did that for the bar charts +3. We did that for the barcharts 4. pick a theme you like -::: +::: {.callout-note collapse="true" icon="false"} + +## More hints +If you are experiencing issues with the legend position and theme, try modifying the order of the layers. + +::: + +::: ::: {.callout-caution collapse="true" icon="false"} ## Solution -```{r} +```{r message=FALSE} ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) + geom_point() + geom_smooth(method = lm) + @@ -353,7 +375,8 @@ ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Tim ## Activity 5: Boxplot (`geom_boxplot()`) -A boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to investigate whether their understanding of open science depends on whether or not students had research experience. Our default boxplot would look like this: +A boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to explore whether students' understanding of open science varies based on whether or not they have research experience. Our default boxplot would look like this: + ```{r} # default boxplot @@ -361,14 +384,15 @@ ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) + geom_boxplot() ``` -Tada. As usual, we can make the plot pretty by adding various layers. Click on the tabs to see how + +Tada! As usual, we can enhance the plot by adding various layers. Click on each tab below to see how. ::: {.panel-tabset} ## Colour -We can change the colour by adding a fill argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` layer If you have specific colours in mind, you would use `scale_fill_manual()` or if you want to stick with pre-defined ones, like brewer `scale_fill_brewer()`. +We can change the colour by adding a `fill` argument inside the `aes()`. To customise the colours further, we can add a `scale_fill_?` layer. If you have specific colours in mind, use `scale_fill_manual()`. If you prefer pre-defined palettes, such as Brewer, you can use `scale_fill_brewer()`. Btw, this is exactly the same code we used for the barcharts. @@ -381,7 +405,8 @@ ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Re ## Axes labels -We need to relabel the axes labels for x and y with `scale_x_discrete()` and `scale_y_continuous()`. We can also tidy the labels for the groups and the breaks on the y-axis (in steps of 1 rather than 2) within the same functions +We need to relabel the axes. The function to use depends on the variable type. Here, we need `scale_x_discrete()` for the x-axis and `scale_y_continuous()` for the y-axis. We can also tidy up the group labels and adjust the breaks on the y-axis (e.g., in steps of 1 instead of 2) within these same functions. + ```{r} ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) + @@ -403,9 +428,10 @@ ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Re ## Legend and Theme -The legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = "none"` into the `scale_fill` function. +The legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = "none"` to the `scale_fill_?` function. + +Let's pick a theme we haven't used yet: `theme_dark()`. -Let's pick a theme we haven't used yet: `theme_dark()` ```{r} ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) + @@ -432,7 +458,7 @@ ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Re ## Activity 6: Violin plot (`geom_violin()`) -An alternative to display a continuous variable with categorical grouping variable is a violin plot. Here, we want to create a violin plot to investigate whether the perception of supervisor support depended on planning to pre-register the dissertation. Our default violin plot would look like this: +An alternative way to display a **continuous variable with a categorical grouping variable** is a violin plot. Here, we want to create a violin plot to explore whether the perception of supervisor support depends on whether students plan to pre-register their dissertation. Our default violin plot would look like this: ```{r} # default boxplot @@ -444,7 +470,7 @@ ggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) + ## Your Turn -Adjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to +Adjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to * [ ] change the colours either manually or using a pre-defined colour palette * [ ] tidy the axes labels and group names @@ -477,11 +503,12 @@ ggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Pl ## Activity 7: Violin-boxplots -So far, we've only added one `geom_` to our plots. Due to the layered system, we could add multiple geoms, for example, when creating a violin-boxplot. +So far, we’ve only added one `geom_?` layer to our plots. However, thanks to ggplot's layered system, we can add multiple geoms, for example, when creating a violin-boxplot. + +Remember, the order of the layers can sometimes make a difference. We've seen this already - adding a theme at the end can override earlier arguments like the legend position. Similarly, `ggplot + violinplot + boxplot` will look different from `ggplot + boxplot + violinplot`. -Remember, the order of the layers makes a difference sometimes. We've seen already, with themes we added at the very end that could override the argument for a legend position earlier. Here, `ggplot + violinplot + boxplot` looks different to `ggplot + boxplot + violinplot`. +Let's use the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade. -Let's take the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade. ```{r eval=FALSE} ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) + @@ -510,14 +537,15 @@ vb + bv + plot_layout(nrow = 1) ``` -Customising a few elements such as width of the boxes and the colour. +See the tabs below to learn how to customise various elements, such as the width of the boxes, and the colour or opacity. ::: {.panel-tabset} ## Width of the boxes -If we want to get any information from the boxplot, we need to use order 1. But still, the boxplot is pretty wide and covers up important information from the violin. We could **adjust the width of the boxes** to make information more visible. This takes a bit of trial and error which width looks appropriate. +If we want to get any information from the boxplot, we need to place it "on top of" the violin plot. But still, the boxplot is pretty wide and covers important details from the violin plot. To make the information more visible, we can **adjust the width of the boxes**. Finding an appropriate width might take some trial and error. + ```{r, fig-vb_width, fig.cap="Default violin-boxplot: adjusting width of the box"} ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) + @@ -527,9 +555,10 @@ ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) + ## Colour -Adding colour should be pretty straight forward by now. This code is no different to the one we used in the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, and a `scale_fill_?`layer. +Adding colour should be pretty straightforward by now. The code is no different from what we used for the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, along with a `scale_fill_?` layer. + +However, we can further customise the plot by adding an **opacity argument** using `alpha` to the violin plot geom. -However, we can modify this further by adding an opacity argument `alpha` to the violin plot geom. ```{r fig-vb_colour, fig.cap="Violin-boxplot with a different colour palette"} ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) + @@ -570,11 +599,12 @@ ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fi ## Activity 8: Faceting - adding another grouping variable -Faceting is really useful when you have subsets in the data. We will use this here on the violin-boxplot from above, but you could add this to pretty much any plot. The function to split up the plots into facets is called `facet_wrap()`. +Faceting is really useful when you have subsets in the data. We will apply it to the violin-boxplot from above, but you could add this layer to pretty much any plot. The function to split the plots into facets is called `facet_wrap()`. -Let's **add another grouping variable**, `Pre_reg_group`, so we can see separate plots for the yes and no groups. +Let's **add another grouping variable**, `Pre_reg_group`, to create separate plots for the yes and no groups. + +Since the group labels on the x-axis are quite long, we will need to adjust them for better readability. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows. -Since the group labels on the x-axis are quite long, we need to adjust them. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows. ```{r fig-vb_pretty_facet, fig.cap="Pretty violin-boxplot split into pre-registration groups (yes and no)"} @@ -592,7 +622,7 @@ ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fi ::: {.callout-tip} -The labels of `Pre_reg_group` are displayed as numbers 1 and 2. If this bugs you, fix the labels in the dataset. This would be less hassle than trying to fix it in the plot. +You may have noticed that the labels of `Pre_reg_group` are displayed as numbers (1 and 2). If this bugs you, fix the labels in the data object. This would be less hassle than trying to adjust the facet headings in the plot. ::: @@ -602,7 +632,9 @@ The labels of `Pre_reg_group` are displayed as numbers 1 and 2. If this bugs you ## Special case: Variables with subscales -For example, we want to show the relationship between SATs scores at timepoints 1 and 2, separately for all 4 subscales of the SATs questionnaires, we would need to wrangle the data first. The dataframe we need should look like this: +For example, if we want to show the relationship between SATs scores at Timepoints 1 and 2, separately for all four subscales of the SATs questionnaire, we need to wrangle the data so that the scores for Time 1 and Time 2 are in separate columns, but each participant has four rows (one for each subscale). The dataframe should look like this: + + ```{r include=FALSE} data_facet <- data_prp_viz %>% @@ -616,16 +648,34 @@ data_facet <- data_prp_viz %>% head(data_facet, n=5) ``` -Try wrangling the data so that it looks like `data_facet` above + +::: {.callout-note icon="false"} + +## Your Turn + +Create a new data object `data_facet` and wrangle the data from `data_prp_viz` so that it looks like the table above. ::: {.callout-note collapse="true" icon="false"} -## Hints +## Broad hints + +* **Step 1**: select variables of interest from `data_prp_viz` +* **Step 2**: pivot the data +* **Step 3**: try to access information on subscales and timepoints from the variable names +* **Step 4**: pivot in the other direction + +::: + +::: {.callout-note collapse="true" icon="false"} + +## More specific hints + +* **Step 1**: The variables of interest are the participants' ID and all columns that start with SATS. +* **Step 2**: Pivot all columns, except the participant ID, from wide to long format. +* **Step 3**: Get some inspiration from Activity 4 in {#sec-wrangling2}. +* **Step 4**: At this point, subscales and timepoints should be in long format. However, only the subscale information should remain in long format. Pivot the data from long to wide format so that Time 1 and Time 2 are in separate columns. -* step 1: select variables of interest -* step 2: pivot -* step 3: try to access information on subscales and timepoints from the variable names -* step 4: pivot in the other direction +::: ::: {.callout-caution collapse="true" icon="false"} @@ -643,7 +693,7 @@ data_facet <- data_prp_viz %>% ::: -Then we can build a scatterplot with facets for the subscales +Now we can build a scatterplot with facets for the subscales. ```{r} ggplot(data_facet, aes(x = Time1, y = Time2)) + @@ -658,22 +708,441 @@ ggplot(data_facet, aes(x = Time1, y = Time2)) + ## [Pair-coding]{style="color: #F39C12; text-transform: uppercase;"} {.unnumbered} -Provide a barchart, a violin-boxplot, and a scatterplot from the loneliness data and the students have to try and recreate one of those in the lab. If they are overly fast, they can do the other 2. +### Task 1: Open the R project for the lab {.unnumbered} + +### Task 2: Create a new `.Rmd` file {.unnumbered} + +... and name it something useful. If you need help, have a look at @sec-rmd. + +### Task 3: Load in the library and read in the data {.unnumbered} + +The data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip "download"). + +We are using the package `tidyverse` today, and the data file we need to read in is `dog_data_clean_wide.csv`. I've named my data object `dog_data_wide` to shorten the name but feel free to use whatever object name sounds intuitive to you. + +```{r reading in data for me, echo=FALSE, message=FALSE} +library(tidyverse) + +dog_data_wide <- read_csv("data/dog_data_clean_wide.csv") +dog_data_long <- read_csv("data/dog_data_clean_long.csv") +``` + + +### Task 4: Re-create one of the 3 plots below {.unnumbered} +Re-create one of the 3 plot below: +* grouped barchart (easy) +* violin-boxplot (medium) +* scatterplot (hard) +#### Difficulty level: easy {.unnumbered} +```{r echo=FALSE} +data_bar <- dog_data_wide %>% + select(RID, GroupAssignment, Year_of_Study) %>% + mutate(GroupAssignment = factor(GroupAssignment, + levels = c("Direct", "Indirect", "Control")), + Year_of_Study = factor(Year_of_Study, + levels = c("First", "Second", "Third", "Fourth", "Fifth or above"))) + +ggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) + + geom_bar(position = "dodge") + + labs(x = "Experimental Group", y = "Count", fill = "Year of Study") +``` -## [Test your knowledge on Chapters 3 and 4]{style="color: #F39C12; text-transform: uppercase;"} {.unnumbered} +::: {.callout-tip collapse="true" icon="false"} +## Hints -Which plot would you choose for +* I've created a new data object `data_bar` to select the relevant variables but you could also work straight from `dog_data_wide`. +* Consider turning the 2 categorical variables into factors before plotting +* Plotting should be relatively straightforward - these are default colours and you would only need to change the axes labels/ legend title. -### Activity 5: Your Turn -Pick **any single or two categorical variables** from the dataset and **choose one of the appropriate plot choices**. Start with a base plot and add other layers if you please. +::: {.callout-tip collapse="true" icon="false"} +## More hints -Save your plot and share it with us on Teams. +We can change all of the 3 labels in one go. Check out the *## Prettier grouped barchart* in @sec-adv_bar, where we did exactly that. + +::: + +::: {.callout-caution collapse="true" icon="false"} +## Solution for `data_bar` + +```{r eval=FALSE} +data_bar <- dog_data_wide %>% + select(RID, GroupAssignment, Year_of_Study) %>% + mutate(GroupAssignment = factor(GroupAssignment, + levels = c("Direct", "Indirect", "Control")), + Year_of_Study = factor(Year_of_Study, + levels = c("First", "Second", "Third", "Fourth", "Fifth or above"))) +``` +::: + +::: {.callout-caution collapse="true" icon="false"} +## Solution for the barchart + +```{r eval=FALSE} +ggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) + + geom_bar(position = "dodge") + + labs(x = "Experimental Group", y = "Count", fill = "Year of Study") +``` +::: + + +::: + +#### Difficulty level: medium {.unnumbered} + +```{r echo=FALSE} +data_vb <- dog_data_wide %>% + select(RID, Year_of_Study, Loneliness_post) %>% + mutate(Year_of_Study = factor(Year_of_Study, + levels = c("First", "Second", "Third", "Fourth", "Fifth or above"))) + +ggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) + + geom_violin(alpha = 0.5) + + geom_boxplot(width = 0.25) + + scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)), + limits = c(1, 4)) + + scale_fill_viridis_d(option = "magma", + guide = "none") + + labs(x = "Year of Study", y = "Loneliness scores post intervention") + + theme_classic() +``` + +::: {.callout-warning collapse="true" icon="false"} +## Hints + +* I've created a new data object `data_vb` to select the relevant variables but you could also work straight from `dog_data_wide`. +* Consider turning the categorical variable into a factor before plotting +* Plotting tips: + * the colour scale is one of the viridis options + * it's a bit of trial and error for the opacity of the violin and the box width of the boxes (it is totally fine if it looks approximately right) + * the tricky part might be adjusting the y-axis ticks. Take inspiration from the histogram in @sec-hist (Tab *Axes labels, margins, and breaks*) + + +::: {.callout-caution collapse="true" icon="false"} +## Solution for `data_vb` + +```{r eval=FALSE} +data_vb <- dog_data_wide %>% + select(RID, Year_of_Study, Loneliness_post) %>% + mutate(Year_of_Study = factor(Year_of_Study, + levels = c("First", "Second", "Third", "Fourth", "Fifth or above"))) +``` + +::: + +::: {.callout-caution collapse="true" icon="false"} +## Solution for the violin-boxplot + +```{r eval=FALSE} +ggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) + + geom_violin(alpha = 0.5) + + geom_boxplot(width = 0.25) + + scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)), + limits = c(1, 4)) + + scale_fill_viridis_d(option = "magma", + guide = "none") + + labs(x = "Year of Study", y = "Loneliness scores post intervention") + + theme_classic() +``` + +::: + +::: + +#### Difficulty level: hard {.unnumbered} + +```{r echo=FALSE, warning=FALSE, message=FALSE} +data_scatter <- dog_data_wide %>% + select(RID, starts_with("PANAS")) %>% + pivot_longer(cols = -RID, names_to = "Q", values_to = "Values") %>% + separate(Q, into = c(NA, "Subscale", "Timepoint"), sep = "_") %>% + pivot_wider(names_from = Timepoint, values_from = Values) %>% + mutate(Subscale = case_match(Subscale, + "NA" ~ "Negative Affect", + "PA" ~ "Positive Affect"), + Subscale = factor(Subscale)) %>% + drop_na() + + +ggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) + + geom_point() + + geom_smooth(method = lm, colour = "#7570b3") + + facet_wrap(~Subscale) + + labs(x = "Pre-Intervention (Timepoint 1)", + y = "Post-Intervention (Timepoint 2)") + + scale_colour_brewer(palette = "Dark2", + guide = "none") + + theme_bw() + +``` + + +::: {.callout-important collapse="true" icon="false"} +## Hints + +* **Data wrangling**: Even though we cleaned the data, it may not be in the shape for the task at hand. Have a look what the data object `dog_data_wide` looks like and think about how you'd need to restructure it to be able to plot the scatterplot. As always, I would suggest creating a new data object for the scatterplot (e.g., `data_scatter`). +* Once you have the data in the right shape, start **plotting**. Start with a basic scatterplot and then add various layers and change elements you notice. +* Remember, some finetuning might need to be done in `data_scatter` rather than plot itself. + +::: {.callout-note collapse="true" icon="false"} +## Data structure you have + +```{r echo=FALSE} +data_scatter <- dog_data_wide %>% + select(RID, starts_with("PANAS")) + +head(data_scatter, n = 5) +``` + +::: + +::: {.callout-note collapse="true" icon="false"} +## Data structure you need + +```{r echo=FALSE} +data_scatter <- dog_data_wide %>% + select(RID, starts_with("PANAS")) %>% + pivot_longer(cols = -RID, names_to = "Q", values_to = "Values") %>% + separate(Q, into = c(NA, "Subscale", "Timepoint"), sep = "_") %>% + pivot_wider(names_from = Timepoint, values_from = Values) %>% + mutate(Subscale = case_match(Subscale, + "NA" ~ "Negative Affect", + "PA" ~ "Positive Affect"), + Subscale = factor(Subscale)) %>% + drop_na() + +head(data_scatter, n = 5) +``` +::: + + +::: {.callout-note collapse="true" icon="false"} +## Hints for `data_scatter` + +* **Step 1**: select the variables you need from `dog_data_wide`. +* **Step 2**: pivot all columns (bar the Participant ID) into long format +* **Step 3**: think about how to separate information of the subscales and timepoints +* **Step 4**: pivot from long into wide format. Take some inspiration from the `Special case: Variables with subscales` scenario above. + +::: + + +::: {.callout-note collapse="true" icon="false"} +## Hints for the plot + +* The colour scheme is Dark2 from the colour palette brewer +* The colour of the trendline is #7570b3 +* Think about how to make the Negative and Positive Affect points different colours. The solution is in @sec-scatter +* Renaming the different facets is one of those things that should be fixed in the data object instead + +::: + + +::: {.callout-caution collapse="true" icon="false"} +## Solution for `data_scatter` + +```{r eval=FALSE} +data_scatter <- dog_data_wide %>% + select(RID, starts_with("PANAS")) %>% + pivot_longer(cols = -RID, names_to = "Q", values_to = "Values") %>% + separate(Q, into = c(NA, "Subscale", "Timepoint"), sep = "_") %>% + pivot_wider(names_from = Timepoint, values_from = Values) %>% + mutate(Subscale = case_match(Subscale, + "NA" ~ "Negative Affect", + "PA" ~ "Positive Affect"), + Subscale = factor(Subscale)) %>% + drop_na() +``` + +::: + +::: {.callout-caution collapse="true" icon="false"} +## Solution for the scatterplot + +```{r eval=FALSE} +ggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) + + geom_point() + + geom_smooth(method = lm, colour = "#7570b3") + + facet_wrap(~Subscale) + + labs(x = "Pre-Intervention (Timepoint 1)", + y = "Post-Intervention (Timepoint 2)") + + scale_colour_brewer(palette = "Dark2", + guide = "none") + + theme_bw() +``` + + +::: + +::: + +If you are extremely fast, challenge yourself and re-create one of the other plots. + + +## [Test your knowledge]{style="color: #F39C12; text-transform: uppercase;"} {.unnumbered} + + +### Knowledge check {.unnumbered} + + +#### Question 1 {.unnumbered} + +Why would this line of code not create a barplot, assuming you already loaded all data and libraries and you spelt the data and column names correctly? + +```{r, eval = FALSE} +ggplot(summarydata, aes(x = sex)) + + geom_barplot() +``` + +`r longmcq(sample(c(answer = "because there is no geom_barplot() and it should be geom_bar()", x = "because you have piped the barplot and not added it", x = "because you have not included a y axis", x = "because this would create a histogram")))` + + + +#### Question 2 {.unnumbered} + +If I wanted precisely 5 bars in my histogram, what argument would I use? + +`r longmcq(sample(c(answer = "ggplot() + geom_histogram(bins = 5)", x = "ggplot() + geom_histogram(bars = 5)", x = "ggplot() + geom_histogram(binwidth = 5)", x = "ggplot() + geom_histogram()")))` + +::: {.callout-caution collapse="true" icon="false"} + +## Explain this answer + +* `ggplot() + geom_histogram(bins = 5)`. This is the **correct** answer as you are asking ggplot2 to give you the plot organised into 5 bins. +* `ggplot() + geom_histogram(bars = 5)`. This is incorrect as you bars is not the right argument name. You want 5 bars, but the argument is bins. +* `ggplot() + geom_histogram(binwidth = 5)`. This is incorrect as binwidth controls the x-axis range to include per bar, rather than the number of bars. +* `ggplot() + geom_histogram()`. This is incorrect as you did not control the number of bins, so it will default to 30. + +::: + + +#### Question 3 {.unnumbered} + +You want to create a scatterplot to show the correlation between two continuous variables, which geom would you use? + +`r longmcq(sample(c(answer = "geom_point()", x = "geom_histogram()", x = "geom_violin()", x = "geom_boxplot()")))` + + +#### Question 4 {.unnumbered} + +True or False? To showcase different groups in a scatterplot, you could specify a grouping variable using the `fill` argument to change the colour of the points. `r torf(FALSE)` + +::: {.callout-caution collapse="true" icon="false"} + +## Explain this answer + +Have a look in the scatterplot section again. There, we explained that we cannot "colour in" the area of the points using `fill`, but need to change the outline colour of the points with `colour` instead. +::: + + +### Error mode {.unnumbered} + +Some of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them. + +Let's go back to the Palmer penguins for this part. + +```{r} +library(palmerpenguins) +``` + + +#### Question 5 {.unnumbered} + +We want to plot the number of penguins across the different islands. + +```{r error=TRUE} +ggplot(penguins, aes(x = islands)) %>% + geom_bar() +``` + +The error message is incredibly useful. So that should be an easy fix! + +::: {.callout-caution collapse="true" icon="false"} + +## Explain the solution + +To fix the error, all we need to do is turn the pipe `%>%` into a plus `+`. + +```{r} +ggplot(penguins, aes(x = island)) + + geom_bar() +``` + +::: + + +#### Question 6 {.unnumbered} + +We want to create a violin-boxplot showing the bill length across species, separately for male and female penguins. + +```{r error=TRUE} +penguins_no_na <- penguins %>% + drop_na(island, flipper_length_mm, sex) + +ggplot(penguins_no_na, aes(x = island, y = flipper_length_mm, fill = sex)) + + geom_violin() + + geom_boxplot(width = 0.2) + + labs(x = "Island", y = "Bill length in mm") + + facet_wrap(~sex) +``` + +Hmmm. We got a plot, but is it the one we aimed for? `r torf (FALSE)` + +::: {.callout-caution collapse="true" icon="false"} + +## Explain the solution + +This was a tricky one. The code runs, so we think everything is fine. Despite having re-labelled the y-axis "Bill length in mm", it's really showing the flipper length. Did you spot this??? + +```{r} +penguins_no_na <- penguins %>% + drop_na(island, bill_length_mm, sex) + +ggplot(penguins_no_na, aes(x = island, y = bill_length_mm, fill = sex)) + + geom_violin() + + geom_boxplot(width = 0.2) + + labs(x = "Island", y = "Bill length in mm") + + facet_wrap(~sex) +``` + +::: + +#### Question 7 {.unnumbered} + +This time, we want to show the relationship between flipper length and body mass of the penguins. The following code runs, but the x- and y-axes are misbehaving somehow. Not what we wanted. Any idea why? + +```{r} +ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) + + geom_point() + + scale_colour_viridis_d(name = "Island") + + scale_y_discrete(name = "Body mass in g", + breaks = seq(2500, 6500, 500), + limits = c(2500, 6500)) + + scale_x_discrete(name = "Flipper length in mm") +``` + + +::: {.callout-caution collapse="true" icon="false"} + +## Explain the solution + +In this example, we used the wrong function for continuous variables. We used `scale_y_discrete` and `scale_y_discrete`, instead of `scale_x_continuous` and `scale_y_continuous`. We must honour the variable type when we customise the plot, so think about what type of variable is on each axis and which function lets you edit it. + +```{r eval=FALSE} +ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) + + geom_point() + + scale_colour_viridis_d(name = "Island") + + scale_y_continuous(name = "Body mass in g", + breaks = seq(2500, 6500, 500), + limits = c(2500, 6500)) + + scale_x_continuous(name = "Flipper length in mm") +``` +::: diff --git a/_freeze/04-dataviz/execute-results/html.json b/_freeze/04-dataviz/execute-results/html.json index e8dac19..e353daa 100644 --- a/_freeze/04-dataviz/execute-results/html.json +++ b/_freeze/04-dataviz/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "a0658998945d62ceacfc2d301080a2be", + "hash": "47f954a1418e3731174a328af3ddce00", "result": { - "markdown": "# Data viz I {#sec-dataviz}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter, you should be able to:\n\n- explain the layered grammar of graphics\n- choose an appropriate plot for categorical variables\n- create a basic version of an appropriate plot\n- apply additional layers to modify the appearance of the plot\n\n\nIt is time to think about selecting the most appropriate plot for your data. Different types of variables call for different kinds of plots, which depends on how many variables you’re aiming to plot and what their data types are. In this chapter, we will focus on **plots for categorical data**. Next week, we will explore plots for continuous variables and learn which plots work best when combining continuous and categorical data.\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n## Building plots\n\nWe are using the package `ggplot2` to create data visualisations. It's part of the tidyverse package. Actually, most people call th package `ggplot` but it's official name is `ggplot2`.\n\nWe’ll be using the `ggplot2` package to create data visualisations. It’s part of the `tidyverse` suite of packages. Although many people refer to it simply as `ggplot`, its official name is `ggplot2`.\n\n::: grid\n::: g-col-6\n**ggplot2** uses a layered grammar of graphics, where plots are constructed through a series of layers. You start with a base layer (by calling `ggplot`), then add **data** and **aesthetics**, followed by selecting the appropriate **geometries** for the plot.\n\nThese first 3 layers will give you the most simple version of a complete plot. However, you can enhance the plot’s clarity and appearance by adding additional layers such as **scales**, **facets**, **coordinates**, **labels** and **themes**.\n\n:::\n\n::: g-col-6\n![gg layers [(Presentation by Ryan Safner)](https://metricsf20.classes.ryansafner.com/slides/1.3-slides#20){target=\"_blank\"}](images/gglayers.png){width=\"90%\"}\n:::\n:::\n\nTo give you a brief overview of the layering system, we will use the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}). This dataset contains information about penguins, including bill length and depth, flipper length, body mass, and more.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(penguins)\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | bill_length_mm| bill_depth_mm| flipper_length_mm| body_mass_g|sex | year|\n|:-------|:---------|--------------:|-------------:|-----------------:|-----------:|:------|----:|\n|Adelie |Torgersen | 39.1| 18.7| 181| 3750|male | 2007|\n|Adelie |Torgersen | 39.5| 17.4| 186| 3800|female | 2007|\n|Adelie |Torgersen | 40.3| 18.0| 195| 3250|female | 2007|\n|Adelie |Torgersen | NA| NA| NA| NA|NA | 2007|\n|Adelie |Torgersen | 36.7| 19.3| 193| 3450|female | 2007|\n|Adelie |Torgersen | 39.3| 20.6| 190| 3650|male | 2007|\n\n
\n:::\n:::\n\n\nLet's build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will customise plots further later on in the individual plots. This is just a quick overview of the different layers.\n\nLet’s build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will further customise the plots in subsequent sections, but for now, this will provide a quick overview of the different layers.\n\n* **Layer 1** creates the base plot that we build upon.\n* **Layer 2** adds the `data` and some `aesthetics`:\n * The data is passed as the first argument.\n * Aesthetics are added via the mapping argument, where you define your variables (e.g., x or both x and y). This also allows you to specify general properties, like the color for grouping variables, etc.\n* **Layer 3** adds geometries, or `geom_?` for short. This tells ggplot how to display the data points. Remember to add these layers with a `+`, rather than using a pipe (`%>%`). You can also add multiple geoms if needed, for example, combining a violin plot with a boxplot.\n* **Layer 4** includes `scale_?` functions, which let you customise aesthetics like color. You can do much more with scales, but we'll explore later.\n* **Layer 5** introduces facets, such as `facet_wrap()`, allowing you to add an extra dimension to your plot by showing the relationship you are interested in for each level of a categorical variable.\n* **Layer 6** involves coordinates, where `coord_cartesian()` controls the limits for the x- and y-axes (xlim and ylim), enabling you to zoom in or out of the plot.\n* **Layer 7** helps you modify axis labels.\n* **Layer 8** controls the overall style of the plot, including background color, text size, and borders. R provides several predefined themes, such as `theme_classic`, `theme_bw`, `theme_minimal`, and `theme_light`.\n\nClick on the tabs below to see how each layer contributes to refining the plot.\n\n::: {.panel-tabset}\n## Layer 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-3-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThere’s not much to see at this stage - this is basically an empty plot layer.\n\n## Layer 2\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-4-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou won’t see any data points yet because we haven’t specified how to display them. However, we have mapped the aesthetics, indicating that we want to plot `body_mass` on the x-axis and `flipper_length` on the y-axis. This also sets the axis titles, as well as the axis values and breakpoints.\n\n::: callout-tip\nYou won't need to add `data =` or `mapping =` if you keep those arguments in exactly that order. Likewise, the first column name you enter within the `aes()` function will always be interpreted as x, and the second as y, so you could omit them if you wish.\n\nYou don’t need to include `data =` or `mapping =` if you keep those arguments in the default order. Similarly, the first column name you enter in the `aes()` function will automatically be interpreted as the x variable, and the second as y, so you can omit specifying `x` and `y` if you prefer.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(body_mass_g, flipper_length_mm))\n```\n:::\n\n\nwill give you the same output as the code above.\n:::\n\n## Layer 3\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-6-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we are telling `ggplot` to add a scatterplot. You may notice a warning indicating that some rows were removed due to missing values.\n\nThe `colour` argument adds colour to the points based on a grouping variable (in this case, `sex`). If you want all the points to be black — representing only two dimensions rather than three — simply omit the `colour` argument.\n\n## Layer 4\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point() +\n # changes colour palette\n scale_colour_brewer(palette = \"Dark2\") + \n # add breaks from 2500 to 6500 in increasing steps of 500\n scale_x_continuous(breaks = seq(from = 2500, to = 6500, by = 500)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `scale_?` functions allow us to modify the color palette of the plot, adjust axis breaks, and more. You could change the axis labels within `scale_x_continuous()` as well or leave it for Layer 7.\n\n## Layer 5\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n # split main plot up into different subplots by species \n facet_wrap(~ species) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nIn this step, we’re using faceting to split the plot by species.\n\n## Layer 6\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n # limits the range of the y axis\n coord_cartesian(ylim = c(0, 250)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we adjust the limits of the y-axis to zoom out of the plot. If you want to zoom in or out of the x-axis, you can add the `xlim` argument to the `coord_cartesian()` function.\n\n## Layer 7\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", # labels the x axis\n y = \"Flipper length (in mm)\", # labels the y axis\n colour = \"Sex\") # labels the grouping variable in the legend\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-10-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou can change the axis labels using the `labs()` function, or you can modify them when adjusting the scales (e.g., within the `scale_x_continuous()` function).\n\n## Layer 8\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", \n y = \"Flipper length (in mm)\",\n colour = \"Sex\") +\n # add a theme\n theme_classic()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `theme_classic()` function is applied to change the overall appearance of the plot.\n\n:::\n\n::: callout-important\n\nYou need to stick to the first three layers to create your base plot. Everything else is optional, meaning you don’t need to use all eight layers. Additionally, layers 4-8 can be added in any order (more or less), whereas layers 1-3 must follow a fixed sequence.\n\n:::\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data from Pownall et al. (2023), so **open your project**.\n* However, let’s start with a fresh R Markdown file: **Create a new `.Rmd` file** and save it in your project folder. Give it a meaningful name (e.g., \"chapter_04.Rmd\" or \"04_data_viz.Rmd\"). If you need guidance, refer to @sec-rmd. Delete everything below line 12, but keep the setup code chunk.\n* We previously aggregated the data in @sec-wrangling and @sec-wrangling2. If you want a fresh copy, download the data here: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\"). Make sure to place the csv file in the project folder.\n* If you need a reminder about the data and variables, check the codebook or refer back to @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we will be using the `tidyverse` package and the dataset `data_prp_for_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- read_csv(???)\n```\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nAs mentioned in @sec-familiarise, it is always a good idea to take a glimpse at the data to see how many variables and observations are in the dataset, as well as the data types.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## glimpse output\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(data_prp_viz)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 89\nColumns: 28\n$ Code \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age 22, 20, 22, 26, 22, 20, 21, 21, …\n$ Ethnicity \"White European\", \"White British…\n$ Secondyeargrade 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT \"Research methods in first year\"…\n$ Research_exp 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ Pre_reg_group 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ SATS28_Affect_Time1_mean 4.000000, 3.833333, 5.000000, 5.…\n$ SATS28_CognitiveCompetence_Time1_mean 5.166667, 5.166667, 5.666667, 4.…\n$ SATS28_Value_Time1_mean 6.000000, 6.666667, 5.222222, 5.…\n$ SATS28_Difficulty_Time1_mean 3.571429, 2.428571, 3.571429, 3.…\n$ QRPs_Acceptance_Time1_mean 5.909091, 6.090909, 6.545455, 5.…\n$ Time1_Understanding_OS 5.500000, 3.166667, 4.500000, 3.…\n$ Other_OS_behav_2 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS 5.583333, 3.333333, 5.416667, 4.…\n$ Mean_Supervisor_Support 5.230769, 6.285714, 6.857143, 2.…\n```\n:::\n:::\n\n\n:::\n\n\nWe can see that some of the categorical data in `data_prp_viz` was read in as numeric variables which makes them continuous. This will haunt us big time when building the plots. We would be better off addressing these changes in the dataset before we start plotting (and potentially getting frustrated with R and data viz in general).\n\nLet’s convert some of the categorical variables into factors. We’ll use the `factor()` function, which requires the `variable` to convert, the `levels` (where we can re-order them as needed), and the corresponding `labels`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n\n## Activity 3: Barchart (`geom_bar()`)\n\nA bar chart is the best choice when you want to plot a single categorical variable.\n\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a **barplot**. This is done with `geom_bar()` in the third layer. Since the counting is done automatically in the background, the `aes()` function only requires an x value (i.e., the name of your variable).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n```\n\n::: {.cell-output-display}\n![Default barchart](04-dataviz_files/figure-html/fig-bc-base-1.png){#fig-bc-base fig-align='center' width=100%}\n:::\n:::\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the **labels** could be clearer, or you might want to add a splash **colour**. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own `.Rmd` file.\n\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` argument. If you have specific colours in mind, you would use `scale_fill_manual()`, or if you prefer to stick with pre-defined options like viridis, you can use `scale_fill_viridis_d()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels & margins\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the `scale_x_discrete()` function and the `labels =` argument. Just make sure to order the labels according to the order in the dataframe.\n\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the `expansion()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_minimal()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n## Activity 4: Column plot (`geom_col()`)\n\nIf the counts had already been summarised for you, `geom_bar()` would not work. Instead, you’d need to use `geom_col()` to display the pre-aggregated data.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngender_count <- data_prp_viz %>% \n count(Gender)\n\ngender_count\n```\n\n::: {.cell-output-display}\n
\n\n|Gender | n|\n|:----------|--:|\n|females | 69|\n|males | 17|\n|non-binary | 3|\n\n
\n:::\n:::\n\n\nThe mapping for `geom_col()` requires both **x** and **y** aesthetics. In this example, **x** would represent the categorical variable (e.g., `Gender`), while **y** would refer to the column storing the summarised values (e.g., `n`). Notice how the axis title now reflects `n` instead of `count` in the base version.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col()\n```\n\n::: {.cell-output-display}\n![Column plot with different coloured bars](04-dataviz_files/figure-html/fig-col-1.png){#fig-col fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"}\n\n\n## Your Turn: Make the column plot pretty\n\nThe other layers to change the colour scheme, axes labels and margins, removing the legend and altering the theme require exactly the same functions as with the boxplot above. Test yourself to see if you can...\n\n* [ ] change the colour scheme (e.g., viridis or [any other colour palettes](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/){target=\"_blank\"})\n* [ ] remove the legend\n* [ ] change the titles of the x and y axes\n* [ ] make the bars start directly on the x-axis\n* [ ] add a theme of your liking\n\n\n\n::: {.callout-tip collapse=\"true\"}\n\n## Possible solution code for the column plot (with a different colour palette and a different theme)\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col() +\n # replaced vidiris with the brewer palette\n scale_fill_brewer(\n palette = \"Set1\", # try \"Set2\" or \"Dark2\" for some variety\n guide = \"none\") + # legend removed\n # labels of the categories changed\n scale_x_discrete(labels = c(\"Male\", \"Female\", \"Non-Binary\")) + \n scale_y_continuous(\n # change y axis label\n name = \"Count\",\n # starts bars on x axis without any gaps but leaves some space at the top (this time 10%)\n expand = expansion(mult = c(0, 0.1)) \n ) +\n # different theme\n theme_light()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n\n## Activity 5: Stacked, Percent Stacked, and Grouped Barchart\n\nWhen dealing with **two categorical variables**, you have three options for displaying stacked barcharts: the \"normal\" **Stacked Barchart** (the default option), a **Percent Stacked Barchart**, or a **Grouped Barchart**.\n\nFor this activity, we will explore the variable `Plan_prereg`, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and `Pre_reg_group`, which tracks whether they actually followed through with a pre-registration for their dissertation.\n\nOne way to display this data is by creating either a **Stacked Barchart** (the default) or a **Percent Stacked Barchart**. In both cases, the subgroups are displayed on top of each other. To make comparison easier, we will place the two plots side by side and move the legend to the bottom of the chart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar() + # no position argument added\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Percent stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Stacked barchart (left), and Percent stacked barchart (right)](04-dataviz_files/figure-html/fig-barcharts_stacked-1.png){#fig-barcharts_stacked fig-align='center' width=100%}\n:::\n:::\n\n\n\nIn the **stacked barchart** (@fig-barcharts_stacked, left plot), you can display participant numbers. From this, we can see that the highest number of students were unsure whether they wanted to pre-register their dissertation, followed closely by those who answered \"yes.\" We also see that the number of students who did not end up with a pre-registered dissertation (blue category) is the same for both those who had planned to pre-register and those who did not want to. However, since the \"No\" category has significantly fewer participants than the other two, it’s difficult to tell if the ratio remains consistent across all three groups.\n\nIf we want to highlight this ratio, a **Percent Stacked Barchart** (@fig-barcharts_stacked, right plot) would be more appropriate. This plot shows that approximately 80% of the students who had planned to pre-register their dissertations, 50% of the students who were initially unsure, and only 33% of the students who had no plan to pre-register ended up with a pre-registered dissertation. BUT! We would lose the information about the raw values in the sample.\n\n**It’s all a trade-off, and the plot you choose depends on the \"story\" you want the data to tell.**\n\n\n::: callout-note\n\nThe position argument `position = \"stack\"` is the default. Adding this argument to the code for the left plot in @fig-barcharts_stacked would produce the same plot as leaving the argument out.\n\n\n:::\n\n\nThe other option is a **Grouped Barchart**, which displays the bars next to each other. You can achieve this by changing the `position` argument to `\"dodge\"`. You can see the default version of the plot in @fig-barchart_grouped on the left, and one with additional layers on the right.\n\nInstead of using a pre-existing colour palette, we manually changed the colours using hex codes. These are some of the colours Gaby used in her PhD thesis, but you can:\n\n* create your own colour hex codes by using [this website](https://www.hexcolortool.com/){target=\"_blank\"}, OR\n* use pre-defined colour names like \"green\" or \"purple\" instead. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\nFeel free to explore.\n\nSince the legend title for the second plot is a bit long, we displayed the legend content across two rows by adding the layer `guides(fill = guide_legend(nrow = 2))` at the end.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Default grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Prettier grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n # changing labels for x, y, and fill category - alternative method\n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registered dissertation\") +\n # manual colour change for values\n scale_fill_manual(values = c('#648FFF', '#DC267F'),\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(\n # remove the space below the bars, but keep a tiny bit (5%) above\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_classic() + \n # need to move this following line to the end otherwise the `theme_*` overrides it\n theme(legend.position = \"bottom\") + \n # display across 2 rows\n guides(fill = guide_legend(nrow = 2))\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right)](04-dataviz_files/figure-html/fig-barchart_grouped-1.png){#fig-barchart_grouped fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Categorical variables with missing values\n\nIf we had chosen a different categorical variable that contains missing values, such as `Closely_follow`, our plots would have included those missing values by default. To change the colour of the missing value bars, you would need to specify this using the `na.value =` argument within the `scale_fill()` function. Here’s an example of a grouped barchart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n## Prettier grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right) for a variable with missing values](04-dataviz_files/figure-html/fig-barchart_grouped_na-1.png){#fig-barchart_grouped_na fig-align='center' width=100%}\n:::\n:::\n\n\n\nIf you don’t want the missing values to appear in the plot, you will need to do some data wrangling to remove them first. The function for this is `drop_na()`. Here we applied `drop_na()` to `Closely_follow` only.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# remove NA\nprereg_plan_follow <- data_prp_viz %>% \n select(Code, Plan_prereg, Closely_follow) %>% \n drop_na(Closely_follow)\n```\n:::\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## check NAs have been removed\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# check NA have been removed\nprereg_plan_follow %>% \n distinct(Plan_prereg, Closely_follow) %>% \n arrange(Plan_prereg, Closely_follow)\n```\n\n::: {.cell-output-display}\n
\n\n|Plan_prereg |Closely_follow |\n|:-----------|:--------------------|\n|Yes |Followed it somewhat |\n|Yes |Followed it exactly |\n|Unsure |Followed it somewhat |\n|Unsure |Followed it exactly |\n|No |Followed it somewhat |\n|No |Followed it exactly |\n\n
\n:::\n:::\n\n\n:::\n\nBut keep in mind that it could misrepresent the data, e.g., giving a wrong impression about proportions. As a comparison...\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# with NA\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n# without NA\nggplot(prereg_plan_follow, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Percent stacked barchart with (left) and without missing values (right)](04-dataviz_files/figure-html/fig-barchart_na_no_na-1.png){#fig-barchart_na_no_na fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n## Activity 6: Save your plots\n\nYou can save your figures using the `ggsave()` function, which will save them to your project folder.\n\nThere are two ways to use `ggsave()`. If you don’t specify which plot to save, by **default** it will **save the last plot you created**. In our case, the last plot was the one without `NA` from the special case scenario (@fig-barchart_na_no_na). However, if you did not follow along with the special case scenario, your last plot will be the grouped bar chart on the right from @fig-barchart_grouped.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"last_plot.png\")\n```\n:::\n\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our last plot saved\n\n![](images/last_plot.png)\n:::\n\n\nThe second option is to save the plot as an object and refer to the object within `ggsave()`. As an example, let's save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\nThe second option is to save the plot as an object and then refer to that object within `ggsave()`. For example, let’s save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngrouped_bar <- ggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n\nThen, you can run the following line:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar.png\", \n plot = grouped_bar)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nSaving 7 x 5 in image\n```\n:::\n:::\n\n\nThe `filename` is the name you want your PNG file to have, and `plot` refers to the name of the plot object.\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our saved `grouped_bar.png` would look like this:\n\n![](images/grouped_bar.png)\n:::\n\nThis is the plot saved with the default settings. If you like it, feel free to keep it as is. However, if it seems a bit \"off\", you can adjust the width, height, and units (e.g., \"cm\", \"mm\", \"in\", \"px\"). You might need to experiment with the dimensions until it feels about right.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar2.png\", \n plot = grouped_bar, \n width = 16, height = 9, units = \"cm\")\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## `grouped_bar.png` with different dimensions\n\n![](images/grouped_bar2.png)\n:::\n\n\n\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the datafile we should read in is `dog_data_clean_wide.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Create an appropriate plot {.unnumbered}\n\nPick **any single or two categorical variables** from the Binfet dataset and **choose one of the appropriate plot choices**. Things to think about: \n\n* [ ] Select your categorical variable(s): `GroupAssignment`, `Year_of_Study`, `Live_Pets`, and/or `Consumer_BARK`\n* [ ] Decide on the plot you want to display: barchart, stacked barchart, percent stacked barchart, or grouped barchart\n* [ ] You may need to convert your variables into factors\n* [ ] Think about what you want to do with missing data\n* [ ] Pick a colour scheme (manual or pre-defined colour palette)\n* [ ] Tidy the axes labels\n* [ ] Decide whether you need a legend or not, and if so, where you would want to place it\n* [ ] Remove the gap between the bottom of the chart and the bars\n* [ ] Pick a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 1 categorical variable\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = Year_of_Study, fill = Year_of_Study)) +\n geom_bar() + \n scale_fill_brewer(\n palette = \"Dark2\",\n guide = \"none\") + \n scale_x_discrete(name = \"Year of Study\") + \n scale_y_continuous(name = \"Count\",\n expand = expansion(mult = c(0, 0.05))) + \n theme_classic()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-37-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 2 categorical variables\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = GroupAssignment , fill = Live_Pets)) +\n geom_bar(position = \"fill\") + \n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Pets at Home\") +\n scale_fill_manual(values = c('deeppink', 'springgreen2'), na.value = 'orangered',\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n theme_classic() + \n theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-39-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nLet's go back to the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}), and assume you have the following data available:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n\npenguin_selection <- penguins %>% \n group_by(species, island) %>% \n summarise(penguin_count = n())\n\npenguin_selection\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | penguin_count|\n|:---------|:---------|-------------:|\n|Adelie |Biscoe | 44|\n|Adelie |Dream | 56|\n|Adelie |Torgersen | 52|\n|Chinstrap |Dream | 68|\n|Gentoo |Biscoe | 124|\n\n
\n:::\n:::\n\n\n\n\n### Knowledge check {.unnumbered}\n\n#### Question 1 {.unnumbered}\n\nWhat `geom` would you use to plot penguin count for each species? \n\n\n#### Question 2 {.unnumbered}\n\nWhat mapping would you use to display penguin count across species?
\n\n\n\n#### Question 3 {.unnumbered}\n\nWhat `geom` would you use to count the number of species on each island? \n\n\n#### Question 4 {.unnumbered}\n\nWhat mapping would you use to display the count of species per island?
\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain these answers\n\n**Question 1**: `geom_col()` is the appropriate choice for bar charts with predefined y-values, such as `penguin_count`.\n\n**Question 2**: The correct aesthetic mapping places the categorical variable (`species`) on the x-axis and the numeric variable (number of observed penguins) on the y-axis. Using `aes(x = penguin_count, y = species)` would flip the axes, placing the number of penguins on the x-axis and species on the y-axis, which doesn’t match the conventional structure of a bar chart.\n\n**Question 3**: `geom_bar()` is the appropriate choice when you want to automatically count the number of observations within each category, such as counting the number of penguin species on each island.\n\n**Question 4**: For a simple count of species per island, you only need to map the categorical variable (`island`) to the x-axis. The y-axis will automatically represent counts when using `geom_bar()`.\n\n:::\n\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) +\n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `check_aesthetics()`:\n! Aesthetics must be either length 1 or the same as the data (344).\n✖ Fix the following mappings: `x`.\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe error message consists of 2 parts. Part 1 is perhaps a bit trickier to interpret, but part 2 gives some useful hints:\n\n* *\"Aesthetics must be either length 1 or the same as the data (344)\"*: This means that the variable mapped to `x` should either be a constant (like a single value) or a column that has 344 entries (matching the number of rows in the penguins dataset).\n* *\"Fix the following mappings: `x`\"*: The issue is specifically with the `x` aesthetic, meaning `islands` is either misspelled or doesn’t exist in the dataset.\n\nTo check the `penguins` data, you can use `glimpse()`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\n\nTo fix the error, you need to correct the column name. The correct column in the `penguins` dataset is called `island` (without the \"s\" at the end). The `island` column has 344 entries, just like the rest of the dataset, so the mapping now works properly.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nNext, we want to create a grouped bar chart displaying species per island, using the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in scale_fill_viridis(): could not find function \"scale_fill_viridis\"\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe function `scale_fill_viridis()` is incorrect; the correct function is called `scale_fill_viridis_d()`.\n\nFIX: correct the function name to display the grouped bar chart with the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-45-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n#### Question 7 {.unnumbered}\n\nWe want to create a grouped bar chart showing the number of penguins on each island, broken down by year.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = year)) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The following aesthetics were dropped during statistical transformation: fill.\nℹ This can happen when ggplot fails to infer the correct grouping structure in\n the data.\nℹ Did you forget to specify a `group` aesthetic or to convert a numerical\n variable into a factor?\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-46-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but certainly not the one we intended. The warning message mentions something about the grouping structure and gives some additional hints.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe grouping variable needs to be a factor. R helpfully asks if we’ve forgotten to convert a numerical variable into a factor!!! Oh, let's check that in the `penguins` data using the `glimpse()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\nIndeed, `year` is currently stored as a numeric (integer) variable. To fix this, we need to convert `year` to a factor. We can do this directly within the `ggplot()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = as.factor(year))) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-48-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n#### Question 8 {.unnumbered}\n\nWe want to create a percent stacked bar chart that displays the ratio of penguins' sex on each island, using a manual color palette. Female penguins should be displayed in blue, males in green, and `NA` values in red.\n\n**Note**: This task is trickier than it looks. Although the code runs and produces a plot, there are three mistakes to identify and fix.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"dodge\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-49-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 1\n\nThe task was to create a **percent stacked barchart**, but the current plot is displaying a grouped barchart. You will need to adjust the argument that defines the type of plot to achieve the correct visualisation.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-50-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 2\n\nYou may have also noticed that the colours are mapped to the islands, not to the penguins' sex, which needs to be corrected.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 2\n\nWe need to switch the columns mapped to the `x` and `fill` aesthetics.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-51-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 3\n\nNow that the correct variables are mapped to the x-axis and the fill argument, the colour scheme no longer matches the instructions. According to the guidelines, missing values should be displayed in red.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 3\n\nChanging the colour of missing values is a special case that requires the argument `na.value =`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\"), na.value = \"red\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-52-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n:::\n", + "markdown": "# Data viz I {#sec-dataviz}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter, you should be able to:\n\n- explain the layered grammar of graphics\n- choose an appropriate plot for categorical variables\n- create a basic version of an appropriate plot\n- apply additional layers to modify the appearance of the plot\n\n\nIt is time to think about selecting the most appropriate plot for your data. Different types of variables call for different kinds of plots, which depends on how many variables you’re aiming to plot and what their data types are. In this chapter, we will focus on **plots for categorical data**. Next week, we will explore plots for continuous variables and learn which plots work best when combining continuous and categorical data.\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n## Building plots\n\nWe are using the package `ggplot2` to create data visualisations. It's part of the tidyverse package. Actually, most people call th package `ggplot` but it's official name is `ggplot2`.\n\nWe’ll be using the `ggplot2` package to create data visualisations. It’s part of the `tidyverse` suite of packages. Although many people refer to it simply as `ggplot`, its official name is `ggplot2`.\n\n::: grid\n::: g-col-6\n**ggplot2** uses a layered grammar of graphics, where plots are constructed through a series of layers. You start with a base layer (by calling `ggplot`), then add **data** and **aesthetics**, followed by selecting the appropriate **geometries** for the plot.\n\nThese first 3 layers will give you the most simple version of a complete plot. However, you can enhance the plot’s clarity and appearance by adding additional layers such as **scales**, **facets**, **coordinates**, **labels** and **themes**.\n\n:::\n\n::: g-col-6\n![gg layers [(Presentation by Ryan Safner)](https://metricsf20.classes.ryansafner.com/slides/1.3-slides#20){target=\"_blank\"}](images/gglayers.png){width=\"90%\"}\n:::\n:::\n\nTo give you a brief overview of the layering system, we will use the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}). This dataset contains information about penguins, including bill length and depth, flipper length, body mass, and more.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(penguins)\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | bill_length_mm| bill_depth_mm| flipper_length_mm| body_mass_g|sex | year|\n|:-------|:---------|--------------:|-------------:|-----------------:|-----------:|:------|----:|\n|Adelie |Torgersen | 39.1| 18.7| 181| 3750|male | 2007|\n|Adelie |Torgersen | 39.5| 17.4| 186| 3800|female | 2007|\n|Adelie |Torgersen | 40.3| 18.0| 195| 3250|female | 2007|\n|Adelie |Torgersen | NA| NA| NA| NA|NA | 2007|\n|Adelie |Torgersen | 36.7| 19.3| 193| 3450|female | 2007|\n|Adelie |Torgersen | 39.3| 20.6| 190| 3650|male | 2007|\n\n
\n:::\n:::\n\n\nLet's build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will customise plots further later on in the individual plots. This is just a quick overview of the different layers.\n\nLet’s build a basic scatterplot to show the relationship between `flipper_length` and `body_mass`. We will further customise the plots in subsequent sections, but for now, this will provide a quick overview of the different layers.\n\n* **Layer 1** creates the base plot that we build upon.\n* **Layer 2** adds the `data` and some `aesthetics`:\n * The data is passed as the first argument.\n * Aesthetics are added via the mapping argument, where you define your variables (e.g., x or both x and y). This also allows you to specify general properties, like the color for grouping variables, etc.\n* **Layer 3** adds geometries, or `geom_?` for short. This tells ggplot how to display the data points. Remember to add these layers with a `+`, rather than using a pipe (`%>%`). You can also add multiple geoms if needed, for example, combining a violin plot with a boxplot.\n* **Layer 4** includes `scale_?` functions, which let you customise aesthetics like color. You can do much more with scales, but we'll explore later.\n* **Layer 5** introduces facets, such as `facet_wrap()`, allowing you to add an extra dimension to your plot by showing the relationship you are interested in for each level of a categorical variable.\n* **Layer 6** involves coordinates, where `coord_cartesian()` controls the limits for the x- and y-axes (xlim and ylim), enabling you to zoom in or out of the plot.\n* **Layer 7** helps you modify axis labels.\n* **Layer 8** controls the overall style of the plot, including background color, text size, and borders. R provides several predefined themes, such as `theme_classic`, `theme_bw`, `theme_minimal`, and `theme_light`.\n\nClick on the tabs below to see how each layer contributes to refining the plot.\n\n::: {.panel-tabset}\n## Layer 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-3-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThere’s not much to see at this stage - this is basically an empty plot layer.\n\n## Layer 2\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-4-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou won’t see any data points yet because we haven’t specified how to display them. However, we have mapped the aesthetics, indicating that we want to plot `body_mass` on the x-axis and `flipper_length` on the y-axis. This also sets the axis titles, as well as the axis values and breakpoints.\n\n::: callout-tip\nYou won't need to add `data =` or `mapping =` if you keep those arguments in exactly that order. Likewise, the first column name you enter within the `aes()` function will always be interpreted as x, and the second as y, so you could omit them if you wish.\n\nYou don’t need to include `data =` or `mapping =` if you keep those arguments in the default order. Similarly, the first column name you enter in the `aes()` function will automatically be interpreted as the x variable, and the second as y, so you can omit specifying `x` and `y` if you prefer.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(body_mass_g, flipper_length_mm))\n```\n:::\n\n\nwill give you the same output as the code above.\n:::\n\n## Layer 3\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-6-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we are telling `ggplot` to add a scatterplot. You may notice a warning indicating that some rows were removed due to missing values.\n\nThe `colour` argument adds colour to the points based on a grouping variable (in this case, `sex`). If you want all the points to be black — representing only two dimensions rather than three — simply omit the `colour` argument.\n\n## Layer 4\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x = body_mass_g, y = flipper_length_mm, colour = sex)) +\n geom_point() +\n # changes colour palette\n scale_colour_brewer(palette = \"Dark2\") + \n # add breaks from 2500 to 6500 in increasing steps of 500\n scale_x_continuous(breaks = seq(from = 2500, to = 6500, by = 500)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `scale_?` functions allow us to modify the color palette of the plot, adjust axis breaks, and more. You could change the axis labels within `scale_x_continuous()` as well or leave it for Layer 7.\n\n## Layer 5\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n # split main plot up into different subplots by species \n facet_wrap(~ species) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nIn this step, we’re using faceting to split the plot by species.\n\n## Layer 6\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n # limits the range of the y axis\n coord_cartesian(ylim = c(0, 250)) \n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHere we adjust the limits of the y-axis to zoom out of the plot. If you want to zoom in or out of the x-axis, you can add the `xlim` argument to the `coord_cartesian()` function.\n\n## Layer 7\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", # labels the x axis\n y = \"Flipper length (in mm)\", # labels the y axis\n colour = \"Sex\") # labels the grouping variable in the legend\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-10-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nYou can change the axis labels using the `labs()` function, or you can modify them when adjusting the scales (e.g., within the `scale_x_continuous()` function).\n\n## Layer 8\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data = penguins, mapping = aes(x=body_mass_g, y=flipper_length_mm, colour=sex)) +\n geom_point() +\n scale_colour_brewer(palette = \"Dark2\") + \n facet_wrap(~ species) +\n labs(x = \"Body Mass (in g)\", \n y = \"Flipper length (in mm)\",\n colour = \"Sex\") +\n # add a theme\n theme_classic()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 11 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nThe `theme_classic()` function is applied to change the overall appearance of the plot.\n\n:::\n\n::: callout-important\n\nYou need to stick to the first three layers to create your base plot. Everything else is optional, meaning you don’t need to use all eight layers. Additionally, layers 4-8 can be added in any order (more or less), whereas layers 1-3 must follow a fixed sequence.\n\n:::\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data from Pownall et al. (2023), so **open your project**.\n* However, let’s start with a fresh R Markdown file: **Create a new `.Rmd` file** and save it in your project folder. Give it a meaningful name (e.g., \"chapter_04.Rmd\" or \"04_data_viz.Rmd\"). If you need guidance, refer to @sec-rmd. Delete everything below line 12, but keep the setup code chunk.\n* We previously aggregated the data in @sec-wrangling and @sec-wrangling2. If you want a fresh copy, download the data here: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\"). Make sure to place the csv file in the project folder.\n* If you need a reminder about the data and variables, check the codebook or refer back to @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we will be using the `tidyverse` package and the dataset `data_prp_for_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- read_csv(???)\n```\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nAs mentioned in @sec-familiarise, it is always a good idea to take a glimpse at the data to see how many variables and observations are in the dataset, as well as the data types.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## glimpse output\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(data_prp_viz)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 89\nColumns: 28\n$ Code \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age 22, 20, 22, 26, 22, 20, 21, 21, …\n$ Ethnicity \"White European\", \"White British…\n$ Secondyeargrade 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT \"Research methods in first year\"…\n$ Research_exp 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ Pre_reg_group 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ SATS28_Affect_Time1_mean 4.000000, 3.833333, 5.000000, 5.…\n$ SATS28_CognitiveCompetence_Time1_mean 5.166667, 5.166667, 5.666667, 4.…\n$ SATS28_Value_Time1_mean 6.000000, 6.666667, 5.222222, 5.…\n$ SATS28_Difficulty_Time1_mean 3.571429, 2.428571, 3.571429, 3.…\n$ QRPs_Acceptance_Time1_mean 5.909091, 6.090909, 6.545455, 5.…\n$ Time1_Understanding_OS 5.500000, 3.166667, 4.500000, 3.…\n$ Other_OS_behav_2 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS 5.583333, 3.333333, 5.416667, 4.…\n$ Mean_Supervisor_Support 5.230769, 6.285714, 6.857143, 2.…\n```\n:::\n:::\n\n\n:::\n\n\nWe can see that some of the categorical data in `data_prp_viz` was read in as numeric variables which makes them continuous. This will haunt us big time when building the plots. We would be better off addressing these changes in the dataset before we start plotting (and potentially getting frustrated with R and data viz in general).\n\nLet’s convert some of the categorical variables into factors. We’ll use the `factor()` function, which requires the `variable` to convert, the `levels` (where we can re-order them as needed), and the corresponding `labels`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n\n## Activity 3: Barchart (`geom_bar()`)\n\nA barchart is the best choice when you want to plot a single categorical variable.\n\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a **barplot**. This is done with `geom_bar()` in the third layer. Since the counting is done automatically in the background, the `aes()` function only requires an x value (i.e., the name of your variable).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n```\n\n::: {.cell-output-display}\n![Default barchart](04-dataviz_files/figure-html/fig-bc-base-1.png){#fig-bc-base fig-align='center' width=100%}\n:::\n:::\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the **labels** could be clearer, or you might want to add a splash **colour**. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own `.Rmd` file.\n\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` argument. If you have specific colours in mind, you would use `scale_fill_manual()`, or if you prefer to stick with pre-defined options like viridis, you can use `scale_fill_viridis_d()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels & margins\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the `scale_x_discrete()` function and the `labels =` argument. Just make sure to order the labels according to the order in the dataframe.\n\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the `expansion()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_minimal()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n## Activity 4: Column plot (`geom_col()`)\n\nIf the counts had already been summarised for you, `geom_bar()` would not work. Instead, you’d need to use `geom_col()` to display the pre-aggregated data.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngender_count <- data_prp_viz %>% \n count(Gender)\n\ngender_count\n```\n\n::: {.cell-output-display}\n
\n\n|Gender | n|\n|:----------|--:|\n|females | 69|\n|males | 17|\n|non-binary | 3|\n\n
\n:::\n:::\n\n\nThe mapping for `geom_col()` requires both **x** and **y** aesthetics. In this example, **x** would represent the categorical variable (e.g., `Gender`), while **y** would refer to the column storing the summarised values (e.g., `n`). Notice how the axis title now reflects `n` instead of `count` in the base version.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col()\n```\n\n::: {.cell-output-display}\n![Column plot with different coloured bars](04-dataviz_files/figure-html/fig-col-1.png){#fig-col fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"}\n\n\n## Your Turn: Make the column plot pretty\n\nThe other layers to change the colour scheme, axes labels and margins, removing the legend and altering the theme require exactly the same functions as with the boxplot above. Test yourself to see if you can...\n\n* [ ] change the colour scheme (e.g., viridis or [any other colour palettes](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/){target=\"_blank\"})\n* [ ] remove the legend\n* [ ] change the titles of the x and y axes\n* [ ] make the bars start directly on the x-axis\n* [ ] add a theme of your liking\n\n\n\n::: {.callout-tip collapse=\"true\"}\n\n## Possible solution code for the column plot (with a different colour palette and a different theme)\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col() +\n # replaced vidiris with the brewer palette\n scale_fill_brewer(\n palette = \"Set1\", # try \"Set2\" or \"Dark2\" for some variety\n guide = \"none\") + # legend removed\n # labels of the categories changed\n scale_x_discrete(labels = c(\"Male\", \"Female\", \"Non-Binary\")) + \n scale_y_continuous(\n # change y axis label\n name = \"Count\",\n # starts bars on x axis without any gaps but leaves some space at the top (this time 10%)\n expand = expansion(mult = c(0, 0.1)) \n ) +\n # different theme\n theme_light()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n\n## Activity 5: Stacked, Percent Stacked, and Grouped Barchart {#sec-adv_bar}\n\nWhen dealing with **two categorical variables**, you have three options for displaying stacked barcharts: the \"normal\" **Stacked Barchart** (the default option), a **Percent Stacked Barchart**, or a **Grouped Barchart**.\n\nFor this activity, we will explore the variable `Plan_prereg`, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and `Pre_reg_group`, which tracks whether they actually followed through with a pre-registration for their dissertation.\n\nOne way to display this data is by creating either a **Stacked Barchart** (the default) or a **Percent Stacked Barchart**. In both cases, the subgroups are displayed on top of each other. To make comparison easier, we will place the two plots side by side and move the legend to the bottom of the chart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar() + # no position argument added\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Percent stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Stacked barchart (left), and Percent stacked barchart (right)](04-dataviz_files/figure-html/fig-barcharts_stacked-1.png){#fig-barcharts_stacked fig-align='center' width=100%}\n:::\n:::\n\n\n\nIn the **stacked barchart** (@fig-barcharts_stacked, left plot), you can display participant numbers. From this, we can see that the highest number of students were unsure whether they wanted to pre-register their dissertation, followed closely by those who answered \"yes.\" We also see that the number of students who did not end up with a pre-registered dissertation (blue category) is the same for both those who had planned to pre-register and those who did not want to. However, since the \"No\" category has significantly fewer participants than the other two, it’s difficult to tell if the ratio remains consistent across all three groups.\n\nIf we want to highlight this ratio, a **Percent Stacked Barchart** (@fig-barcharts_stacked, right plot) would be more appropriate. This plot shows that approximately 80% of the students who had planned to pre-register their dissertations, 50% of the students who were initially unsure, and only 33% of the students who had no plan to pre-register ended up with a pre-registered dissertation. BUT! We would lose the information about the raw values in the sample.\n\n**It’s all a trade-off, and the plot you choose depends on the \"story\" you want the data to tell.**\n\n\n::: callout-note\n\nThe position argument `position = \"stack\"` is the default. Adding this argument to the code for the left plot in @fig-barcharts_stacked would produce the same plot as leaving the argument out.\n\n\n:::\n\n\nThe other option is a **Grouped Barchart**, which displays the bars next to each other. You can achieve this by changing the `position` argument to `\"dodge\"`. You can see the default version of the plot in @fig-barchart_grouped on the left, and one with additional layers on the right.\n\nInstead of using a pre-existing colour palette, we manually changed the colours using hex codes. These are some of the colours Gaby used in her PhD thesis, but you can:\n\n* create your own colour hex codes by using [this website](https://www.hexcolortool.com/){target=\"_blank\"}, OR\n* use pre-defined colour names like \"green\" or \"purple\" instead. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\nFeel free to explore.\n\nSince the legend title for the second plot is a bit long, we displayed the legend content across two rows by adding the layer `guides(fill = guide_legend(nrow = 2))` at the end.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## Default grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Prettier grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n # changing labels for x, y, and fill category - alternative method\n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registered dissertation\") +\n # manual colour change for values\n scale_fill_manual(values = c('#648FFF', '#DC267F'),\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(\n # remove the space below the bars, but keep a tiny bit (5%) above\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_classic() + \n # need to move this following line to the end otherwise the `theme_*` overrides it\n theme(legend.position = \"bottom\") + \n # display across 2 rows\n guides(fill = guide_legend(nrow = 2))\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right)](04-dataviz_files/figure-html/fig-barchart_grouped-1.png){#fig-barchart_grouped fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Categorical variables with missing values\n\nIf we had chosen a different categorical variable that contains missing values, such as `Closely_follow`, our plots would have included those missing values by default. To change the colour of the missing value bars, you would need to specify this using the `na.value =` argument within the `scale_fill()` function. Here’s an example of a grouped barchart.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n## Prettier grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default grouped barchart (left) and one with a few more layers added (right) for a variable with missing values](04-dataviz_files/figure-html/fig-barchart_grouped_na-1.png){#fig-barchart_grouped_na fig-align='center' width=100%}\n:::\n:::\n\n\n\nIf you don’t want the missing values to appear in the plot, you will need to do some data wrangling to remove them first. The function for this is `drop_na()`. Here we applied `drop_na()` to `Closely_follow` only.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# remove NA\nprereg_plan_follow <- data_prp_viz %>% \n select(Code, Plan_prereg, Closely_follow) %>% \n drop_na(Closely_follow)\n```\n:::\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## check NAs have been removed\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# check NA have been removed\nprereg_plan_follow %>% \n distinct(Plan_prereg, Closely_follow) %>% \n arrange(Plan_prereg, Closely_follow)\n```\n\n::: {.cell-output-display}\n
\n\n|Plan_prereg |Closely_follow |\n|:-----------|:--------------------|\n|Yes |Followed it somewhat |\n|Yes |Followed it exactly |\n|Unsure |Followed it somewhat |\n|Unsure |Followed it exactly |\n|No |Followed it somewhat |\n|No |Followed it exactly |\n\n
\n:::\n:::\n\n\n:::\n\nBut keep in mind that it could misrepresent the data, e.g., giving a wrong impression about proportions. As a comparison...\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# with NA\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n# without NA\nggplot(prereg_plan_follow, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Percent stacked barchart with (left) and without missing values (right)](04-dataviz_files/figure-html/fig-barchart_na_no_na-1.png){#fig-barchart_na_no_na fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n## Activity 6: Save your plots\n\nYou can save your figures using the `ggsave()` function, which will save them to your project folder.\n\nThere are two ways to use `ggsave()`. If you don’t specify which plot to save, by **default** it will **save the last plot you created**. In our case, the last plot was the one without `NA` from the special case scenario (@fig-barchart_na_no_na). However, if you did not follow along with the special case scenario, your last plot will be the grouped bar chart on the right from @fig-barchart_grouped.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"last_plot.png\")\n```\n:::\n\n\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our last plot saved\n\n![](images/last_plot.png)\n:::\n\n\nThe second option is to save the plot as an object and refer to the object within `ggsave()`. As an example, let's save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\nThe second option is to save the plot as an object and then refer to that object within `ggsave()`. For example, let’s save the grouped barchart that contained missing values (@fig-barchart_grouped) as an object called `grouped_bar`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ngrouped_bar <- ggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n```\n:::\n\n\nThen, you can run the following line:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar.png\", \n plot = grouped_bar)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nSaving 7 x 5 in image\n```\n:::\n:::\n\n\nThe `filename` is the name you want your PNG file to have, and `plot` refers to the name of the plot object.\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Our saved `grouped_bar.png` would look like this:\n\n![](images/grouped_bar.png)\n:::\n\nThis is the plot saved with the default settings. If you like it, feel free to keep it as is. However, if it seems a bit \"off\", you can adjust the width, height, and units (e.g., \"cm\", \"mm\", \"in\", \"px\"). You might need to experiment with the dimensions until it feels about right.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggsave(filename = \"grouped_bar2.png\", \n plot = grouped_bar, \n width = 16, height = 9, units = \"cm\")\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## `grouped_bar.png` with different dimensions\n\n![](images/grouped_bar2.png)\n:::\n\n\n\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the datafile we should read in is `dog_data_clean_wide.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Create an appropriate plot {.unnumbered}\n\nPick **any single or two categorical variables** from the Binfet dataset and **choose one of the appropriate plot choices**. Things to think about: \n\n* [ ] Select your categorical variable(s): `GroupAssignment`, `Year_of_Study`, `Live_Pets`, and/or `Consumer_BARK`\n* [ ] Decide on the plot you want to display: barchart, stacked barchart, percent stacked barchart, or grouped barchart\n* [ ] You may need to convert your variables into factors\n* [ ] Think about what you want to do with missing data\n* [ ] Pick a colour scheme (manual or pre-defined colour palette)\n* [ ] Tidy the axes labels\n* [ ] Decide whether you need a legend or not, and if so, where you would want to place it\n* [ ] Remove the gap between the bottom of the chart and the bars\n* [ ] Pick a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 1 categorical variable\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = Year_of_Study, fill = Year_of_Study)) +\n geom_bar() + \n scale_fill_brewer(\n palette = \"Dark2\",\n guide = \"none\") + \n scale_x_discrete(name = \"Year of Study\") + \n scale_y_continuous(name = \"Count\",\n expand = expansion(mult = c(0, 0.05))) + \n theme_classic()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-37-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Possible solution for a plot with 2 categorical variables\n\n**Converting some variables into factors**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndog_data_wide <- dog_data_wide %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")))\n```\n:::\n\n\n**Now we can plot**\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(dog_data_wide, aes(x = GroupAssignment , fill = Live_Pets)) +\n geom_bar(position = \"fill\") + \n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Pets at Home\") +\n scale_fill_manual(values = c('deeppink', 'springgreen2'), na.value = 'orangered',\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n theme_classic() + \n theme(legend.position = \"bottom\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-39-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nLet's go back to the `palmerpenguins` package ([https://allisonhorst.github.io/palmerpenguins/](https://allisonhorst.github.io/palmerpenguins/){target=\"_blank\"}), and assume you have the following data available:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n\npenguin_selection <- penguins %>% \n group_by(species, island) %>% \n summarise(penguin_count = n())\n\npenguin_selection\n```\n\n::: {.cell-output-display}\n
\n\n|species |island | penguin_count|\n|:---------|:---------|-------------:|\n|Adelie |Biscoe | 44|\n|Adelie |Dream | 56|\n|Adelie |Torgersen | 52|\n|Chinstrap |Dream | 68|\n|Gentoo |Biscoe | 124|\n\n
\n:::\n:::\n\n\n\n\n### Knowledge check {.unnumbered}\n\n#### Question 1 {.unnumbered}\n\nWhat `geom` would you use to plot penguin count for each species? \n\n\n#### Question 2 {.unnumbered}\n\nWhat mapping would you use to display penguin count across species?
\n\n\n\n#### Question 3 {.unnumbered}\n\nWhat `geom` would you use to count the number of species on each island? \n\n\n#### Question 4 {.unnumbered}\n\nWhat mapping would you use to display the count of species per island?
\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain these answers\n\n**Question 1**: `geom_col()` is the appropriate choice for bar charts with predefined y-values, such as `penguin_count`.\n\n**Question 2**: The correct aesthetic mapping places the categorical variable (`species`) on the x-axis and the numeric variable (number of observed penguins) on the y-axis. Using `aes(x = penguin_count, y = species)` would flip the axes, placing the number of penguins on the x-axis and species on the y-axis, which doesn’t match the conventional structure of a bar chart.\n\n**Question 3**: `geom_bar()` is the appropriate choice when you want to automatically count the number of observations within each category, such as counting the number of penguin species on each island.\n\n**Question 4**: For a simple count of species per island, you only need to map the categorical variable (`island`) to the x-axis. The y-axis will automatically represent counts when using `geom_bar()`.\n\n:::\n\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) +\n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `check_aesthetics()`:\n! Aesthetics must be either length 1 or the same as the data (344).\n✖ Fix the following mappings: `x`.\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe error message consists of 2 parts. Part 1 is perhaps a bit trickier to interpret, but part 2 gives some useful hints:\n\n* *\"Aesthetics must be either length 1 or the same as the data (344)\"*: This means that the variable mapped to `x` should either be a constant (like a single value) or a column that has 344 entries (matching the number of rows in the penguins dataset).\n* *\"Fix the following mappings: `x`\"*: The issue is specifically with the `x` aesthetic, meaning `islands` is either misspelled or doesn’t exist in the dataset.\n\nTo check the `penguins` data, you can use `glimpse()`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\n\nTo fix the error, you need to correct the column name. The correct column in the `penguins` dataset is called `island` (without the \"s\" at the end). The `island` column has 344 entries, just like the rest of the dataset, so the mapping now works properly.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nNext, we want to create a grouped bar chart displaying species per island, using the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in scale_fill_viridis(): could not find function \"scale_fill_viridis\"\n```\n:::\n:::\n\n\nWhat does this error message mean and how do you fix it?\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe function `scale_fill_viridis()` is incorrect; the correct function is called `scale_fill_viridis_d()`.\n\nFIX: correct the function name to display the grouped bar chart with the viridis color palette.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis_d()\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-45-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n#### Question 7 {.unnumbered}\n\nWe want to create a grouped bar chart showing the number of penguins on each island, broken down by year.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = year)) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: The following aesthetics were dropped during statistical transformation: fill.\nℹ This can happen when ggplot fails to infer the correct grouping structure in\n the data.\nℹ Did you forget to specify a `group` aesthetic or to convert a numerical\n variable into a factor?\n```\n:::\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-46-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but certainly not the one we intended. The warning message mentions something about the grouping structure and gives some additional hints.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThe grouping variable needs to be a factor. R helpfully asks if we’ve forgotten to convert a numerical variable into a factor!!! Oh, let's check that in the `penguins` data using the `glimpse()` function.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nglimpse(penguins)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nRows: 344\nColumns: 8\n$ species Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex male, female, female, NA, female, male, female, male…\n$ year 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n:::\n:::\n\n\nIndeed, `year` is currently stored as a numeric (integer) variable. To fix this, we need to convert `year` to a factor. We can do this directly within the `ggplot()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = as.factor(year))) +\n geom_bar(position = \"dodge\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-48-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n#### Question 8 {.unnumbered}\n\nWe want to create a percent stacked bar chart that displays the ratio of penguins' sex on each island, using a manual color palette. Female penguins should be displayed in blue, males in green, and `NA` values in red.\n\n**Note**: This task is trickier than it looks. Although the code runs and produces a plot, there are three mistakes to identify and fix.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"dodge\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-49-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 1\n\nThe task was to create a **percent stacked barchart**, but the current plot is displaying a grouped barchart. You will need to adjust the argument that defines the type of plot to achieve the correct visualisation.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 1\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-50-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 2\n\nYou may have also noticed that the colours are mapped to the islands, not to the penguins' sex, which needs to be corrected.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 2\n\nWe need to switch the columns mapped to the `x` and `fill` aesthetics.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-51-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hint for Mistake 3\n\nNow that the correct variables are mapped to the x-axis and the fill argument, the colour scheme no longer matches the instructions. According to the guidelines, missing values should be displayed in red.\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Fixing of Mistake 3\n\nChanging the colour of missing values is a special case that requires the argument `na.value =`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\"), na.value = \"red\")\n```\n\n::: {.cell-output-display}\n![](04-dataviz_files/figure-html/unnamed-chunk-52-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n:::\n", "supporting": [ "04-dataviz_files" ], diff --git a/_freeze/05-dataviz2/execute-results/html.json b/_freeze/05-dataviz2/execute-results/html.json index 83521fd..c9544c2 100644 --- a/_freeze/05-dataviz2/execute-results/html.json +++ b/_freeze/05-dataviz2/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "95f73437e1db40d9de50e88a434c53bd", + "hash": "b6258efe5544313db086c348b4efd5b1", "result": { - "markdown": "# Data viz II {#sec-dataviz2}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter you should be able to:\n\n- choose an appropriate plot for continuous variables \n- choose an appropriate plot when you've got a mix of continuous/categorical variables \n- create a basic version of an appropriate plot\n- apply extra layers to change the appearance of the plot\n\nIn this chapter, we are continuing our journey of appropriate plots. Last week, we looked at which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**.\n\n\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n## Activity 1: Set-up and data for today\n\n- We are still working with the data by Pownall et al. (2023). **Open the project**.\n- You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you.\n- The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download it again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\").\n- If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nWe need the package `tidyverse` today, and the data `data_prp_ch3.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- ???\n```\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nThis is the same as last week. We need to turn our categorical variables into factors to make plotting easier.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n::: {.callout-tip}\n\nIf you are working within the same `.Rmd` file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week's `.Rmd` file to load tidyverse into the library, read in the data, and convert some of the variables into factors.\n\n:::\n\n\n## Activity 3: Histogram (`geom_histogram()`)\n\nIf you wanted to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting \"in the background\".\n\nA histogram splits the data into “bins” (i.e., groupings displayed in a single bar). These values are plotted along the x-axis and shows the count of how many observations are in each bin along the y-axis. It's basically a bar chart for continuous variables.\n\nLet's have a look at the age distribution in our dataset.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram() \n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Default histogram](05-dataviz2_files/figure-html/fig-hist-1.png){#fig-hist fig-align='center' width=100%}\n:::\n:::\n\n\n\nThe default **bin number** is 30 (as displayed in @fig-hist above). Changing the number of bins (argument `bins`) can help to show more or less fine tuning in the data. Bigger numbers of bins means more finetuning.\n\nPerhaps it's more intuitive to modify the **width of each bin** instead via the argument (`binwidth`). So for example `binwidth = 1` for category age would mean 1 year of age per \"age group\"; `binwidth = 5` would mean 5 years of age span per \"age group\", etc. The plots below show modifications for both `bin` and `binwidth`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n#less finetuning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(bins = 10) \n\n# more fineturning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1) \n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).\nRemoved 2 rows containing non-finite outside the scale range (`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Bins vs binwidth arguments](05-dataviz2_files/figure-html/fig-bins-1.png){#fig-bins fig-align='center' width=100%}\n:::\n:::\n\n\nThe **warning message** telling us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `hp_data` to see if you can decipher the warning message.\n\nThey were removed because .\n\nColours are getting manipulated slightly differently to the barchart. Click through each tab to see how you can alter colour, axes labels, margins and breaks, and add a different theme.\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument manipulates the colour of the bars, and the `colour` argument changes the outline of the bars. Pay attention that they are **added directly to the `geom_histogram` arguments**, not the overall `aes()` like we did with the boxplot.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: callout-tip\n\nYou could use \n\n* hex codes for fill and color, like we used here, `geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")`. If you want create your own colours, check out [this website](https://www.hexcolortool.com/){target=\"_blank\"}. OR\n* a pre-defined colour name `geom_histogram(binwidth = 1, fill = \"purple\", colour = \"green\")`. See a full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"} OR\n\n\n:::\n\n## Axes labels, margins, and breaks\n\nHere we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y axis is now displayed in increasing steps of 5 (rather than 10), and the x axis has 1-year increments rather than 5. \n\nNotice how the `breaks =` argument manipulates the labels of the break ticks but not the limit of the scale. You can manipulate the limits of the scale with the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values for 15 to 19, and 44 to 50 do not have a label. You would need to adjust that in `breaks =` argument.\n\nThe `expansion()` function removes the gap between x axis and bars. It is exactly the same code as we used in @sec-dataviz .\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 20, to = 43, by = 1),\n # Experimenting with\n limits = c(15, 50)\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_bw()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 19, to = 44, by = 1)\n ) +\n # pick a theme\n theme_bw()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n\n## Activity 4: Scatterplot (`geom_point()`)\n\nScatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and 2. The default scatterplot would be created with `geom_point()`.\n\nWe could also **add a trendline** by adding `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = lm` into `geom_smooth()` function.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth()\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth(method = lm)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![Default Scatterplot with added trendline - loess (left) and linear (right)](05-dataviz2_files/figure-html/fig-scatter-1.png){#fig-scatter fig-align='center' width=100%}\n:::\n:::\n\n\n\n\nCustomising the colour of plot is slightly different to the other plots we've encountered so far. Technically, the point is not a \"filled-in black area\", but an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument, but have to switch to the `colour` argument (like we did for the outline of the histogram). See the tabs below how to change the colour for all points or if you want to change the colour according to groupings.\n\n\n::: {.panel-tabset}\n\n## Colour for all points\n\nIf we want to **change the colour of all the points**, we would add the `colour` argument to the `geom_point()` function. Likewise, changing the **colour of the trendline** would also require a `colour` argument. Here we went with pre-defined colour names, but HEX codes would work too\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# colour of all points and the trendline\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point(colour = 'magenta') +\n geom_smooth(method = lm, colour = 'turquoise')\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n## Colour with grouping\n\nIf we wanted the points to **change colour due to another grouping variable**, the colour argument would go into the `aes()` If you don't want to define the colours manually, use a colour palette like brewer (`scale_colour_brewer()`) or viridis (`scale_colour_viridis_d()`).\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## adding grouping variable Pre_reg_group and changing the colour values manually\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-12-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n## Legend title and labels\n\nYou can tidy the legend title and group labels via the `scale_colour_?` function\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\"))\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-13-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAll other layers would be exactly the same as in other plots. Try to add layers to the make the plot above prettier: \n\n* [ ] 1. relabel axes x and y\n* [ ] 2. set the x and y axis range from 1 to 7\n* [ ] 3. move the legend to a different position (either top, left, or bottom)\n* [ ] 4. add a theme\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## Hints\n\n1. can be done in 2 different ways - labs() or scale_x_?\n2. we did that for the histogram\n3. We did that for the bar charts\n4. pick a theme you like\n\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\")) +\n labs (x = \"Acceptance of Questionable Research Practices (Time 1)\", \n y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_light() + # place before moving the legend position\n theme(legend.position = \"top\") # move legend to the top\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-14-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n\n\n\n\n\n## Activity 5: Boxplot (`geom_boxplot()`)\n\nA boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to investigate whether their understanding of open science depends on whether or not students had research experience. Our default boxplot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-15-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nTada. As usual, we can make the plot pretty by adding various layers. Click on the tabs to see how\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a fill argument in the `aes()`. If we want to modify these colours further, we would add a `scale_fill_?` layer If you have specific colours in mind, you would use `scale_fill_manual()` or if you want to stick with pre-defined ones, like brewer `scale_fill_brewer()`.\n\nBtw, this is exactly the same code we used for the barcharts.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n # customise colour\n scale_fill_brewer(palette = \"Dark2\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-16-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels\n\nWe need to relabel the axes labels for x and y with `scale_x_discrete()` and `scale_y_continuous()`. We can also tidy the labels for the groups and the breaks on the y-axis (in steps of 1 rather than 2) within the same functions\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n scale_x_discrete(\n # changing the label of x\n name = \"Research Experience\",\n # changing the group labels of the 2 groups\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Confidence in Understanding Open Science (Time 1)\",\n # changing break labels\n breaks = c(seq(from = 1, to = 7, by = 1))\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend and Theme\n\nThe legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = \"none\"` into the `scale_fill` function.\n\nLet's pick a theme we haven't used yet: `theme_dark()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\",\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(\n name = \"Research Experience\",\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n name = \"Confidence in Understanding Open Science (Time 1)\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_dark()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## Activity 6: Violin plot (`geom_violin()`)\n\nAn alternative to display a continuous variable with categorical grouping variable is a violin plot. Here, we want to create a violin plot to investigate whether the perception of supervisor support depended on planning to pre-register the dissertation. Our default violin plot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +\n geom_violin()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAdjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to \n\n* [ ] change the colours either manually or using a pre-defined colour palette \n* [ ] tidy the axes labels and group names\n* [ ] in case a legend appears, take it off\n* [ ] add a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +\n geom_violin() +\n scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(name = \"Plan to pre-register the dissertation\") + \n scale_y_continuous(\n name = \"Perceived Supervisory Support\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 7: Violin-boxplots\n\nSo far, we've only added one `geom_` to our plots. Due to the layered system, we could add multiple geoms, for example, when creating a violin-boxplot. \n\nRemember, the order of the layers makes a difference sometimes. We've seen already, with themes we added at the very end that could override the argument for a legend position earlier. Here, `ggplot + violinplot + boxplot` looks different to `ggplot + boxplot + violinplot`.\n\nLet's take the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot()\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_boxplot() +\n geom_violin()\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default violin-boxplot: Order of the layer matters](05-dataviz2_files/figure-html/fig-vb_bv-1.png){#fig-vb_bv fig-align='center' width=100%}\n:::\n:::\n\n\n\nCustomising a few elements such as width of the boxes and the colour.\n\n\n::: {.panel-tabset}\n\n## Width of the boxes\n\nIf we want to get any information from the boxplot, we need to use order 1. But still, the boxplot is pretty wide and covers up important information from the violin. We could **adjust the width of the boxes** to make information more visible. This takes a bit of trial and error which width looks appropriate.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot(width = 0.2)\n```\n\n::: {.cell-output-display}\n![Default violin-boxplot: adjusting width of the box](05-dataviz2_files/figure-html/fig-vb_width-1.png){#fig-vb_width fig-align='center' width=100%}\n:::\n:::\n\n\n## Colour\n\nAdding colour should be pretty straight forward by now. This code is no different to the one we used in the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, and a `scale_fill_?`layer.\n\nHowever, we can modify this further by adding an opacity argument `alpha` to the violin plot geom.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) + # alpha for opacity\n geom_boxplot(width = 0.2) + # change width of the boxes\n scale_fill_brewer(palette = \"RdPu\") # customise colour\n```\n\n::: {.cell-output-display}\n![Violin-boxplot with a different colour palette](05-dataviz2_files/figure-html/fig-vb_colour-1.png){#fig-vb_colour fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\n* [ ] Change the x- and y-axis labels\n* [ ] Remove the legend\n* [ ] add a theme\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + # removes the legend\n # change labels of x and y\n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 8: Faceting - adding another grouping variable\n\nFaceting is really useful when you have subsets in the data. We will use this here on the violin-boxplot from above, but you could add this to pretty much any plot. The function to split up the plots into facets is called `facet_wrap()`. \n\nLet's **add another grouping variable**, `Pre_reg_group`, so we can see separate plots for the yes and no groups. \n\nSince the group labels on the x-axis are quite long, we need to adjust them. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + \n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic() +\n facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no\n scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows\n```\n\n::: {.cell-output-display}\n![Pretty violin-boxplot split into pre-registration groups (yes and no)](05-dataviz2_files/figure-html/fig-vb_pretty_facet-1.png){#fig-vb_pretty_facet fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-tip}\n\nThe labels of `Pre_reg_group` are displayed as numbers 1 and 2. If this bugs you, fix the labels in the dataset. This would be less hassle than trying to fix it in the plot.\n\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Variables with subscales\n\nFor example, we want to show the relationship between SATs scores at timepoints 1 and 2, separately for all 4 subscales of the SATs questionnaires, we would need to wrangle the data first. The dataframe we need should look like this:\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(data_facet, n=5)\n```\n\n::: {.cell-output-display}\n
\n\n|Code |Subscale | Time1| Time2|\n|:----|:-------------------|--------:|--------:|\n|Tr10 |Affect | 4.000000| 3.500000|\n|Tr10 |CognitiveCompetence | 5.166667| 4.166667|\n|Tr10 |Value | 6.000000| 3.000000|\n|Tr10 |Difficulty | 3.571429| 2.857143|\n|Bi07 |Affect | 3.833333| 3.166667|\n\n
\n:::\n:::\n\n\nTry wrangling the data so that it looks like `data_facet` above\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Hints\n\n* step 1: select variables of interest\n* step 2: pivot\n* step 3: try to access information on subscales and timepoints from the variable names\n* step 4: pivot in the other direction\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_facet <- data_prp_viz %>% \n select(Code, starts_with(\"SATS\")) %>% \n pivot_longer(cols = starts_with(\"SATS\"), names_to = \"Variable\", values_to = \"Mean_Scores\") %>% \n separate(Variable, into = c(NA, \"Subscale\", \"Timepoint\", NA), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Mean_Scores)\n```\n:::\n\n\n:::\n\n:::\n\nThen we can build a scatterplot with facets for the subscales\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_facet, aes(x = Time1, y = Time2)) +\n geom_point() +\n facet_wrap(~Subscale)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-26-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nProvide a barchart, a violin-boxplot, and a scatterplot from the loneliness data and the students have to try and recreate one of those in the lab. If they are overly fast, they can do the other 2.\n\n\n\n\n\n\n\n## [Test your knowledge on Chapters 3 and 4]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\nWhich plot would you choose for\n\n### Activity 5: Your Turn\n\nPick **any single or two categorical variables** from the dataset and **choose one of the appropriate plot choices**. Start with a base plot and add other layers if you please. \n\nSave your plot and share it with us on Teams.\n\n\n", + "markdown": "# Data viz II {#sec-dataviz2}\n\n\n\n\n\n## Intended Learning Outcomes {.unnumbered}\n\nBy the end of this chapter you should be able to:\n\n- choose an appropriate plot for continuous variables \n- choose an appropriate plot for a mix of continuous/categorical variables \n- create a basic version of an appropriate plot\n- apply extra layers to change the appearance of the plot\n\nIn this chapter, we continue our journey of appropriate plots. Last week, we examined which **plots are appropriate for categorical variables**. Today, we'll focus on continuous variables and which plots to choose with a **mix of continuous and categorical variables**.\n\n\n\n## [Individual Walkthrough]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n## Activity 1: Set-up and data for today\n\n* We are still working with the data by Pownall et al. (2023). **Open the project**.\n* You could use the same `.Rmd` file as last week if you want to keep all plotting in one document or create a new `.Rmd` to separate plots for categorical and continuous variables. Up to you.\n* The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download the csv again and place it in your project folder: [data_prp_for_ch4.csv](data/data_prp_for_ch4.csv \"download\").\n* If you need a reminder about the data and variables, have a look at the codebook and/or @sec-download_data_ch1.\n\n\n\n## Activity 2: Load in libraries, read in data, and adjust data types\n\nToday, we need to load the package `tidyverse`, and read in the data `data_prp_ch4.csv`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## packages \n???\n\n## data\ndata_prp_viz <- ???\n```\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n```\n:::\n\n\n:::\n\n\n\n\n\n\n\n\nThis is the same code as last week. We need to turn our categorical variables into factors to make plotting easier.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n```\n:::\n\n\n\n::: {.callout-tip}\n\nIf you are working within the same `.Rmd` file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week's `.Rmd` file to load tidyverse into the library, read in the data, and convert some of the variables into factors.\n\n:::\n\n\n## Activity 3: Histogram (`geom_histogram()`) {#sec-hist}\n\nIf you want to show the **distribution of a continuous variable**, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to `geom_bar()`, `geom_histogram()` only requires an `x` variable as it does the counting \"in the background\".\n\nA histogram divides the data into \"bins\" (i.e., groupings displayed in a single bar). These bins are plotted along the x-axis, with the y-axis showing the count of observations in each bin. It's basically a barchart for continuous variables.\n\nLet's have a look at the age distribution in our dataset.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram() \n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Default histogram](05-dataviz2_files/figure-html/fig-hist-1.png){#fig-hist fig-align='center' width=100%}\n:::\n:::\n\n\n\nThe default **number of bins** is 30 (as shown in @fig-hist above). Changing the number of bins (argument `bins`) allows for more or less fine-tuning of the data. A higher number of bins results in more detailed granularity.\n\nPerhaps it’s more intuitive to modify the **width of each bin** using the `binwidth` argument. For example, `binwidth = 1` for the age category would mean each \"age group\" represents 1 year, while `binwidth = 5` would group ages into 5-year spans. The plots below show modifications for both `bins` and `binwidth`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n#less finetuning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(bins = 10) \n\n# more fineturning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1) \n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).\nRemoved 2 rows containing non-finite outside the scale range (`stat_bin()`).\n```\n:::\n\n::: {.cell-output-display}\n![Bins vs binwidth arguments](05-dataviz2_files/figure-html/fig-bins-1.png){#fig-bins fig-align='center' width=100%}\n:::\n:::\n\n\nThe **warning message** tells us 2 row of data were removed due to containing `non-finite values outside the scale range`. Have a look at the age column in `data_prp_viz` to see if you can decipher the warning message.\n\nThe rows were removed because .\n\nColours are manipulated slightly differently than in the barchart. Click through each tab to see how you can modify colours, axis labels, margins, and breaks, and apply a different theme.\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the plot colours by adding a **fill argument** and a **colour argument**. The `fill` argument changes the colour of the bars, while the `colour` argument modifies the outline of the bars. Note that these arguments are **added directly to the `geom_histogram()`**, rather than within the overall `aes()`, as we did with the barchart.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-7-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: callout-tip\n\nYou could use:\n\n* Hex codes for `fill` and `colour`, as we did here: `geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")`. If you want to create your own colours, check out [this website](https://www.hexcolortool.com/){target=\"_blank\"}.\n\n* Pre-defined colour names: `geom_histogram(binwidth = 1, fill = \"purple\", colour = \"green\")`. See the full list [here](https://www.datanovia.com/en/blog/awesome-list-of-657-r-color-names/){target=\"_blank\"}.\n\n:::\n\n\n## Axes labels, margins, and breaks\n\nHere we **removed the label** for the y axes `Count` (to show you some variety) and **modified the breaks**. The y-axis is now displayed in increasing steps of 5 (rather than 10), and the x-axis has 1-year increments instead of 5. \n\nNotice how the `breaks =` argument changes the labels of the break ticks but not the scale limits. You can adjust the limits of the scale using the `limits =` argument. To exaggerate, we set the limits to 15 and 50. See how the values from 15 to 19, and 44 to 50 do not have labels? You would need to adjust that using the `breaks =` argument.\n\n\nThe `expansion()` function removes the gap between the x-axis and the bars. It is exactly the same code we used in @sec-dataviz.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 20, to = 43, by = 1),\n # Experimenting with\n limits = c(15, 50)\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-8-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Themes\n\nLet's experiment with the themes. For this plot we have chosen `theme_bw()`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 19, to = 44, by = 1)\n ) +\n # pick a theme\n theme_bw()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-9-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n\n## Activity 4: Scatterplot (`geom_point()`) {#sec-scatter}\n\nScatterplots are appropriate when you want to plot **two continuous variables**. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and Time point 2. The default scatterplot can be created with `geom_point()`.\n\nWe can also **add a trendline** by using `geom_smooth()`. The default trendline is `loess`. If you want a linear trendline, you would need to add `method = \"lm\"` inside the `geom_smooth()` function.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth()\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth(method = lm)\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default Scatterplot with added trendline - loess (left) and linear (right)](05-dataviz2_files/figure-html/fig-scatter-1.png){#fig-scatter fig-align='center' width=100%}\n:::\n:::\n\n\n\n\nCustomising the colour of a scatterplot is slightly different from the other plots we’ve encountered so far. Technically, the point is not a \"filled-in black area\" but rather an extremely wide outline of a circle. Therefore, we cannot use the usual `fill` argument and instead need to use the `colour` argument, similar to how we customised the outline of the histogram. \n\nSee the tabs below to learn how to change the colour for all points or how to adjust the colour based on groupings.\n\n\n::: {.panel-tabset}\n\n\n## Colour for all points\n\nIf we want to **change the colour of all the points**, we can add the `colour` argument to the `geom_point()` function. Likewise, to change the **colour of the trendline**, we would also use the `colour` argument. Here, we used pre-defined colour names, but HEX codes would work just as well.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# colour of all points and the trendline\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point(colour = 'magenta') +\n geom_smooth(method = lm, colour = 'turquoise')\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-11-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n## Colour with grouping\n\nIf we want the points to **change colour based on another grouping variable**, the `colour` argument should go inside the `aes()`. If you don’t want to define the colours manually, you can use a colour palette like Brewer (`scale_colour_brewer()`) or Viridis (`scale_colour_viridis_d()`).\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n## adding grouping variable Pre_reg_group and changing the colour values manually\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-12-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n## Legend title and labels\n\nYou can tidy the legend title and group labels using the `scale_colour_?` function, depending on the palette you’re using (e.g., `scale_colour_manual()`, `scale_colour_brewer` and many more).\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\"))\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-13-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAll other layers remain exactly the same as in other plots. Try adding layers to make the plot above prettier:\n\n* [ ] 1. relabel axes x and y\n* [ ] 2. set the x and y axis range from 1 to 7\n* [ ] 3. move the legend to a different position (either top, left, or bottom)\n* [ ] 4. add a theme\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## Hints\n\n1. can be done in 2 different ways - labs() or scale_x_?\n2. we did that for the histogram\n3. We did that for the barcharts\n4. pick a theme you like\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"} \n\n## More hints\n\nIf you are experiencing issues with the legend position and theme, try modifying the order of the layers.\n\n:::\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\")) +\n labs (x = \"Acceptance of Questionable Research Practices (Time 1)\", \n y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_light() + # place before moving the legend position\n theme(legend.position = \"top\") # move legend to the top\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-14-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n:::\n\n:::\n\n\n\n\n\n\n## Activity 5: Boxplot (`geom_boxplot()`)\n\nA boxplot is one of the options to display a **continuous variable with categorical grouping variable**. Here, we want to create a boxplot to explore whether students' understanding of open science varies based on whether or not they have research experience. Our default boxplot would look like this:\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-15-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\nTada! As usual, we can enhance the plot by adding various layers. Click on each tab below to see how.\n\n\n::: {.panel-tabset}\n\n## Colour\n\nWe can change the colour by adding a `fill` argument inside the `aes()`. To customise the colours further, we can add a `scale_fill_?` layer. If you have specific colours in mind, use `scale_fill_manual()`. If you prefer pre-defined palettes, such as Brewer, you can use `scale_fill_brewer()`.\n\nBtw, this is exactly the same code we used for the barcharts.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n # customise colour\n scale_fill_brewer(palette = \"Dark2\")\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-16-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Axes labels\n\nWe need to relabel the axes. The function to use depends on the variable type. Here, we need `scale_x_discrete()` for the x-axis and `scale_y_continuous()` for the y-axis. We can also tidy up the group labels and adjust the breaks on the y-axis (e.g., in steps of 1 instead of 2) within these same functions.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n scale_x_discrete(\n # changing the label of x\n name = \"Research Experience\",\n # changing the group labels of the 2 groups\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Confidence in Understanding Open Science (Time 1)\",\n # changing break labels\n breaks = c(seq(from = 1, to = 7, by = 1))\n )\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-17-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n## Legend and Theme\n\nThe legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument `guide = \"none\"` to the `scale_fill_?` function.\n\nLet's pick a theme we haven't used yet: `theme_dark()`.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\",\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(\n name = \"Research Experience\",\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n name = \"Confidence in Understanding Open Science (Time 1)\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_dark()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-18-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## Activity 6: Violin plot (`geom_violin()`)\n\nAn alternative way to display a **continuous variable with a categorical grouping variable** is a violin plot. Here, we want to create a violin plot to explore whether the perception of supervisor support depends on whether students plan to pre-register their dissertation. Our default violin plot would look like this:\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\n# default boxplot\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +\n geom_violin()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-19-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nAdjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to\n\n* [ ] change the colours either manually or using a pre-defined colour palette \n* [ ] tidy the axes labels and group names\n* [ ] in case a legend appears, take it off\n* [ ] add a theme\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +\n geom_violin() +\n scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(name = \"Plan to pre-register the dissertation\") + \n scale_y_continuous(\n name = \"Perceived Supervisory Support\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_minimal()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-20-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 7: Violin-boxplots\n\nSo far, we’ve only added one `geom_?` layer to our plots. However, thanks to ggplot's layered system, we can add multiple geoms, for example, when creating a violin-boxplot.\n\nRemember, the order of the layers can sometimes make a difference. We've seen this already - adding a theme at the end can override earlier arguments like the legend position. Similarly, `ggplot + violinplot + boxplot` will look different from `ggplot + boxplot + violinplot`.\n\nLet's use the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot()\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_boxplot() +\n geom_violin()\n```\n:::\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![Default violin-boxplot: Order of the layer matters](05-dataviz2_files/figure-html/fig-vb_bv-1.png){#fig-vb_bv fig-align='center' width=100%}\n:::\n:::\n\n\n\nSee the tabs below to learn how to customise various elements, such as the width of the boxes, and the colour or opacity.\n\n\n::: {.panel-tabset}\n\n## Width of the boxes\n\nIf we want to get any information from the boxplot, we need to place it \"on top of\" the violin plot. But still, the boxplot is pretty wide and covers important details from the violin plot. To make the information more visible, we can **adjust the width of the boxes**. Finding an appropriate width might take some trial and error.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot(width = 0.2)\n```\n\n::: {.cell-output-display}\n![Default violin-boxplot: adjusting width of the box](05-dataviz2_files/figure-html/fig-vb_width-1.png){#fig-vb_width fig-align='center' width=100%}\n:::\n:::\n\n\n## Colour\n\nAdding colour should be pretty straightforward by now. The code is no different from what we used for the boxplot or violin plot. We need to add the `fill` argument within the `aes()`, along with a `scale_fill_?` layer.\n\nHowever, we can further customise the plot by adding an **opacity argument** using `alpha` to the violin plot geom.\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) + # alpha for opacity\n geom_boxplot(width = 0.2) + # change width of the boxes\n scale_fill_brewer(palette = \"RdPu\") # customise colour\n```\n\n::: {.cell-output-display}\n![Violin-boxplot with a different colour palette](05-dataviz2_files/figure-html/fig-vb_colour-1.png){#fig-vb_colour fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\n* [ ] Change the x- and y-axis labels\n* [ ] Remove the legend\n* [ ] add a theme\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## One possible Solution \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + # removes the legend\n # change labels of x and y\n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-22-1.png){fig-align='center' width=100%}\n:::\n:::\n\n:::\n\n:::\n\n\n## Activity 8: Faceting - adding another grouping variable\n\nFaceting is really useful when you have subsets in the data. We will apply it to the violin-boxplot from above, but you could add this layer to pretty much any plot. The function to split the plots into facets is called `facet_wrap()`.\n\nLet's **add another grouping variable**, `Pre_reg_group`, to create separate plots for the yes and no groups.\n\nSince the group labels on the x-axis are quite long, we will need to adjust them for better readability. Adding `guide = guide_axis(n.dodge = 2)` to the `scale_x_discrete()` function helps to display labels across multiple rows.\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + \n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic() +\n facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no\n scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows\n```\n\n::: {.cell-output-display}\n![Pretty violin-boxplot split into pre-registration groups (yes and no)](05-dataviz2_files/figure-html/fig-vb_pretty_facet-1.png){#fig-vb_pretty_facet fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-tip}\n\nYou may have noticed that the labels of `Pre_reg_group` are displayed as numbers (1 and 2). If this bugs you, fix the labels in the data object. This would be less hassle than trying to adjust the facet headings in the plot.\n\n:::\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n\n## Special case: Variables with subscales\n\nFor example, if we want to show the relationship between SATs scores at Timepoints 1 and 2, separately for all four subscales of the SATs questionnaire, we need to wrangle the data so that the scores for Time 1 and Time 2 are in separate columns, but each participant has four rows (one for each subscale). The dataframe should look like this:\n\n\n\n\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nhead(data_facet, n=5)\n```\n\n::: {.cell-output-display}\n
\n\n|Code |Subscale | Time1| Time2|\n|:----|:-------------------|--------:|--------:|\n|Tr10 |Affect | 4.000000| 3.500000|\n|Tr10 |CognitiveCompetence | 5.166667| 4.166667|\n|Tr10 |Value | 6.000000| 3.000000|\n|Tr10 |Difficulty | 3.571429| 2.857143|\n|Bi07 |Affect | 3.833333| 3.166667|\n\n
\n:::\n:::\n\n\n\n::: {.callout-note icon=\"false\"} \n\n## Your Turn\n\nCreate a new data object `data_facet` and wrangle the data from `data_prp_viz` so that it looks like the table above.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## Broad hints\n\n* **Step 1**: select variables of interest from `data_prp_viz`\n* **Step 2**: pivot the data\n* **Step 3**: try to access information on subscales and timepoints from the variable names\n* **Step 4**: pivot in the other direction\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n\n## More specific hints\n\n* **Step 1**: The variables of interest are the participants' ID and all columns that start with SATS.\n* **Step 2**: Pivot all columns, except the participant ID, from wide to long format.\n* **Step 3**: Get some inspiration from Activity 4 in {#sec-wrangling2}.\n* **Step 4**: At this point, subscales and timepoints should be in long format. However, only the subscale information should remain in long format. Pivot the data from long to wide format so that Time 1 and Time 2 are in separate columns.\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Solution\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_facet <- data_prp_viz %>% \n select(Code, starts_with(\"SATS\")) %>% \n pivot_longer(cols = starts_with(\"SATS\"), names_to = \"Variable\", values_to = \"Mean_Scores\") %>% \n separate(Variable, into = c(NA, \"Subscale\", \"Timepoint\", NA), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Mean_Scores)\n```\n:::\n\n\n:::\n\n:::\n\nNow we can build a scatterplot with facets for the subscales.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_facet, aes(x = Time1, y = Time2)) +\n geom_point() +\n facet_wrap(~Subscale)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-26-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n\n\n## [Pair-coding]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n### Task 1: Open the R project for the lab {.unnumbered}\n\n### Task 2: Create a new `.Rmd` file {.unnumbered}\n\n... and name it something useful. If you need help, have a look at @sec-rmd.\n\n### Task 3: Load in the library and read in the data {.unnumbered}\n\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: [data_pair_coding](data/data_pair_coding.zip \"download\").\n\nWe are using the package `tidyverse` today, and the data file we need to read in is `dog_data_clean_wide.csv`. I've named my data object `dog_data_wide` to shorten the name but feel free to use whatever object name sounds intuitive to you.\n\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n\n\n### Task 4: Re-create one of the 3 plots below {.unnumbered}\n\nRe-create one of the 3 plot below:\n\n* grouped barchart (easy)\n* violin-boxplot (medium)\n* scatterplot (hard)\n\n#### Difficulty level: easy {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-27-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n## Hints \n\n* I've created a new data object `data_bar` to select the relevant variables but you could also work straight from `dog_data_wide`.\n* Consider turning the 2 categorical variables into factors before plotting\n* Plotting should be relatively straightforward - these are default colours and you would only need to change the axes labels/ legend title. \n\n\n::: {.callout-tip collapse=\"true\" icon=\"false\"}\n## More hints \n\nWe can change all of the 3 labels in one go. Check out the *## Prettier grouped barchart* in @sec-adv_bar, where we did exactly that.\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_bar`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_bar <- dog_data_wide %>% \n select(RID, GroupAssignment, Year_of_Study) %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")),\n Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the barchart\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) +\n geom_bar(position = \"dodge\") +\n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Year of Study\")\n```\n:::\n\n:::\n\n\n:::\n\n#### Difficulty level: medium {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-30-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n::: {.callout-warning collapse=\"true\" icon=\"false\"}\n## Hints \n\n* I've created a new data object `data_vb` to select the relevant variables but you could also work straight from `dog_data_wide`.\n* Consider turning the categorical variable into a factor before plotting\n* Plotting tips:\n * the colour scale is one of the viridis options \n * it's a bit of trial and error for the opacity of the violin and the box width of the boxes (it is totally fine if it looks approximately right)\n * the tricky part might be adjusting the y-axis ticks. Take inspiration from the histogram in @sec-hist (Tab *Axes labels, margins, and breaks*)\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_vb`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_vb <- dog_data_wide %>% \n select(RID, Year_of_Study, Loneliness_post) %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n```\n:::\n\n\n::: \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the violin-boxplot\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.25) +\n scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)),\n limits = c(1, 4)) +\n scale_fill_viridis_d(option = \"magma\",\n guide = \"none\") +\n labs(x = \"Year of Study\", y = \"Loneliness scores post intervention\") +\n theme_classic()\n```\n:::\n\n\n::: \n\n:::\n\n#### Difficulty level: hard {.unnumbered}\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-33-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-important collapse=\"true\" icon=\"false\"}\n## Hints \n\n* **Data wrangling**: Even though we cleaned the data, it may not be in the shape for the task at hand. Have a look what the data object `dog_data_wide` looks like and think about how you'd need to restructure it to be able to plot the scatterplot. As always, I would suggest creating a new data object for the scatterplot (e.g., `data_scatter`).\n* Once you have the data in the right shape, start **plotting**. Start with a basic scatterplot and then add various layers and change elements you notice. \n* Remember, some finetuning might need to be done in `data_scatter` rather than plot itself.\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Data structure you have\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n
\n\n| RID| PANAS_PA_pre| PANAS_PA_post| PANAS_NA_pre| PANAS_NA_post|\n|---:|------------:|-------------:|------------:|-------------:|\n| 1| 3.2| 3.8| 1.0| 1.2|\n| 2| 3.0| 3.2| 1.8| 1.0|\n| 3| 2.8| 3.0| 1.6| 1.6|\n| 4| 4.2| 3.8| 1.8| 1.6|\n| 5| 3.4| 4.0| 2.2| 1.6|\n\n
\n:::\n:::\n\n\n:::\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Data structure you need\n\n\n::: {.cell layout-align=\"center\"}\n::: {.cell-output-display}\n
\n\n| RID|Subscale | pre| post|\n|---:|:---------------|---:|----:|\n| 1|Positive Affect | 3.2| 3.8|\n| 1|Negative Affect | 1.0| 1.2|\n| 2|Positive Affect | 3.0| 3.2|\n| 2|Negative Affect | 1.8| 1.0|\n| 3|Positive Affect | 2.8| 3.0|\n\n
\n:::\n:::\n\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Hints for `data_scatter`\n\n* **Step 1**: select the variables you need from `dog_data_wide`.\n* **Step 2**: pivot all columns (bar the Participant ID) into long format\n* **Step 3**: think about how to separate information of the subscales and timepoints\n* **Step 4**: pivot from long into wide format. Take some inspiration from the `Special case: Variables with subscales` scenario above.\n\n:::\n\n\n::: {.callout-note collapse=\"true\" icon=\"false\"}\n## Hints for the plot\n\n* The colour scheme is Dark2 from the colour palette brewer\n* The colour of the trendline is #7570b3\n* Think about how to make the Negative and Positive Affect points different colours. The solution is in @sec-scatter\n* Renaming the different facets is one of those things that should be fixed in the data object instead\n\n:::\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for `data_scatter`\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\ndata_scatter <- dog_data_wide %>% \n select(RID, starts_with(\"PANAS\")) %>% \n pivot_longer(cols = -RID, names_to = \"Q\", values_to = \"Values\") %>% \n separate(Q, into = c(NA, \"Subscale\", \"Timepoint\"), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Values) %>% \n mutate(Subscale = case_match(Subscale,\n \"NA\" ~ \"Negative Affect\",\n \"PA\" ~ \"Positive Affect\"),\n Subscale = factor(Subscale)) %>% \n drop_na()\n```\n:::\n\n\n:::\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n## Solution for the scatterplot\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) +\n geom_point() +\n geom_smooth(method = lm, colour = \"#7570b3\") +\n facet_wrap(~Subscale) +\n labs(x = \"Pre-Intervention (Timepoint 1)\",\n y = \"Post-Intervention (Timepoint 2)\") +\n scale_colour_brewer(palette = \"Dark2\",\n guide = \"none\") +\n theme_bw()\n```\n:::\n\n\n\n:::\n\n:::\n\nIf you are extremely fast, challenge yourself and re-create one of the other plots.\n\n\n## [Test your knowledge]{style=\"color: #F39C12; text-transform: uppercase;\"} {.unnumbered}\n\n\n### Knowledge check {.unnumbered}\n\n\n#### Question 1 {.unnumbered}\n\nWhy would this line of code not create a barplot, assuming you already loaded all data and libraries and you spelt the data and column names correctly?\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(summarydata, aes(x = sex)) +\n geom_barplot()\n```\n:::\n\n\n
\n\n\n\n\n#### Question 2 {.unnumbered}\n\nIf I wanted precisely 5 bars in my histogram, what argument would I use? \n\n
\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain this answer\n\n* `ggplot() + geom_histogram(bins = 5)`. This is the **correct** answer as you are asking ggplot2 to give you the plot organised into 5 bins. \n* `ggplot() + geom_histogram(bars = 5)`. This is incorrect as you bars is not the right argument name. You want 5 bars, but the argument is bins. \n* `ggplot() + geom_histogram(binwidth = 5)`. This is incorrect as binwidth controls the x-axis range to include per bar, rather than the number of bars. \n* `ggplot() + geom_histogram()`. This is incorrect as you did not control the number of bins, so it will default to 30. \n\n:::\n\n\n#### Question 3 {.unnumbered}\n\nYou want to create a scatterplot to show the correlation between two continuous variables, which geom would you use? \n\n
\n\n\n\n#### Question 4 {.unnumbered}\n\nTrue or False? To showcase different groups in a scatterplot, you could specify a grouping variable using the `fill` argument to change the colour of the points. \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"} \n\n## Explain this answer\n\nHave a look in the scatterplot section again. There, we explained that we cannot \"colour in\" the area of the points using `fill`, but need to change the outline colour of the points with `colour` instead.\n:::\n\n\n### Error mode {.unnumbered}\n\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\n\nLet's go back to the Palmer penguins for this part.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nlibrary(palmerpenguins)\n```\n:::\n\n\n\n#### Question 5 {.unnumbered}\n\nWe want to plot the number of penguins across the different islands.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = islands)) %>% \n geom_bar()\n```\n\n::: {.cell-output .cell-output-error}\n```\nError in `geom_bar()`:\n! `mapping` must be created by `aes()`.\nℹ Did you use `%>%` or `|>` instead of `+`?\n```\n:::\n:::\n\n\nThe error message is incredibly useful. So that should be an easy fix!\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nTo fix the error, all we need to do is turn the pipe `%>%` into a plus `+`.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = island)) +\n geom_bar()\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-41-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n\n#### Question 6 {.unnumbered}\n\nWe want to create a violin-boxplot showing the bill length across species, separately for male and female penguins.\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npenguins_no_na <- penguins %>% \n drop_na(island, flipper_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = flipper_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-42-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\nHmmm. We got a plot, but is it the one we aimed for? \n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nThis was a tricky one. The code runs, so we think everything is fine. Despite having re-labelled the y-axis \"Bill length in mm\", it's really showing the flipper length. Did you spot this???\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\npenguins_no_na <- penguins %>% \n drop_na(island, bill_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = bill_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n```\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-43-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n:::\n\n#### Question 7 {.unnumbered}\n\nThis time, we want to show the relationship between flipper length and body mass of the penguins. The following code runs, but the x- and y-axes are misbehaving somehow. Not what we wanted. Any idea why?\n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_discrete(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_discrete(name = \"Flipper length in mm\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in scale_y_discrete(name = \"Body mass in g\", breaks = seq(2500, : Continuous limits supplied to discrete scale.\nℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n:::\n\n::: {.cell-output-display}\n![](05-dataviz2_files/figure-html/unnamed-chunk-44-1.png){fig-align='center' width=100%}\n:::\n:::\n\n\n\n::: {.callout-caution collapse=\"true\" icon=\"false\"}\n\n## Explain the solution\n\nIn this example, we used the wrong function for continuous variables. We used `scale_y_discrete` and `scale_y_discrete`, instead of `scale_x_continuous` and `scale_y_continuous`. We must honour the variable type when we customise the plot, so think about what type of variable is on each axis and which function lets you edit it. \n\n\n::: {.cell layout-align=\"center\"}\n\n```{.r .cell-code}\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_continuous(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_continuous(name = \"Flipper length in mm\")\n```\n:::\n\n:::\n\n", "supporting": [ "05-dataviz2_files" ], diff --git a/_freeze/05-dataviz2/figure-html/unnamed-chunk-27-1.png b/_freeze/05-dataviz2/figure-html/unnamed-chunk-27-1.png new file mode 100644 index 0000000000000000000000000000000000000000..7f6085fa1b7af85403e5a24155b7994d5d18c66e GIT binary patch literal 24365 zcmeHv2|QHo`?rQcgS3bw6crVw5M`Z;qO1{RZ$tJ%c4Ja`O131GFeOU~6~;QIlBga` z*ARF5XeZu)M(d!~M5l|4XF 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t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;et===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

    ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=Y(t,{elementContext:"reference"}),s=Y(t,{altBoundary:!0}),f=he(a,r),c=he(s,o,i),p=me(f),u=me(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},ge=K({defaultModifiers:[Z,$,ne,re]}),ye=[Z,$,ne,re,oe,pe,le,de,ve],be=K({defaultModifiers:ye});e.applyStyles=re,e.arrow=de,e.computeStyles=ne,e.createPopper=be,e.createPopperLite=ge,e.defaultModifiers=ye,e.detectOverflow=Y,e.eventListeners=Z,e.flip=pe,e.hide=ve,e.offset=oe,e.popperGenerator=K,e.popperOffsets=$,e.preventOverflow=le,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/_freeze/05-dataviz2/libs/quarto-html/quarto-syntax-highlighting.css b/_freeze/05-dataviz2/libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/_freeze/05-dataviz2/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of 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a/_quarto.yml +++ b/_quarto.yml @@ -54,6 +54,7 @@ book: - 02-wrangling.qmd - 03-wrangling2.qmd - 04-dataviz.qmd + - 05-dataviz2.qmd appendices: - appendix-a-installing-r.qmd - appendix-b-updating-packages.qmd diff --git a/docs/01-basics.html b/docs/01-basics.html index 22ccc1d..6b5d430 100644 --- a/docs/01-basics.html +++ b/docs/01-basics.html @@ -188,6 +188,12 @@     4  Data viz I + +
  1. +
    + + 5  Data viz II +
  2. @@ -881,7 +887,7 @@

    Research shows that questionable research practices (QRPs) are present in undergraduate final-year dissertation projects. One entry-level Open Science practice proposed to mitigate QRPs is “study preregistration,” through which researchers outline their research questions, design, method, and analysis plans before data collection and/or analysis. In this study, we aimed to empirically test the effectiveness of preregistration as a pedagogic tool in undergraduate dissertations using a quasi-experimental design. A total of 89 UK psychology students were recruited, including students who preregistered their empirical quantitative dissertation (n = 52; experimental group) and students who did not (n = 37; control group). Attitudes toward statistics, acceptance of QRPs, and perceived understanding of Open Science were measured both before and after dissertation completion. Exploratory measures included capability, opportunity, and motivation to engage with preregistration, measured at Time 1 only. This study was conducted as a Registered Report; Stage 1 protocol: https://osf.io/9hjbw (date of in-principle acceptance: September 21, 2021). Study preregistration did not significantly affect attitudes toward statistics or acceptance of QRPs. However, students who preregistered reported greater perceived understanding of Open Science concepts from Time 1 to Time 2 compared with students who did not preregister. Exploratory analyses indicated that students who preregistered reported significantly greater capability, opportunity, and motivation to preregister. Qualitative responses revealed that preregistration was perceived to improve clarity and organization of the dissertation, prevent QRPs, and promote rigor. Disadvantages and barriers included time, perceived rigidity, and need for training. These results contribute to discussions surrounding embedding Open Science principles into research training.

    Changes made to the dataset

    -

    We made some changes to the dataset for the purpose of increasing difficulty for data wrangling (Chapter 2 and Chapter 3) and data visualisation (Chapter 4 and ?sec-dataviz2). This will ensure some “teachable moments”. The changes are as follows:

    +

    We made some changes to the dataset for the purpose of increasing difficulty for data wrangling (Chapter 2 and Chapter 3) and data visualisation (Chapter 4 and Chapter 5). This will ensure some “teachable moments”. The changes are as follows:

    • We removed some of the variables to make the data more manageable for teaching purposes.
    • We recoded some values from numeric responses to labels (e.g., understanding).
      • @@ -1365,7 +1371,7 @@

      -

      A factor is a specific type of integer or character that lets you assign the order of the categories. This becomes useful when you want to display certain categories in “the correct order” either in a dataframe (see arrange) or when plotting (see Chapter 4/ ?sec-dataviz2).

      +

      A factor is a specific type of integer or character that lets you assign the order of the categories. This becomes useful when you want to display certain categories in “the correct order” either in a dataframe (see arrange) or when plotting (see Chapter 4/ Chapter 5).

      1.6.3 Variable types

      You’ve already encountered them in Level 1 but let’s refresh. Variables can be classified as continuous (numbers) or categorical (labels).

      @@ -1381,7 +1387,7 @@

    -

    Knowing your variable and data types will help later on when deciding on an appropriate plot (see Chapter 4 and ?sec-dataviz2) or which inferential test to run (?sec-nhstI to ?sec-factorial).

    +

    Knowing your variable and data types will help later on when deciding on an appropriate plot (see Chapter 4 and Chapter 5) or which inferential test to run (?sec-nhstI to ?sec-factorial).

    diff --git a/docs/02-wrangling.html b/docs/02-wrangling.html index bc226b1..fecbfb0 100644 --- a/docs/02-wrangling.html +++ b/docs/02-wrangling.html @@ -188,6 +188,12 @@ 4  Data viz I
    +
    3. +
  3. +
    + + 5  Data viz II +
  4. @@ -296,7 +302,7 @@

    realise there are several ways of getting to the results

  5. export data objects as csv files
    6. -

    The main purpose of this chapter and Chapter 3 is to wrangle your data into shape for data visualisation (Chapter 4 and ?sec-dataviz2). For the two chapters, we will:

    +

    The main purpose of this chapter and Chapter 3 is to wrangle your data into shape for data visualisation (Chapter 4 and Chapter 5). For the two chapters, we will:

    1. calculate demographics
    2. tidy 3 different questionnaires with varying degrees of complexity
      3. diff --git a/docs/03-wrangling2.html b/docs/03-wrangling2.html index d044ced..b19a824 100644 --- a/docs/03-wrangling2.html +++ b/docs/03-wrangling2.html @@ -188,6 +188,12 @@ 4  Data viz I + +
    3. +
      + + 5  Data viz II +
    4. @@ -287,7 +293,7 @@

      read and interpret error messages

    5. realise there are several ways of getting to the results
      6. -

      In this chapter, we will pick up where we left off in Chapter 2. We will calculate average scores for two of the questionnaires, address an error mode problem, and finally, join all data objects together. This will finalise our data for the upcoming data visualization sections (Chapter 4 and ?sec-dataviz2).

      +

      In this chapter, we will pick up where we left off in Chapter 2. We will calculate average scores for two of the questionnaires, address an error mode problem, and finally, join all data objects together. This will finalise our data for the upcoming data visualization sections (Chapter 4 and Chapter 5).

      Individual Walkthrough

      3.1 Activity 1: Setup

      @@ -1172,7 +1178,7 @@

      More info on joins can be found https://www.tidyverse.org/blog/2023/01/dplyr-1-1-0-joins/

      -

      And this is basically the dataset we need for Chapter 4 and ?sec-dataviz2.

      +

      And this is basically the dataset we need for Chapter 4 and Chapter 5.

      3.7 Activity 7: Knit and export

      Knit the .Rmd file to ensure everything runs as expected. Once it does, export the data object data_prp_final as a csv for use in the Chapter 4. Name it something meaningful, something like data_prp_for_ch4.csv.

      diff --git a/docs/04-dataviz.html b/docs/04-dataviz.html index 4d9a226..666b822 100644 --- a/docs/04-dataviz.html +++ b/docs/04-dataviz.html @@ -60,7 +60,7 @@ - + @@ -188,6 +188,12 @@ 4  Data viz I + +
    6. +
      + + 5  Data viz II +
    7. @@ -245,7 +251,7 @@
    8. 4.3 Activity 2: Load in libraries, read in data, and adjust data types
    9. 4.4 Activity 3: Barchart (geom_bar())
    10. 4.5 Activity 4: Column plot (geom_col())
      11. -
    11. 4.6 Activity 5: Stacked, Percent Stacked, and Grouped Barchart
      12. +
    12. 4.6 Activity 5: Stacked, Percent Stacked, and Grouped Barchart
    13. 4.7 Activity 6: Save your plots
    14. Pair-coding @@ -727,7 +733,7 @@

      15. 4.4 Activity 3: Barchart (geom_bar())

      -

      A bar chart is the best choice when you want to plot a single categorical variable.

      +

      A barchart is the best choice when you want to plot a single categorical variable.

      For example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a barplot. This is done with geom_bar() in the third layer. Since the counting is done automatically in the background, the aes() function only requires an x value (i.e., the name of your variable).

      ggplot(data_prp_viz, aes(x = Gender)) +
@@ -939,7 +945,7 @@
      -

      +

      4.6 Activity 5: Stacked, Percent Stacked, and Grouped Barchart

      When dealing with two categorical variables, you have three options for displaying stacked barcharts: the “normal” Stacked Barchart (the default option), a Percent Stacked Barchart, or a Grouped Barchart.

      For this activity, we will explore the variable Plan_prereg, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and Pre_reg_group, which tracks whether they actually followed through with a pre-registration for their dissertation.

      @@ -1400,8 +1406,8 @@

      geom_col

      Question 2

      What mapping would you use to display penguin count across species? -
      - +
      +

      Question 3

      What geom would you use to count the number of species on each island?

      Question 4

      What mapping would you use to display the count of species per island? -
      - +
      +
      - - A  Installing R + + 5  Data viz II
      diff --git a/docs/05-dataviz2.html b/docs/05-dataviz2.html new file mode 100644 index 0000000..0022945 --- /dev/null +++ b/docs/05-dataviz2.html @@ -0,0 +1,2426 @@ + + + + + + + +Analysis - 5  Data viz II + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + +
      + + + +
      +

      5  Data viz II

      +
      + + + +
      + + + + +
      + + +

      Intended Learning Outcomes

      +

      By the end of this chapter you should be able to:

      +
        +
      • choose an appropriate plot for continuous variables
        • +
      • choose an appropriate plot for a mix of continuous/categorical variables
        • +
      • create a basic version of an appropriate plot
        • +
      • apply extra layers to change the appearance of the plot
        • +
      +

      In this chapter, we continue our journey of appropriate plots. Last week, we examined which plots are appropriate for categorical variables. Today, we’ll focus on continuous variables and which plots to choose with a mix of continuous and categorical variables.

      +

      Individual Walkthrough

      +

      +5.1 Activity 1: Set-up and data for today

      +
        +
      • We are still working with the data by Pownall et al. (2023). Open the project.
        • +
      • You could use the same .Rmd file as last week if you want to keep all plotting in one document or create a new .Rmd to separate plots for categorical and continuous variables. Up to you.
        • +
      • The aggregated data is the same as last week. It should be in your project folder but in case it got lost, download the csv again and place it in your project folder: data_prp_for_ch4.csv.
        • +
      • If you need a reminder about the data and variables, have a look at the codebook and/or Section 1.4.
        • +

      +5.2 Activity 2: Load in libraries, read in data, and adjust data types

      +

      Today, we need to load the package tidyverse, and read in the data data_prp_ch4.csv.

      +
      +
      ## packages 
+???
+
+## data
+data_prp_viz <- ???
      +
      +
      + +
      +
      +
      +
      library(tidyverse)
+data_prp_viz <- read_csv("data_prp_for_ch4.csv")
      +
      +
      +
      +
      +

      This is the same code as last week. We need to turn our categorical variables into factors to make plotting easier.

      +
      +
      data_prp_viz <- data_prp_viz %>% 
+  mutate(Gender = factor(Gender,
+                         levels = c(2, 1, 3),
+                         labels = c("females", "males", "non-binary")),
+         Secondyeargrade = factor(Secondyeargrade,
+                                  levels = c(1, 2, 3, 4, 5),
+                                  labels = c("≥ 70% (1st class grade)", "60-69% (2:1 grade)", "50-59% (2:2 grade)", "40-49% (3rd class)", "< 40%")),
+         Plan_prereg = factor(Plan_prereg,
+                              levels = c(1, 3, 2),
+                              labels = c("Yes", "Unsure", "No")),
+         Closely_follow = factor(Closely_follow,
+                                 levels = c(2, 3),
+                                 labels = c("Followed it somewhat", "Followed it exactly")),
+         Research_exp = factor(Research_exp),
+         Pre_reg_group = factor(Pre_reg_group))
      +
      +
      +
      +
      + +
      +
      +Tip +
      +
      +
      +

      If you are working within the same .Rmd file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week’s .Rmd file to load tidyverse into the library, read in the data, and convert some of the variables into factors.

      +
      +
      +

      +5.3 Activity 3: Histogram (geom_histogram())

      +

      If you want to show the distribution of a continuous variable, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to geom_bar(), geom_histogram() only requires an x variable as it does the counting “in the background”.

      +

      A histogram divides the data into “bins” (i.e., groupings displayed in a single bar). These bins are plotted along the x-axis, with the y-axis showing the count of observations in each bin. It’s basically a barchart for continuous variables.

      +

      Let’s have a look at the age distribution in our dataset.

      +
      +
      ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram()
      +
      +
      `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
      +
      +
      +
      Warning: Removed 2 rows containing non-finite outside the scale range
+(`stat_bin()`).
      +
      +
      +
      +

      +
      Figure 5.1: Default histogram
      +
      +
      +
      +

      The default number of bins is 30 (as shown in Figure 5.1 above). Changing the number of bins (argument bins) allows for more or less fine-tuning of the data. A higher number of bins results in more detailed granularity.

      +

      Perhaps it’s more intuitive to modify the width of each bin using the binwidth argument. For example, binwidth = 1 for the age category would mean each “age group” represents 1 year, while binwidth = 5 would group ages into 5-year spans. The plots below show modifications for both bins and binwidth.

      +
      +
      #less finetuning
+ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram(bins = 10) 
+
+# more fineturning
+ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram(binwidth = 1)
      +
      +
      +
      +
      Warning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).
+Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).
      +
      +
      +
      +

      +
      Figure 5.2: Bins vs binwidth arguments
      +
      +
      +
      +

      The warning message tells us 2 row of data were removed due to containing non-finite values outside the scale range. Have a look at the age column in data_prp_viz to see if you can decipher the warning message.

      +

      The rows were removed because .

      +

      Colours are manipulated slightly differently than in the barchart. Click through each tab to see how you can modify colours, axis labels, margins, and breaks, and apply a different theme.

      +
      + +
      +
      +

      We can change the plot colours by adding a fill argument and a colour argument. The fill argument changes the colour of the bars, while the colour argument modifies the outline of the bars. Note that these arguments are added directly to the geom_histogram(), rather than within the overall aes(), as we did with the barchart.

      +
      +
      ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE")
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +Tip +
      +
      +
      +

      You could use:

      +
        +

      • Hex codes for fill and colour, as we did here: geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE"). If you want to create your own colours, check out this website.

        • +

      • Pre-defined colour names: geom_histogram(binwidth = 1, fill = "purple", colour = "green"). See the full list here.

        • +
      +
      +
      +
      +
      +

      Here we removed the label for the y axes Count (to show you some variety) and modified the breaks. The y-axis is now displayed in increasing steps of 5 (rather than 10), and the x-axis has 1-year increments instead of 5.

      +

      Notice how the breaks = argument changes the labels of the break ticks but not the scale limits. You can adjust the limits of the scale using the limits = argument. To exaggerate, we set the limits to 15 and 50. See how the values from 15 to 19, and 44 to 50 do not have labels? You would need to adjust that using the breaks = argument.

      +

      The expansion() function removes the gap between the x-axis and the bars. It is exactly the same code we used in Chapter 4.

      +
      +
      ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE") +
+  labs(x = "Age (in years)", # renaming x axis label
+       y = "") + # removing the y axis label
+  scale_y_continuous(
+    # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)
+    expand = expansion(mult = c(0, 0.05)),
+    # changing break points on y axis
+    breaks = seq(from = 0, to = 30, by = 5)
+    ) +
+  scale_x_continuous(
+    # changing break points on x axis
+    breaks = seq(from = 20, to = 43, by = 1),
+    # Experimenting with
+    limits = c(15, 50)
+    )
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +

      Let’s experiment with the themes. For this plot we have chosen theme_bw()

      +
      +
      ggplot(data_prp_viz, aes(x = Age)) +
+  geom_histogram(binwidth = 1, fill = "#586cfd", colour = "#FC58BE") +
+  labs(x = "Age (in years)", # renaming x axis label
+       y = "") + # removing the y axis label
+  scale_y_continuous(
+    # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)
+    expand = expansion(mult = c(0, 0.05)),
+    # changing break points on y axis
+    breaks = seq(from = 0, to = 30, by = 5)
+    ) +
+  scale_x_continuous(
+    # changing break points on x axis
+    breaks = seq(from = 19, to = 44, by = 1)
+    ) +
+  # pick a theme
+  theme_bw()
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +

      +5.4 Activity 4: Scatterplot (geom_point())

      +

      Scatterplots are appropriate when you want to plot two continuous variables. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and Time point 2. The default scatterplot can be created with geom_point().

      +

      We can also add a trendline by using geom_smooth(). The default trendline is loess. If you want a linear trendline, you would need to add method = "lm" inside the geom_smooth() function.

      +
      +
      ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +
+  geom_point() +
+  geom_smooth()
+
+ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +
+  geom_point() +
+  geom_smooth(method = lm)
      +
      +
      +
      +
      +

      +
      Figure 5.3: Default Scatterplot with added trendline - loess (left) and linear (right)
      +
      +
      +
      +

      Customising the colour of a scatterplot is slightly different from the other plots we’ve encountered so far. Technically, the point is not a “filled-in black area” but rather an extremely wide outline of a circle. Therefore, we cannot use the usual fill argument and instead need to use the colour argument, similar to how we customised the outline of the histogram.

      +

      See the tabs below to learn how to change the colour for all points or how to adjust the colour based on groupings.

      +
      + +
      +
      +

      If we want to change the colour of all the points, we can add the colour argument to the geom_point() function. Likewise, to change the colour of the trendline, we would also use the colour argument. Here, we used pre-defined colour names, but HEX codes would work just as well.

      +
      +
      # colour of all points and the trendline
+ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +
+  geom_point(colour = 'magenta') +
+  geom_smooth(method = lm, colour = 'turquoise')
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +

      If we want the points to change colour based on another grouping variable, the colour argument should go inside the aes(). If you don’t want to define the colours manually, you can use a colour palette like Brewer (scale_colour_brewer()) or Viridis (scale_colour_viridis_d()).

      +
      +
      ## adding grouping variable Pre_reg_group and changing the colour values manually
+ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +
+  geom_point() +
+  geom_smooth(method = lm) +
+  scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +

      You can tidy the legend title and group labels using the scale_colour_? function, depending on the palette you’re using (e.g., scale_colour_manual(), scale_colour_brewer and many more).

      +
      +
      ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +
+  geom_point() +
+  geom_smooth(method = lm) +
+  scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),
+                      name = "Pre-registered Dissertation",
+                      labels = c("Yes", "No"))
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +Your Turn +
      +
      +
      +

      All other layers remain exactly the same as in other plots. Try adding layers to make the plot above prettier:

      +
        +
      • +1. relabel axes x and y
        • +
      • +2. set the x and y axis range from 1 to 7
        • +
      • +3. move the legend to a different position (either top, left, or bottom)
        • +
      • +4. add a theme
        • +
      +
      + +
      +
      +
        +
      1. can be done in 2 different ways - labs() or scale_x_?
        2. +
      2. we did that for the histogram
        3. +
      3. We did that for the barcharts
        4. +
      4. pick a theme you like
        5. +
      +
      + +
      +
      +

      If you are experiencing issues with the legend position and theme, try modifying the order of the layers.

      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +
      +
      ggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +
+  geom_point() +
+  geom_smooth(method = lm) +
+  scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),
+                      name = "Pre-registered Dissertation",
+                      labels = c("Yes", "No")) +
+  labs (x = "Acceptance of Questionable Research Practices (Time 1)", 
+        y = "Acceptance of Questionable Research Practices (Time 2)") +
+  theme_light() + # place before moving the legend position
+  theme(legend.position = "top") # move legend to the top
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +
      +
      +

      +5.5 Activity 5: Boxplot (geom_boxplot())

      +

      A boxplot is one of the options to display a continuous variable with categorical grouping variable. Here, we want to create a boxplot to explore whether students’ understanding of open science varies based on whether or not they have research experience. Our default boxplot would look like this:

      +
      +
      # default boxplot
+ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +
+  geom_boxplot()
      +
      +
      +

      +
      +
      +
      +
      +

      Tada! As usual, we can enhance the plot by adding various layers. Click on each tab below to see how.

      +
      + +
      +
      +

      We can change the colour by adding a fill argument inside the aes(). To customise the colours further, we can add a scale_fill_? layer. If you have specific colours in mind, use scale_fill_manual(). If you prefer pre-defined palettes, such as Brewer, you can use scale_fill_brewer().

      +

      Btw, this is exactly the same code we used for the barcharts.

      +
      +
      ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +
+  geom_boxplot() +
+  # customise colour
+  scale_fill_brewer(palette = "Dark2")
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +

      We need to relabel the axes. The function to use depends on the variable type. Here, we need scale_x_discrete() for the x-axis and scale_y_continuous() for the y-axis. We can also tidy up the group labels and adjust the breaks on the y-axis (e.g., in steps of 1 instead of 2) within these same functions.

      +
      +
      ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +
+  geom_boxplot() +
+  scale_fill_brewer(palette = "Dark2") +
+  scale_x_discrete(
+    # changing the label of x
+    name = "Research Experience",
+    # changing the group labels of the 2 groups
+    labels = c("Yes", "No")) + 
+  scale_y_continuous(
+    # changing name of the y axis
+    name = "Confidence in Understanding Open Science (Time 1)",
+    # changing break labels
+    breaks = c(seq(from = 1, to = 7, by = 1))
+  )
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +

      The legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument guide = "none" to the scale_fill_? function.

      +

      Let’s pick a theme we haven’t used yet: theme_dark().

      +
      +
      ggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +
+  geom_boxplot() +
+  scale_fill_brewer(palette = "Dark2",
+                    # removing the legend
+                    guide = "none") +
+  scale_x_discrete(
+    name = "Research Experience",
+    labels = c("Yes", "No")) + 
+  scale_y_continuous(
+    name = "Confidence in Understanding Open Science (Time 1)",
+    breaks = c(seq(from = 1, to = 7, by = 1))
+  ) +
+  # pick a theme
+  theme_dark()
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +

      +5.6 Activity 6: Violin plot (geom_violin())

      +

      An alternative way to display a continuous variable with a categorical grouping variable is a violin plot. Here, we want to create a violin plot to explore whether the perception of supervisor support depends on whether students plan to pre-register their dissertation. Our default violin plot would look like this:

      +
      +
      # default boxplot
+ggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +
+  geom_violin()
      +
      +
      Warning: Removed 3 rows containing non-finite outside the scale range
+(`stat_ydensity()`).
      +
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +Your Turn +
      +
      +
      +

      Adjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to

      +
        +
      • +change the colours either manually or using a pre-defined colour palette
        • +
      • +tidy the axes labels and group names
        • +
      • +in case a legend appears, take it off
        • +
      • +add a theme
        • +
      +
      + +
      +
      +
      +
      ggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +
+  geom_violin() +
+  scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),
+                    # removing the legend
+                    guide = "none") +
+  scale_x_discrete(name = "Plan to pre-register the dissertation") + 
+  scale_y_continuous(
+    name = "Perceived Supervisory Support",
+    breaks = c(seq(from = 1, to = 7, by = 1))
+  ) +
+  # pick a theme
+  theme_minimal()
      +
      +
      Warning: Removed 3 rows containing non-finite outside the scale range
+(`stat_ydensity()`).
      +
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +
      +
      +

      +5.7 Activity 7: Violin-boxplots

      +

      So far, we’ve only added one geom_? layer to our plots. However, thanks to ggplot’s layered system, we can add multiple geoms, for example, when creating a violin-boxplot.

      +

      Remember, the order of the layers can sometimes make a difference. We’ve seen this already - adding a theme at the end can override earlier arguments like the legend position. Similarly, ggplot + violinplot + boxplot will look different from ggplot + boxplot + violinplot.

      +

      Let’s use the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.

      +
      +
      ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +
+  geom_violin() +
+  geom_boxplot()
+
+
+ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +
+  geom_boxplot() +
+  geom_violin()
      +
      +
      +
      +
      +

      +
      Figure 5.4: Default violin-boxplot: Order of the layer matters
      +
      +
      +
      +

      See the tabs below to learn how to customise various elements, such as the width of the boxes, and the colour or opacity.

      +
      + +
      +
      +

      If we want to get any information from the boxplot, we need to place it “on top of” the violin plot. But still, the boxplot is pretty wide and covers important details from the violin plot. To make the information more visible, we can adjust the width of the boxes. Finding an appropriate width might take some trial and error.

      +
      +
      ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +
+  geom_violin() +
+  geom_boxplot(width = 0.2)
      +
      +
      +

      +
      Figure 5.5: Default violin-boxplot: adjusting width of the box
      +
      +
      +
      +
      +
      +

      Adding colour should be pretty straightforward by now. The code is no different from what we used for the boxplot or violin plot. We need to add the fill argument within the aes(), along with a scale_fill_? layer.

      +

      However, we can further customise the plot by adding an opacity argument using alpha to the violin plot geom.

      +
      +
      ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +
+  geom_violin(alpha = 0.4) + # alpha for opacity
+  geom_boxplot(width = 0.2) + # change width of the boxes
+  scale_fill_brewer(palette = "RdPu") # customise colour
      +
      +
      +

      +
      Figure 5.6: Violin-boxplot with a different colour palette
      +
      +
      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +Your Turn +
      +
      +
      +
        +
      • +Change the x- and y-axis labels
        • +
      • +Remove the legend
        • +
      • +add a theme
        • +
      +
      + +
      +
      +
      +
      ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +
+  geom_violin(alpha = 0.4) +
+  geom_boxplot(width = 0.2) +
+  scale_fill_brewer(palette = "RdPu",
+                    guide = "none") + # removes the legend
+  # change labels of x and y
+  labs (x = "Second-year Grade", y = "Acceptance of Questionable Research Practices (Time 2)") +
+  theme_classic()
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +
      +
      +

      +5.8 Activity 8: Faceting - adding another grouping variable

      +

      Faceting is really useful when you have subsets in the data. We will apply it to the violin-boxplot from above, but you could add this layer to pretty much any plot. The function to split the plots into facets is called facet_wrap().

      +

      Let’s add another grouping variable, Pre_reg_group, to create separate plots for the yes and no groups.

      +

      Since the group labels on the x-axis are quite long, we will need to adjust them for better readability. Adding guide = guide_axis(n.dodge = 2) to the scale_x_discrete() function helps to display labels across multiple rows.

      +
      +
      ggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +
+  geom_violin(alpha = 0.5) +
+  geom_boxplot(width = 0.2) +
+  scale_fill_brewer(palette = "RdPu",
+                    guide = "none") + 
+  labs (x = "Second-year Grade", y = "Acceptance of Questionable Research Practices (Time 2)") +
+  theme_classic() +
+  facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no
+  scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows
      +
      +
      +

      +
      Figure 5.7: Pretty violin-boxplot split into pre-registration groups (yes and no)
      +
      +
      +
      +
      +
      +
      + +
      +
      +Tip +
      +
      +
      +

      You may have noticed that the labels of Pre_reg_group are displayed as numbers (1 and 2). If this bugs you, fix the labels in the data object. This would be less hassle than trying to adjust the facet headings in the plot.

      +
      +
      +
      + +
      +
      +

      For example, if we want to show the relationship between SATs scores at Timepoints 1 and 2, separately for all four subscales of the SATs questionnaire, we need to wrangle the data so that the scores for Time 1 and Time 2 are in separate columns, but each participant has four rows (one for each subscale). The dataframe should look like this:

      +
      +
      head(data_facet, n=5)
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      CodeSubscaleTime1Time2
      Tr10Affect4.0000003.500000
      Tr10CognitiveCompetence5.1666674.166667
      Tr10Value6.0000003.000000
      Tr10Difficulty3.5714292.857143
      Bi07Affect3.8333333.166667
      +
      +
      +
      +
      +
      +
      + +
      +
      +Your Turn +
      +
      +
      +

      Create a new data object data_facet and wrangle the data from data_prp_viz so that it looks like the table above.

      +
      + +
      +
      +
        +
      • +Step 1: select variables of interest from data_prp_viz +
        • +
      • +Step 2: pivot the data
        • +
      • +Step 3: try to access information on subscales and timepoints from the variable names
        • +
      • +Step 4: pivot in the other direction
        • +
      +
      +
      +
      +
      + +
      +
      +
        +
      • +Step 1: The variables of interest are the participants’ ID and all columns that start with SATS.
        • +
      • +Step 2: Pivot all columns, except the participant ID, from wide to long format.
        • +
      • +Step 3: Get some inspiration from Activity 4 in {#sec-wrangling2}.
        • +
      • +Step 4: At this point, subscales and timepoints should be in long format. However, only the subscale information should remain in long format. Pivot the data from long to wide format so that Time 1 and Time 2 are in separate columns.
        • +
      +
      +
      +
      +
      + +
      +
      +
      +
      data_facet <- data_prp_viz %>% 
+  select(Code, starts_with("SATS")) %>% 
+  pivot_longer(cols = starts_with("SATS"), names_to = "Variable", values_to = "Mean_Scores") %>% 
+  separate(Variable, into = c(NA, "Subscale", "Timepoint", NA), sep = "_") %>% 
+  pivot_wider(names_from = Timepoint, values_from = Mean_Scores)
      +
      +
      +
      +
      +
      +
      +

      Now we can build a scatterplot with facets for the subscales.

      +
      +
      ggplot(data_facet, aes(x = Time1, y = Time2)) +
+  geom_point() +
+  facet_wrap(~Subscale)
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +

      Pair-coding

      +

      Task 1: Open the R project for the lab

      +

      Task 2: Create a new .Rmd file

      +

      … and name it something useful. If you need help, have a look at Section 1.3.

      +

      Task 3: Load in the library and read in the data

      +

      The data should already be in your project folder. If you want a fresh copy, you can download the data again here: data_pair_coding.

      +

      We are using the package tidyverse today, and the data file we need to read in is dog_data_clean_wide.csv. I’ve named my data object dog_data_wide to shorten the name but feel free to use whatever object name sounds intuitive to you.

      +

      Task 4: Re-create one of the 3 plots below

      +

      Re-create one of the 3 plot below:

      +
        +
      • grouped barchart (easy)
        • +
      • violin-boxplot (medium)
        • +
      • scatterplot (hard)
        • +
      +

      Difficulty level: easy

      +
      +
      +
      +

      +
      +
      +
      +
      +
      + +
      +
      +
        +
      • I’ve created a new data object data_bar to select the relevant variables but you could also work straight from dog_data_wide.
        • +
      • Consider turning the 2 categorical variables into factors before plotting
        • +
      • Plotting should be relatively straightforward - these are default colours and you would only need to change the axes labels/ legend title.
        • +
      +
      + +
      +
      +

      We can change all of the 3 labels in one go. Check out the ## Prettier grouped barchart in Section 4.6, where we did exactly that.

      +
      +
      +
      +
      + +
      +
      +
      +
      data_bar <- dog_data_wide %>% 
+  select(RID, GroupAssignment, Year_of_Study) %>% 
+  mutate(GroupAssignment = factor(GroupAssignment,
+                                  levels = c("Direct", "Indirect", "Control")),
+         Year_of_Study = factor(Year_of_Study,
+                                levels = c("First", "Second", "Third", "Fourth", "Fifth or above")))
      +
      +
      +
      +
      +
      + +
      +
      +
      +
      ggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) +
+  geom_bar(position = "dodge") +
+  labs(x = "Experimental Group", y = "Count", fill = "Year of Study")
      +
      +
      +
      +
      +
      +
      +
      +

      Difficulty level: medium

      +
      +
      +
      +

      +
      +
      +
      +
      +
      + +
      +
      +
        +
      • I’ve created a new data object data_vb to select the relevant variables but you could also work straight from dog_data_wide.
        • +
      • Consider turning the categorical variable into a factor before plotting
        • +
      • Plotting tips: +
          +
        • the colour scale is one of the viridis options
          • +
        • it’s a bit of trial and error for the opacity of the violin and the box width of the boxes (it is totally fine if it looks approximately right)
          • +
        • the tricky part might be adjusting the y-axis ticks. Take inspiration from the histogram in Section 5.3 (Tab Axes labels, margins, and breaks)
          • +
        +
        • +
      +
      + +
      +
      +
      +
      data_vb <- dog_data_wide %>% 
+  select(RID, Year_of_Study, Loneliness_post) %>% 
+  mutate(Year_of_Study = factor(Year_of_Study,
+                                levels = c("First", "Second", "Third", "Fourth", "Fifth or above")))
      +
      +
      +
      +
      +
      + +
      +
      +
      +
      ggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) +
+  geom_violin(alpha = 0.5) +
+  geom_boxplot(width = 0.25) +
+  scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)),
+                     limits = c(1, 4)) +
+  scale_fill_viridis_d(option = "magma",
+                       guide = "none") +
+  labs(x = "Year of Study", y = "Loneliness scores post intervention") +
+  theme_classic()
      +
      +
      +
      +
      +
      +
      +
      +

      Difficulty level: hard

      +
      +
      +
      +

      +
      +
      +
      +
      +
      + +
      +
      +
        +
      • +Data wrangling: Even though we cleaned the data, it may not be in the shape for the task at hand. Have a look what the data object dog_data_wide looks like and think about how you’d need to restructure it to be able to plot the scatterplot. As always, I would suggest creating a new data object for the scatterplot (e.g., data_scatter).
        • +
      • Once you have the data in the right shape, start plotting. Start with a basic scatterplot and then add various layers and change elements you notice.
        • +
      • Remember, some finetuning might need to be done in data_scatter rather than plot itself.
        • +
      +
      + +
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      RIDPANAS_PA_prePANAS_PA_postPANAS_NA_prePANAS_NA_post
      13.23.81.01.2
      23.03.21.81.0
      32.83.01.61.6
      44.23.81.81.6
      53.44.02.21.6
      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      RIDSubscaleprepost
      1Positive Affect3.23.8
      1Negative Affect1.01.2
      2Positive Affect3.03.2
      2Negative Affect1.81.0
      3Positive Affect2.83.0
      +
      +
      +
      +
      +
      +
      +
      + +
      +
      +
        +
      • +Step 1: select the variables you need from dog_data_wide.
        • +
      • +Step 2: pivot all columns (bar the Participant ID) into long format
        • +
      • +Step 3: think about how to separate information of the subscales and timepoints
        • +
      • +Step 4: pivot from long into wide format. Take some inspiration from the Special case: Variables with subscales scenario above.
        • +
      +
      +
      +
      +
      + +
      +
      +
        +
      • The colour scheme is Dark2 from the colour palette brewer
        • +
      • The colour of the trendline is #7570b3
        • +
      • Think about how to make the Negative and Positive Affect points different colours. The solution is in Section 5.4 +
        • +
      • Renaming the different facets is one of those things that should be fixed in the data object instead
        • +
      +
      +
      +
      +
      + +
      +
      +
      +
      data_scatter <- dog_data_wide %>% 
+  select(RID, starts_with("PANAS")) %>% 
+  pivot_longer(cols = -RID, names_to = "Q", values_to = "Values") %>% 
+  separate(Q, into = c(NA, "Subscale", "Timepoint"), sep = "_") %>% 
+  pivot_wider(names_from = Timepoint, values_from = Values) %>% 
+  mutate(Subscale = case_match(Subscale,
+                               "NA" ~ "Negative Affect",
+                               "PA" ~ "Positive Affect"),
+         Subscale = factor(Subscale)) %>% 
+  drop_na()
      +
      +
      +
      +
      +
      + +
      +
      +
      +
      ggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) +
+  geom_point() +
+  geom_smooth(method = lm, colour = "#7570b3") +
+  facet_wrap(~Subscale) +
+  labs(x = "Pre-Intervention (Timepoint 1)",
+       y = "Post-Intervention (Timepoint 2)") +
+  scale_colour_brewer(palette = "Dark2",
+                      guide = "none") +
+  theme_bw()
      +
      +
      +
      +
      +
      +
      +
      +

      If you are extremely fast, challenge yourself and re-create one of the other plots.

      +

      Test your knowledge

      +

      Knowledge check

      +

      Question 1

      +

      Why would this line of code not create a barplot, assuming you already loaded all data and libraries and you spelt the data and column names correctly?

      +
      +
      ggplot(summarydata, aes(x = sex)) +
+  geom_barplot()
      +
      +
      + +
      +

      Question 2

      +

      If I wanted precisely 5 bars in my histogram, what argument would I use?

      +
      + +
      +
      + +
      +
      +
        +
      • +ggplot() + geom_histogram(bins = 5). This is the correct answer as you are asking ggplot2 to give you the plot organised into 5 bins.
        • +
      • +ggplot() + geom_histogram(bars = 5). This is incorrect as you bars is not the right argument name. You want 5 bars, but the argument is bins.
        • +
      • +ggplot() + geom_histogram(binwidth = 5). This is incorrect as binwidth controls the x-axis range to include per bar, rather than the number of bars.
        • +
      • +ggplot() + geom_histogram(). This is incorrect as you did not control the number of bins, so it will default to 30.
        • +
      +
      +
      +
      +

      Question 3

      +

      You want to create a scatterplot to show the correlation between two continuous variables, which geom would you use?

      +
      + +
      +

      Question 4

      +

      True or False? To showcase different groups in a scatterplot, you could specify a grouping variable using the fill argument to change the colour of the points.

      +
      + +
      +
      +

      Have a look in the scatterplot section again. There, we explained that we cannot “colour in” the area of the points using fill, but need to change the outline colour of the points with colour instead.

      +
      +
      +
      +

      Error mode

      +

      Some of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.

      +

      Let’s go back to the Palmer penguins for this part.

      +
      +
      library(palmerpenguins)
      +
      +

      Question 5

      +

      We want to plot the number of penguins across the different islands.

      +
      +
      ggplot(penguins, aes(x = islands)) %>% 
+  geom_bar()
      +
      +
      Error in `geom_bar()`:
+! `mapping` must be created by `aes()`.
+ℹ Did you use `%>%` or `|>` instead of `+`?
      +
      +
      +

      The error message is incredibly useful. So that should be an easy fix!

      +
      + +
      +
      +

      To fix the error, all we need to do is turn the pipe %>% into a plus +.

      +
      +
      ggplot(penguins, aes(x = island)) +
+  geom_bar()
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +

      Question 6

      +

      We want to create a violin-boxplot showing the bill length across species, separately for male and female penguins.

      +
      +
      penguins_no_na <- penguins %>% 
+  drop_na(island, flipper_length_mm, sex)
+
+ggplot(penguins_no_na, aes(x = island, y = flipper_length_mm, fill = sex)) +
+  geom_violin() +
+  geom_boxplot(width = 0.2) +
+  labs(x = "Island", y = "Bill length in mm") +
+  facet_wrap(~sex)
      +
      +
      +

      +
      +
      +
      +
      +

      Hmmm. We got a plot, but is it the one we aimed for?

      +
      + +
      +
      +

      This was a tricky one. The code runs, so we think everything is fine. Despite having re-labelled the y-axis “Bill length in mm”, it’s really showing the flipper length. Did you spot this???

      +
      +
      penguins_no_na <- penguins %>% 
+  drop_na(island, bill_length_mm, sex)
+
+ggplot(penguins_no_na, aes(x = island, y = bill_length_mm, fill = sex)) +
+  geom_violin() +
+  geom_boxplot(width = 0.2) +
+  labs(x = "Island", y = "Bill length in mm") +
+  facet_wrap(~sex)
      +
      +
      +

      +
      +
      +
      +
      +
      +
      +
      +

      Question 7

      +

      This time, we want to show the relationship between flipper length and body mass of the penguins. The following code runs, but the x- and y-axes are misbehaving somehow. Not what we wanted. Any idea why?

      +
      +
      ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +
+  geom_point() +
+  scale_colour_viridis_d(name = "Island") +
+  scale_y_discrete(name = "Body mass in g",
+                     breaks = seq(2500, 6500, 500),
+                     limits = c(2500, 6500)) +
+  scale_x_discrete(name = "Flipper length in mm")
      +
      +
      Warning in scale_y_discrete(name = "Body mass in g", breaks = seq(2500, : Continuous limits supplied to discrete scale.
+ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?
      +
      +
      +
      Warning: Removed 2 rows containing missing values or values outside the scale range
+(`geom_point()`).
      +
      +
      +
      +

      +
      +
      +
      +
      +
      + +
      +
      +

      In this example, we used the wrong function for continuous variables. We used scale_y_discrete and scale_y_discrete, instead of scale_x_continuous and scale_y_continuous. We must honour the variable type when we customise the plot, so think about what type of variable is on each axis and which function lets you edit it.

      +
      +
      ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +
+  geom_point() +
+  scale_colour_viridis_d(name = "Island") +
+  scale_y_continuous(name = "Body mass in g",
+                     breaks = seq(2500, 6500, 500),
+                     limits = c(2500, 6500)) +
+  scale_x_continuous(name = "Flipper length in mm")
      +
      +
      +
      +
      + + +
      +
      +
      +
      +   +
      +
      CC-BY (2024) Gaby Mahrholz Carolina E. Kuepper-Tetzel
      +
      +   +
      +
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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

        ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void 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this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof 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e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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0000000..d9fd98f --- /dev/null +++ b/docs/05-dataviz2_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/docs/05-dataviz2_files/libs/quarto-html/tippy.css b/docs/05-dataviz2_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/docs/05-dataviz2_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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content: "\f857"; } +.bi-sign-intersection-y-fill::before { content: "\f858"; } +.bi-sign-intersection-y::before { content: "\f859"; } +.bi-sign-intersection::before { content: "\f85a"; } +.bi-sign-merge-left-fill::before { content: "\f85b"; } +.bi-sign-merge-left::before { content: "\f85c"; } +.bi-sign-merge-right-fill::before { content: "\f85d"; } +.bi-sign-merge-right::before { content: "\f85e"; } +.bi-sign-no-left-turn-fill::before { content: "\f85f"; } +.bi-sign-no-left-turn::before { content: "\f860"; } +.bi-sign-no-parking-fill::before { content: "\f861"; } +.bi-sign-no-parking::before { content: "\f862"; } +.bi-sign-no-right-turn-fill::before { content: "\f863"; } +.bi-sign-no-right-turn::before { content: "\f864"; } +.bi-sign-railroad-fill::before { content: "\f865"; } +.bi-sign-railroad::before { content: "\f866"; } +.bi-building-add::before { content: "\f867"; } +.bi-building-check::before { content: "\f868"; } +.bi-building-dash::before { content: "\f869"; } +.bi-building-down::before { content: "\f86a"; } +.bi-building-exclamation::before { content: "\f86b"; } +.bi-building-fill-add::before { content: "\f86c"; } +.bi-building-fill-check::before { content: "\f86d"; } +.bi-building-fill-dash::before { content: "\f86e"; } +.bi-building-fill-down::before { content: "\f86f"; } +.bi-building-fill-exclamation::before { content: "\f870"; } +.bi-building-fill-gear::before { content: "\f871"; } +.bi-building-fill-lock::before { content: "\f872"; } +.bi-building-fill-slash::before { content: "\f873"; } +.bi-building-fill-up::before { content: "\f874"; } +.bi-building-fill-x::before { content: "\f875"; } +.bi-building-fill::before { content: "\f876"; } +.bi-building-gear::before { content: "\f877"; } +.bi-building-lock::before { content: "\f878"; } +.bi-building-slash::before { content: "\f879"; } +.bi-building-up::before { content: "\f87a"; } +.bi-building-x::before { content: "\f87b"; } +.bi-buildings-fill::before { content: "\f87c"; } +.bi-buildings::before { content: "\f87d"; } +.bi-bus-front-fill::before { content: "\f87e"; } +.bi-bus-front::before { content: "\f87f"; } +.bi-ev-front-fill::before { content: "\f880"; } +.bi-ev-front::before { content: "\f881"; } +.bi-globe-americas::before { content: "\f882"; } +.bi-globe-asia-australia::before { content: "\f883"; } +.bi-globe-central-south-asia::before { content: "\f884"; } +.bi-globe-europe-africa::before { content: "\f885"; } +.bi-house-add-fill::before { content: "\f886"; } +.bi-house-add::before { content: "\f887"; } +.bi-house-check-fill::before { content: "\f888"; } +.bi-house-check::before { content: "\f889"; } +.bi-house-dash-fill::before { content: "\f88a"; } +.bi-house-dash::before { content: "\f88b"; } +.bi-house-down-fill::before { content: "\f88c"; } +.bi-house-down::before { content: "\f88d"; } +.bi-house-exclamation-fill::before { content: "\f88e"; } +.bi-house-exclamation::before { content: "\f88f"; } +.bi-house-gear-fill::before { content: "\f890"; } +.bi-house-gear::before { content: "\f891"; } +.bi-house-lock-fill::before { content: "\f892"; } +.bi-house-lock::before { content: "\f893"; } +.bi-house-slash-fill::before { content: "\f894"; } +.bi-house-slash::before { content: "\f895"; } +.bi-house-up-fill::before { content: "\f896"; } +.bi-house-up::before { content: "\f897"; } +.bi-house-x-fill::before { content: "\f898"; } +.bi-house-x::before { content: "\f899"; } +.bi-person-add::before { content: "\f89a"; } +.bi-person-down::before { content: "\f89b"; } +.bi-person-exclamation::before { content: "\f89c"; } +.bi-person-fill-add::before { content: "\f89d"; } +.bi-person-fill-check::before { content: "\f89e"; } +.bi-person-fill-dash::before { content: "\f89f"; } +.bi-person-fill-down::before { content: "\f8a0"; } +.bi-person-fill-exclamation::before { content: "\f8a1"; } +.bi-person-fill-gear::before { content: "\f8a2"; } +.bi-person-fill-lock::before { content: "\f8a3"; } +.bi-person-fill-slash::before { content: "\f8a4"; } +.bi-person-fill-up::before { content: "\f8a5"; } +.bi-person-fill-x::before { content: "\f8a6"; } +.bi-person-gear::before { content: "\f8a7"; } +.bi-person-lock::before { content: "\f8a8"; } +.bi-person-slash::before { content: "\f8a9"; } +.bi-person-up::before { content: "\f8aa"; } +.bi-scooter::before { content: "\f8ab"; } +.bi-taxi-front-fill::before { content: "\f8ac"; } +.bi-taxi-front::before { content: "\f8ad"; } +.bi-amd::before { content: "\f8ae"; } +.bi-database-add::before { content: "\f8af"; } +.bi-database-check::before { content: "\f8b0"; } +.bi-database-dash::before { content: "\f8b1"; } +.bi-database-down::before { content: "\f8b2"; } +.bi-database-exclamation::before { content: "\f8b3"; } +.bi-database-fill-add::before { content: "\f8b4"; } +.bi-database-fill-check::before { content: "\f8b5"; } +.bi-database-fill-dash::before { content: "\f8b6"; } +.bi-database-fill-down::before { content: "\f8b7"; } +.bi-database-fill-exclamation::before { content: "\f8b8"; } +.bi-database-fill-gear::before { content: "\f8b9"; } +.bi-database-fill-lock::before { content: "\f8ba"; } +.bi-database-fill-slash::before { content: "\f8bb"; } +.bi-database-fill-up::before { content: "\f8bc"; } +.bi-database-fill-x::before { content: "\f8bd"; } +.bi-database-fill::before { content: "\f8be"; } +.bi-database-gear::before { content: "\f8bf"; } +.bi-database-lock::before { content: "\f8c0"; } +.bi-database-slash::before { content: "\f8c1"; } +.bi-database-up::before { content: "\f8c2"; } +.bi-database-x::before { content: "\f8c3"; } +.bi-database::before { content: "\f8c4"; } +.bi-houses-fill::before { content: "\f8c5"; } +.bi-houses::before { content: "\f8c6"; } +.bi-nvidia::before { content: "\f8c7"; } +.bi-person-vcard-fill::before { content: "\f8c8"; } +.bi-person-vcard::before { content: "\f8c9"; } +.bi-sina-weibo::before { content: "\f8ca"; } +.bi-tencent-qq::before { content: "\f8cb"; } +.bi-wikipedia::before { content: "\f8cc"; } diff --git 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      + +
    15. +
      + + 5  Data viz II +
    16. @@ -840,8 +846,8 @@

      Ho

      17. +
    17. +
      + + 5  Data viz II +
    18. diff --git a/docs/appendix-d-symbols.html b/docs/appendix-d-symbols.html index e31cb33..91890f6 100644 --- a/docs/appendix-d-symbols.html +++ b/docs/appendix-d-symbols.html @@ -154,6 +154,12 @@ 4  Data viz I
      +
      19. +
    19. +
      + + 5  Data viz II +
    20. diff --git a/docs/appendix-x-How-to-cite-R.html b/docs/appendix-x-How-to-cite-R.html index 9e1d089..4e52225 100644 --- a/docs/appendix-x-How-to-cite-R.html +++ b/docs/appendix-x-How-to-cite-R.html @@ -188,6 +188,12 @@ 4  Data viz I
      +
      21. +
    21. +
      + + 5  Data viz II +
    22. diff --git a/docs/appendix-y-license.html b/docs/appendix-y-license.html index 3af2a2c..a9a0703 100644 --- a/docs/appendix-y-license.html +++ b/docs/appendix-y-license.html @@ -171,6 +171,12 @@ 4  Data viz I
      +
      23. +
    23. +
      + + 5  Data viz II +
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z`&CX+-Kp{y$D@~Y0|pC=PeiK~IyDV~ zRi|<3GzAkj6L+VgKLR7?}U@q5spue{(!D|AYPuXe6*Q diff --git a/docs/index.html b/docs/index.html index 6660de3..2d67f14 100644 --- a/docs/index.html +++ b/docs/index.html @@ -153,6 +153,12 @@ 4  Data viz I
      +
      25. +
    25. +
      + + 5  Data viz II +
    26. diff --git a/docs/search.json b/docs/search.json index f692d0a..05ded1b 100644 --- a/docs/search.json +++ b/docs/search.json @@ -46,7 +46,7 @@ "href": "01-basics.html#sec-download_data_ch1", "title": "1  Projects and R Markdown", "section": "\n1.4 Activity 3: Download the data", - "text": "1.4 Activity 3: Download the data\nThe data for chapters 1-3. Download it here: data_ch1.zip. There are 2 csv files contained in a zip folder. One is the data file we are going to use today prp_data_reduced.csv and the other is an Excel file prp_codebook that explains the variables in the data.\nThe first step is to unzip the zip folder so that the files are placed within the same folder as your project.\n\nPlace the zip folder within your 2A_chapter1 folder\nRight mouse click –> Extract All...\n\nCheck the folder location is the one to extract the files to\nCheck the extracted files are placed next to the project icon\nFiles and project should be visible in the Output pane in RStudio\n\n\n\n\n\n\n\nScreenshots for “unzipping a zip folder”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUnzipping a zip folder\n\n\n\n\n\n\nThe paper by Pownall et al. was a registered report published in 2023, and the original data can be found on OSF (https://osf.io/5qshg/).\nCitation\n\nPownall, M., Pennington, C. R., Norris, E., Juanchich, M., Smailes, D., Russell, S., Gooch, D., Evans, T. R., Persson, S., Mak, M. H. C., Tzavella, L., Monk, R., Gough, T., Benwell, C. S. Y., Elsherif, M., Farran, E., Gallagher-Mitchell, T., Kendrick, L. T., Bahnmueller, J., . . . Clark, K. (2023). Evaluating the Pedagogical Effectiveness of Study Preregistration in the Undergraduate Dissertation. Advances in Methods and Practices in Psychological Science, 6(4). https://doi.org/10.1177/25152459231202724\n\nAbstract\n\nResearch shows that questionable research practices (QRPs) are present in undergraduate final-year dissertation projects. One entry-level Open Science practice proposed to mitigate QRPs is “study preregistration,” through which researchers outline their research questions, design, method, and analysis plans before data collection and/or analysis. In this study, we aimed to empirically test the effectiveness of preregistration as a pedagogic tool in undergraduate dissertations using a quasi-experimental design. A total of 89 UK psychology students were recruited, including students who preregistered their empirical quantitative dissertation (n = 52; experimental group) and students who did not (n = 37; control group). Attitudes toward statistics, acceptance of QRPs, and perceived understanding of Open Science were measured both before and after dissertation completion. Exploratory measures included capability, opportunity, and motivation to engage with preregistration, measured at Time 1 only. This study was conducted as a Registered Report; Stage 1 protocol: https://osf.io/9hjbw (date of in-principle acceptance: September 21, 2021). Study preregistration did not significantly affect attitudes toward statistics or acceptance of QRPs. However, students who preregistered reported greater perceived understanding of Open Science concepts from Time 1 to Time 2 compared with students who did not preregister. Exploratory analyses indicated that students who preregistered reported significantly greater capability, opportunity, and motivation to preregister. Qualitative responses revealed that preregistration was perceived to improve clarity and organization of the dissertation, prevent QRPs, and promote rigor. Disadvantages and barriers included time, perceived rigidity, and need for training. These results contribute to discussions surrounding embedding Open Science principles into research training.\n\nChanges made to the dataset\nWe made some changes to the dataset for the purpose of increasing difficulty for data wrangling (Chapter 2 and Chapter 3) and data visualisation (Chapter 4 and ?sec-dataviz2). This will ensure some “teachable moments”. The changes are as follows:\n\nWe removed some of the variables to make the data more manageable for teaching purposes.\nWe recoded some values from numeric responses to labels (e.g., understanding).\nWe added the word “years” to one of the Age entries.\nWe tidied a messy column Ethnicity but introduced a similar but easier-to-solve “messiness pattern” when recoding the understanding data.\nThe scores in the original file were already corrected from reverse-coded responses. We reversed that process to present raw data here." + "text": "1.4 Activity 3: Download the data\nThe data for chapters 1-3. Download it here: data_ch1.zip. There are 2 csv files contained in a zip folder. One is the data file we are going to use today prp_data_reduced.csv and the other is an Excel file prp_codebook that explains the variables in the data.\nThe first step is to unzip the zip folder so that the files are placed within the same folder as your project.\n\nPlace the zip folder within your 2A_chapter1 folder\nRight mouse click –> Extract All...\n\nCheck the folder location is the one to extract the files to\nCheck the extracted files are placed next to the project icon\nFiles and project should be visible in the Output pane in RStudio\n\n\n\n\n\n\n\nScreenshots for “unzipping a zip folder”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUnzipping a zip folder\n\n\n\n\n\n\nThe paper by Pownall et al. was a registered report published in 2023, and the original data can be found on OSF (https://osf.io/5qshg/).\nCitation\n\nPownall, M., Pennington, C. R., Norris, E., Juanchich, M., Smailes, D., Russell, S., Gooch, D., Evans, T. R., Persson, S., Mak, M. H. C., Tzavella, L., Monk, R., Gough, T., Benwell, C. S. Y., Elsherif, M., Farran, E., Gallagher-Mitchell, T., Kendrick, L. T., Bahnmueller, J., . . . Clark, K. (2023). Evaluating the Pedagogical Effectiveness of Study Preregistration in the Undergraduate Dissertation. Advances in Methods and Practices in Psychological Science, 6(4). https://doi.org/10.1177/25152459231202724\n\nAbstract\n\nResearch shows that questionable research practices (QRPs) are present in undergraduate final-year dissertation projects. One entry-level Open Science practice proposed to mitigate QRPs is “study preregistration,” through which researchers outline their research questions, design, method, and analysis plans before data collection and/or analysis. In this study, we aimed to empirically test the effectiveness of preregistration as a pedagogic tool in undergraduate dissertations using a quasi-experimental design. A total of 89 UK psychology students were recruited, including students who preregistered their empirical quantitative dissertation (n = 52; experimental group) and students who did not (n = 37; control group). Attitudes toward statistics, acceptance of QRPs, and perceived understanding of Open Science were measured both before and after dissertation completion. Exploratory measures included capability, opportunity, and motivation to engage with preregistration, measured at Time 1 only. This study was conducted as a Registered Report; Stage 1 protocol: https://osf.io/9hjbw (date of in-principle acceptance: September 21, 2021). Study preregistration did not significantly affect attitudes toward statistics or acceptance of QRPs. However, students who preregistered reported greater perceived understanding of Open Science concepts from Time 1 to Time 2 compared with students who did not preregister. Exploratory analyses indicated that students who preregistered reported significantly greater capability, opportunity, and motivation to preregister. Qualitative responses revealed that preregistration was perceived to improve clarity and organization of the dissertation, prevent QRPs, and promote rigor. Disadvantages and barriers included time, perceived rigidity, and need for training. These results contribute to discussions surrounding embedding Open Science principles into research training.\n\nChanges made to the dataset\nWe made some changes to the dataset for the purpose of increasing difficulty for data wrangling (Chapter 2 and Chapter 3) and data visualisation (Chapter 4 and Chapter 5). This will ensure some “teachable moments”. The changes are as follows:\n\nWe removed some of the variables to make the data more manageable for teaching purposes.\nWe recoded some values from numeric responses to labels (e.g., understanding).\nWe added the word “years” to one of the Age entries.\nWe tidied a messy column Ethnicity but introduced a similar but easier-to-solve “messiness pattern” when recoding the understanding data.\nThe scores in the original file were already corrected from reverse-coded responses. We reversed that process to present raw data here." }, { "objectID": "01-basics.html#activity-4-installing-packages-loading-packages-and-reading-in-data", @@ -60,7 +60,7 @@ "href": "01-basics.html#sec-familiarise", "title": "1  Projects and R Markdown", "section": "\n1.6 Activity 5: Familiarise yourself with the data", - "text": "1.6 Activity 5: Familiarise yourself with the data\n\nLook at the Codebook to get a feel of the variables in the dataset and how they have been measured. Note that some of the columns were deleted in the dataset you have been given.\nYou’ll notice that some questionnaire data was collected at 2 different time points (i.e., SATS28, QRPs, Understanding_OS)\nsome of the data was only collected at one time point (i.e., supervisor judgements, OS_behav items, and Included_prereg variables are t2-only variables)\n\n\n1.6.1 First glimpse at the data\nBefore you start wrangling your data, it is important to understand what kind of data you’re working with and what the format of your dataframe looks like.\nAs you may have noticed, read_csv() provides a message listing the data types in your dataset and how many columns are of each type. Plus, it shows a few examples columns for each data type.\nTo obtain more detailed information about your data, you have several options. Click on the individual tabs to see the different options available. Test them out in your own .Rmd file and use whichever method you prefer (but do it).\n\n\n\n\n\n\nWarning\n\n\n\nSome of the output is a bit long because we do have quite a few variables in the data file.\n\n\n\n\nvisual inspection 1\nglimpse()\nspec()\nvisual inspection 2\n\n\n\nIn the Global Environment, click the blue arrow icon next to the object name data_prp. This action will expand the object, revealing details about its columns. The $ symbol is commonly used in Base R to access a specific column within your dataframe.\n\n\nVisual inspection of the data\n\nCon: When you have quite a few variables, not all of them are shown.\n\n\nUse glimpse() if you want a more detailed overview you can see on your screen. The output will display rows and column numbers, and some examples of the first couple of observations for each variable.\n\nglimpse(data_prp)\n\nRows: 89\nColumns: 91\n$ Code <chr> \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age <chr> \"22\", \"20\", \"22\", \"26\", \"22\", \"2…\n$ Ethnicity <chr> \"White European\", \"White British…\n$ Secondyeargrade <dbl> 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod <dbl> 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT <chr> \"Research methods in first year\"…\n$ Research_exp <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT <chr> NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg <dbl> 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ SATS28_1_Affect_Time1 <dbl> 4, 5, 5, 6, 2, 1, 6, 3, 2, 5, 2,…\n$ SATS28_2_Affect_Time1 <dbl> 5, 6, 3, 3, 6, 1, 2, 2, 7, 3, 4,…\n$ SATS28_3_Affect_Time1 <dbl> 3, 2, 5, 2, 6, 7, 2, 6, 6, 5, 2,…\n$ SATS28_4_Affect_Time1 <dbl> 4, 5, 2, 2, 6, 6, 5, 5, 5, 5, 2,…\n$ SATS28_5_Affect_Time1 <dbl> 5, 5, 5, 6, 1, 1, 5, 1, 2, 5, 2,…\n$ SATS28_6_Affect_Time1 <dbl> 5, 6, 2, 5, 6, 7, 4, 5, 5, 3, 5,…\n$ SATS28_7_CognitiveCompetence_Time1 <dbl> 4, 2, 2, 5, 6, 7, 2, 5, 5, 2, 2,…\n$ SATS28_8_CognitiveCompetence_Time1 <dbl> 2, 2, 2, 1, 6, 7, 2, 5, 3, 2, 3,…\n$ SATS28_9_CognitiveCompetence_Time1 <dbl> 2, 2, 2, 3, 3, 7, 2, 6, 3, 3, 1,…\n$ SATS28_10_CognitiveCompetence_Time1 <dbl> 6, 7, 6, 6, 4, 2, 6, 4, 5, 6, 5,…\n$ SATS28_11_CognitiveCompetence_Time1 <dbl> 4, 3, 5, 5, 3, 1, 6, 2, 5, 6, 5,…\n$ SATS28_12_CognitiveCompetence_Time1 <dbl> 3, 5, 3, 5, 5, 7, 3, 4, 7, 2, 3,…\n$ SATS28_13_Value_Time1 <dbl> 1, 1, 2, 1, 3, 7, 1, 2, 1, 2, 4,…\n$ SATS28_14_Value_Time1 <dbl> 7, 7, 6, 6, 5, 1, 6, 5, 7, 6, 2,…\n$ SATS28_15_Value_Time1 <dbl> 7, 7, 6, 6, 3, 5, 6, 6, 6, 5, 5,…\n$ SATS28_16_Value_Time1 <dbl> 2, 1, 3, 2, 6, 5, 3, 7, 2, 2, 2,…\n$ SATS28_17_Value_Time1 <dbl> 1, 1, 3, 3, 7, 7, 2, 7, 2, 2, 5,…\n$ SATS28_18_Value_Time1 <dbl> 3, 6, 5, 3, 1, 1, 5, 1, 5, 2, 2,…\n$ SATS28_19_Value_Time1 <dbl> 3, 3, 3, 3, 7, 7, 4, 5, 3, 5, 6,…\n$ SATS28_20_Value_Time1 <dbl> 2, 1, 4, 2, 7, 7, 2, 4, 2, 2, 7,…\n$ SATS28_21_Value_Time1 <dbl> 2, 1, 3, 2, 6, 7, 2, 5, 1, 3, 5,…\n$ SATS28_22_Difficulty_Time1 <dbl> 3, 2, 5, 3, 2, 1, 4, 2, 2, 5, 3,…\n$ SATS28_23_Difficulty_Time1 <dbl> 5, 6, 5, 6, 6, 7, 4, 6, 7, 5, 6,…\n$ SATS28_24_Difficulty_Time1 <dbl> 2, 2, 2, 3, 1, 4, 4, 2, 2, 2, 2,…\n$ SATS28_25_Difficulty_Time1 <dbl> 6, 7, 5, 5, 6, 7, 5, 6, 5, 5, 5,…\n$ SATS28_26_Difficulty_Time1 <dbl> 4, 2, 2, 2, 6, 7, 4, 5, 3, 5, 3,…\n$ SATS28_27_Difficulty_Time1 <dbl> 4, 5, 5, 3, 6, 7, 4, 3, 5, 3, 6,…\n$ SATS28_28_Difficulty_Time1 <dbl> 1, 7, 5, 5, 6, 6, 5, 4, 4, 4, 2,…\n$ QRPs_1_Time1 <dbl> 7, 7, 7, 7, 7, 7, 6, 2, 7, 6, 7,…\n$ QRPs_2_Time1 <dbl> 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 5,…\n$ QRPs_3_Time1 <dbl> 5, 2, 6, 2, 6, 4, 6, 3, 7, 3, 3,…\n$ QRPs_4_Time1 <dbl> 7, 7, 6, 6, 7, 4, 6, 7, 7, 7, 6,…\n$ QRPs_5_Time1 <dbl> 3, 3, 7, 7, 2, 7, 4, 6, 7, 3, 2,…\n$ QRPs_6_Time1 <dbl> 4, 7, 6, 5, 7, 4, 4, 5, 7, 6, 5,…\n$ QRPs_7_Time1 <dbl> 5, 7, 7, 7, 7, 4, 5, 6, 7, 7, 5,…\n$ QRPs_8_Time1 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 7,…\n$ QRPs_9_Time1 <dbl> 6, 7, 7, 4, 7, 7, 3, 7, 6, 6, 2,…\n$ QRPs_10_Time1 <dbl> 7, 6, 5, 2, 5, 4, 2, 6, 7, 7, 2,…\n$ QRPs_11_Time1 <dbl> 7, 7, 7, 4, 7, 7, 4, 6, 7, 7, 5,…\n$ QRPs_12NotQRP_Time1 <dbl> 2, 2, 1, 4, 1, 4, 2, 4, 2, 2, 1,…\n$ QRPs_13NotQRP_Time1 <dbl> 1, 1, 1, 1, 1, 4, 2, 4, 1, 1, 1,…\n$ QRPs_14NotQRP_Time1 <dbl> 1, 4, 3, 4, 1, 4, 2, 3, 3, 4, 3,…\n$ QRPs_15NotQRP_Time1 <dbl> 2, 4, 2, 2, 1, 4, 2, 1, 4, 4, 2,…\n$ Understanding_OS_1_Time1 <chr> \"2\", \"2\", \"6\", \"2\", \"6\", \"Not at…\n$ Understanding_OS_2_Time1 <chr> \"2\", \"Not at all confident\", \"2\"…\n$ Understanding_OS_3_Time1 <chr> \"2\", \"Not at all confident\", \"3\"…\n$ Understanding_OS_4_Time1 <chr> \"6\", \"Not at all confident\", \"6\"…\n$ Understanding_OS_5_Time1 <chr> \"Entirely confident\", \"6\", \"6\", …\n$ Understanding_OS_6_Time1 <chr> \"Entirely confident\", \"Entirely …\n$ Understanding_OS_7_Time1 <chr> \"6\", \"Not at all confident\", \"2\"…\n$ Understanding_OS_8_Time1 <chr> \"6\", \"3\", \"5\", \"3\", \"5\", \"Not at…\n$ Understanding_OS_9_Time1 <chr> \"Entirely confident\", \"6\", \"5\", …\n$ Understanding_OS_10_Time1 <chr> \"Entirely confident\", \"6\", \"5\", …\n$ Understanding_OS_11_Time1 <chr> \"Entirely confident\", \"2\", \"4\", …\n$ Understanding_OS_12_Time1 <chr> \"Entirely confident\", \"2\", \"5\", …\n$ Pre_reg_group <dbl> 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ Other_OS_behav_2 <dbl> 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 <dbl> 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 <dbl> NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow <dbl> 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean <dbl> 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean <dbl> 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean <dbl> 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean <dbl> 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean <dbl> 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS <dbl> 5.583333, 3.333333, 5.416667, 4.…\n$ Supervisor_1 <dbl> 5, 7, 7, 1, 7, 1, 7, 6, 7, 5, 6,…\n$ Supervisor_2 <dbl> 5, 6, 7, 4, 6, 2, 7, 5, 6, 5, 5,…\n$ Supervisor_3 <dbl> 6, 7, 7, 1, 7, 1, 7, 5, 6, 6, 7,…\n$ Supervisor_4 <dbl> 6, 7, 7, 1, 7, 1, 7, 6, 7, 6, 6,…\n$ Supervisor_5 <dbl> 5, 7, 7, 4, 7, 3, 7, 7, 6, 6, 6,…\n$ Supervisor_6 <dbl> 5, 7, 7, 4, 6, 3, 7, 6, 7, 6, 6,…\n$ Supervisor_7 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…\n$ Supervisor_8 <dbl> 5, 5, 7, 1, 7, 1, 7, 5, 7, 5, 6,…\n$ Supervisor_9 <dbl> 6, 7, 7, 4, 7, 3, 7, 5, 7, 6, 7,…\n$ Supervisor_10 <dbl> 5, 7, 7, 1, 7, 1, 7, 6, 7, 6, 6,…\n$ Supervisor_11 <dbl> NA, 7, 7, NA, 7, 1, 7, 5, 7, 6, …\n$ Supervisor_12 <dbl> 4, 5, 7, 1, 4, 1, 7, 3, 6, 6, 5,…\n$ Supervisor_13 <dbl> 4, 2, 5, 1, 2, 1, 6, 3, 5, 6, 5,…\n$ Supervisor_14 <dbl> 5, 7, 7, 1, 7, 1, 7, 5, 7, 6, 6,…\n$ Supervisor_15_R <dbl> 1, 1, 1, 4, 1, 7, 1, 2, 1, 2, 1,…\n\n\n\n\nYou can also use spec() as suggested in the message above and then it shows you a list of the data type in every single column. But it doesn’t show you the number of rows and columns.\n\nspec(data_prp)\n\ncols(\n Code = col_character(),\n Gender = col_double(),\n Age = col_character(),\n Ethnicity = col_character(),\n Secondyeargrade = col_double(),\n Opptional_mod = col_double(),\n Opptional_mod_1_TEXT = col_character(),\n Research_exp = col_double(),\n Research_exp_1_TEXT = col_character(),\n Plan_prereg = col_double(),\n SATS28_1_Affect_Time1 = col_double(),\n SATS28_2_Affect_Time1 = col_double(),\n SATS28_3_Affect_Time1 = col_double(),\n SATS28_4_Affect_Time1 = col_double(),\n SATS28_5_Affect_Time1 = col_double(),\n SATS28_6_Affect_Time1 = col_double(),\n SATS28_7_CognitiveCompetence_Time1 = col_double(),\n SATS28_8_CognitiveCompetence_Time1 = col_double(),\n SATS28_9_CognitiveCompetence_Time1 = col_double(),\n SATS28_10_CognitiveCompetence_Time1 = col_double(),\n SATS28_11_CognitiveCompetence_Time1 = col_double(),\n SATS28_12_CognitiveCompetence_Time1 = col_double(),\n SATS28_13_Value_Time1 = col_double(),\n SATS28_14_Value_Time1 = col_double(),\n SATS28_15_Value_Time1 = col_double(),\n SATS28_16_Value_Time1 = col_double(),\n SATS28_17_Value_Time1 = col_double(),\n SATS28_18_Value_Time1 = col_double(),\n SATS28_19_Value_Time1 = col_double(),\n SATS28_20_Value_Time1 = col_double(),\n SATS28_21_Value_Time1 = col_double(),\n SATS28_22_Difficulty_Time1 = col_double(),\n SATS28_23_Difficulty_Time1 = col_double(),\n SATS28_24_Difficulty_Time1 = col_double(),\n SATS28_25_Difficulty_Time1 = col_double(),\n SATS28_26_Difficulty_Time1 = col_double(),\n SATS28_27_Difficulty_Time1 = col_double(),\n SATS28_28_Difficulty_Time1 = col_double(),\n QRPs_1_Time1 = col_double(),\n QRPs_2_Time1 = col_double(),\n QRPs_3_Time1 = col_double(),\n QRPs_4_Time1 = col_double(),\n QRPs_5_Time1 = col_double(),\n QRPs_6_Time1 = col_double(),\n QRPs_7_Time1 = col_double(),\n QRPs_8_Time1 = col_double(),\n QRPs_9_Time1 = col_double(),\n QRPs_10_Time1 = col_double(),\n QRPs_11_Time1 = col_double(),\n QRPs_12NotQRP_Time1 = col_double(),\n QRPs_13NotQRP_Time1 = col_double(),\n QRPs_14NotQRP_Time1 = col_double(),\n QRPs_15NotQRP_Time1 = col_double(),\n Understanding_OS_1_Time1 = col_character(),\n Understanding_OS_2_Time1 = col_character(),\n Understanding_OS_3_Time1 = col_character(),\n Understanding_OS_4_Time1 = col_character(),\n Understanding_OS_5_Time1 = col_character(),\n Understanding_OS_6_Time1 = col_character(),\n Understanding_OS_7_Time1 = col_character(),\n Understanding_OS_8_Time1 = col_character(),\n Understanding_OS_9_Time1 = col_character(),\n Understanding_OS_10_Time1 = col_character(),\n Understanding_OS_11_Time1 = col_character(),\n Understanding_OS_12_Time1 = col_character(),\n Pre_reg_group = col_double(),\n Other_OS_behav_2 = col_double(),\n Other_OS_behav_4 = col_double(),\n Other_OS_behav_5 = col_double(),\n Closely_follow = col_double(),\n SATS28_Affect_Time2_mean = col_double(),\n SATS28_CognitiveCompetence_Time2_mean = col_double(),\n SATS28_Value_Time2_mean = col_double(),\n SATS28_Difficulty_Time2_mean = col_double(),\n QRPs_Acceptance_Time2_mean = col_double(),\n Time2_Understanding_OS = col_double(),\n Supervisor_1 = col_double(),\n Supervisor_2 = col_double(),\n Supervisor_3 = col_double(),\n Supervisor_4 = col_double(),\n Supervisor_5 = col_double(),\n Supervisor_6 = col_double(),\n Supervisor_7 = col_double(),\n Supervisor_8 = col_double(),\n Supervisor_9 = col_double(),\n Supervisor_10 = col_double(),\n Supervisor_11 = col_double(),\n Supervisor_12 = col_double(),\n Supervisor_13 = col_double(),\n Supervisor_14 = col_double(),\n Supervisor_15_R = col_double()\n)\n\n\n\n\nIn the Global Environment, click on the object name data_prp. This action will open the data in a new tab. Hovering over the column headings with your mouse will also reveal their data type. However, it seems to be a fairly tedious process when you have loads of columns.\n\n\n\n\n\n\nHang on, where is the rest of my data? Why do I only see 50 columns?\n\n\n\n\n\nOne common source of confusion is not seeing all your columns when you open up a data object as a tab. This is because RStudio shows you a maximum of 50 columns at a time. If you have more than 50 columns, navigate with the arrows to see the remaining columns.\n\n\nShowing 50 columns at a time\n\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nNow that you have tested out all the options in your own .Rmd file, you can probably answer the following questions:\n\nHow many observations? \n\nHow many variables? \n\nHow many columns are col_character or chr data type? \n\nHow many columns are col_double or dbl data type? \n\n\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThe visual inspections shows you the number of observations and variables. glimpse() also gives you that information but calls them rows and columns respectively.\nThe data type information actually comes from the output when using the read_csv() function. Did you notice the information on Column specification (see screenshot below)?\n\n\nmessage from read_csv() when reading in the data\n\nWhilst spec() is quite useful for data type information per individual column, it doesn’t give you the total count of each data type. So it doesn’t really help with answering the questions here - unless you want to count manually from its extremely long output.\n\n\n\nIn your .Rmd, include a new heading level 2 called “Information about the data” (or something equally meaningful) and jot down some notes about data_prp. You could include the citation and/or the abstract, and whatever information you think you should note about this dataset (e.g., any observations from looking at the codebook?). You could also include some notes on the functions used so far and what they do. Try to incorporate some bold, italic or bold and italic emphasis and perhaps a bullet point or two.\n\n\n\n\n\n\nPossible solution\n\n\n\n\n\n## Information about the data\nThe data is from Pownall et al. (2023), and I can find the paper here: https://doi.org/10.1177/25152459231202724.\nI’ve noticed in the prp codebook that the SATS-28 questionnaire has quite a few *reverse-coded items*, and the supervisor support questionnaire also has a reverse-coded item.\nSo far, I think I prefer **glimpse()** to show me some more detail about the data. Specs() is too text-heavy for me which makes it hard to read.\nThings to keep in mind:\n\n**don’t forget to load in tidyverse first!!!**\nalways read in the data with **read_csv**, ***never ever use read.csv***!!!\n\n\n\nThe output rendered in a knitted html file\n\n\n\n\n\n\n\n1.6.2 Data types\nEach variable has a data type, such as numeric (numbers), character (text), and logical (TRUE/FALSE values), or a special class of factor. As you have just seen, our data_prp only has character and numeric columns (so far).\nNumeric data can be double (dbl) or integer (int). Doubles can have decimal places (e.g., 1.1). Integers are the whole numbers (e.g., 1, 2, -1) and are displayed with the suffix L (e.g., 1L). This is not overly important but might leave you less puzzled the next time you see an L after a number.\nCharacters (also called “strings”) is anything written between quotation marks. This is usually text, but in special circumstances, a number can be a character if it placed within quotation marks. This can happen when you are recoding variables. It might not be too obvious at the time, but you won’t be able to calculate anything if the number is a character\n\n\nExample data types\nnumeric computation\ncharacter computation\n\n\n\n\ntypeof(1)\ntypeof(1L)\ntypeof(\"1\")\ntypeof(\"text\")\n\n[1] \"double\"\n[1] \"integer\"\n[1] \"character\"\n[1] \"character\"\n\n\n\n\nNo problems here…\n\n1+1\n\n[1] 2\n\n\n\n\nWhen the data type is incorrect, you won’t be able to compute anything, despite your numbers being shown as numeric values in the dataframe. The error message tells you exactly what’s wrong with it, i.e., that you have non-numeric arguments.\n\n\"1\"+\"1\" # ERROR\n\nError in \"1\" + \"1\": non-numeric argument to binary operator\n\n\n\n\n\nLogical data (also sometimes called “Boolean” values) are one of two values: TRUE or FALSE (written in uppercase). They become really important when we use filter() or mutate() with conditional statements such as case_when(). More about those in Chapter 3.\nSome commonly used logical operators:\n\n\noperator\ndescription\n\n\n\n>\ngreater than\n\n\n>=\ngreater than or equal to\n\n\n<\nless than\n\n\n<=\nless than or equal to\n\n\n==\nequal to\n\n\n!=\nnot equal to\n\n\n%in%\nTRUE if any element is in the following vector\n\n\n\nA factor is a specific type of integer or character that lets you assign the order of the categories. This becomes useful when you want to display certain categories in “the correct order” either in a dataframe (see arrange) or when plotting (see Chapter 4/ ?sec-dataviz2).\n\n1.6.3 Variable types\nYou’ve already encountered them in Level 1 but let’s refresh. Variables can be classified as continuous (numbers) or categorical (labels).\nCategorical variables are properties you can count. They can be nominal, where the categories don’t have an order (e.g., gender) or ordinal (e.g., Likert scales either with numeric values 1-7 or with character labels such as “agree”, “neither agree nor disagree”, “disagree”). Categorical data may also be factors rather than characters.\nContinuous variables are properties you can measure and calculate sums/ means/ etc. They may be rounded to the nearest whole number, but it should make sense to have a value between them. Continuous variables always have a numeric data type (i.e. integer or double).\n\n\n\n\n\n\nWhy is this important you may ask?\n\n\n\nKnowing your variable and data types will help later on when deciding on an appropriate plot (see Chapter 4 and ?sec-dataviz2) or which inferential test to run (?sec-nhstI to ?sec-factorial).\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nAs we’ve seen earlier, data_prp only had character and numeric variables which hardly tests your understanding to see if you can identify a variety of data types and variable types. So, for this little quiz, we’ve spiced it up a bit. We’ve selected a few columns, shortened some of the column names, and modified some of the data types. Here you can see the first few rows of the new object data_quiz. You can find the code with explanations at the end of this section.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCode\nAge\nGender\nEthnicity\nSecondyeargrade\nQRP_item\nQRPs_mean\nUnderstanding_item\nQRP_item > 4\n\n\n\nTr10\n22\n2\nWhite European\n60-69% (2:1 grade)\n5\n5.636364\n2\nTRUE\n\n\nBi07\n20\n2\nWhite British\n50-59% (2:2 grade)\n2\n5.454546\n2\nFALSE\n\n\nSK03\n22\n2\nWhite British\n≥ 70% (1st class grade)\n6\n6.272727\n6\nTRUE\n\n\nSM95\n26\n2\nWhite British\n60-69% (2:1 grade)\n2\n5.000000\n2\nFALSE\n\n\nSt01\n22\n2\nWhite British\n60-69% (2:1 grade)\n6\n5.545454\n6\nTRUE\n\n\n\n\n\n\n\nglimpse(data_quiz)\n\nRows: 89\nColumns: 9\n$ Code <chr> \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", \"St01\", \"St10\", \"Wa…\n$ Age <chr> \"22\", \"20\", \"22\", \"26\", \"22\", \"20\", \"21\", \"21\", \"22…\n$ Gender <fct> 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, …\n$ Ethnicity <chr> \"White European\", \"White British\", \"White British\",…\n$ Secondyeargrade <fct> 60-69% (2:1 grade), 50-59% (2:2 grade), ≥ 70% (1st …\n$ QRP_item <dbl> 5, 2, 6, 2, 6, 4, 6, 3, 7, 3, 3, 4, 4, 4, 4, 6, 3, …\n$ QRPs_mean <dbl> 5.636364, 5.454545, 6.272727, 5.000000, 5.545455, 6…\n$ Understanding_item <chr> \"2\", \"2\", \"6\", \"2\", \"6\", \"Not at all confident\", \"4…\n$ `QRP_item > 4` <lgl> TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE,…\n\n\nSelect from the dropdown menu the variable type and their data types for each of the columns.\n\n\n\n\n\n\n\nColumn\nVariable type\nData type\n\n\n\nAge\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nGender\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nEthinicity\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nSecondyeargrade\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRP_item\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRPs_mean\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nUnderstanding_item\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRP_item > 4\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\n\n\n\n\n\n\n\n\n\nRevealing the mystery code that created data_quiz\n\n\n\n\n\nThe code might look a bit complex for the minute despite the line-by-line explanations below. Come back to it after completing chapter 2.\n\ndata_quiz <- data_prp %>% \n select(Code, Age, Gender, Ethnicity, Secondyeargrade, QRP_item = QRPs_3_Time1, QRPs_mean = QRPs_Acceptance_Time2_mean, Understanding_item = Understanding_OS_1_Time1) %>% \n mutate(Gender = factor(Gender),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n `QRP_item > 4` = case_when(\n QRP_item > 4 ~ TRUE, \n .default = FALSE))\n\nLets go through this line by line:\n\n\nline 1: creates a new object called data_quiz and it is based on the already existing data object data_prp\n\n\nline 2: we are selecting a few variables of interest, such as Code, Age etc. Some of those variables were renamed in the process according to the structure new_name = old_name, for example QRP item 3 at time point 1 got renamed as QRP_item.\n\n\nline 3: The function mutate() is used to create a new column called Gender that turns the existing column Gender from a numeric value into a factor. R simply overwrites the existing column of the same name. If we had named the new column Gender_factor, we would have been able to retain the original Gender column and Gender_factor would have been added as the last column.\n\nline 4-6: See how the line starts with an indent which indicates we are still within the mutate() function. You can also see this by counting brackets - in line 3 there are 2 opening brackets but only 1 closes.\n\nSimilar to Gender, we are replacing the “old” Secondyeargrade with the new Secondyeargrade column that is now a factor.\nTurning our variable Secondyeargrade into a factor, spot the difference between this attempt and the one we used for Gender? Here we are using a lot more arguments in that factor function, namely levels and labels. Levels describes the unique values we have for that column, and in labels we want to define how these levels will be shown in the data object. If you don’t add the levels and labels argument, the labels will be the labels (as you can see in the Gender column in which we kept the numbers).\n\n\n\nline 7: Doesn’t start with a function name and has an indent, which means we are still within the mutate() function - count the opening and closing brackets to confirm.\n\nHere, we are creating a new column called QRP_item > 4. Notice the two backticks we have to use to make this weird column name work? This is because it has spaces (and we did mention that R doesn’t like spaces). So the backticks help R to group it as a unit/ a single name.\nNext we have a case_when() function which helps executing conditional statements. We are using it to check whether a statement is TRUE or FALSE. Here, we ask whether the QRP item (column QRP_item) is larger than 4 (midpoint of the scale) using the Boolean operator >. If the statement is TRUE, the label TRUE should appear in column QRP_item > 4. Otherwise, if the value is equal to 4 or smaller, the label should read FALSE. We will come back to conditional statements in Chapter 2. But long story short, this Boolean expression created the only logical data type in data_quiz.\n\n\n\n\n\n\nAnd with this, we are done with the individual walkthrough. Well done :)" + "text": "1.6 Activity 5: Familiarise yourself with the data\n\nLook at the Codebook to get a feel of the variables in the dataset and how they have been measured. Note that some of the columns were deleted in the dataset you have been given.\nYou’ll notice that some questionnaire data was collected at 2 different time points (i.e., SATS28, QRPs, Understanding_OS)\nsome of the data was only collected at one time point (i.e., supervisor judgements, OS_behav items, and Included_prereg variables are t2-only variables)\n\n\n1.6.1 First glimpse at the data\nBefore you start wrangling your data, it is important to understand what kind of data you’re working with and what the format of your dataframe looks like.\nAs you may have noticed, read_csv() provides a message listing the data types in your dataset and how many columns are of each type. Plus, it shows a few examples columns for each data type.\nTo obtain more detailed information about your data, you have several options. Click on the individual tabs to see the different options available. Test them out in your own .Rmd file and use whichever method you prefer (but do it).\n\n\n\n\n\n\nWarning\n\n\n\nSome of the output is a bit long because we do have quite a few variables in the data file.\n\n\n\n\nvisual inspection 1\nglimpse()\nspec()\nvisual inspection 2\n\n\n\nIn the Global Environment, click the blue arrow icon next to the object name data_prp. This action will expand the object, revealing details about its columns. The $ symbol is commonly used in Base R to access a specific column within your dataframe.\n\n\nVisual inspection of the data\n\nCon: When you have quite a few variables, not all of them are shown.\n\n\nUse glimpse() if you want a more detailed overview you can see on your screen. The output will display rows and column numbers, and some examples of the first couple of observations for each variable.\n\nglimpse(data_prp)\n\nRows: 89\nColumns: 91\n$ Code <chr> \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", …\n$ Gender <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,…\n$ Age <chr> \"22\", \"20\", \"22\", \"26\", \"22\", \"2…\n$ Ethnicity <chr> \"White European\", \"White British…\n$ Secondyeargrade <dbl> 2, 3, 1, 2, 2, 2, 2, 2, 1, 1, 1,…\n$ Opptional_mod <dbl> 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,…\n$ Opptional_mod_1_TEXT <chr> \"Research methods in first year\"…\n$ Research_exp <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…\n$ Research_exp_1_TEXT <chr> NA, NA, NA, NA, NA, NA, NA, NA, …\n$ Plan_prereg <dbl> 1, 3, 1, 2, 1, 1, 3, 3, 2, 2, 2,…\n$ SATS28_1_Affect_Time1 <dbl> 4, 5, 5, 6, 2, 1, 6, 3, 2, 5, 2,…\n$ SATS28_2_Affect_Time1 <dbl> 5, 6, 3, 3, 6, 1, 2, 2, 7, 3, 4,…\n$ SATS28_3_Affect_Time1 <dbl> 3, 2, 5, 2, 6, 7, 2, 6, 6, 5, 2,…\n$ SATS28_4_Affect_Time1 <dbl> 4, 5, 2, 2, 6, 6, 5, 5, 5, 5, 2,…\n$ SATS28_5_Affect_Time1 <dbl> 5, 5, 5, 6, 1, 1, 5, 1, 2, 5, 2,…\n$ SATS28_6_Affect_Time1 <dbl> 5, 6, 2, 5, 6, 7, 4, 5, 5, 3, 5,…\n$ SATS28_7_CognitiveCompetence_Time1 <dbl> 4, 2, 2, 5, 6, 7, 2, 5, 5, 2, 2,…\n$ SATS28_8_CognitiveCompetence_Time1 <dbl> 2, 2, 2, 1, 6, 7, 2, 5, 3, 2, 3,…\n$ SATS28_9_CognitiveCompetence_Time1 <dbl> 2, 2, 2, 3, 3, 7, 2, 6, 3, 3, 1,…\n$ SATS28_10_CognitiveCompetence_Time1 <dbl> 6, 7, 6, 6, 4, 2, 6, 4, 5, 6, 5,…\n$ SATS28_11_CognitiveCompetence_Time1 <dbl> 4, 3, 5, 5, 3, 1, 6, 2, 5, 6, 5,…\n$ SATS28_12_CognitiveCompetence_Time1 <dbl> 3, 5, 3, 5, 5, 7, 3, 4, 7, 2, 3,…\n$ SATS28_13_Value_Time1 <dbl> 1, 1, 2, 1, 3, 7, 1, 2, 1, 2, 4,…\n$ SATS28_14_Value_Time1 <dbl> 7, 7, 6, 6, 5, 1, 6, 5, 7, 6, 2,…\n$ SATS28_15_Value_Time1 <dbl> 7, 7, 6, 6, 3, 5, 6, 6, 6, 5, 5,…\n$ SATS28_16_Value_Time1 <dbl> 2, 1, 3, 2, 6, 5, 3, 7, 2, 2, 2,…\n$ SATS28_17_Value_Time1 <dbl> 1, 1, 3, 3, 7, 7, 2, 7, 2, 2, 5,…\n$ SATS28_18_Value_Time1 <dbl> 3, 6, 5, 3, 1, 1, 5, 1, 5, 2, 2,…\n$ SATS28_19_Value_Time1 <dbl> 3, 3, 3, 3, 7, 7, 4, 5, 3, 5, 6,…\n$ SATS28_20_Value_Time1 <dbl> 2, 1, 4, 2, 7, 7, 2, 4, 2, 2, 7,…\n$ SATS28_21_Value_Time1 <dbl> 2, 1, 3, 2, 6, 7, 2, 5, 1, 3, 5,…\n$ SATS28_22_Difficulty_Time1 <dbl> 3, 2, 5, 3, 2, 1, 4, 2, 2, 5, 3,…\n$ SATS28_23_Difficulty_Time1 <dbl> 5, 6, 5, 6, 6, 7, 4, 6, 7, 5, 6,…\n$ SATS28_24_Difficulty_Time1 <dbl> 2, 2, 2, 3, 1, 4, 4, 2, 2, 2, 2,…\n$ SATS28_25_Difficulty_Time1 <dbl> 6, 7, 5, 5, 6, 7, 5, 6, 5, 5, 5,…\n$ SATS28_26_Difficulty_Time1 <dbl> 4, 2, 2, 2, 6, 7, 4, 5, 3, 5, 3,…\n$ SATS28_27_Difficulty_Time1 <dbl> 4, 5, 5, 3, 6, 7, 4, 3, 5, 3, 6,…\n$ SATS28_28_Difficulty_Time1 <dbl> 1, 7, 5, 5, 6, 6, 5, 4, 4, 4, 2,…\n$ QRPs_1_Time1 <dbl> 7, 7, 7, 7, 7, 7, 6, 2, 7, 6, 7,…\n$ QRPs_2_Time1 <dbl> 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 5,…\n$ QRPs_3_Time1 <dbl> 5, 2, 6, 2, 6, 4, 6, 3, 7, 3, 3,…\n$ QRPs_4_Time1 <dbl> 7, 7, 6, 6, 7, 4, 6, 7, 7, 7, 6,…\n$ QRPs_5_Time1 <dbl> 3, 3, 7, 7, 2, 7, 4, 6, 7, 3, 2,…\n$ QRPs_6_Time1 <dbl> 4, 7, 6, 5, 7, 4, 4, 5, 7, 6, 5,…\n$ QRPs_7_Time1 <dbl> 5, 7, 7, 7, 7, 4, 5, 6, 7, 7, 5,…\n$ QRPs_8_Time1 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 7,…\n$ QRPs_9_Time1 <dbl> 6, 7, 7, 4, 7, 7, 3, 7, 6, 6, 2,…\n$ QRPs_10_Time1 <dbl> 7, 6, 5, 2, 5, 4, 2, 6, 7, 7, 2,…\n$ QRPs_11_Time1 <dbl> 7, 7, 7, 4, 7, 7, 4, 6, 7, 7, 5,…\n$ QRPs_12NotQRP_Time1 <dbl> 2, 2, 1, 4, 1, 4, 2, 4, 2, 2, 1,…\n$ QRPs_13NotQRP_Time1 <dbl> 1, 1, 1, 1, 1, 4, 2, 4, 1, 1, 1,…\n$ QRPs_14NotQRP_Time1 <dbl> 1, 4, 3, 4, 1, 4, 2, 3, 3, 4, 3,…\n$ QRPs_15NotQRP_Time1 <dbl> 2, 4, 2, 2, 1, 4, 2, 1, 4, 4, 2,…\n$ Understanding_OS_1_Time1 <chr> \"2\", \"2\", \"6\", \"2\", \"6\", \"Not at…\n$ Understanding_OS_2_Time1 <chr> \"2\", \"Not at all confident\", \"2\"…\n$ Understanding_OS_3_Time1 <chr> \"2\", \"Not at all confident\", \"3\"…\n$ Understanding_OS_4_Time1 <chr> \"6\", \"Not at all confident\", \"6\"…\n$ Understanding_OS_5_Time1 <chr> \"Entirely confident\", \"6\", \"6\", …\n$ Understanding_OS_6_Time1 <chr> \"Entirely confident\", \"Entirely …\n$ Understanding_OS_7_Time1 <chr> \"6\", \"Not at all confident\", \"2\"…\n$ Understanding_OS_8_Time1 <chr> \"6\", \"3\", \"5\", \"3\", \"5\", \"Not at…\n$ Understanding_OS_9_Time1 <chr> \"Entirely confident\", \"6\", \"5\", …\n$ Understanding_OS_10_Time1 <chr> \"Entirely confident\", \"6\", \"5\", …\n$ Understanding_OS_11_Time1 <chr> \"Entirely confident\", \"2\", \"4\", …\n$ Understanding_OS_12_Time1 <chr> \"Entirely confident\", \"2\", \"5\", …\n$ Pre_reg_group <dbl> 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,…\n$ Other_OS_behav_2 <dbl> 1, NA, NA, NA, 1, NA, NA, 1, NA,…\n$ Other_OS_behav_4 <dbl> 1, NA, NA, NA, NA, NA, NA, NA, N…\n$ Other_OS_behav_5 <dbl> NA, NA, NA, NA, 1, 1, NA, NA, NA…\n$ Closely_follow <dbl> 2, 2, 2, NA, 3, 3, 3, NA, NA, 2,…\n$ SATS28_Affect_Time2_mean <dbl> 3.500000, 3.166667, 4.833333, 4.…\n$ SATS28_CognitiveCompetence_Time2_mean <dbl> 4.166667, 4.666667, 6.166667, 5.…\n$ SATS28_Value_Time2_mean <dbl> 3.000000, 6.222222, 6.000000, 4.…\n$ SATS28_Difficulty_Time2_mean <dbl> 2.857143, 2.857143, 4.000000, 2.…\n$ QRPs_Acceptance_Time2_mean <dbl> 5.636364, 5.454545, 6.272727, 5.…\n$ Time2_Understanding_OS <dbl> 5.583333, 3.333333, 5.416667, 4.…\n$ Supervisor_1 <dbl> 5, 7, 7, 1, 7, 1, 7, 6, 7, 5, 6,…\n$ Supervisor_2 <dbl> 5, 6, 7, 4, 6, 2, 7, 5, 6, 5, 5,…\n$ Supervisor_3 <dbl> 6, 7, 7, 1, 7, 1, 7, 5, 6, 6, 7,…\n$ Supervisor_4 <dbl> 6, 7, 7, 1, 7, 1, 7, 6, 7, 6, 6,…\n$ Supervisor_5 <dbl> 5, 7, 7, 4, 7, 3, 7, 7, 6, 6, 6,…\n$ Supervisor_6 <dbl> 5, 7, 7, 4, 6, 3, 7, 6, 7, 6, 6,…\n$ Supervisor_7 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…\n$ Supervisor_8 <dbl> 5, 5, 7, 1, 7, 1, 7, 5, 7, 5, 6,…\n$ Supervisor_9 <dbl> 6, 7, 7, 4, 7, 3, 7, 5, 7, 6, 7,…\n$ Supervisor_10 <dbl> 5, 7, 7, 1, 7, 1, 7, 6, 7, 6, 6,…\n$ Supervisor_11 <dbl> NA, 7, 7, NA, 7, 1, 7, 5, 7, 6, …\n$ Supervisor_12 <dbl> 4, 5, 7, 1, 4, 1, 7, 3, 6, 6, 5,…\n$ Supervisor_13 <dbl> 4, 2, 5, 1, 2, 1, 6, 3, 5, 6, 5,…\n$ Supervisor_14 <dbl> 5, 7, 7, 1, 7, 1, 7, 5, 7, 6, 6,…\n$ Supervisor_15_R <dbl> 1, 1, 1, 4, 1, 7, 1, 2, 1, 2, 1,…\n\n\n\n\nYou can also use spec() as suggested in the message above and then it shows you a list of the data type in every single column. But it doesn’t show you the number of rows and columns.\n\nspec(data_prp)\n\ncols(\n Code = col_character(),\n Gender = col_double(),\n Age = col_character(),\n Ethnicity = col_character(),\n Secondyeargrade = col_double(),\n Opptional_mod = col_double(),\n Opptional_mod_1_TEXT = col_character(),\n Research_exp = col_double(),\n Research_exp_1_TEXT = col_character(),\n Plan_prereg = col_double(),\n SATS28_1_Affect_Time1 = col_double(),\n SATS28_2_Affect_Time1 = col_double(),\n SATS28_3_Affect_Time1 = col_double(),\n SATS28_4_Affect_Time1 = col_double(),\n SATS28_5_Affect_Time1 = col_double(),\n SATS28_6_Affect_Time1 = col_double(),\n SATS28_7_CognitiveCompetence_Time1 = col_double(),\n SATS28_8_CognitiveCompetence_Time1 = col_double(),\n SATS28_9_CognitiveCompetence_Time1 = col_double(),\n SATS28_10_CognitiveCompetence_Time1 = col_double(),\n SATS28_11_CognitiveCompetence_Time1 = col_double(),\n SATS28_12_CognitiveCompetence_Time1 = col_double(),\n SATS28_13_Value_Time1 = col_double(),\n SATS28_14_Value_Time1 = col_double(),\n SATS28_15_Value_Time1 = col_double(),\n SATS28_16_Value_Time1 = col_double(),\n SATS28_17_Value_Time1 = col_double(),\n SATS28_18_Value_Time1 = col_double(),\n SATS28_19_Value_Time1 = col_double(),\n SATS28_20_Value_Time1 = col_double(),\n SATS28_21_Value_Time1 = col_double(),\n SATS28_22_Difficulty_Time1 = col_double(),\n SATS28_23_Difficulty_Time1 = col_double(),\n SATS28_24_Difficulty_Time1 = col_double(),\n SATS28_25_Difficulty_Time1 = col_double(),\n SATS28_26_Difficulty_Time1 = col_double(),\n SATS28_27_Difficulty_Time1 = col_double(),\n SATS28_28_Difficulty_Time1 = col_double(),\n QRPs_1_Time1 = col_double(),\n QRPs_2_Time1 = col_double(),\n QRPs_3_Time1 = col_double(),\n QRPs_4_Time1 = col_double(),\n QRPs_5_Time1 = col_double(),\n QRPs_6_Time1 = col_double(),\n QRPs_7_Time1 = col_double(),\n QRPs_8_Time1 = col_double(),\n QRPs_9_Time1 = col_double(),\n QRPs_10_Time1 = col_double(),\n QRPs_11_Time1 = col_double(),\n QRPs_12NotQRP_Time1 = col_double(),\n QRPs_13NotQRP_Time1 = col_double(),\n QRPs_14NotQRP_Time1 = col_double(),\n QRPs_15NotQRP_Time1 = col_double(),\n Understanding_OS_1_Time1 = col_character(),\n Understanding_OS_2_Time1 = col_character(),\n Understanding_OS_3_Time1 = col_character(),\n Understanding_OS_4_Time1 = col_character(),\n Understanding_OS_5_Time1 = col_character(),\n Understanding_OS_6_Time1 = col_character(),\n Understanding_OS_7_Time1 = col_character(),\n Understanding_OS_8_Time1 = col_character(),\n Understanding_OS_9_Time1 = col_character(),\n Understanding_OS_10_Time1 = col_character(),\n Understanding_OS_11_Time1 = col_character(),\n Understanding_OS_12_Time1 = col_character(),\n Pre_reg_group = col_double(),\n Other_OS_behav_2 = col_double(),\n Other_OS_behav_4 = col_double(),\n Other_OS_behav_5 = col_double(),\n Closely_follow = col_double(),\n SATS28_Affect_Time2_mean = col_double(),\n SATS28_CognitiveCompetence_Time2_mean = col_double(),\n SATS28_Value_Time2_mean = col_double(),\n SATS28_Difficulty_Time2_mean = col_double(),\n QRPs_Acceptance_Time2_mean = col_double(),\n Time2_Understanding_OS = col_double(),\n Supervisor_1 = col_double(),\n Supervisor_2 = col_double(),\n Supervisor_3 = col_double(),\n Supervisor_4 = col_double(),\n Supervisor_5 = col_double(),\n Supervisor_6 = col_double(),\n Supervisor_7 = col_double(),\n Supervisor_8 = col_double(),\n Supervisor_9 = col_double(),\n Supervisor_10 = col_double(),\n Supervisor_11 = col_double(),\n Supervisor_12 = col_double(),\n Supervisor_13 = col_double(),\n Supervisor_14 = col_double(),\n Supervisor_15_R = col_double()\n)\n\n\n\n\nIn the Global Environment, click on the object name data_prp. This action will open the data in a new tab. Hovering over the column headings with your mouse will also reveal their data type. However, it seems to be a fairly tedious process when you have loads of columns.\n\n\n\n\n\n\nHang on, where is the rest of my data? Why do I only see 50 columns?\n\n\n\n\n\nOne common source of confusion is not seeing all your columns when you open up a data object as a tab. This is because RStudio shows you a maximum of 50 columns at a time. If you have more than 50 columns, navigate with the arrows to see the remaining columns.\n\n\nShowing 50 columns at a time\n\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nNow that you have tested out all the options in your own .Rmd file, you can probably answer the following questions:\n\nHow many observations? \n\nHow many variables? \n\nHow many columns are col_character or chr data type? \n\nHow many columns are col_double or dbl data type? \n\n\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThe visual inspections shows you the number of observations and variables. glimpse() also gives you that information but calls them rows and columns respectively.\nThe data type information actually comes from the output when using the read_csv() function. Did you notice the information on Column specification (see screenshot below)?\n\n\nmessage from read_csv() when reading in the data\n\nWhilst spec() is quite useful for data type information per individual column, it doesn’t give you the total count of each data type. So it doesn’t really help with answering the questions here - unless you want to count manually from its extremely long output.\n\n\n\nIn your .Rmd, include a new heading level 2 called “Information about the data” (or something equally meaningful) and jot down some notes about data_prp. You could include the citation and/or the abstract, and whatever information you think you should note about this dataset (e.g., any observations from looking at the codebook?). You could also include some notes on the functions used so far and what they do. Try to incorporate some bold, italic or bold and italic emphasis and perhaps a bullet point or two.\n\n\n\n\n\n\nPossible solution\n\n\n\n\n\n## Information about the data\nThe data is from Pownall et al. (2023), and I can find the paper here: https://doi.org/10.1177/25152459231202724.\nI’ve noticed in the prp codebook that the SATS-28 questionnaire has quite a few *reverse-coded items*, and the supervisor support questionnaire also has a reverse-coded item.\nSo far, I think I prefer **glimpse()** to show me some more detail about the data. Specs() is too text-heavy for me which makes it hard to read.\nThings to keep in mind:\n\n**don’t forget to load in tidyverse first!!!**\nalways read in the data with **read_csv**, ***never ever use read.csv***!!!\n\n\n\nThe output rendered in a knitted html file\n\n\n\n\n\n\n\n1.6.2 Data types\nEach variable has a data type, such as numeric (numbers), character (text), and logical (TRUE/FALSE values), or a special class of factor. As you have just seen, our data_prp only has character and numeric columns (so far).\nNumeric data can be double (dbl) or integer (int). Doubles can have decimal places (e.g., 1.1). Integers are the whole numbers (e.g., 1, 2, -1) and are displayed with the suffix L (e.g., 1L). This is not overly important but might leave you less puzzled the next time you see an L after a number.\nCharacters (also called “strings”) is anything written between quotation marks. This is usually text, but in special circumstances, a number can be a character if it placed within quotation marks. This can happen when you are recoding variables. It might not be too obvious at the time, but you won’t be able to calculate anything if the number is a character\n\n\nExample data types\nnumeric computation\ncharacter computation\n\n\n\n\ntypeof(1)\ntypeof(1L)\ntypeof(\"1\")\ntypeof(\"text\")\n\n[1] \"double\"\n[1] \"integer\"\n[1] \"character\"\n[1] \"character\"\n\n\n\n\nNo problems here…\n\n1+1\n\n[1] 2\n\n\n\n\nWhen the data type is incorrect, you won’t be able to compute anything, despite your numbers being shown as numeric values in the dataframe. The error message tells you exactly what’s wrong with it, i.e., that you have non-numeric arguments.\n\n\"1\"+\"1\" # ERROR\n\nError in \"1\" + \"1\": non-numeric argument to binary operator\n\n\n\n\n\nLogical data (also sometimes called “Boolean” values) are one of two values: TRUE or FALSE (written in uppercase). They become really important when we use filter() or mutate() with conditional statements such as case_when(). More about those in Chapter 3.\nSome commonly used logical operators:\n\n\noperator\ndescription\n\n\n\n>\ngreater than\n\n\n>=\ngreater than or equal to\n\n\n<\nless than\n\n\n<=\nless than or equal to\n\n\n==\nequal to\n\n\n!=\nnot equal to\n\n\n%in%\nTRUE if any element is in the following vector\n\n\n\nA factor is a specific type of integer or character that lets you assign the order of the categories. This becomes useful when you want to display certain categories in “the correct order” either in a dataframe (see arrange) or when plotting (see Chapter 4/ Chapter 5).\n\n1.6.3 Variable types\nYou’ve already encountered them in Level 1 but let’s refresh. Variables can be classified as continuous (numbers) or categorical (labels).\nCategorical variables are properties you can count. They can be nominal, where the categories don’t have an order (e.g., gender) or ordinal (e.g., Likert scales either with numeric values 1-7 or with character labels such as “agree”, “neither agree nor disagree”, “disagree”). Categorical data may also be factors rather than characters.\nContinuous variables are properties you can measure and calculate sums/ means/ etc. They may be rounded to the nearest whole number, but it should make sense to have a value between them. Continuous variables always have a numeric data type (i.e. integer or double).\n\n\n\n\n\n\nWhy is this important you may ask?\n\n\n\nKnowing your variable and data types will help later on when deciding on an appropriate plot (see Chapter 4 and Chapter 5) or which inferential test to run (?sec-nhstI to ?sec-factorial).\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nAs we’ve seen earlier, data_prp only had character and numeric variables which hardly tests your understanding to see if you can identify a variety of data types and variable types. So, for this little quiz, we’ve spiced it up a bit. We’ve selected a few columns, shortened some of the column names, and modified some of the data types. Here you can see the first few rows of the new object data_quiz. You can find the code with explanations at the end of this section.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCode\nAge\nGender\nEthnicity\nSecondyeargrade\nQRP_item\nQRPs_mean\nUnderstanding_item\nQRP_item > 4\n\n\n\nTr10\n22\n2\nWhite European\n60-69% (2:1 grade)\n5\n5.636364\n2\nTRUE\n\n\nBi07\n20\n2\nWhite British\n50-59% (2:2 grade)\n2\n5.454546\n2\nFALSE\n\n\nSK03\n22\n2\nWhite British\n≥ 70% (1st class grade)\n6\n6.272727\n6\nTRUE\n\n\nSM95\n26\n2\nWhite British\n60-69% (2:1 grade)\n2\n5.000000\n2\nFALSE\n\n\nSt01\n22\n2\nWhite British\n60-69% (2:1 grade)\n6\n5.545454\n6\nTRUE\n\n\n\n\n\n\n\nglimpse(data_quiz)\n\nRows: 89\nColumns: 9\n$ Code <chr> \"Tr10\", \"Bi07\", \"SK03\", \"SM95\", \"St01\", \"St10\", \"Wa…\n$ Age <chr> \"22\", \"20\", \"22\", \"26\", \"22\", \"20\", \"21\", \"21\", \"22…\n$ Gender <fct> 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, …\n$ Ethnicity <chr> \"White European\", \"White British\", \"White British\",…\n$ Secondyeargrade <fct> 60-69% (2:1 grade), 50-59% (2:2 grade), ≥ 70% (1st …\n$ QRP_item <dbl> 5, 2, 6, 2, 6, 4, 6, 3, 7, 3, 3, 4, 4, 4, 4, 6, 3, …\n$ QRPs_mean <dbl> 5.636364, 5.454545, 6.272727, 5.000000, 5.545455, 6…\n$ Understanding_item <chr> \"2\", \"2\", \"6\", \"2\", \"6\", \"Not at all confident\", \"4…\n$ `QRP_item > 4` <lgl> TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE,…\n\n\nSelect from the dropdown menu the variable type and their data types for each of the columns.\n\n\n\n\n\n\n\nColumn\nVariable type\nData type\n\n\n\nAge\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nGender\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nEthinicity\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nSecondyeargrade\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRP_item\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRPs_mean\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nUnderstanding_item\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\nQRP_item > 4\n\ncontinuous\nnominal\nordinal\n\nnumeric\ncharacter\nlogical\nfactor\n\n\n\n\n\n\n\n\n\n\n\nRevealing the mystery code that created data_quiz\n\n\n\n\n\nThe code might look a bit complex for the minute despite the line-by-line explanations below. Come back to it after completing chapter 2.\n\ndata_quiz <- data_prp %>% \n select(Code, Age, Gender, Ethnicity, Secondyeargrade, QRP_item = QRPs_3_Time1, QRPs_mean = QRPs_Acceptance_Time2_mean, Understanding_item = Understanding_OS_1_Time1) %>% \n mutate(Gender = factor(Gender),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n `QRP_item > 4` = case_when(\n QRP_item > 4 ~ TRUE, \n .default = FALSE))\n\nLets go through this line by line:\n\n\nline 1: creates a new object called data_quiz and it is based on the already existing data object data_prp\n\n\nline 2: we are selecting a few variables of interest, such as Code, Age etc. Some of those variables were renamed in the process according to the structure new_name = old_name, for example QRP item 3 at time point 1 got renamed as QRP_item.\n\n\nline 3: The function mutate() is used to create a new column called Gender that turns the existing column Gender from a numeric value into a factor. R simply overwrites the existing column of the same name. If we had named the new column Gender_factor, we would have been able to retain the original Gender column and Gender_factor would have been added as the last column.\n\nline 4-6: See how the line starts with an indent which indicates we are still within the mutate() function. You can also see this by counting brackets - in line 3 there are 2 opening brackets but only 1 closes.\n\nSimilar to Gender, we are replacing the “old” Secondyeargrade with the new Secondyeargrade column that is now a factor.\nTurning our variable Secondyeargrade into a factor, spot the difference between this attempt and the one we used for Gender? Here we are using a lot more arguments in that factor function, namely levels and labels. Levels describes the unique values we have for that column, and in labels we want to define how these levels will be shown in the data object. If you don’t add the levels and labels argument, the labels will be the labels (as you can see in the Gender column in which we kept the numbers).\n\n\n\nline 7: Doesn’t start with a function name and has an indent, which means we are still within the mutate() function - count the opening and closing brackets to confirm.\n\nHere, we are creating a new column called QRP_item > 4. Notice the two backticks we have to use to make this weird column name work? This is because it has spaces (and we did mention that R doesn’t like spaces). So the backticks help R to group it as a unit/ a single name.\nNext we have a case_when() function which helps executing conditional statements. We are using it to check whether a statement is TRUE or FALSE. Here, we ask whether the QRP item (column QRP_item) is larger than 4 (midpoint of the scale) using the Boolean operator >. If the statement is TRUE, the label TRUE should appear in column QRP_item > 4. Otherwise, if the value is equal to 4 or smaller, the label should read FALSE. We will come back to conditional statements in Chapter 2. But long story short, this Boolean expression created the only logical data type in data_quiz.\n\n\n\n\n\n\nAnd with this, we are done with the individual walkthrough. Well done :)" }, { "objectID": "01-basics.html#pair-coding", @@ -81,7 +81,7 @@ "href": "02-wrangling.html#intended-learning-outcomes", "title": "2  Data wrangling I", "section": "Intended Learning Outcomes", - "text": "Intended Learning Outcomes\nIn the next two chapters, we will build on the data wrangling skills from level 1. We will revisit all the functions you have already encountered (and might have forgotten over the summer break) and introduce 2 or 3 new functions. These two chapters will provide an opportunity to revise and apply the functions to a novel dataset.\nBy the end of this chapter, you should be able to:\n\napply familiar data wrangling functions to novel datasets\nread and interpret error messages\nrealise there are several ways of getting to the results\nexport data objects as csv files\n\nThe main purpose of this chapter and Chapter 3 is to wrangle your data into shape for data visualisation (Chapter 4 and ?sec-dataviz2). For the two chapters, we will:\n\ncalculate demographics\ntidy 3 different questionnaires with varying degrees of complexity\nsolve an error mode problem\njoin all data objects together" + "text": "Intended Learning Outcomes\nIn the next two chapters, we will build on the data wrangling skills from level 1. We will revisit all the functions you have already encountered (and might have forgotten over the summer break) and introduce 2 or 3 new functions. These two chapters will provide an opportunity to revise and apply the functions to a novel dataset.\nBy the end of this chapter, you should be able to:\n\napply familiar data wrangling functions to novel datasets\nread and interpret error messages\nrealise there are several ways of getting to the results\nexport data objects as csv files\n\nThe main purpose of this chapter and Chapter 3 is to wrangle your data into shape for data visualisation (Chapter 4 and Chapter 5). For the two chapters, we will:\n\ncalculate demographics\ntidy 3 different questionnaires with varying degrees of complexity\nsolve an error mode problem\njoin all data objects together" }, { "objectID": "02-wrangling.html#individual-walkthrough", @@ -151,7 +151,7 @@ "href": "03-wrangling2.html#intended-learning-outcomes", "title": "3  Data wrangling II", "section": "Intended Learning Outcomes", - "text": "Intended Learning Outcomes\nBy the end of this chapter, you should be able to:\n\napply familiar data wrangling functions to novel datasets\nread and interpret error messages\nrealise there are several ways of getting to the results\n\nIn this chapter, we will pick up where we left off in Chapter 2. We will calculate average scores for two of the questionnaires, address an error mode problem, and finally, join all data objects together. This will finalise our data for the upcoming data visualization sections (Chapter 4 and ?sec-dataviz2)." + "text": "Intended Learning Outcomes\nBy the end of this chapter, you should be able to:\n\napply familiar data wrangling functions to novel datasets\nread and interpret error messages\nrealise there are several ways of getting to the results\n\nIn this chapter, we will pick up where we left off in Chapter 2. We will calculate average scores for two of the questionnaires, address an error mode problem, and finally, join all data objects together. This will finalise our data for the upcoming data visualization sections (Chapter 4 and Chapter 5)." }, { "objectID": "03-wrangling2.html#individual-walkthrough", @@ -200,7 +200,7 @@ "href": "03-wrangling2.html#activity-6-join-everything-together-with-_join", "title": "3  Data wrangling II", "section": "\n3.6 Activity 6: Join everything together with ???_join()\n", - "text": "3.6 Activity 6: Join everything together with ???_join()\n\nTime to join all the relevant data files into a single dataframe, which will be used in the next chapters on data visualization. There are four ways to join data: inner_join(), left_join(), right_join(), and full_join(). Each function behaves differently in terms of what information is retained from the two data objects. Here is a quick overview:\n\n\n\n\n\n\nInfo on mutating joins\n\n\n\nYou have 4 types of join functions you could make use of. Click on the panels to know more\n\n\ninner_join()\nleft_join()\nright_join()\nfull_join()\n\n\n\ninner_join() returns only the rows where the values in the column specified in the by = statement match in both tables.\n\n\ninner_join(): gif by Garrick Aden-Buie\n\n\n\nleft_join() retains the complete first (left) table and adds values from the second (right) table that have matching values in the column specified in the by = statement. Rows in the left table with no match in the right table will have missing values (NA) in the new columns.\n\n\nleft_join(): gif by Garrick Aden-Buie\n\n\n\nright_join() retains the complete second (right) table and adds values from the first (left) table that have matching values in the column specified in the by = statement. Rows in the right table with no match in the left table will have missing values (NA) in the new columns.\n\n\nright_join(): gif by Garrick Aden-Buie\n\n\n\nfull_join() returns all rows and all columns from both tables. NA values fill unmatched rows.\n\n\nfull_join(): gif by Garrick Aden-Buie\n\n\n\n\n\n\nFrom our original data_prp, we need to select demographics data and all summarised questionnaire data from time point 2. Next, we will join this with all other aggregated datasets from time point 1 which are currently stored in separate data objects in the Global Environment.\nWhile you may be familiar with inner_join() from last year, for this task, we want to retain all data from all the data objects. Therefore, we will use full_join(). Keep in mind, you can only join two data objects at a time, so the upcoming code chunk will involve a fair bit of piping and joining.\nNote: Since I (Gaby) like my columns arranged in a meaningful way, I will use select() at the end to order them better.\n\ndata_prp_final <- data_prp %>% \n select(Code:Plan_prereg, Pre_reg_group:Time2_Understanding_OS) %>% \n full_join(qrp_t1) %>% \n full_join(understanding_t1) %>% \n full_join(sats_t1) %>% \n full_join(super) %>% \n select(Code:Plan_prereg, Pre_reg_group, SATS28_Affect_Time1_mean, SATS28_CognitiveCompetence_Time1_mean, SATS28_Value_Time1_mean, SATS28_Difficulty_Time1_mean, QRPs_Acceptance_Time1_mean, Time1_Understanding_OS, Other_OS_behav_2:Time2_Understanding_OS, Mean_Supervisor_Support)\n\n\n\n\n\n\n\nNo by argument in the code above?\n\n\n\nNote how I didn’t include a by argument in the code above. If you leave by = out, R will join the 2 data objects by ALL columns that have the same name.\nSpecial case 1: matching column names but different values\nIf you want more control, you should include the by argument; for example, if both data objects include a column age but data was recorded at 2 different time points. In that case, the information from both age columns should be retained and the by argument would not include age.\nSpecial case 2: different column names but matching values\nAnother special case presents when both data objects contain identical information but the variable names don’t match. Let’s say, both data objects contain gender information, but in one data object the variable is named gender and in the other one gender_label. In that case, your by argument needs to be modified as: by = join_by(gender == gender_label).\nMore info on joins can be found https://www.tidyverse.org/blog/2023/01/dplyr-1-1-0-joins/\n\n\nAnd this is basically the dataset we need for Chapter 4 and ?sec-dataviz2." + "text": "3.6 Activity 6: Join everything together with ???_join()\n\nTime to join all the relevant data files into a single dataframe, which will be used in the next chapters on data visualization. There are four ways to join data: inner_join(), left_join(), right_join(), and full_join(). Each function behaves differently in terms of what information is retained from the two data objects. Here is a quick overview:\n\n\n\n\n\n\nInfo on mutating joins\n\n\n\nYou have 4 types of join functions you could make use of. Click on the panels to know more\n\n\ninner_join()\nleft_join()\nright_join()\nfull_join()\n\n\n\ninner_join() returns only the rows where the values in the column specified in the by = statement match in both tables.\n\n\ninner_join(): gif by Garrick Aden-Buie\n\n\n\nleft_join() retains the complete first (left) table and adds values from the second (right) table that have matching values in the column specified in the by = statement. Rows in the left table with no match in the right table will have missing values (NA) in the new columns.\n\n\nleft_join(): gif by Garrick Aden-Buie\n\n\n\nright_join() retains the complete second (right) table and adds values from the first (left) table that have matching values in the column specified in the by = statement. Rows in the right table with no match in the left table will have missing values (NA) in the new columns.\n\n\nright_join(): gif by Garrick Aden-Buie\n\n\n\nfull_join() returns all rows and all columns from both tables. NA values fill unmatched rows.\n\n\nfull_join(): gif by Garrick Aden-Buie\n\n\n\n\n\n\nFrom our original data_prp, we need to select demographics data and all summarised questionnaire data from time point 2. Next, we will join this with all other aggregated datasets from time point 1 which are currently stored in separate data objects in the Global Environment.\nWhile you may be familiar with inner_join() from last year, for this task, we want to retain all data from all the data objects. Therefore, we will use full_join(). Keep in mind, you can only join two data objects at a time, so the upcoming code chunk will involve a fair bit of piping and joining.\nNote: Since I (Gaby) like my columns arranged in a meaningful way, I will use select() at the end to order them better.\n\ndata_prp_final <- data_prp %>% \n select(Code:Plan_prereg, Pre_reg_group:Time2_Understanding_OS) %>% \n full_join(qrp_t1) %>% \n full_join(understanding_t1) %>% \n full_join(sats_t1) %>% \n full_join(super) %>% \n select(Code:Plan_prereg, Pre_reg_group, SATS28_Affect_Time1_mean, SATS28_CognitiveCompetence_Time1_mean, SATS28_Value_Time1_mean, SATS28_Difficulty_Time1_mean, QRPs_Acceptance_Time1_mean, Time1_Understanding_OS, Other_OS_behav_2:Time2_Understanding_OS, Mean_Supervisor_Support)\n\n\n\n\n\n\n\nNo by argument in the code above?\n\n\n\nNote how I didn’t include a by argument in the code above. If you leave by = out, R will join the 2 data objects by ALL columns that have the same name.\nSpecial case 1: matching column names but different values\nIf you want more control, you should include the by argument; for example, if both data objects include a column age but data was recorded at 2 different time points. In that case, the information from both age columns should be retained and the by argument would not include age.\nSpecial case 2: different column names but matching values\nAnother special case presents when both data objects contain identical information but the variable names don’t match. Let’s say, both data objects contain gender information, but in one data object the variable is named gender and in the other one gender_label. In that case, your by argument needs to be modified as: by = join_by(gender == gender_label).\nMore info on joins can be found https://www.tidyverse.org/blog/2023/01/dplyr-1-1-0-joins/\n\n\nAnd this is basically the dataset we need for Chapter 4 and Chapter 5." }, { "objectID": "03-wrangling2.html#activity-7-knit-and-export", @@ -263,7 +263,7 @@ "href": "04-dataviz.html#activity-3-barchart-geom_bar", "title": "4  Data viz I", "section": "\n4.4 Activity 3: Barchart (geom_bar())", - "text": "4.4 Activity 3: Barchart (geom_bar())\nA bar chart is the best choice when you want to plot a single categorical variable.\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a barplot. This is done with geom_bar() in the third layer. Since the counting is done automatically in the background, the aes() function only requires an x value (i.e., the name of your variable).\n\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n\n\n\nFigure 4.1: Default barchart\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the labels could be clearer, or you might want to add a splash colour. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own .Rmd file.\n\n\nColour\nAxes labels & margins\nLegend\nThemes\n\n\n\nWe can change the colour by adding a fill argument in the aes(). If we want to modify these colours further, we would add a scale_fill_? argument. If you have specific colours in mind, you would use scale_fill_manual(), or if you prefer to stick with pre-defined options like viridis, you can use scale_fill_viridis_d().\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n\n\n\n\n\n\n\n\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the scale_x_discrete() function and the labels = argument. Just make sure to order the labels according to the order in the dataframe.\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the expansion() function.\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n\n\n\n\n\n\n\n\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument guide = \"none\" to the scale_fill function.\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n\n\n\n\n\n\n\n\n\nLet’s experiment with the themes. For this plot we have chosen theme_minimal().\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()" + "text": "4.4 Activity 3: Barchart (geom_bar())\nA barchart is the best choice when you want to plot a single categorical variable.\nFor example, let’s say we want to count some demographic data, such as gender. To visualise the gender counts, we would use a barplot. This is done with geom_bar() in the third layer. Since the counting is done automatically in the background, the aes() function only requires an x value (i.e., the name of your variable).\n\nggplot(data_prp_viz, aes(x = Gender)) +\n geom_bar() \n\n\n\nFigure 4.1: Default barchart\n\n\n\nThis is the base plot done. You can customise it by adding different layers. For example, the labels could be clearer, or you might want to add a splash colour. Click on the tabs below to see examples of additional customisations, and try applying them to your base plot in your own .Rmd file.\n\n\nColour\nAxes labels & margins\nLegend\nThemes\n\n\n\nWe can change the colour by adding a fill argument in the aes(). If we want to modify these colours further, we would add a scale_fill_? argument. If you have specific colours in mind, you would use scale_fill_manual(), or if you prefer to stick with pre-defined options like viridis, you can use scale_fill_viridis_d().\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n # customise colour\n scale_fill_viridis_d()\n\n\n\n\n\n\n\n\n\nThe x-axis label is fine, but the categories need to be relabelled. You can achieve this with the scale_x_discrete() function and the labels = argument. Just make sure to order the labels according to the order in the dataframe.\nThere is also a gap between the bottom of the chart and the bars that looks a bit odd. You can remove it by using the expansion() function.\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d() +\n # changing group labels on the breaks of the x axis\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Count\",\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05))\n )\n\n\n\n\n\n\n\n\n\nThe legend does not add any useful information because the labels are already provided on the x-axis. We can remove the legend by adding the argument guide = \"none\" to the scale_fill function.\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n # remove the legend\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n )\n\n\n\n\n\n\n\n\n\nLet’s experiment with the themes. For this plot we have chosen theme_minimal().\n\nggplot(data_prp_viz, aes(x = Gender, fill = Gender)) +\n geom_bar() +\n scale_fill_viridis_d(\n guide = \"none\") +\n scale_x_discrete(labels = c(\"Female\", \"Male\", \"Non-Binary\")) +\n scale_y_continuous(\n name = \"Count\",\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_minimal()" }, { "objectID": "04-dataviz.html#activity-4-column-plot-geom_col", @@ -273,8 +273,8 @@ "text": "4.5 Activity 4: Column plot (geom_col())\nIf the counts had already been summarised for you, geom_bar() would not work. Instead, you’d need to use geom_col() to display the pre-aggregated data.\n\ngender_count <- data_prp_viz %>% \n count(Gender)\n\ngender_count\n\n\n\n\nGender\nn\n\n\n\nfemales\n69\n\n\nmales\n17\n\n\nnon-binary\n3\n\n\n\n\n\n\nThe mapping for geom_col() requires both x and y aesthetics. In this example, x would represent the categorical variable (e.g., Gender), while y would refer to the column storing the summarised values (e.g., n). Notice how the axis title now reflects n instead of count in the base version.\n\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col()\n\n\n\nFigure 4.2: Column plot with different coloured bars\n\n\n\n\n\n\n\n\n\nYour Turn: Make the column plot pretty\n\n\n\nThe other layers to change the colour scheme, axes labels and margins, removing the legend and altering the theme require exactly the same functions as with the boxplot above. Test yourself to see if you can…\n\n\nchange the colour scheme (e.g., viridis or any other colour palettes)\n\nremove the legend\n\nchange the titles of the x and y axes\n\nmake the bars start directly on the x-axis\n\nadd a theme of your liking\n\n\n\n\n\n\n\nPossible solution code for the column plot (with a different colour palette and a different theme)\n\n\n\n\n\n\nggplot(gender_count, aes(x = Gender, y = n, fill = Gender)) +\n geom_col() +\n # replaced vidiris with the brewer palette\n scale_fill_brewer(\n palette = \"Set1\", # try \"Set2\" or \"Dark2\" for some variety\n guide = \"none\") + # legend removed\n # labels of the categories changed\n scale_x_discrete(labels = c(\"Male\", \"Female\", \"Non-Binary\")) + \n scale_y_continuous(\n # change y axis label\n name = \"Count\",\n # starts bars on x axis without any gaps but leaves some space at the top (this time 10%)\n expand = expansion(mult = c(0, 0.1)) \n ) +\n # different theme\n theme_light()" }, { - "objectID": "04-dataviz.html#activity-5-stacked-percent-stacked-and-grouped-barchart", - "href": "04-dataviz.html#activity-5-stacked-percent-stacked-and-grouped-barchart", + "objectID": "04-dataviz.html#sec-adv_bar", + "href": "04-dataviz.html#sec-adv_bar", "title": "4  Data viz I", "section": "\n4.6 Activity 5: Stacked, Percent Stacked, and Grouped Barchart", "text": "4.6 Activity 5: Stacked, Percent Stacked, and Grouped Barchart\nWhen dealing with two categorical variables, you have three options for displaying stacked barcharts: the “normal” Stacked Barchart (the default option), a Percent Stacked Barchart, or a Grouped Barchart.\nFor this activity, we will explore the variable Plan_prereg, which measures whether students planned to pre-register their undergraduate dissertation at time point 1, and Pre_reg_group, which tracks whether they actually followed through with a pre-registration for their dissertation.\nOne way to display this data is by creating either a Stacked Barchart (the default) or a Percent Stacked Barchart. In both cases, the subgroups are displayed on top of each other. To make comparison easier, we will place the two plots side by side and move the legend to the bottom of the chart.\n\n## Stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar() + # no position argument added\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Percent stacked barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n\n\n\n\nFigure 4.3: Stacked barchart (left), and Percent stacked barchart (right)\n\n\n\nIn the stacked barchart (Figure 4.3, left plot), you can display participant numbers. From this, we can see that the highest number of students were unsure whether they wanted to pre-register their dissertation, followed closely by those who answered “yes.” We also see that the number of students who did not end up with a pre-registered dissertation (blue category) is the same for both those who had planned to pre-register and those who did not want to. However, since the “No” category has significantly fewer participants than the other two, it’s difficult to tell if the ratio remains consistent across all three groups.\nIf we want to highlight this ratio, a Percent Stacked Barchart (Figure 4.3, right plot) would be more appropriate. This plot shows that approximately 80% of the students who had planned to pre-register their dissertations, 50% of the students who were initially unsure, and only 33% of the students who had no plan to pre-register ended up with a pre-registered dissertation. BUT! We would lose the information about the raw values in the sample.\nIt’s all a trade-off, and the plot you choose depends on the “story” you want the data to tell.\n\n\n\n\n\n\nNote\n\n\n\nThe position argument position = \"stack\" is the default. Adding this argument to the code for the left plot in Figure 4.3 would produce the same plot as leaving the argument out.\n\n\nThe other option is a Grouped Barchart, which displays the bars next to each other. You can achieve this by changing the position argument to \"dodge\". You can see the default version of the plot in Figure 4.4 on the left, and one with additional layers on the right.\nInstead of using a pre-existing colour palette, we manually changed the colours using hex codes. These are some of the colours Gaby used in her PhD thesis, but you can:\n\ncreate your own colour hex codes by using this website, OR\nuse pre-defined colour names like “green” or “purple” instead. See a full list here.\n\nFeel free to explore.\nSince the legend title for the second plot is a bit long, we displayed the legend content across two rows by adding the layer guides(fill = guide_legend(nrow = 2)) at the end.\n\n## Default grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n theme(legend.position = \"bottom\") # move legend to the bottom\n\n## Prettier grouped barchart\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Pre_reg_group)) +\n geom_bar(position = \"dodge\") + # add position argument here\n # changing labels for x, y, and fill category - alternative method\n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registered dissertation\") +\n # manual colour change for values\n scale_fill_manual(values = c('#648FFF', '#DC267F'),\n labels = c(\"Yes\", \"No\")) +\n scale_y_continuous(\n # remove the space below the bars, but keep a tiny bit (5%) above\n expand = expansion(mult = c(0, 0.05))\n ) +\n # pick a theme\n theme_classic() + \n # need to move this following line to the end otherwise the `theme_*` overrides it\n theme(legend.position = \"bottom\") + \n # display across 2 rows\n guides(fill = guide_legend(nrow = 2))\n\n\n\n\n\nFigure 4.4: Default grouped barchart (left) and one with a few more layers added (right)\n\n\n\n\n\n\n\n\n\nSpecial case: Categorical variables with missing values\n\n\n\n\n\nIf we had chosen a different categorical variable that contains missing values, such as Closely_follow, our plots would have included those missing values by default. To change the colour of the missing value bars, you would need to specify this using the na.value = argument within the scale_fill() function. Here’s an example of a grouped barchart.\n\n# default grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n## Prettier grouped barchart with missing values\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"dodge\") + \n labs(x = \"Pre-registration planned\", y = \"Count\", fill = \"Pre-registration followed\") +\n # manual colour change for values of the factor and the NA responses\n scale_fill_manual(values = c('#648FFF', '#DC267F'), na.value = '#FFB000') +\n scale_y_continuous(\n expand = expansion(mult = c(0, 0.05))\n ) +\n theme_classic() + \n theme(legend.position = \"bottom\") + \n guides(fill = guide_legend(nrow = 3)) # display across 3 rows\n\n\n\n\n\nFigure 4.5: Default grouped barchart (left) and one with a few more layers added (right) for a variable with missing values\n\n\n\nIf you don’t want the missing values to appear in the plot, you will need to do some data wrangling to remove them first. The function for this is drop_na(). Here we applied drop_na() to Closely_follow only.\n\n# remove NA\nprereg_plan_follow <- data_prp_viz %>% \n select(Code, Plan_prereg, Closely_follow) %>% \n drop_na(Closely_follow)\n\n\n\n\n\n\n\ncheck NAs have been removed\n\n\n\n\n\n\n# check NA have been removed\nprereg_plan_follow %>% \n distinct(Plan_prereg, Closely_follow) %>% \n arrange(Plan_prereg, Closely_follow)\n\n\n\n\nPlan_prereg\nClosely_follow\n\n\n\nYes\nFollowed it somewhat\n\n\nYes\nFollowed it exactly\n\n\nUnsure\nFollowed it somewhat\n\n\nUnsure\nFollowed it exactly\n\n\nNo\nFollowed it somewhat\n\n\nNo\nFollowed it exactly\n\n\n\n\n\n\n\n\n\nBut keep in mind that it could misrepresent the data, e.g., giving a wrong impression about proportions. As a comparison…\n\n# with NA\nggplot(data_prp_viz, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n# without NA\nggplot(prereg_plan_follow, aes(x = Plan_prereg, fill = Closely_follow)) +\n geom_bar(position = \"fill\") + # add position argument here\n theme(legend.position = \"bottom\") + # move legend to the bottom\n guides(fill = guide_legend(nrow = 2)) # display across 2 rows\n\n\n\n\n\nFigure 4.6: Percent stacked barchart with (left) and without missing values (right)" @@ -300,6 +300,90 @@ "section": "Test your knowledge", "text": "Test your knowledge\nLet’s go back to the palmerpenguins package (https://allisonhorst.github.io/palmerpenguins/), and assume you have the following data available:\n\nlibrary(palmerpenguins)\n\npenguin_selection <- penguins %>% \n group_by(species, island) %>% \n summarise(penguin_count = n())\n\npenguin_selection\n\n\n\n\nspecies\nisland\npenguin_count\n\n\n\nAdelie\nBiscoe\n44\n\n\nAdelie\nDream\n56\n\n\nAdelie\nTorgersen\n52\n\n\nChinstrap\nDream\n68\n\n\nGentoo\nBiscoe\n124\n\n\n\n\n\n\nKnowledge check\nQuestion 1\nWhat geom would you use to plot penguin count for each species? \ngeom_bar\ngeom_col\nQuestion 2\nWhat mapping would you use to display penguin count across species?\n\naes(x = penguin_count, y = species)aes(x = species, y = penguin_count)aes(x = species)aes(x = penguin_count)\n\nQuestion 3\nWhat geom would you use to count the number of species on each island? \ngeom_bar\ngeom_col\nQuestion 4\nWhat mapping would you use to display the count of species per island?\n\naes(x = island, y = species)aes(x = species, y = island)aes(x = island)aes(x = species)\n\n\n\n\n\n\n\nExplain these answers\n\n\n\n\n\nQuestion 1: geom_col() is the appropriate choice for bar charts with predefined y-values, such as penguin_count.\nQuestion 2: The correct aesthetic mapping places the categorical variable (species) on the x-axis and the numeric variable (number of observed penguins) on the y-axis. Using aes(x = penguin_count, y = species) would flip the axes, placing the number of penguins on the x-axis and species on the y-axis, which doesn’t match the conventional structure of a bar chart.\nQuestion 3: geom_bar() is the appropriate choice when you want to automatically count the number of observations within each category, such as counting the number of penguin species on each island.\nQuestion 4: For a simple count of species per island, you only need to map the categorical variable (island) to the x-axis. The y-axis will automatically represent counts when using geom_bar().\n\n\n\nError mode\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\nQuestion 5\nWe want to plot the number of penguins across the different islands.\n\nggplot(penguins, aes(x = islands)) +\n geom_bar()\n\nError in `geom_bar()`:\n! Problem while computing aesthetics.\nℹ Error occurred in the 1st layer.\nCaused by error in `check_aesthetics()`:\n! Aesthetics must be either length 1 or the same as the data (344).\n✖ Fix the following mappings: `x`.\n\n\nWhat does this error message mean and how do you fix it?\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThe error message consists of 2 parts. Part 1 is perhaps a bit trickier to interpret, but part 2 gives some useful hints:\n\n\n“Aesthetics must be either length 1 or the same as the data (344)”: This means that the variable mapped to x should either be a constant (like a single value) or a column that has 344 entries (matching the number of rows in the penguins dataset).\n\n“Fix the following mappings: x”: The issue is specifically with the x aesthetic, meaning islands is either misspelled or doesn’t exist in the dataset.\n\nTo check the penguins data, you can use glimpse().\n\nglimpse(penguins)\n\nRows: 344\nColumns: 8\n$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex <fct> male, female, female, NA, female, male, female, male…\n$ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n\n\nTo fix the error, you need to correct the column name. The correct column in the penguins dataset is called island (without the “s” at the end). The island column has 344 entries, just like the rest of the dataset, so the mapping now works properly.\n\nggplot(penguins, aes(x = island)) +\n geom_bar()\n\n\n\n\n\n\n\n\n\n\nQuestion 6\nNext, we want to create a grouped bar chart displaying species per island, using the viridis color palette.\n\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis()\n\nError in scale_fill_viridis(): could not find function \"scale_fill_viridis\"\n\n\nWhat does this error message mean and how do you fix it?\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThe function scale_fill_viridis() is incorrect; the correct function is called scale_fill_viridis_d().\nFIX: correct the function name to display the grouped bar chart with the viridis color palette.\n\nggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"dodge\") +\n scale_fill_viridis_d()\n\n\n\n\n\n\n\n\n\n\nQuestion 7\nWe want to create a grouped bar chart showing the number of penguins on each island, broken down by year.\n\nggplot(penguins, aes(x = island, fill = year)) +\n geom_bar(position = \"dodge\")\n\nWarning: The following aesthetics were dropped during statistical transformation: fill.\nℹ This can happen when ggplot fails to infer the correct grouping structure in\n the data.\nℹ Did you forget to specify a `group` aesthetic or to convert a numerical\n variable into a factor?\n\n\n\n\n\n\n\n\nHmmm. We got a plot, but certainly not the one we intended. The warning message mentions something about the grouping structure and gives some additional hints.\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThe grouping variable needs to be a factor. R helpfully asks if we’ve forgotten to convert a numerical variable into a factor!!! Oh, let’s check that in the penguins data using the glimpse() function.\n\nglimpse(penguins)\n\nRows: 344\nColumns: 8\n$ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex <fct> male, female, female, NA, female, male, female, male…\n$ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n\n\nIndeed, year is currently stored as a numeric (integer) variable. To fix this, we need to convert year to a factor. We can do this directly within the ggplot() function.\n\nggplot(penguins, aes(x = island, fill = as.factor(year))) +\n geom_bar(position = \"dodge\")\n\n\n\n\n\n\n\n\n\n\nQuestion 8\nWe want to create a percent stacked bar chart that displays the ratio of penguins’ sex on each island, using a manual color palette. Female penguins should be displayed in blue, males in green, and NA values in red.\nNote: This task is trickier than it looks. Although the code runs and produces a plot, there are three mistakes to identify and fix.\n\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"dodge\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\n\n\n\n\n\n\nHint for Mistake 1\n\n\n\n\n\nThe task was to create a percent stacked barchart, but the current plot is displaying a grouped barchart. You will need to adjust the argument that defines the type of plot to achieve the correct visualisation.\n\n\n\n\n\n\nFixing of Mistake 1\n\n\n\n\n\n\nggplot(penguins, aes(x = sex, fill = island)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHint for Mistake 2\n\n\n\n\n\nYou may have also noticed that the colours are mapped to the islands, not to the penguins’ sex, which needs to be corrected.\n\n\n\n\n\n\nFixing of Mistake 2\n\n\n\n\n\nWe need to switch the columns mapped to the x and fill aesthetics.\n\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\", \"red\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHint for Mistake 3\n\n\n\n\n\nNow that the correct variables are mapped to the x-axis and the fill argument, the colour scheme no longer matches the instructions. According to the guidelines, missing values should be displayed in red.\n\n\n\n\n\n\nFixing of Mistake 3\n\n\n\n\n\nChanging the colour of missing values is a special case that requires the argument na.value =.\n\nggplot(penguins, aes(x = island, fill = sex)) +\n geom_bar(position = \"fill\") +\n scale_fill_manual(values = c(\"blue\", \"green\"), na.value = \"red\")" }, + { + "objectID": "05-dataviz2.html#intended-learning-outcomes", + "href": "05-dataviz2.html#intended-learning-outcomes", + "title": "5  Data viz II", + "section": "Intended Learning Outcomes", + "text": "Intended Learning Outcomes\nBy the end of this chapter you should be able to:\n\nchoose an appropriate plot for continuous variables\nchoose an appropriate plot for a mix of continuous/categorical variables\ncreate a basic version of an appropriate plot\napply extra layers to change the appearance of the plot\n\nIn this chapter, we continue our journey of appropriate plots. Last week, we examined which plots are appropriate for categorical variables. Today, we’ll focus on continuous variables and which plots to choose with a mix of continuous and categorical variables." + }, + { + "objectID": "05-dataviz2.html#individual-walkthrough", + "href": "05-dataviz2.html#individual-walkthrough", + "title": "5  Data viz II", + "section": "Individual Walkthrough", + "text": "Individual Walkthrough" + }, + { + "objectID": "05-dataviz2.html#activity-1-set-up-and-data-for-today", + "href": "05-dataviz2.html#activity-1-set-up-and-data-for-today", + "title": "5  Data viz II", + "section": "\n5.1 Activity 1: Set-up and data for today", + "text": "5.1 Activity 1: Set-up and data for today\n\nWe are still working with the data by Pownall et al. (2023). Open the project.\nYou could use the same .Rmd file as last week if you want to keep all plotting in one document or create a new .Rmd to separate plots for categorical and continuous variables. Up to you.\nThe aggregated data is the same as last week. It should be in your project folder but in case it got lost, download the csv again and place it in your project folder: data_prp_for_ch4.csv.\nIf you need a reminder about the data and variables, have a look at the codebook and/or Section 1.4." + }, + { + "objectID": "05-dataviz2.html#activity-2-load-in-libraries-read-in-data-and-adjust-data-types", + "href": "05-dataviz2.html#activity-2-load-in-libraries-read-in-data-and-adjust-data-types", + "title": "5  Data viz II", + "section": "\n5.2 Activity 2: Load in libraries, read in data, and adjust data types", + "text": "5.2 Activity 2: Load in libraries, read in data, and adjust data types\nToday, we need to load the package tidyverse, and read in the data data_prp_ch4.csv.\n\n## packages \n???\n\n## data\ndata_prp_viz <- ???\n\n\n\n\n\n\n\nSolution\n\n\n\n\n\n\nlibrary(tidyverse)\ndata_prp_viz <- read_csv(\"data_prp_for_ch4.csv\")\n\n\n\n\nThis is the same code as last week. We need to turn our categorical variables into factors to make plotting easier.\n\ndata_prp_viz <- data_prp_viz %>% \n mutate(Gender = factor(Gender,\n levels = c(2, 1, 3),\n labels = c(\"females\", \"males\", \"non-binary\")),\n Secondyeargrade = factor(Secondyeargrade,\n levels = c(1, 2, 3, 4, 5),\n labels = c(\"≥ 70% (1st class grade)\", \"60-69% (2:1 grade)\", \"50-59% (2:2 grade)\", \"40-49% (3rd class)\", \"< 40%\")),\n Plan_prereg = factor(Plan_prereg,\n levels = c(1, 3, 2),\n labels = c(\"Yes\", \"Unsure\", \"No\")),\n Closely_follow = factor(Closely_follow,\n levels = c(2, 3),\n labels = c(\"Followed it somewhat\", \"Followed it exactly\")),\n Research_exp = factor(Research_exp),\n Pre_reg_group = factor(Pre_reg_group))\n\n\n\n\n\n\n\nTip\n\n\n\nIf you are working within the same .Rmd file as last week, you can skip these initial steps but you have to run the code you had already placed at the start of last-week’s .Rmd file to load tidyverse into the library, read in the data, and convert some of the variables into factors." + }, + { + "objectID": "05-dataviz2.html#sec-hist", + "href": "05-dataviz2.html#sec-hist", + "title": "5  Data viz II", + "section": "\n5.3 Activity 3: Histogram (geom_histogram())", + "text": "5.3 Activity 3: Histogram (geom_histogram())\nIf you want to show the distribution of a continuous variable, you can use a histogram. As with every plot, you need at least 3 layers to create a base version of the plot. Similar to geom_bar(), geom_histogram() only requires an x variable as it does the counting “in the background”.\nA histogram divides the data into “bins” (i.e., groupings displayed in a single bar). These bins are plotted along the x-axis, with the y-axis showing the count of observations in each bin. It’s basically a barchart for continuous variables.\nLet’s have a look at the age distribution in our dataset.\n\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram() \n\n`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n\n\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_bin()`).\n\n\n\n\nFigure 5.1: Default histogram\n\n\n\nThe default number of bins is 30 (as shown in Figure 5.1 above). Changing the number of bins (argument bins) allows for more or less fine-tuning of the data. A higher number of bins results in more detailed granularity.\nPerhaps it’s more intuitive to modify the width of each bin using the binwidth argument. For example, binwidth = 1 for the age category would mean each “age group” represents 1 year, while binwidth = 5 would group ages into 5-year spans. The plots below show modifications for both bins and binwidth.\n\n#less finetuning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(bins = 10) \n\n# more fineturning\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1) \n\n\n\nWarning: Removed 2 rows containing non-finite outside the scale range (`stat_bin()`).\nRemoved 2 rows containing non-finite outside the scale range (`stat_bin()`).\n\n\n\n\nFigure 5.2: Bins vs binwidth arguments\n\n\n\nThe warning message tells us 2 row of data were removed due to containing non-finite values outside the scale range. Have a look at the age column in data_prp_viz to see if you can decipher the warning message.\nThe rows were removed because \nthey fall outside of the plot range\nthey contain missing values.\nColours are manipulated slightly differently than in the barchart. Click through each tab to see how you can modify colours, axis labels, margins, and breaks, and apply a different theme.\n\n\nColour\nAxes labels, margins, and breaks\nThemes\n\n\n\nWe can change the plot colours by adding a fill argument and a colour argument. The fill argument changes the colour of the bars, while the colour argument modifies the outline of the bars. Note that these arguments are added directly to the geom_histogram(), rather than within the overall aes(), as we did with the barchart.\n\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\")\n\n\n\n\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nYou could use:\n\nHex codes for fill and colour, as we did here: geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\"). If you want to create your own colours, check out this website.\nPre-defined colour names: geom_histogram(binwidth = 1, fill = \"purple\", colour = \"green\"). See the full list here.\n\n\n\n\n\nHere we removed the label for the y axes Count (to show you some variety) and modified the breaks. The y-axis is now displayed in increasing steps of 5 (rather than 10), and the x-axis has 1-year increments instead of 5.\nNotice how the breaks = argument changes the labels of the break ticks but not the scale limits. You can adjust the limits of the scale using the limits = argument. To exaggerate, we set the limits to 15 and 50. See how the values from 15 to 19, and 44 to 50 do not have labels? You would need to adjust that using the breaks = argument.\nThe expansion() function removes the gap between the x-axis and the bars. It is exactly the same code we used in Chapter 4.\n\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 20, to = 43, by = 1),\n # Experimenting with\n limits = c(15, 50)\n )\n\n\n\n\n\n\n\n\n\nLet’s experiment with the themes. For this plot we have chosen theme_bw()\n\nggplot(data_prp_viz, aes(x = Age)) +\n geom_histogram(binwidth = 1, fill = \"#586cfd\", colour = \"#FC58BE\") +\n labs(x = \"Age (in years)\", # renaming x axis label\n y = \"\") + # removing the y axis label\n scale_y_continuous(\n # remove the space below the bars (first number), but keep a tiny bit (5%) above (second number)\n expand = expansion(mult = c(0, 0.05)),\n # changing break points on y axis\n breaks = seq(from = 0, to = 30, by = 5)\n ) +\n scale_x_continuous(\n # changing break points on x axis\n breaks = seq(from = 19, to = 44, by = 1)\n ) +\n # pick a theme\n theme_bw()" + }, + { + "objectID": "05-dataviz2.html#sec-scatter", + "href": "05-dataviz2.html#sec-scatter", + "title": "5  Data viz II", + "section": "\n5.4 Activity 4: Scatterplot (geom_point())", + "text": "5.4 Activity 4: Scatterplot (geom_point())\nScatterplots are appropriate when you want to plot two continuous variables. Here, we want to display the relationship between Acceptance of QRPs at Time point 1 and Time point 2. The default scatterplot can be created with geom_point().\nWe can also add a trendline by using geom_smooth(). The default trendline is loess. If you want a linear trendline, you would need to add method = \"lm\" inside the geom_smooth() function.\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth()\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point() +\n geom_smooth(method = lm)\n\n\n\n\n\nFigure 5.3: Default Scatterplot with added trendline - loess (left) and linear (right)\n\n\n\nCustomising the colour of a scatterplot is slightly different from the other plots we’ve encountered so far. Technically, the point is not a “filled-in black area” but rather an extremely wide outline of a circle. Therefore, we cannot use the usual fill argument and instead need to use the colour argument, similar to how we customised the outline of the histogram.\nSee the tabs below to learn how to change the colour for all points or how to adjust the colour based on groupings.\n\n\nColour for all points\nColour with grouping\nLegend title and labels\n\n\n\nIf we want to change the colour of all the points, we can add the colour argument to the geom_point() function. Likewise, to change the colour of the trendline, we would also use the colour argument. Here, we used pre-defined colour names, but HEX codes would work just as well.\n\n# colour of all points and the trendline\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean)) +\n geom_point(colour = 'magenta') +\n geom_smooth(method = lm, colour = 'turquoise')\n\n\n\n\n\n\n\n\n\nIf we want the points to change colour based on another grouping variable, the colour argument should go inside the aes(). If you don’t want to define the colours manually, you can use a colour palette like Brewer (scale_colour_brewer()) or Viridis (scale_colour_viridis_d()).\n\n## adding grouping variable Pre_reg_group and changing the colour values manually\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'))\n\n\n\n\n\n\n\n\n\nYou can tidy the legend title and group labels using the scale_colour_? function, depending on the palette you’re using (e.g., scale_colour_manual(), scale_colour_brewer and many more).\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nAll other layers remain exactly the same as in other plots. Try adding layers to make the plot above prettier:\n\n\n1. relabel axes x and y\n\n2. set the x and y axis range from 1 to 7\n\n3. move the legend to a different position (either top, left, or bottom)\n\n4. add a theme\n\n\n\n\n\n\n\nHints\n\n\n\n\n\n\ncan be done in 2 different ways - labs() or scale_x_?\nwe did that for the histogram\nWe did that for the barcharts\npick a theme you like\n\n\n\n\n\n\n\nMore hints\n\n\n\n\n\nIf you are experiencing issues with the legend position and theme, try modifying the order of the layers.\n\n\n\n\n\n\n\n\n\n\n\n\nSolution\n\n\n\n\n\n\nggplot(data_prp_viz, aes(x = QRPs_Acceptance_Time1_mean, y = QRPs_Acceptance_Time2_mean, colour = Pre_reg_group)) +\n geom_point() +\n geom_smooth(method = lm) +\n scale_colour_manual(values = c('mediumvioletred', 'steelblue1'),\n name = \"Pre-registered Dissertation\",\n labels = c(\"Yes\", \"No\")) +\n labs (x = \"Acceptance of Questionable Research Practices (Time 1)\", \n y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_light() + # place before moving the legend position\n theme(legend.position = \"top\") # move legend to the top" + }, + { + "objectID": "05-dataviz2.html#activity-5-boxplot-geom_boxplot", + "href": "05-dataviz2.html#activity-5-boxplot-geom_boxplot", + "title": "5  Data viz II", + "section": "\n5.5 Activity 5: Boxplot (geom_boxplot())", + "text": "5.5 Activity 5: Boxplot (geom_boxplot())\nA boxplot is one of the options to display a continuous variable with categorical grouping variable. Here, we want to create a boxplot to explore whether students’ understanding of open science varies based on whether or not they have research experience. Our default boxplot would look like this:\n\n# default boxplot\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS)) +\n geom_boxplot()\n\n\n\n\n\n\n\nTada! As usual, we can enhance the plot by adding various layers. Click on each tab below to see how.\n\n\nColour\nAxes labels\nLegend and Theme\n\n\n\nWe can change the colour by adding a fill argument inside the aes(). To customise the colours further, we can add a scale_fill_? layer. If you have specific colours in mind, use scale_fill_manual(). If you prefer pre-defined palettes, such as Brewer, you can use scale_fill_brewer().\nBtw, this is exactly the same code we used for the barcharts.\n\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n # customise colour\n scale_fill_brewer(palette = \"Dark2\")\n\n\n\n\n\n\n\n\n\nWe need to relabel the axes. The function to use depends on the variable type. Here, we need scale_x_discrete() for the x-axis and scale_y_continuous() for the y-axis. We can also tidy up the group labels and adjust the breaks on the y-axis (e.g., in steps of 1 instead of 2) within these same functions.\n\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n scale_x_discrete(\n # changing the label of x\n name = \"Research Experience\",\n # changing the group labels of the 2 groups\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n # changing name of the y axis\n name = \"Confidence in Understanding Open Science (Time 1)\",\n # changing break labels\n breaks = c(seq(from = 1, to = 7, by = 1))\n )\n\n\n\n\n\n\n\n\n\nThe legend is superfluous; best to take it off. As before, we can remove the legend by adding the argument guide = \"none\" to the scale_fill_? function.\nLet’s pick a theme we haven’t used yet: theme_dark().\n\nggplot(data_prp_viz, aes(x = Research_exp, y = Time1_Understanding_OS, fill = Research_exp)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\",\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(\n name = \"Research Experience\",\n labels = c(\"Yes\", \"No\")) + \n scale_y_continuous(\n name = \"Confidence in Understanding Open Science (Time 1)\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_dark()" + }, + { + "objectID": "05-dataviz2.html#activity-6-violin-plot-geom_violin", + "href": "05-dataviz2.html#activity-6-violin-plot-geom_violin", + "title": "5  Data viz II", + "section": "\n5.6 Activity 6: Violin plot (geom_violin())", + "text": "5.6 Activity 6: Violin plot (geom_violin())\nAn alternative way to display a continuous variable with a categorical grouping variable is a violin plot. Here, we want to create a violin plot to explore whether the perception of supervisor support depends on whether students plan to pre-register their dissertation. Our default violin plot would look like this:\n\n# default boxplot\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support)) +\n geom_violin()\n\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nAdjusting the violin plot would be exactly the same as the boxplot. Try to add layers to the base plot above to\n\n\nchange the colours either manually or using a pre-defined colour palette\n\ntidy the axes labels and group names\n\nin case a legend appears, take it off\n\nadd a theme\n\n\n\n\n\n\n\nOne possible Solution\n\n\n\n\n\n\nggplot(data_prp_viz, aes(x = Plan_prereg, y = Mean_Supervisor_Support, fill = Plan_prereg)) +\n geom_violin() +\n scale_fill_manual(values = c('mediumspringgreen', 'orangered', 'slateblue'),\n # removing the legend\n guide = \"none\") +\n scale_x_discrete(name = \"Plan to pre-register the dissertation\") + \n scale_y_continuous(\n name = \"Perceived Supervisory Support\",\n breaks = c(seq(from = 1, to = 7, by = 1))\n ) +\n # pick a theme\n theme_minimal()\n\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_ydensity()`)." + }, + { + "objectID": "05-dataviz2.html#activity-7-violin-boxplots", + "href": "05-dataviz2.html#activity-7-violin-boxplots", + "title": "5  Data viz II", + "section": "\n5.7 Activity 7: Violin-boxplots", + "text": "5.7 Activity 7: Violin-boxplots\nSo far, we’ve only added one geom_? layer to our plots. However, thanks to ggplot’s layered system, we can add multiple geoms, for example, when creating a violin-boxplot.\nRemember, the order of the layers can sometimes make a difference. We’ve seen this already - adding a theme at the end can override earlier arguments like the legend position. Similarly, ggplot + violinplot + boxplot will look different from ggplot + boxplot + violinplot.\nLet’s use the example of QRPs at timepoint 2 and a grouping variable of Second-year Grade.\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot()\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_boxplot() +\n geom_violin()\n\n\n\n\n\nFigure 5.4: Default violin-boxplot: Order of the layer matters\n\n\n\nSee the tabs below to learn how to customise various elements, such as the width of the boxes, and the colour or opacity.\n\n\nWidth of the boxes\nColour\n\n\n\nIf we want to get any information from the boxplot, we need to place it “on top of” the violin plot. But still, the boxplot is pretty wide and covers important details from the violin plot. To make the information more visible, we can adjust the width of the boxes. Finding an appropriate width might take some trial and error.\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean)) +\n geom_violin() +\n geom_boxplot(width = 0.2)\n\n\n\nFigure 5.5: Default violin-boxplot: adjusting width of the box\n\n\n\n\n\nAdding colour should be pretty straightforward by now. The code is no different from what we used for the boxplot or violin plot. We need to add the fill argument within the aes(), along with a scale_fill_? layer.\nHowever, we can further customise the plot by adding an opacity argument using alpha to the violin plot geom.\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) + # alpha for opacity\n geom_boxplot(width = 0.2) + # change width of the boxes\n scale_fill_brewer(palette = \"RdPu\") # customise colour\n\n\n\nFigure 5.6: Violin-boxplot with a different colour palette\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\n\n\nChange the x- and y-axis labels\n\nRemove the legend\n\nadd a theme\n\n\n\n\n\n\n\nOne possible Solution\n\n\n\n\n\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.4) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + # removes the legend\n # change labels of x and y\n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic()" + }, + { + "objectID": "05-dataviz2.html#activity-8-faceting---adding-another-grouping-variable", + "href": "05-dataviz2.html#activity-8-faceting---adding-another-grouping-variable", + "title": "5  Data viz II", + "section": "\n5.8 Activity 8: Faceting - adding another grouping variable", + "text": "5.8 Activity 8: Faceting - adding another grouping variable\nFaceting is really useful when you have subsets in the data. We will apply it to the violin-boxplot from above, but you could add this layer to pretty much any plot. The function to split the plots into facets is called facet_wrap().\nLet’s add another grouping variable, Pre_reg_group, to create separate plots for the yes and no groups.\nSince the group labels on the x-axis are quite long, we will need to adjust them for better readability. Adding guide = guide_axis(n.dodge = 2) to the scale_x_discrete() function helps to display labels across multiple rows.\n\nggplot(data_prp_viz, aes(x = Secondyeargrade, y = QRPs_Acceptance_Time2_mean, fill = Secondyeargrade)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.2) +\n scale_fill_brewer(palette = \"RdPu\",\n guide = \"none\") + \n labs (x = \"Second-year Grade\", y = \"Acceptance of Questionable Research Practices (Time 2)\") +\n theme_classic() +\n facet_wrap(~Pre_reg_group) + # faceting to split into subplots for yes and no\n scale_x_discrete(guide = guide_axis(n.dodge = 2)) # want display labels in 2 rows\n\n\n\nFigure 5.7: Pretty violin-boxplot split into pre-registration groups (yes and no)\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nYou may have noticed that the labels of Pre_reg_group are displayed as numbers (1 and 2). If this bugs you, fix the labels in the data object. This would be less hassle than trying to adjust the facet headings in the plot.\n\n\n\n\n\n\n\n\nSpecial case: Variables with subscales\n\n\n\n\n\nFor example, if we want to show the relationship between SATs scores at Timepoints 1 and 2, separately for all four subscales of the SATs questionnaire, we need to wrangle the data so that the scores for Time 1 and Time 2 are in separate columns, but each participant has four rows (one for each subscale). The dataframe should look like this:\n\nhead(data_facet, n=5)\n\n\n\n\nCode\nSubscale\nTime1\nTime2\n\n\n\nTr10\nAffect\n4.000000\n3.500000\n\n\nTr10\nCognitiveCompetence\n5.166667\n4.166667\n\n\nTr10\nValue\n6.000000\n3.000000\n\n\nTr10\nDifficulty\n3.571429\n2.857143\n\n\nBi07\nAffect\n3.833333\n3.166667\n\n\n\n\n\n\n\n\n\n\n\n\nYour Turn\n\n\n\nCreate a new data object data_facet and wrangle the data from data_prp_viz so that it looks like the table above.\n\n\n\n\n\n\nBroad hints\n\n\n\n\n\n\n\nStep 1: select variables of interest from data_prp_viz\n\n\nStep 2: pivot the data\n\nStep 3: try to access information on subscales and timepoints from the variable names\n\nStep 4: pivot in the other direction\n\n\n\n\n\n\n\n\n\n\nMore specific hints\n\n\n\n\n\n\n\nStep 1: The variables of interest are the participants’ ID and all columns that start with SATS.\n\nStep 2: Pivot all columns, except the participant ID, from wide to long format.\n\nStep 3: Get some inspiration from Activity 4 in {#sec-wrangling2}.\n\nStep 4: At this point, subscales and timepoints should be in long format. However, only the subscale information should remain in long format. Pivot the data from long to wide format so that Time 1 and Time 2 are in separate columns.\n\n\n\n\n\n\n\n\n\n\nSolution\n\n\n\n\n\n\ndata_facet <- data_prp_viz %>% \n select(Code, starts_with(\"SATS\")) %>% \n pivot_longer(cols = starts_with(\"SATS\"), names_to = \"Variable\", values_to = \"Mean_Scores\") %>% \n separate(Variable, into = c(NA, \"Subscale\", \"Timepoint\", NA), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Mean_Scores)\n\n\n\n\n\n\nNow we can build a scatterplot with facets for the subscales.\n\nggplot(data_facet, aes(x = Time1, y = Time2)) +\n geom_point() +\n facet_wrap(~Subscale)" + }, + { + "objectID": "05-dataviz2.html#pair-coding", + "href": "05-dataviz2.html#pair-coding", + "title": "5  Data viz II", + "section": "Pair-coding", + "text": "Pair-coding\nTask 1: Open the R project for the lab\nTask 2: Create a new .Rmd file\n… and name it something useful. If you need help, have a look at Section 1.3.\nTask 3: Load in the library and read in the data\nThe data should already be in your project folder. If you want a fresh copy, you can download the data again here: data_pair_coding.\nWe are using the package tidyverse today, and the data file we need to read in is dog_data_clean_wide.csv. I’ve named my data object dog_data_wide to shorten the name but feel free to use whatever object name sounds intuitive to you.\nTask 4: Re-create one of the 3 plots below\nRe-create one of the 3 plot below:\n\ngrouped barchart (easy)\nviolin-boxplot (medium)\nscatterplot (hard)\n\nDifficulty level: easy\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHints\n\n\n\n\n\n\nI’ve created a new data object data_bar to select the relevant variables but you could also work straight from dog_data_wide.\nConsider turning the 2 categorical variables into factors before plotting\nPlotting should be relatively straightforward - these are default colours and you would only need to change the axes labels/ legend title.\n\n\n\n\n\n\n\nMore hints\n\n\n\n\n\nWe can change all of the 3 labels in one go. Check out the ## Prettier grouped barchart in Section 4.6, where we did exactly that.\n\n\n\n\n\n\n\n\n\nSolution for data_bar\n\n\n\n\n\n\ndata_bar <- dog_data_wide %>% \n select(RID, GroupAssignment, Year_of_Study) %>% \n mutate(GroupAssignment = factor(GroupAssignment,\n levels = c(\"Direct\", \"Indirect\", \"Control\")),\n Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n\n\n\n\n\n\n\n\n\n\nSolution for the barchart\n\n\n\n\n\n\nggplot(data_bar, aes(x = GroupAssignment, fill = Year_of_Study)) +\n geom_bar(position = \"dodge\") +\n labs(x = \"Experimental Group\", y = \"Count\", fill = \"Year of Study\")\n\n\n\n\n\n\n\nDifficulty level: medium\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHints\n\n\n\n\n\n\nI’ve created a new data object data_vb to select the relevant variables but you could also work straight from dog_data_wide.\nConsider turning the categorical variable into a factor before plotting\nPlotting tips:\n\nthe colour scale is one of the viridis options\nit’s a bit of trial and error for the opacity of the violin and the box width of the boxes (it is totally fine if it looks approximately right)\nthe tricky part might be adjusting the y-axis ticks. Take inspiration from the histogram in Section 5.3 (Tab Axes labels, margins, and breaks)\n\n\n\n\n\n\n\n\n\nSolution for data_vb\n\n\n\n\n\n\ndata_vb <- dog_data_wide %>% \n select(RID, Year_of_Study, Loneliness_post) %>% \n mutate(Year_of_Study = factor(Year_of_Study,\n levels = c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth or above\")))\n\n\n\n\n\n\n\n\n\n\nSolution for the violin-boxplot\n\n\n\n\n\n\nggplot(data_vb, aes(x = Year_of_Study, y = Loneliness_post, fill = Year_of_Study)) +\n geom_violin(alpha = 0.5) +\n geom_boxplot(width = 0.25) +\n scale_y_continuous(breaks = c(seq(from = 1, to = 4, by = 0.5)),\n limits = c(1, 4)) +\n scale_fill_viridis_d(option = \"magma\",\n guide = \"none\") +\n labs(x = \"Year of Study\", y = \"Loneliness scores post intervention\") +\n theme_classic()\n\n\n\n\n\n\n\nDifficulty level: hard\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHints\n\n\n\n\n\n\n\nData wrangling: Even though we cleaned the data, it may not be in the shape for the task at hand. Have a look what the data object dog_data_wide looks like and think about how you’d need to restructure it to be able to plot the scatterplot. As always, I would suggest creating a new data object for the scatterplot (e.g., data_scatter).\nOnce you have the data in the right shape, start plotting. Start with a basic scatterplot and then add various layers and change elements you notice.\nRemember, some finetuning might need to be done in data_scatter rather than plot itself.\n\n\n\n\n\n\n\nData structure you have\n\n\n\n\n\n\n\n\n\n\nRID\nPANAS_PA_pre\nPANAS_PA_post\nPANAS_NA_pre\nPANAS_NA_post\n\n\n\n1\n3.2\n3.8\n1.0\n1.2\n\n\n2\n3.0\n3.2\n1.8\n1.0\n\n\n3\n2.8\n3.0\n1.6\n1.6\n\n\n4\n4.2\n3.8\n1.8\n1.6\n\n\n5\n3.4\n4.0\n2.2\n1.6\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData structure you need\n\n\n\n\n\n\n\n\n\n\nRID\nSubscale\npre\npost\n\n\n\n1\nPositive Affect\n3.2\n3.8\n\n\n1\nNegative Affect\n1.0\n1.2\n\n\n2\nPositive Affect\n3.0\n3.2\n\n\n2\nNegative Affect\n1.8\n1.0\n\n\n3\nPositive Affect\n2.8\n3.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHints for data_scatter\n\n\n\n\n\n\n\nStep 1: select the variables you need from dog_data_wide.\n\nStep 2: pivot all columns (bar the Participant ID) into long format\n\nStep 3: think about how to separate information of the subscales and timepoints\n\nStep 4: pivot from long into wide format. Take some inspiration from the Special case: Variables with subscales scenario above.\n\n\n\n\n\n\n\n\n\n\nHints for the plot\n\n\n\n\n\n\nThe colour scheme is Dark2 from the colour palette brewer\nThe colour of the trendline is #7570b3\nThink about how to make the Negative and Positive Affect points different colours. The solution is in Section 5.4\n\nRenaming the different facets is one of those things that should be fixed in the data object instead\n\n\n\n\n\n\n\n\n\n\nSolution for data_scatter\n\n\n\n\n\n\ndata_scatter <- dog_data_wide %>% \n select(RID, starts_with(\"PANAS\")) %>% \n pivot_longer(cols = -RID, names_to = \"Q\", values_to = \"Values\") %>% \n separate(Q, into = c(NA, \"Subscale\", \"Timepoint\"), sep = \"_\") %>% \n pivot_wider(names_from = Timepoint, values_from = Values) %>% \n mutate(Subscale = case_match(Subscale,\n \"NA\" ~ \"Negative Affect\",\n \"PA\" ~ \"Positive Affect\"),\n Subscale = factor(Subscale)) %>% \n drop_na()\n\n\n\n\n\n\n\n\n\n\nSolution for the scatterplot\n\n\n\n\n\n\nggplot(data_scatter, aes(x = pre, y = post, colour = Subscale)) +\n geom_point() +\n geom_smooth(method = lm, colour = \"#7570b3\") +\n facet_wrap(~Subscale) +\n labs(x = \"Pre-Intervention (Timepoint 1)\",\n y = \"Post-Intervention (Timepoint 2)\") +\n scale_colour_brewer(palette = \"Dark2\",\n guide = \"none\") +\n theme_bw()\n\n\n\n\n\n\n\nIf you are extremely fast, challenge yourself and re-create one of the other plots." + }, + { + "objectID": "05-dataviz2.html#test-your-knowledge", + "href": "05-dataviz2.html#test-your-knowledge", + "title": "5  Data viz II", + "section": "Test your knowledge", + "text": "Test your knowledge\nKnowledge check\nQuestion 1\nWhy would this line of code not create a barplot, assuming you already loaded all data and libraries and you spelt the data and column names correctly?\n\nggplot(summarydata, aes(x = sex)) +\n geom_barplot()\n\n\nbecause you have piped the barplot and not added itbecause there is no geom_barplot() and it should be geom_bar()because you have not included a y axisbecause this would create a histogram\n\nQuestion 2\nIf I wanted precisely 5 bars in my histogram, what argument would I use?\n\nggplot() + geom_histogram(bins = 5)ggplot() + geom_histogram()ggplot() + geom_histogram(binwidth = 5)ggplot() + geom_histogram(bars = 5)\n\n\n\n\n\n\n\nExplain this answer\n\n\n\n\n\n\n\nggplot() + geom_histogram(bins = 5). This is the correct answer as you are asking ggplot2 to give you the plot organised into 5 bins.\n\nggplot() + geom_histogram(bars = 5). This is incorrect as you bars is not the right argument name. You want 5 bars, but the argument is bins.\n\nggplot() + geom_histogram(binwidth = 5). This is incorrect as binwidth controls the x-axis range to include per bar, rather than the number of bars.\n\nggplot() + geom_histogram(). This is incorrect as you did not control the number of bins, so it will default to 30.\n\n\n\n\nQuestion 3\nYou want to create a scatterplot to show the correlation between two continuous variables, which geom would you use?\n\ngeom_histogram()geom_boxplot()geom_point()geom_violin()\n\nQuestion 4\nTrue or False? To showcase different groups in a scatterplot, you could specify a grouping variable using the fill argument to change the colour of the points. \nTRUE\nFALSE\n\n\n\n\n\n\nExplain this answer\n\n\n\n\n\nHave a look in the scatterplot section again. There, we explained that we cannot “colour in” the area of the points using fill, but need to change the outline colour of the points with colour instead.\n\n\n\nError mode\nSome of the code chunks contain mistakes and result in errors, while others do not produce the expected results. Your task is to identify any issues, explain why they occurred, and, if possible, fix them.\nLet’s go back to the Palmer penguins for this part.\n\nlibrary(palmerpenguins)\n\nQuestion 5\nWe want to plot the number of penguins across the different islands.\n\nggplot(penguins, aes(x = islands)) %>% \n geom_bar()\n\nError in `geom_bar()`:\n! `mapping` must be created by `aes()`.\nℹ Did you use `%>%` or `|>` instead of `+`?\n\n\nThe error message is incredibly useful. So that should be an easy fix!\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nTo fix the error, all we need to do is turn the pipe %>% into a plus +.\n\nggplot(penguins, aes(x = island)) +\n geom_bar()\n\n\n\n\n\n\n\n\n\n\nQuestion 6\nWe want to create a violin-boxplot showing the bill length across species, separately for male and female penguins.\n\npenguins_no_na <- penguins %>% \n drop_na(island, flipper_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = flipper_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n\n\n\n\n\n\n\nHmmm. We got a plot, but is it the one we aimed for? \nTRUE\nFALSE\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nThis was a tricky one. The code runs, so we think everything is fine. Despite having re-labelled the y-axis “Bill length in mm”, it’s really showing the flipper length. Did you spot this???\n\npenguins_no_na <- penguins %>% \n drop_na(island, bill_length_mm, sex)\n\nggplot(penguins_no_na, aes(x = island, y = bill_length_mm, fill = sex)) +\n geom_violin() +\n geom_boxplot(width = 0.2) +\n labs(x = \"Island\", y = \"Bill length in mm\") +\n facet_wrap(~sex)\n\n\n\n\n\n\n\n\n\n\nQuestion 7\nThis time, we want to show the relationship between flipper length and body mass of the penguins. The following code runs, but the x- and y-axes are misbehaving somehow. Not what we wanted. Any idea why?\n\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_discrete(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_discrete(name = \"Flipper length in mm\")\n\nWarning in scale_y_discrete(name = \"Body mass in g\", breaks = seq(2500, : Continuous limits supplied to discrete scale.\nℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?\n\n\nWarning: Removed 2 rows containing missing values or values outside the scale range\n(`geom_point()`).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplain the solution\n\n\n\n\n\nIn this example, we used the wrong function for continuous variables. We used scale_y_discrete and scale_y_discrete, instead of scale_x_continuous and scale_y_continuous. We must honour the variable type when we customise the plot, so think about what type of variable is on each axis and which function lets you edit it.\n\nggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +\n geom_point() +\n scale_colour_viridis_d(name = \"Island\") +\n scale_y_continuous(name = \"Body mass in g\",\n breaks = seq(2500, 6500, 500),\n limits = c(2500, 6500)) +\n scale_x_continuous(name = \"Flipper length in mm\")" + }, { "objectID": "appendix-a-installing-r.html#how-to-install-r-and-rstudio", "href": "appendix-a-installing-r.html#how-to-install-r-and-rstudio",