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| "markdown": "---\ntitle: \"Coordinate systems and axes\"\nauthor: \"Claus O. Wilke\"\ndate: last-modified\nformat: \n revealjs:\n theme:\n - default\n - Wilke-theme.scss\n auto-stretch: false\n chalkboard: true\nexecute:\n fig-format: svg\n fig-asp: 0.618\n fig-width: 5.5\n---\n\n\n\n\n\n## Most data visualizations use Cartesian coordinates\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=55%}\n:::\n:::\n\n\n\n## Changing units does not change the plot\n\n::: {.columns}\n\n::: {.column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n\n::: {.fragment .column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n\n:::\n\n## If scale units are unrelated, aspect ratio is arbitrary\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=65%}\n:::\n:::\n\n\n\n# Non-linear scales and coordinate systems\n\n## Logarithmic scales (log scales)\n\n::: {.small-font .move-down-1em}\nVisualize these five values: 1, 3.16, 10, 31.6, 100\n:::\n\n<br>\n\n::: {.fragment .nogap}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=80%}\n:::\n:::\n\n\n:::\n\n::: {.fragment .nogap}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=80%}\n:::\n:::\n\n\n:::\n\n::: {.fragment .nogap}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=80%}\n:::\n:::\n\n\n:::\n\n## Example: Population number of Texas counties\n\n[A linear scale emphasizes large counties]{.small-font}\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=80%}\n:::\n:::\n\n\n## Example: Population number of Texas counties\n\n[A log scale shows symmetry around the median]{.small-font}\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=80%}\n:::\n:::\n\n\n\n## Nonlinear coordinate systems: Polar coordinates\n\n::: {.columns}\n\n::: {.column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n\n::: {.fragment .column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n\n:::\n\n## Cartesian vs polar example\n\n<br>\n\n::: {.columns}\n\n::: {.column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=100%}\n:::\n:::\n\n\n:::\n\n::: {.column width=\"50%\"}\n\n\n::: {.cell}\n::: {.cell-output-display}\n{width=100%}\n:::\n:::\n\n\n:::\n\n:::\n\n<!-- Segment ends here -->\n\n# Scales and coordinate systems in **ggplot2**\n\n## Getting the data\n\n[The `boxoffice` dataset:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code}\nboxoffice <- tibble(\n rank = 1:5,\n title = c(\"Star Wars\", \"Jumanji\", \"Pitch Perfect 3\", \"Greatest Showman\", \"Ferdinand\"),\n amount = c(71.57, 36.17, 19.93, 8.81, 7.32) # million USD\n)\n```\n:::\n\n\n:::\n\n::: {.fragment}\n[The `tx_counties` dataset:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntx_counties <- read_csv(\"https://wilkelab.org/SDS375/datasets/US_census.csv\") |> \n filter(state == \"Texas\") |>\n mutate(popratio = pop2010/median(pop2010)) |>\n arrange(desc(popratio)) |>\n mutate(index = 1:n())\n```\n:::\n\n\n:::\n\n:::\n\n::: {.fragment}\n[The `temperatures` dataset:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntemperatures <- read_csv(\"https://wilkelab.org/SDS375/datasets/tempnormals.csv\") |>\n mutate(\n location = factor(\n location, levels = c(\"Death Valley\", \"Houston\", \"San Diego\", \"Chicago\")\n )\n ) |>\n select(location, station_id, day_of_year, month, temperature)\n```\n:::\n\n\n:::\n\n:::\n\n## Scale functions customize the x and y axes\n\n[Recall the box-office example from a prior lecture:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col()\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n## Scale functions customize the x and y axes\n\n[Add scale functions (no change in figure so far):]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"4-5\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous() +\n scale_y_discrete()\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n## Scale functions customize the x and y axes\n\n[The parameter `name` sets the axis title:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"5,8\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous(\n name = \"weekend gross (million USD)\"\n ) +\n scale_y_discrete(\n name = NULL # no axis title\n )\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n::: {.fragment .small-font}\nNote: We could do the same with `xlab()` and `ylab()`\n:::\n\n\n## Scale functions customize the x and y axes\n\n[The parameter `limits` sets the scale limits:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"6\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous(\n name = \"weekend gross (million USD)\",\n limits = c(0, 80)\n ) +\n scale_y_discrete(\n name = NULL\n )\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n::: {.fragment .small-font}\nNote: We could do the same with `xlim()` and `ylim()` but I advise against it, as these functions can have unexpected side-effects\n:::\n\n\n## Scale functions customize the x and y axes\n\n[The parameter `breaks` sets the axis tick positions:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"7\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous(\n name = \"weekend gross (million USD)\",\n limits = c(0, 80),\n breaks = c(0, 25, 50, 75)\n ) +\n scale_y_discrete(\n name = NULL\n )\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n## Scale functions customize the x and y axes\n\n[The parameter `labels` sets the axis tick labels:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"8\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous(\n name = \"weekend gross (million USD)\",\n limits = c(0, 80),\n breaks = c(0, 25, 50, 75),\n labels = c(\"0\", \"$25M\", \"$50M\", \"$75M\")\n ) +\n scale_y_discrete(\n name = NULL\n )\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n## Scale functions customize the x and y axes\n\n[The parameter `expand` sets the axis expansion:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell output-location='column'}\n\n```{.r .cell-code code-line-numbers=\"9\"}\nggplot(boxoffice) +\n aes(amount, fct_reorder(title, amount)) +\n geom_col() +\n scale_x_continuous(\n name = \"weekend gross (million USD)\",\n limits = c(0, 80),\n breaks = c(0, 25, 50, 75),\n labels = c(\"0\", \"$25M\", \"$50M\", \"$75M\"),\n expand = expansion(mult = c(0, 0.06))\n ) +\n scale_y_discrete(\n name = NULL\n )\n```\n\n::: {.cell-output-display}\n\n:::\n:::\n\n\n:::\n\n## Scale functions define transformations\n\n::: {.columns}\n\n::: {.column width=\"50%\"}\n[Linear y scale:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"4\"}\nggplot(tx_counties) +\n aes(x = index, y = popratio) +\n geom_point() +\n scale_y_continuous() \n```\n\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n:::\n\n::: {.fragment .column width=\"50%\"}\n[Log y scale:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"4\"}\nggplot(tx_counties) +\n aes(x = index, y = popratio) +\n geom_point() +\n scale_y_log10()\n```\n\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n:::\n\n:::\n\n## Parameters work the same for all scale functions\n\n::: {.columns}\n\n::: {.column width=\"50%\"}\n[Linear y scale:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"5-7\"}\nggplot(tx_counties) +\n aes(x = index, y = popratio) +\n geom_point() +\n scale_y_continuous(\n name = \"population number / median\",\n breaks = c(0, 100, 200),\n labels = c(\"0\", \"100\", \"200\")\n )\n```\n\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n:::\n\n::: {.column width=\"50%\"}\n[Log y scale:]{.small-font}\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"5-7\"}\nggplot(tx_counties) +\n aes(x = index, y = popratio) +\n geom_point() +\n scale_y_log10(\n name = \"population number / median\",\n breaks = c(0.01, 1, 100),\n labels = c(\"0.01\", \"1\", \"100\")\n )\n```\n\n::: {.cell-output-display}\n{width=90%}\n:::\n:::\n\n\n:::\n:::\n\n:::\n\n## Coords define the coordinate system\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(temperatures, aes(day_of_year, temperature, color = location)) +\n geom_line() +\n coord_cartesian() # cartesian coords are the default\n```\n\n::: {.cell-output-display}\n{width=60%}\n:::\n:::\n\n\n:::\n\n## Coords define the coordinate system\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"3\"}\nggplot(temperatures, aes(day_of_year, temperature, color = location)) +\n geom_line() +\n coord_polar() # polar coords\n```\n\n::: {.cell-output-display}\n{width=60%}\n:::\n:::\n\n\n:::\n\n## Coords define the coordinate system\n\n::: {.tiny-font}\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"4\"}\nggplot(temperatures, aes(day_of_year, temperature, color = location)) +\n geom_line() +\n coord_polar() +\n scale_y_continuous(limits = c(0, 105)) # fix up temperature limits\n```\n\n::: {.cell-output-display}\n{width=60%}\n:::\n:::\n\n\n:::\n\n\n<!-- Segment ends here -->\n\n## Further reading\n\n::: {.small-font}\n- Fundamentals of Data Visualization: [Chapter 3: Coordinate systems and axes](https://clauswilke.com/dataviz/coordinate-systems-axes.html)\n- **ggplot2** reference documentation: [Scales](https://ggplot2.tidyverse.org/reference/index.html#section-scales)\n- **ggplot2** reference documentation: [Coordinate systems](https://ggplot2.tidyverse.org/reference/index.html#section-coordinate-systems)\n- **ggplot2** book: [Position scales](https://ggplot2-book.org/scale-position.html)\n- **ggplot2** book: [Coordinate systems](https://ggplot2-book.org/coord.html)\n:::", | ||
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