SWC_fall2018_Lesson4_ggplot2

Software Carpentry Workshop: Lesson4: Data visualization with ggplot2

Instructor: Rich Time: 1.5 hours

Note: add "?both" to the end of the URL if you would like to view both the markdown and rendered document side-by-side.

Introduction

R is one of the most powerful pieces of software for visualizing data. It is even possible to produce publication-quality graphics quite easily and reproducibly. Today, we will give you a flavor of some of the quality plotting you can produce quite readily using the R package ggplot2. ggplot2 is perhaps the best piece of software for intuitively producing high-quality figures, regardless of programming language (with some packages in Python probably being second best). We will continue using the gapminder dataset and will visualize some of the trends in these data in R with ggplot2. Check out some of the interesting dynamic plots available from the Gapminder website. By the end of this lesson you will know the basic principles necessary to produce similar, static versions of many of the example plots you are looking at.

Note: This lesson is modified from the Software Carpentry workshop on data visualization in R.

Objectives

To be able to use ggplot2 to generate publication quality graphics.
To understand the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups.

Keypoints

Use ggplot2 to create plots.
Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.

Background

Plotting our data is one of the best ways to quickly explore it and the various relationships between variables.

There are three main plotting systems in R, the base plotting system, the lattice package, and the ggplot2 package.

Today we'll be learning about the ggplot2 package, because it is the most effective for creating publication quality graphics.

ggplot2 is built on the grammar of graphics, the idea that any plot can be expressed from the same set of components: a data set, a coordinate system, and a set of geoms--the visual representation of data points.

The key to understanding ggplot2 is thinking about a figure in layers. This idea may be familiar to you if you have used image editing programs like Photoshop, Illustrator, or Inkscape. Main components of ggplot

Quickstart

Start in SWC_fall2018 directory. Make a new folder called ggplot. Copy gapminder.txt from Data folder to ggplot folder. Go to ggplot folder. Set your working directory to ggplot

We must first make sure our gapminder dataset has been loaded into R, if it isn't already.

gapminder <- read.table("gapminder.txt", header=TRUE, sep="\t")

Let's start off with an example:

#install ggplot2 package
install.packages("ggplot2")
#load package into R
library(ggplot2)

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

So the first thing we do is call the ggplot function. This function lets R know that we're creating a new plot, and any of the arguments we give the ggplot function are the global options for the plot: they apply to all layers on the plot.

We've passed in two arguments to ggplot. First, we tell ggplot what data we want to show on our figure, in this example the gapminder data we read in earlier. For the second argument we passed in the aes function, which tells ggplot how variables in the data map to aesthetic properties of the figure, in this case the x and y locations. Here we told ggplot we want to plot the "gdpPercap" column of the gapminder data frame on the x-axis, and the "lifeExp" column on the y-axis. Notice that we didn't need to explicitly pass aes these columns (e.g. x = gapminder[, "gdpPercap"]), this is because ggplot is smart enough to know to look in the data for that column!

By itself, the call to ggplot isn't enough to draw a figure:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp))

We need to tell ggplot how we want to visually represent the data, which we do by adding a new geom layer. In our example, we used geom_point, which tells ggplot we want to visually represent the relationship between x and y as a scatterplot of points:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Challenge 1

Modify the example so that the figure shows how life expectancy has changed over time:
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point()
Hint: the gapminder dataset has a column called "year", which should appear on the x-axis.
Solution to challenge 1

Here is one possible solution:
ggplot(data = gapminder, aes(x = year, y = lifeExp)) + geom_point()
Challenge 2

In the previous examples and challenge we've used the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the previous challenge to color the points by the "continent" column. What trends do you see in the data? Are they what you expected?
Solution to challenge 2

In the previous examples and challenge we've used the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the previous challenge to color the points by the "continent" column. What trends do you see in the data? Are they what you expected?
ggplot(data = gapminder, aes(x = year, y = lifeExp, color=continent)) +
  geom_point()

Layers

Using a scatterplot probably isn't the best for visualizing change over time. Instead, let's tell ggplot to visualize the data as a line plot:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line()

Instead of adding a geom_point layer, we've added a geom_line layer. We've added the by aesthetic, which tells ggplot to draw a line for each country.

But what if we want to visualize both lines and points on the plot? We can simply add another layer to the plot:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line() + geom_point()

It's important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here's a demonstration:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
  geom_line(aes(color=continent)) + geom_point()

In this example, the aesthetic mapping of color has been moved from the global plot options in ggplot to the geom_line layer so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.

Tip: Setting an aesthetic to a value instead of a mapping

So far, we've seen how to use an aesthetic (such as color) as a mapping to a variable in the data. For example, when we use geom_line(aes(color=continent)), ggplot will give a different color to each continent. But what if we want to change the colour of all lines to blue? You may think that geom_line(aes(color="blue")) should work, but it doesn't. Since we don't want to create a mapping to a specific variable, we simply move the color specification outside of the aes() function, like this: geom_line(color="blue").

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
     geom_line(color="blue") + geom_point()

Challenge 3

Switch the order of the point and line layers from the previous example. What happened?
Solution to challenge 3

Switch the order of the point and line layers from the previous example. What happened?
ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
 geom_point() + geom_line(aes(color=continent))
The lines now get drawn over the points!

Transformations and statistics

ggplot also makes it easy to overlay statistical models over the data. To demonstrate we'll go back to our first example:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, color=continent)) +
  geom_point()

Currently it's hard to see the relationship between the points due to some strong outliers in GDP per capita. We can change the scale of units on the x axis using the scale functions. These control the mapping between the data values and visual values of an aesthetic. We can also modify the transparency of the points, using the alpha function, which is especially helpful when you have a large amount of data which is very clustered.

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10()

The log10 function applied a transformation to the values of the gdpPercap column before rendering them on the plot, so that each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita of 1,000 is now 3 on the x axis, a value of 10,000 corresponds to 4 on the x axis and so on. This makes it easier to visualize the spread of data on the x-axis.

Tip Reminder: Setting an aesthetic to a value instead of a mapping

Notice that we used geom_point(alpha = 0.5). As the previous tip mentioned, using a setting outside of the aes() function will cause this value to be used for all points, which is what we want in this case. But just like any other aesthetic setting, alpha can also be mapped to a variable in the data. For example, we can give a different transparency to each continent with geom_point(aes(alpha = continent)).

We can fit a simple relationship to the data by adding another layer, geom_smooth:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm")

We can make the line thicker by setting the size aesthetic in the geom_smooth layer:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm", size=1.5)

There are two ways an aesthetic can be specified. Here we set the size aesthetic by passing it as an argument to geom_smooth. Previously in the lesson we've used the aes function to define a mapping between data variables and their visual representation.

Challenge 4a

Modify the color and size of the points on the point layer in the previous example.

Hint: do not use the aes function.
Solution to challenge 4a

Modify the color and size of the points on the point layer in the previous example.

Hint: do not use the aes function.
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
 geom_point(size=3, color="orange") + scale_x_log10() +
 geom_smooth(method="lm", size=1.5)

Challenge 4b

Modify your solution to Challenge 4a so that the points are now a different shape and are colored by continent with new trendlines. Hint: The color argument can be used inside the aesthetic.
Solution to challenge 4b

Modify Challenge 4 so that the points are now a different shape and are colored by continent with new trendlines.

Hint: The color argument can be used inside the aesthetic.
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point(size=3, shape=17) + scale_x_log10() +
geom_smooth(method="lm", size=1.5)

Multi-panel figures

Earlier we visualized the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of facet panels. Focusing only on those countries with names that start with the letter "A" or "Z".

Tip

We start by subsetting the data. We use the substr function to pull out a part of a character string; in this case, the letters that occur in positions start through stop, inclusive, of the gapminder$country vector. The operator %in% allows us to make multiple comparisons rather than write out long subsetting conditions (in this case, starts.with %in% c("A", "Z") is equivalent to starts.with == "A" | starts.with == "Z")

starts.with <- substr(gapminder$country, start = 1, stop = 1)
az.countries <- gapminder[starts.with %in% c("A", "Z"), ]
ggplot(data = az.countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country)

The facet_wrap layer took a "formula" as its argument, denoted by the tilde (~). This tells R to draw a panel for each unique value in the country column of the gapminder dataset.

Modifying text

To clean this figure up for a publication we need to change some of the text elements. The x-axis is too cluttered, and the y axis should read "Life expectancy", rather than the column name in the data frame.

We can do this by adding a couple of different layers. The theme layer controls the axis text, and overall text size. Labels for the axes, plot title and any legend can be set using the labs function. Legend titles are set using the same names we used in the aes specification. Thus below the color legend title is set using color = "Continent", while the title of a fill legend would be set using fill = "MyTitle".

ggplot(data = az.countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())

This is a taste of what you can do with ggplot2. RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the ggplot2 website. Finally, if you have no idea how to change something, a quick Google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!

Challenge 5

Create a density plot of GDP per capita, filled by continent.

Advanced:

Transform the x axis to better visualise the data spread.

Add a facet layer to panel the density plots by year.
Solution to challenge 5

Create a density plot of GDP per capita, filled by continent.

Advanced:

Transform the x axis to better visualise the data spread.

Add a facet layer to panel the density plots by year.
ggplot(data = gapminder, aes(x = gdpPercap, fill=continent)) +
 geom_density(alpha=0.6) + facet_wrap( ~ year) + scale_x_log10()

Demo some more advanced features

If time permits, the following interactive activities can be run to showcase some of the cool stuff you can do with ggplot and R.

Let's do this in terms of one of the cooler visualizations directly from the Gapminder website. This visualization shows the relationship between income and life expectancy for all nations in the dataset. The points are scaled in size based on the population size of the nation and colored based on continent. So there is a lot of details here.

Challenge 6a

Can we create something pretty similar using ggplot? What variables in our dataset needs to be mapped to what aesthetics to achive this?

Solution to challenge 6a

x = income y = life expectancy size = population size color = continent

Challenge 6b

Use these mappings to produce a similar plot for 2007. Bonus points if you can use dplyr to do it.
Solution fo challenge 6b
# note that you can save plot to variable and plot later
library("dplyr")
bubble <- gapminder %>%
   filter(year == 2007) %>%
   ggplot(aes(x = gdpPercap,
              y = lifeExp,
              color = continent,
              size = pop)) +
     geom_point() + 
     labs(
       x = "Income",            # x axis title
       y = "Life expectancy"    # y axis title
     )
bubble

This is great, but you'll notice that it this plot is static, while the online one is interactive. Turns out is is actually pretty easy to create an interactive version when you know a little ggplot, using a package called plotly. Let's demo this.

install.packages("plotly")
library("plotly#)

# plotly has built in support for `ggplot` objects
bubble <- gapminder %>%
    filter(year == 2007) %>%
    ggplot(aes(x = gdpPercap,
               y = lifeExp,
               color = continent,
               size = pop,
               text = country)) +
    geom_point() + 
    labs(
        x = "Income",            # x axis title
        y = "Life expectancy"    # y axis title
    )
    
ggplotly(bubble)

Now you'll see an interactive plot, which is really awesome for exploring and for showing off your data.

The last thing we're missing from the Gapminder plot is the fact that it will cycle through years. We can't mimic this with with interactive plots at each time, but we can loop through plots for each year and make a movie-like animation. A pretty simple loop will do this.

for (yr in unique(gapminder$year)) {
   bubble <- gapminder %>%
      filter(year == yr) %>% 
      ggplot(aes(x = gdpPercap,
                 y = lifeExp,
                 color = continent,
                 size = pop)) +
         geom_point() + 
         labs(
           x = "Income", 
           y = "Life expectancy",
           title = yr
         ) 
    plot(bubble)    # need to do this in loops
    Sys.sleep(5)    # 5 sec pause between plots
}

Very cool. See how easy it is to produce fancy plots inR!

Here is a handy cheatsheet produced by RStudio.

{%pdfhttps://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf%}

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Lesson4_ggplot2.md

Lesson4_ggplot2.md

SWC_fall2018_Lesson4_ggplot2

Software Carpentry Workshop: Lesson4: Data visualization with ggplot2

Introduction

Objectives

Keypoints

Background

Quickstart

Challenge 1

Solution to challenge 1

Challenge 2

Solution to challenge 2

Layers

Tip: Setting an aesthetic to a value instead of a mapping

Challenge 3

Solution to challenge 3

Transformations and statistics

Tip Reminder: Setting an aesthetic to a value instead of a mapping

Challenge 4a

Solution to challenge 4a

Challenge 4b

Solution to challenge 4b

Multi-panel figures

Tip

Modifying text

Challenge 5

Solution to challenge 5

Demo some more advanced features

Challenge 6a

Solution to challenge 6a

Challenge 6b

Solution fo challenge 6b

Files

Lesson4_ggplot2.md

Latest commit

History

Lesson4_ggplot2.md

File metadata and controls

SWC_fall2018_Lesson4_ggplot2

Software Carpentry Workshop: Lesson4: Data visualization with ggplot2

Introduction

Objectives

Keypoints

Background

Quickstart

Challenge 1

Solution to challenge 1

Challenge 2

Solution to challenge 2

Layers

Tip: Setting an aesthetic to a value instead of a mapping

Challenge 3

Solution to challenge 3

Transformations and statistics

Tip Reminder: Setting an aesthetic to a value instead of a mapping

Challenge 4a

Solution to challenge 4a

Challenge 4b

Solution to challenge 4b

Multi-panel figures

Tip

Modifying text

Challenge 5

Solution to challenge 5

Demo some more advanced features

Challenge 6a

Solution to challenge 6a

Challenge 6b

Solution fo challenge 6b