Content from Creating Publication-Quality Graphics with ggplot2

Last updated on 2024-05-14 | Edit this page

Overview

Questions

  • How can I create publication-quality graphics in R?

Objectives

  • Learn how to use the {ggplot2} package to generate publication-quality graphics.
  • Manipulate the aesthetics of a plot using different colors, shapes, and lines.

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

Today we’ll be learning about the ggplot2 package, built on the grammar of graphics:

  • data set,
  • mapping aesthetics,
  • graphical layers

Let’s start off building an example using the gapminder data from earlier.

R 

library("ggplot2")
ggplot(data = gapminder)
Blank plot, before adding any mapping aesthetics to ggplot().

Now we’re going to add in the mapping aesthetics using the aes function. aes tells ggplot how variables in the data map to aesthetic properties of the figure, such as which columns of the data should be used for the x and y locations.

R 

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp))
Plotting area with axes for a scatter plot of life expectancy vs GDP, with no data points visible.

The final part of making our plot is to tell ggplot how we want to visually represent the data. We do this by adding a new layer to the plot using one of the geom functions.

R 

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
Scatter plot of life expectancy vs GDP per capita, now showing the data points.

Modify the example so that the figure shows how life expectancy has changed over time:

R 

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, 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:

R 

ggplot(data = gapminder, 
       mapping = aes(x=year, y=lifeExp, group=country,color=continent)) +
  geom_line()+ 
  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(mapping = aes(color=continent)), ggplot will give a different color to each continent. But what if we want to change the color of all lines to blue? You may think that geom_line(mapping = aes(color="blue")) should work, but it doesn’t. Since we don’t want to create a mapping to a specific variable, we can move the color specification outside of the aes() function, like this: geom_line(color="blue").

Transformations and statistics

ggplot2 also makes it easy to overlay statistical models over the data. To demonstrate we’ll go back to our first example:

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.

R 

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10()
Scatterplot of GDP vs life expectancy showing logarithmic x-axis data spread
Scatterplot of GDP vs life expectancy showing logarithmic x-axis data spread

The scale_x_log10 function applied a transformation to the coordinate system of the plot, so that each multiple of 10 is evenly spaced from left to right. For example, a GDP per capita of 1,000 is the same horizontal distance away from a value of 10,000 as the 10,000 value is from 100,000. This helps to visualize the spread of the data along 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(mapping = aes(alpha = continent)).

Key Points

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