R for Data Science, 2nd Edition

Chapter 9. Layers

Introduction

In Chapter 1, you learned much more than just how to make scatterplots, bar charts, and boxplots. You learned a foundation that you can use to make any type of plot with ggplot2.

In this chapter, you’ll expand on that foundation as you learn about the layered grammar of graphics. We’ll start with a deeper dive into aesthetic mappings, geometric objects, and facets. Then, you will learn about statistical transformations ggplot2 makes under the hood when creating a plot. These transformations are used to calculate new values to plot, such as the heights of bars in a bar plot or medians in a box plot. You will also learn about position adjustments, which modify how geoms are displayed in your plots. Finally, we’ll briefly introduce coordinate systems.

We will not cover every single function and option for each of these layers, but we will walk you through the most important and commonly used functionality provided by ggplot2 as well as introduce you to packages that extend ggplot2.

Prerequisites

This chapter focuses on ggplot2. To access the datasets, help pages, and functions used in this chapter, load the tidyverse by running this code:

library(tidyverse)

Aesthetic Mappings

“The greatest value of a picture is when it forces us to notice what we never expected to see.” —John Tukey

Remember that the mpg data frame bundled with the ggplot2 package contains 234 observations on 38 car models.

mpg
#> # A tibble: 234 × 11
#> manufacturer model ...