Lecture 9: ggplot2

Brian J. Smith

2026-02-19

ggplot2

Create elegant data visualizations using the Grammar of Graphics

https://ggplot2.tidyverse.org/

ggplot2

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

• What is a declarative programming language?
• What is The Grammar of Graphics?

Imperative vs. Declarative Programming

Imperative

• Focus on how in addition to what.
• Provide step-by-step commands.
• Provides more control and transparency.

Declarative

• Focus on what not how.
• Provide a definition of what the final product should look like.
• More concise and abstract.

Imperative vs. Declarative Programming

Examples

---

Imperative

• R
  • (including base R plotting)
• Python
• C++

Declarative

• HTML
• SQL
• JAGS/Stan/NIMBLE
• ggplot2

The Grammar of Graphics

The Grammar of Graphics (2005)

The Grammar of Graphics

ggplot2 (3rd Ed)

Wilkinson (2005) created the grammar of graphics to describe the fundamental features that underlie all statistical graphics. The grammar of graphics is an answer to the question of what is a statistical graphic? ggplot2 (Wickham 2009) builds on Wilkinson’s grammar by focussing on the primacy of layers and adapting it for use in R. In brief, the grammar tells us that a graphic maps the data to the aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also include statistical transformations of the data and information about the plot’s coordinate system. Facetting can be used to plot for different subsets of the data. The combination of these independent components are what make up a graphic.

— https://ggplot2-book.org/introduction.html

ggplot2 Basics

It’s hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).

ggplot2 Basics

• Begin with ggplot()
• The first argument is data, so you can (but don’t have to) specify that.
• You can also easily pipe to ggplot().

library(ggplot2)
library(dplyr)

data("starwars")

starwars %>% 
  filter(hair_color %in% c("blond", "brown", "black", "none"),
         !is.na(mass)) %>% 
  ggplot()

ggplot2 Basics

• The next argument is mapping, which you use to define the aesthetics of your plot using the function aes().
  • This is where you define which channels to use for which attributes.

starwars %>% 
  filter(hair_color %in% c("blond", "brown", "black", "none"),
         !is.na(mass)) %>% 
  ggplot(aes(x = height, y = mass, color = hair_color))

ggplot2 Basics

• Now you can add a geom, i.e., a mark.
• You can add more than one geom in layers.
• After calling ggplot(), all layers and options are added to the plot using the + operator.

starwars %>% 
  filter(hair_color %in% c("blond", "brown", "black", "none"),
         !is.na(mass)) %>% 
  ggplot(aes(x = height, y = mass, color = hair_color)) +
  geom_point()

ggplot2 Basics

• Once you have all the layers, you can start tweaking the appearance of layers, marks, or channels.

starwars %>% 
  filter(hair_color %in% c("blond", "brown", "black", "none"),
         !is.na(mass)) %>% 
  ggplot(aes(x = height, y = mass, color = hair_color)) +
  geom_point() +
  xlab("Height (cm)") +
  ylab("Mass (kg)") +
  ggtitle("Mass by Height and Hair Color",
          subtitle = "in the Star Wars universe") +
  scale_color_manual(name = "Hair Color", 
                     breaks = c("blond", "brown", "black", "none"),
                     values = c("gold", "brown", "black", "gray"))

ggplot2 Basics

• Other tweaks might include:
  • adding facets to change the view.
  • changing the overall appearance with themes.

starwars %>% 
  filter(hair_color %in% c("blond", "brown", "black", "none"),
         !is.na(mass)) %>% 
  ggplot(aes(x = height, y = mass, color = hair_color)) +
  geom_point() +
  xlab("Height (cm)") +
  ylab("Mass (kg)") +
  ggtitle("Mass by Height and Hair Color",
          subtitle = "in the Star Wars universe") +
  scale_color_manual(name = "Hair Color", 
                     breaks = c("blond", "brown", "black", "none"),
                     values = c("gold", "brown", "black", "gray")) +
  facet_wrap(~ hair_color) +
  theme_bw() +
  theme(text = element_text(size = 16))

ggplot2 Cheatsheet

Questions?

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