ggplot2

2024-09-23

ggplot2

The code in this lecture assumes these three libraries are loaded:

library(dplyr)
library(ggplot2)
library(dslabs)

The components of a graph

In today’s lecture we recreate this:

The components of a graph

gg stands for grammar of graphics.
Analogy: we learn verbs and nouns to construct sentences.
The first step in learning ggplot2 is breaking a graph apart into components.
Let’s break down the plot we want to recreate while introducing some ggplot2 terminology.

The components of a graph

The main three components to note are:
- Data: The US murders data table is being summarized. We refer to this as the data component.
- Geometry: The plot above is a scatterplot. This is referred to as the geometry component.
- Aesthetic mapping: How we map visual cues to information provided by the dataset.

Aesthetic mapping

The two most important cues in the plot we are recreating are the point positions on the x-axis and y-axis.
Each point represents a different observation, and we map data about these observations to visual cues like x- and y-scale.
Color is another visual cue that we map to region.
How we define the mapping depends on what geometry we are using.

The components of a graph

We also note that:
- The points are labeled with the state abbreviations.
- The range of the x-axis and y-axis appears to be defined by the range of the data.
- Axes are in the log-scales.
- There are labels, a title, a legend, and we use the style of The Economist magazine.
We will now construct the plot, piece by piece.

`ggplot` objects

Start by defining the dataset:

ggplot(data = murders)

We can also use the pipe:

murders |> ggplot()

We call also assign the output to a variable

p <- ggplot(data = murders)
class(p)

[1] "gg"     "ggplot"

`ggplot` objects

To see the plot we can print it:

print(p)

Geometries

We create graphs by adding layers.
Layers define geometries, compute summary statistics, define what scales to use, or even change styles.
To add layers, we use the symbol +.
In general, a line of code will look like this:

DATA |> ggplot() + LAYER 1 + LAYER 2 + ... + LAYER N

Usually, the first added layer defines the geometry.

Geometries

So if we want to make a scatterplot, what geometry do we use?
Let’s look at the cheat sheet: https://rstudio.github.io/cheatsheets/data-visualization.pdf

Aesthetic mappings

To make a scatter plot we use geom_points.
The help file tells us this is how we use it:

murders |> ggplot() + geom_point(aes(x = population/10^6, y = total))

Aesthetic mappings

Since we defined p earlier, we can add a layer like this:

p + geom_point(aes(population/10^6, total))

Note we are no longer using x= and y =.

Layers

To add text we use geom_text:

p + geom_point(aes(population/10^6, total)) +
  geom_text(aes(population/10^6, total, label = abb))

Layers

As an example of the unique behavior of aes note that this call:

p_test <- p + geom_text(aes(population/10^6, total, label = abb))

is fine, whereas this call:

p_test <- p + geom_text(aes(population/10^6, total), label = abb)

will give you an error since abb is not found because it is outside of the aes function.

The layer geom_text does not know where to find abb: it’s a column name and not a global variable.

Tinkering with arguments

p + geom_point(aes(population/10^6, total), size = 3) +
  geom_text(aes(population/10^6, total, label = abb))

size can be an aesthetic mapping, but here it is not, so all points get bigger.

Tinkering with arguments

p + geom_point(aes(population/10^6, total), size = 3) +
  geom_text(aes(population/10^6, total, label = abb), nudge_x = 1.5)

nudge_x is not an aesthetic mapping.

Global versus local mappings

Note that in we can define a global aes in the ggplot function:

args(ggplot)

function (data = NULL, mapping = aes(), ..., environment = parent.frame()) 
NULL

We refer to this as the global mapping.

Global versus local mappings

All the layers will assume the global mapping unless we explicitly define another one.

p <- murders |> ggplot(aes(population/10^6, total, label = abb))
p + geom_point(size = 3) + geom_text(nudge_x = 1.5)

The two layers use the global mapping.

Global versus local mappings

We can override the global aes by defining one in the geometry functions:

p + geom_point(size = 3) +  
  geom_text(aes(x = 10, y = 800, label = "Hello there!"))

Scales

Layers can define transformations:

p + geom_point(size = 3) +  
  geom_text(nudge_x = 0.05) + 
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10")

Scales

This particular transformation is so common that ggplot2 provides the specialized functions:

p + geom_point(size = 3) +  
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10()

Labels and titles

There are layers for adding labels and titles:

p + geom_point(size = 3) +  
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  xlab("Populations in millions (log scale)") + 
  ylab("Total number of murders (log scale)") +
  ggtitle("US Gun Murders in 2010")

Labels and titles

We can also use the labs function:

p + geom_point(size = 3) +  
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010")

This produces the same graph as in the previous slide.

p + geom_point(size = 3) +  
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010")

Adding color

murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010") +
  geom_point(size = 3, color = "blue")

A mapped color

murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010") +
  geom_point(aes(col = region), size = 3)

A legend is added automatically!

Change legend name

murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010",
       color = "Region") +
  geom_point(aes(col = region), size = 3)

Other adjustments

We want to add a line with intercept the US rate.
Lets compute that

r <- murders |> 
  summarize(rate = sum(total) /  sum(population) * 10^6) |> 
  pull(rate)

Add a line

murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010",
       color = "Region") +
  geom_point(aes(col = region), size = 3) +
  geom_abline(intercept = log10(r), lty = 2, color = "darkgrey")

We are close!

Other adjustments

To make the final adjustments we will save our current draft in p and add layers.

p <- murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_text(nudge_x = 0.05) + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010",
       color = "Region") +
  geom_point(aes(col = region), size = 3) +
  geom_abline(intercept = log10(r), lty = 2, color = "darkgrey")

Add-on packages

The dslabs package can define the look used in the textbook:

ds_theme_set()

Many other themes are added by the package ggthemes.

Add-on packages

ggthemes provides pre-designed themes.

library(ggthemes)
p + theme_economist()

Add-on packages

Here is the FiveThirtyEight theme:

p + theme_fivethirtyeight()

Add-on packages

If you want to ruin the plot use the excel theme:

p + theme_excel()

Add-on packages

ThemePark provides fun themes:

library(ThemePark)
p + theme_starwars()

Add-on packages

This is a fan favorite:

p + theme_barbie()

Add-on packages

To avoid the state abbreviations being on top of each other we can use the ggrepel package.
We change the layer geom_text(nudge_x = 0.05) to geom_text_repel()

Putting it all together

Code
Plot

library(ggthemes)
library(ggrepel)

r <- murders |> 
  summarize(rate = sum(total) /  sum(population) * 10^6) |>
  pull(rate)

murders |> ggplot(aes(population/10^6, total, label = abb)) +   
  geom_abline(intercept = log10(r), lty = 2, color = "darkgrey") +
  geom_point(aes(col = region), size = 3) +
  geom_text_repel() + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Populations in millions (log scale)", 
       y = "Total number of murders (log scale)", 
       title = "US Gun Murders in 2010",
       color = "Region") +
  theme_economist()

Grids of plots

We often want to put plots next to each other.
The gridExtra package permits us to do that:

Code
Plot

library(gridExtra)
p1 <- murders |> 
  ggplot(aes(log10(population))) + 
  geom_histogram()
p2 <- murders |> 
  gplot(aes(log10(population), log10(total))) + 
  geom_point()
grid.arrange(p1, p2, ncol = 2)

Grids of plots

There are several additional packages for combining ggplot2 plots into visually appealing layouts:**
cowplot: A versatile package designed for publication-quality plots, offering seamless integration with ggplot2.
ggpubr: Provides user-friendly functions for combining and annotating ggplot2 plots with minimal effort.
New packages frequently emerge. Explore beyond these options and stay curious—there might be new tools that suit your needs even better!

ggplot2

ggplot2

The components of a graph

The components of a graph

The components of a graph

Aesthetic mapping

The components of a graph

ggplot objects

ggplot objects

Geometries

Geometries

Aesthetic mappings

Aesthetic mappings

Layers

Layers

Tinkering with arguments

Tinkering with arguments

Global versus local mappings

Global versus local mappings

Global versus local mappings

Scales

Scales

Labels and titles

Labels and titles

Almost there

Adding color

A mapped color

Change legend name

Other adjustments

Add a line

We are close!

Other adjustments

Add-on packages

Add-on packages

Add-on packages

Add-on packages

Add-on packages

Add-on packages

Add-on packages

Putting it all together

Grids of plots

Grids of plots

`ggplot` objects

`ggplot` objects