Creating Publication-Quality Graphics with ggplot2

Adapted from Software Carpentry

Overview

Today we will learn to:

• Use ggplot2 to generate publication-quality graphics
• Apply geometry, aesthetic, and statistics layers to a ggplot plot
• Manipulate aesthetics using different colors, shapes, and lines
• Improve data visualization through transforming scales and paneling by group
• Save a plot created with ggplot to disk

Questions

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

Why ggplot2?

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

Three main plotting systems in R:

• Base plotting system
• lattice package
• ggplot2 package (most effective for publication-quality graphics)

Grammar of Graphics

ggplot2 is built on the grammar of graphics. Any plot can be built from:

• Data sets - the data you provide
• Mapping aesthetics - how data connects to graphics (x, y, color, size)
• Layers - the actual graphical output (scatterplot, histogram, etc.)

Loading the Data

First, let’s load our packages and data:

library(ggplot2)

gapminder <- read.csv(
  "https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/main/episodes/data/gapminder_data.csv"
)

Starting with ggplot

The most basic function is ggplot():

ggplot(data = gapminder)

This creates a blank slate - we haven’t told it what to draw yet!

Adding Aesthetics

Use aes() to map variables to visual properties:

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

Now we have axes, but still no data points!

Adding a Geom Layer

Tell ggplot how to represent the data visually:

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

geom_point() creates a scatterplot of points.

Challenge 1

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

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

Hint: the gapminder dataset has a column called “year”, which should appear on the x-axis.

Challenge 1 Solution

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

Challenge 2

Modify the code from Challenge 1 to color the points by the “continent” column.

What trends do you see in the data? Are they what you expected?

Challenge 2 Solution

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

The general trend shows increased life expectancy over the years. Continents with stronger economies show longer life expectancy.

Layers

Let’s visualize change over time with a line plot instead:

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

The result looks jumpy! Let’s separate by country.

Grouping by Country

Use the group aesthetic to draw one line per country:

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

Now each country has its own line, colored by continent.

Combining Layers

Add multiple layers to show both lines and points:

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

Layer Order Matters

Each layer is drawn on top of the previous layer:

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

Here, color only applies to lines, and points are drawn on top.

Setting vs Mapping Aesthetics

Mapping: Use aes() to connect aesthetics to data variables

geom_line(mapping = aes(color = continent))  # Different color per continent

Setting: Put aesthetic outside aes() for a fixed value

geom_line(color = "blue")  # All lines are blue

Challenge 3

Switch the order of the point and line layers from the previous example.

What happened?

Challenge 3 Solution

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

The lines are now drawn over the points!

Transformations and Statistics

Let’s go back to our first scatterplot:

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

Strong outliers in GDP make it hard to see relationships.

Log Scale Transformation

Use scale_x_log10() and alpha for transparency:

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

Each multiple of 10 is now evenly spaced on the x-axis.

Alpha Transparency

The alpha setting (0 to 1) controls transparency:

• alpha = 1 - fully opaque (default)
• alpha = 0.5 - 50% transparent
• alpha = 0 - fully transparent (invisible)

Useful for overlapping points!

Adding a Trend Line

Use geom_smooth() to fit a statistical model:

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + 
  scale_x_log10() + 
  geom_smooth(method = "lm")

The gray shaded area shows 95% confidence intervals.

Customizing the Trend Line

Make the line thicker with linewidth:

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + 
  scale_x_log10() + 
  geom_smooth(method = "lm", linewidth = 1.5)

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.

Hint: The equivalent of linewidth for points is size.

Challenge 4a Solution

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(size = 3, color = "orange") + 
  scale_x_log10() + 
  geom_smooth(method = "lm", linewidth = 1.5)

Color and size are set outside aes() so they apply to all points.

Challenge 4b

Modify your solution to Challenge 4a so that the points are now:

• A different shape
• Colored by continent
• With new trendlines for each continent

Hint: The color argument can be used inside the aesthetic.

Challenge 4b Solution

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(size = 3, shape = 17) + 
  scale_x_log10() + 
  geom_smooth(method = "lm", linewidth = 1.5)

shape is set for all points, while color is mapped to continent.

Multi-panel Figures

Split data into panels with facet_wrap():

ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap(~ country) +
  theme(axis.text.x = element_text(angle = 45))

Understanding facet_wrap()

The formula ~ country tells R to:

• Draw a panel for each unique value in the country column
• The tilde (~) denotes a formula

Modifying Text

Clean up labels with labs() and theme():

ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color = continent)) +
  geom_line() + 
  facet_wrap(~ country) +
  labs(
    x = "Year",
    y = "Life expectancy",
    title = "Figure 1",
    color = "Continent"
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

The labs() Function

labs() sets various text elements:

• x - x-axis title
• y - y-axis title
• title - main plot title
• color - legend title for color aesthetic
• fill - legend title for fill aesthetic

Exporting Plots

Save plots with ggsave():

lifeExp_plot <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color = continent)) +
  geom_line() + 
  facet_wrap(~ country) +
  labs(
    x = "Year",
    y = "Life expectancy",
    title = "Figure 1",
    color = "Continent"
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, 
       width = 12, height = 10, dpi = 300, units = "cm")

ggsave() Features

Two nice things about ggsave():

1. Default to last plot: If you omit the plot argument, it saves the last plot you created

2. Auto-detect format: Determines format from file extension (.png, .pdf, etc.)

Challenge 5

Generate boxplots to compare life expectancy between the different continents during the available years.

Advanced:

• Rename y-axis as “Life Expectancy”
• Remove x-axis labels

Challenge 5 Solution

ggplot(data = gapminder, mapping = aes(x = continent, y = lifeExp, fill = continent)) +
  geom_boxplot() + 
  facet_wrap(~ year) +
  ylab("Life Expectancy") +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  )

Common Geoms

Here are some frequently used geom functions:

Geom	Description
geom_point()	Scatterplot
geom_line()	Line plot
geom_boxplot()	Box plot
geom_histogram()	Histogram
geom_bar()	Bar chart
geom_smooth()	Trend line

Key Points

• Use ggplot2 to create plots
• Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping
• Use aes() to map data to visual properties
• Add layers with + operator
• Customize with labs() and theme()
• Save with ggsave()

Important Reminders

• Each layer is drawn on top of previous layers
• Set aesthetics outside aes() for fixed values
• Map aesthetics inside aes() for data-dependent values
• Use facet_wrap() for multi-panel figures
• Use scale_* functions to transform axes

Resources

• ggplot2 documentation
• RStudio Data Visualization Cheat Sheet
• R Graph Gallery
• Software Carpentry: r-novice-gapminder

Questions?