Mastering ggplot2 Themes

What are Themes?

In ggplot2, the geometry defines the data representation (dots, lines, bars), while the theme controls the non-data ink. This includes fonts, background colors, grid lines, and legend placement.

To customize a plot, you identify a component (e.g., axis.title) and assign it an element function: * element_text(): For titles and labels. * element_line(): For grid lines and axes. * element_rect(): For backgrounds and borders. * element_blank(): To remove a component entirely.

1. Using Preset Themes

ggplot2 comes with several built-in “all-in-one” themes. These are the quickest way to change the entire look of your plot.

Code

library(tidyverse)

# Base Plot
p <- ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() + 
  ggtitle("Weight vs MPG") + 
  xlab("Weight (1000 lbs)") + 
  ylab("Miles per Gallon")

# Comparison of popular presets
# theme_bw(): White background with gray gridlines
# theme_minimal(): No borders, very clean
# theme_classic(): No gridlines, x and y axes only

Code

p + theme_bw() + ggtitle("Theme: Black & White")

Code

p + theme_classic() + ggtitle("Theme: Classic")

Code

p + theme_minimal() + ggtitle("Theme: Minimal")

Code

p + theme_void() + ggtitle("Theme: Void (Completely Empty)")

2. Customizing Specific Components

You can fine-tune every pixel using the theme() function.

Titles and Axes

Code

p + theme(
    plot.title = element_text(color = "steelblue", face = "bold", hjust = 0.5, size = rel(2)),
    axis.title = element_text(color = "red", face = "bold.italic"),
    axis.text = element_text(color = "blue", size = 12),
    axis.line = element_line(linewidth = 1, color = "black")
  )

Backgrounds and Grids

Use element_rect for backgrounds and element_line for the grid system.

Code

p + theme(
    panel.background = element_rect(fill = "lightyellow", color = "lightblue", linewidth = 2),
    plot.background = element_rect(fill = "lightgrey"),
    panel.grid.major = element_line(color = "white", linewidth = 0.5),
    panel.grid.minor = element_blank() # Remove minor grid lines
  )

3. Legend Customization

Legends are controlled via legend.* arguments. You can move them, style their background, or change their text.

Code

p_legend <- ggplot(mtcars, aes(wt, mpg, color = factor(cyl))) + 
  geom_point() + 
  labs(title = "Weight vs MPG by Cyl", color = "Cylinders")

p_legend + theme(
    legend.position = "bottom",
    legend.background = element_rect(fill = "ivory", color = "black"),
    legend.key = element_rect(fill = "white"),
    legend.title = element_text(face = "bold")
  )

4. Facet Styling (Strips)

When using facet_wrap, the labels at the top of each box are called strips.

Code

p_facet <- ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() + 
  facet_wrap(~cyl) + 
  ggtitle("Weight vs MPG by Cylinders")

p_facet + theme(
    strip.background = element_rect(fill = "steelblue", color = "black"),
    strip.text = element_text(color = "white", face = "bold"),
    panel.spacing = unit(2, "lines") # Increase space between panels
  )

5. Global Theme Setting

Instead of adding theme() to every single plot, use theme_set() at the start of your script to establish a systemic “house style.”

Code

# Set a global professional theme
theme_set(
  theme_minimal() +
  theme(
    plot.title = element_text(size = 18, face = "bold", color = "steelblue4", hjust = 0.5),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "gray98", color = NA)
  )
)

# Any plot created now will automatically use these settings
p

Systemic Summary Toolkit

Component	Element Function	Common Arguments
Text (Titles, Labels)	`element_text()`	`family`, `face`, `color`, `size`, `hjust`
Lines (Axes, Grids)	`element_line()`	`color`, `linewidth`, `linetype`
Rectangles (Backgrounds)	`element_rect()`	`fill`, `color`, `linewidth`
To Hide/Remove	`element_blank()`	No arguments needed