2.6.19. patchwork in R

1. Introduction

The patchwork package is a powerful tool for combining multiple ggplot2 plots into a single figure. It offers a flexible and intuitive syntax for arranging plots in grids, stacking, nesting, and customizing layouts, making it ideal for creating publication-ready multi-panel figures.

# Create a dummy ADAM dataset (ADLB - Laboratory Results Analysis Dataset)
set.seed(123)
adsl <- data.frame(
  USUBJID = sprintf("SUBJ%03d", 1:50),
  AGE = round(rnorm(50, mean = 50, sd = 15)),
  SEX = sample(c("M", "F"), 50, replace = TRUE),
  TRT01A = sample(c("Placebo", "Low Dose", "High Dose"), 50, replace = TRUE, prob = c(0.3, 0.3, 0.4))
)

adlb <- data.frame(
  USUBJID = rep(adsl$USUBJID, each = 2),
  PARAMCD = rep(c("ALT", "AST"), times = 50),
  AVAL = c(rnorm(50, 30, 15), rnorm(50, 35, 12)),  # ALT and AST values
  BASE = c(rnorm(50, 28, 10), rnorm(50, 32, 8))    # Baseline values
)

adlb <- merge(adlb, adsl[, c("USUBJID", "TRT01A", "AGE")], by = "USUBJID")

2. Why Use patchwork?

• Combine multiple ggplot2 plots with simple operators.
• Arrange plots side by side, stacked, or in complex layouts.
• Control relative sizes, spacing, and alignment.
• Collect and manage legends across plots.
• Apply themes and annotations to all or selected plots.

3. Basic Plot Combination Syntax

• Use + to add plots together.
• Use | to place plots side by side.
• Use / to stack plots vertically.
• Use parentheses to group plots for nesting.

R Code:

# install.packages("patchwork")
library(patchwork)
library(ggplot2)

# Scatterplot of ALT values by treatment group
p1 <- ggplot(adlb %>% filter(PARAMCD == "ALT"), aes(BASE, AVAL, color = TRT01A)) +
  geom_point() +
  labs(x = "Baseline ALT", y = "ALT Value", color = "Treatment") +
  theme_minimal()

# Scatterplot of AST values by treatment group
p2 <- ggplot(adlb %>% filter(PARAMCD == "AST"), aes(BASE, AVAL, color = TRT01A)) +
  geom_point() +
  labs(x = "Baseline AST", y = "AST Value", color = "Treatment") +
  theme_minimal()

# Combine two plots side by side
p1 | p2

# Stack two plots vertically
p1 / p2

# Complex layout: two plots on top, one below
(p1 | p2) / p1

Expected Outcome:

Combine two plots side by side

Stack two plots vertically

Complex layout: two plots on top, one below

Plots arranged in the specified layout, sharing space as defined by the operators.

4. Empty Spaces and Spacers

• Use plot_spacer() to add empty space in your layout.

R Code:

(plot_spacer() | p2 | plot_spacer()) / p1

Expected Outcome:

p2 is centered above p1 with empty space on either side.

5. Controlling Plot Widths and Heights

• Use plot_layout(widths = ...) and plot_layout(heights = ...) to control relative sizes.

R Code:

p1 | p2 + plot_layout(widths = c(2, 1))
p1 / p2 + plot_layout(heights = c(2, 1))

Expected Outcome:

The first plot is twice as wide (or tall) as the second.

6. Managing Legends and Layouts

• Use plot_layout(guides = "collect") to gather legends in one place.
• Specify number of rows/columns with nrow and ncol.
• Use byrow = FALSE to fill columns before rows.

R Code:

p1 | p2 + plot_layout(guides = "collect")
p1 | p2 + plot_layout(nrow = 2, guides = "collect")

Expected Outcome:

Legends are combined, and plots are arranged as specified.

7. Applying Themes to Plots

• Add a theme to the last plot before the theme call.
• Use * to apply a theme to all nested plots.
• Use & to apply a theme to all plots in the layout.

R Code:

(p1 | p2) * theme_bw()
(p1 | p2) & theme(axis.title.x = element_text(face = "bold"))

Expected Outcome:

Themes are applied to the intended plots.

8. Adding Annotations to the Combined Plot

• Use plot_annotation() to add a title or other annotation to the entire figure.

R Code:

(p1 | p2) + plot_annotation(
  title = "ALT and AST Trends by Treatment Group",
  theme = theme(plot.title = element_text(hjust = 0.5))
)

Expected Outcome:

A multi-panel figure with a shared title and custom theme.

9. Input and Output Table for patchwork Examples

Input and Output Table for patchwork Examples

10. Exploring Beyond Basic patchwork

• Combine patchwork with cowplot or gridExtra for advanced layouts.
• Use plot nesting for complex arrangements.
• Apply different themes to different plot groups.
• Add tags or labels to subplots for reference.

11. Practice Problems

1. Arrange three plots in a 2x2 grid with one empty space.
2. Collect all legends at the bottom of the combined plot.
3. Apply a theme to all plots in a patchwork layout.
4. Add a shared title and subtitle to a multi-panel figure.
5. Use plot_spacer() to control plot spacing.

12. Further Reading and Resources

• patchwork documentation
• R Graph Gallery: patchwork
• R for Data Science: Communicate Results
• Fundamentals of Data Visualization

**Resource download links**

2.6.19.-patchwork-in-R.zip