2.6.18. cowplot in R

1. Introduction

The cowplot package is designed to help you arrange multiple ggplot2 plots into a single grid and apply a consistent theme across all plots. This is especially useful for presentations and publications where you want your visualizations to look polished and consistent.

# 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 cowplot?

• Easily combine multiple ggplot2 plots into a grid.
• Apply a standard, clean theme (theme_cowplot) to all plots.
• Add labels, titles, and control relative sizes of plots.
• Simplifies the process of creating multi-panel figures for reports and presentations.

3. Applying a Consistent Theme

• theme_cowplot() provides a clean, minimal look similar to theme_classic().
• Removes background color and grid lines, leaving only axis lines.

R Code:

# install.packages("cowplot")
library(cowplot)
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_cowplot(12)

# 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_cowplot(12)

Expected Outcome:
Two scatterplots with a consistent, clean theme and different parameters (ALT and AST).

4. Combining Multiple Plots

• Use plot_grid() to arrange plots in a grid.
• Control the number of columns or rows, and relative widths/heights.

R Code:

# Side-by-side
plot_grid(p1, p2, ncol = 2)

# Stacked vertically
plot_grid(p1, p2, ncol = 1)

Expected Outcome:

Plots arranged side by side or stacked, sharing the same theme.

5. Controlling Relative Sizes and Adding Labels

• Use rel_widths or rel_heights to adjust plot sizes in the grid.
• Add labels to plots for easy reference in reports.

R Code:

# Bar plot of ALT counts by treatment group
p3 <- ggplot(adlb %>% filter(PARAMCD == "ALT"), aes(TRT01A)) +
  geom_bar(fill = "dodgerblue") +
  labs(x = "Treatment Group", y = "Count") +
  theme_cowplot(12)

# Combine with relative widths and labels
plot_grid(p1, p3, rel_widths = c(1, 1.3), labels = c("A", "B"))

Expected Outcome:

A grid with two plots, the right plot wider, and labeled "A" and "B".

6. Adding Joint Titles and Complex Layouts

• Use draw_label() and ggdraw() to add a shared title above a grid of plots.
• Combine multiple plot grids for complex layouts.

R Code:

# Combine three plots with a shared title
first_col <- plot_grid(p1, p2, nrow = 2, labels = c('A', 'B'))
three_plots <- plot_grid(first_col, p3, ncol = 2, labels = c('', 'C'), rel_widths = c(1, 1.3))

title <- ggdraw() +
  draw_label("ALT and AST Trends by Treatment Group",
             fontface = 'bold', x = 0, hjust = 0) +
  theme(plot.margin = margin(0, 0, 0, 7))

# Add title above the grid
plot_grid(title, three_plots, ncol = 1, rel_heights = c(0.1, 1))

Expected Outcome:

A multi-panel figure with a shared title and labeled subplots, ready for publication or presentation.

7. Input and Output Table for cowplot Examples

8. Exploring Beyond Basic cowplot

• Use align argument in plot_grid() to align axes.
• Combine cowplot with patchwork or gridExtra for advanced layouts.
• Add annotations, legends, or insets to multi-panel figures.
• Explore cowplot's vignettes for more layout options.

9. Practice Problems

1. Arrange three plots in a single row using plot_grid().
2. Add a shared title above a grid of plots.
3. Make one plot twice as wide as the other in a grid.
4. Label subplots with custom text.
5. Combine cowplot with facets for a complex figure.

10. Further Reading and Resources

• cowplot documentation
• R Graph Gallery: cowplot
• R for Data Science: Communicate Results

**Resource download links**

2.6.18.-cowplot-in-R.zip