A Comprehensive Guide to Adding Shared Legends for Combined ggplot Plots

Introduction

In data visualization, combining multiple plots with shared legends is a common requirement. Particularly when using R's ggplot2 package, effectively managing legend layout presents technical challenges. This article delves into methods for implementing shared legends using the gridExtra package, based on highly-rated solutions from Stack Overflow.

Problem Context

Users often need to arrange two or more ggplot plots horizontally, remove individual legends, and add a unified shared legend at the bottom of the combined visualization. This need is especially prevalent when comparing visualizations across different datasets.

Data Preparation

First, we prepare sample data. Here are simulated datasets for demonstration:

# Dataset 1
df1 <- read.table(text="group   x     y   
group1 -0.212201  0.358867
group2 -0.279756 -0.126194
group3  0.186860 -0.203273
group4  0.417117 -0.002592
group1 -0.212201  0.358867
group2 -0.279756 -0.126194
group3  0.186860 -0.203273
group4  0.186860 -0.203273", header=TRUE)

# Dataset 2
df2 <- read.table(text="group   x     y   
group1  0.211826 -0.306214
group2 -0.072626  0.104988
group3 -0.072626  0.104988
group4 -0.072626  0.104988
group1  0.211826 -0.306214
group2 -0.072626  0.104988
group3 -0.072626  0.104988
group4 -0.072626  0.104988", header=TRUE)

Creating Base Plots

Create two scatter plots using ggplot2 with color grouping:

library(ggplot2)
library(gridExtra)

p1 <- ggplot(df1, aes(x=x, y=y, colour=group)) + 
  geom_point(position=position_jitter(w=0.04, h=0.02), size=1.8) + 
  theme(legend.position="bottom")

p2 <- ggplot(df2, aes(x=x, y=y, colour=group)) + 
  geom_point(position=position_jitter(w=0.04, h=0.02), size=1.8)

Legend Extraction Function

The core step involves creating a function to extract legends from ggplot objects:

g_legend <- function(a.gplot){
  tmp <- ggplot_gtable(ggplot_build(a.gplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}

This function works by converting the ggplot object to a gtable object via ggplot_build and ggplot_gtable, then searching for the element named "guide-box" in the gtable's grobs list, which represents the legend object.

Combining Plots and Legends

Use the extracted legend with grid.arrange for final layout:

mylegend <- g_legend(p1)

p3 <- grid.arrange(arrangeGrob(p1 + theme(legend.position="none"),
                         p2 + theme(legend.position="none"),
                         nrow=1),
             mylegend, nrow=2, heights=c(10, 1))

Key aspects include:

• Using arrangeGrob to horizontally arrange two plots with legends removed
• Adding the extracted legend as a separate element in the second row
• Controlling the height ratio between plot area and legend area via the heights parameter

Technical Analysis

The core advantages of this approach are:

1. Flexibility: Precise control over legend position and size
2. Compatibility: Works with various ggplot plot types
3. Customizability: Easy adjustment of layout parameters to meet specific needs

Note that calculating the legend height lheight ensures layout precision, preventing legend truncation or excessive whitespace.

Alternative Approaches Comparison

Beyond this method, other packages offer more concise solutions:

ggpubr package: Using the ggarrange function with common.legend = TRUE automatically handles shared legends.

patchwork package: Provides more intuitive syntax using the & operator and plot_layout(guides = "collect") to gather legends.

However, the method described in this article offers advantages in control precision and compatibility, particularly in complex scenarios requiring fine-tuned layouts.

Best Practice Recommendations

In practical applications, we recommend:

• Ensuring at least one plot contains a complete legend before extraction
• Adjusting height ratio parameters based on the number of plots and data complexity
• Testing different legend positions (bottom, right, etc.) for optimal visual effect
• Considering modern packages like patchwork for code simplification, but using the described method when precise control is needed

Conclusion

By leveraging the capabilities of ggplot2 and gridExtra packages, we can effectively create combined plots with shared legends. While this approach requires additional code, it offers high flexibility and control precision, making it an important technique in the data visualization toolkit.