R ggplot2 Boxplots Troubleshooting ggpubrs statcomparemeans

As a data scientist, generating informative and visually appealing boxplots is a crucial part of data analysis. R’s ggplot2 package provides a powerful and flexible framework for creating boxplots, but it can be challenging at times to get the plots to display the information we want to convey in an intuitive and concise manner. One particularly useful tool for creating boxplots and comparing different groups is ggpubr’s stat_compare_means function. However, when this function is not working properly, it can be frustrating to troubleshoot.

In this article, we will explore some common issues that may cause stat_compare_means to not work as expected in ggplot2 boxplots and provide solutions to these problems.

What is ggpubr’s stat_compare_means?

Before diving into troubleshooting, let’s first understand what stat_compare_means does and how it works. stat_compare_means is a function in the ggpubr package that allows us to compare the means of different groups in a boxplot. It adds statistical comparison annotations to boxplots and can perform various types of statistical tests, including t-tests, Mann-Whitney tests, and Wilcoxon tests.

To use stat_compare_means , we first need to create a ggplot2 boxplot using the geom_boxplot function, as shown in the following code:

library(ggplot2)
library(ggpubr)
# Create a boxplot
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot()

This code creates a simple boxplot of the Sepal.Length variable in the iris dataset, grouped by the Species variable.

To add statistical comparison annotations to this plot using stat_compare_means , we can simply add the function to our ggplot2 code, as shown below:

ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() +
  stat_compare_means()

This code adds statistical comparison annotations to the boxplot, comparing the means of the Sepal.Length variable between the different Species groups.

Troubleshooting ggpubr’s stat_compare_means

While stat_compare_means can be a useful tool for comparing means in boxplots, it can sometimes fail to work as expected. Here are some common issues that you may encounter and how to troubleshoot them.

Issue 1: Error message “Error: geom_signif requires the following missing aesthetics: xmin, xmax”

One common issue that can occur when using stat_compare_means is an error message that says “Error: geom_signif requires the following missing aesthetics: xmin, xmax”. This error occurs when stat_compare_means cannot find the xmin and xmax aesthetics in the ggplot2 code.

The solution to this issue is to explicitly set the xmin and xmax aesthetics in the geom_boxplot layer of the ggplot2 code, as shown below:

ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(aes(x = Species, y = Sepal.Length, 
                   ymin = stat(y), lower = stat(y), upper = stat(y), ymax = stat(y),
                   fill = Species, alpha = 0.5),
               position = position_dodge(width = 0.75),