Mixing Plots with the ggpubr Package

The ggplot extension packages in R allow using multiple plots, offering a powerful data visualization approach. These packages facilitate data scientists and analysts to create rich and informative visualizations for their projects. Various ggplot extension packages help combine multiple ggplot plots for effective data analysis. Similar to the patchwork and cowplot packages, there is another package, the ggpubr package.

Let’s explore how to create beautiful data visualizations containing multiple plots using ggpubr.

Getting started with ggpubr

The ggpubr package is a widely used package, particularly among those who use ggplot2 for their plotting needs. The package’s most notable function is ggarrange().

The ggarrange() function allows us to easily arrange multiple plots in a single figure while providing further customization options.

Additionally, ggpubr offers a variety of other functions for adding mean lines and marginal rugsIt is a one-dimensional distribution plot used to support two-dimensional plots for visualizing the spread of data on each axis. to histograms, adding p-values to boxplots and violin plots, and sorting bar plot groups globally.

First, let’s load the ggpubr package using the following code:

Plotting with the ggarrange() function

The ggarrange() function in the ggpubr package allows us to control different parts of a plot, like the individual plots, labels, and adding a common legend. Other packages like cowplot and patchwork don’t offer this level of control.

We’ll set theme_bw() as the default theme for all the plots. We’ll also fix the position of the legend to the bottom, rotate the x-axis labels, and define the color palette to eliminate the need to change the overall appearance of each graph separately using the following code:

• Lines 1-3: We use the theme_set() function to change the default gray theme to theme_bw(). We also set the legend.position argument to bottom inside the theme() function, and we specify the angle argument inside the element_text() function to rotate the x-axis labels by 90 degrees.
• Line 4: We create a vector called colors using the c() function with multiple elements where each element represents a hex color code.

Now, we’ll create four ...