Combining Plots with the patchwork Package

Introduction to the patchwork package

The patchwork package in ggplot2 allows us to create individual plots and the faceting system for creating multiple subplots within a single visualization. Faceting is a powerful tool for creating multiple subplots within a single visualization, but it does have limitations. It is best suited for creating multiple subplots sharing the same data, scales, and layers.

However, in some cases, using several plots that are not directly related or have different data, scales, or layers may be necessary. In such cases, we have to use additional packages such as patchwork, cowplot, etc., to enhance the capabilities of ggplot2.

The patchwork package extends the capabilities of ggplot2 by combining multiple plots with minimal effort and time. This powerful package supports using the + operator to combine multiple plots and provides additional operators for working with multiple plots.

Additionally, the patchwork package provides more flexibility in arranging plots, such as overlapping plots or arranging them in a more customized layout, compared to faceting, which is limited to arranging plots in a grid-like structure. More information about the patchwork package can be found here.

To use the patchwork package and its functions, first, let’s load the package using the following command:

Plotting with the patchwork package

After loading the patchwork package, let’s set the default theme for all the plots to theme_bw(). We’ll also fix the position of the legend for all our graphs to the bottom using the theme() function and define the color palette to eliminate the need to change the legend position and the overall appearance of the graph separately.

• Line 1: We create a vector called colors using the c() function with multiple elements where each element represents a hex color code.
• Lines 2–3: 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.

Now, let’s create four distinct plots using ...