patchwork Package in R | Introduction, Tutorial & Programming Examples (2025)

Recently, I have discovered the patchwork package of Thomas Lin Pedersen, and I was impressed how easy it is to combine multiple ggplot2 plots in a plot composition using this package.

In this tutorial, I want to explain the basics of the patchwork package and some of its most important functions.

Before we dive into the examples…

• Here you can find the documentation of the patchwork package.
• Here you can find the CRAN page of the patchwork package.
• Here you can find the homepage of the patchwork package.

Make sure to check out the previous links for detailed instructions on how to use the patchwork package in R.

In the following, however, I’ll give an introduction to the basic functionality of the package. So keep on reading!

Example Data, Add-On Packages & ggplot2 Plots

First, let’s load some example data to R:

data(iris) # Load iris flower data sethead(iris) # Head of iris flower data set# Sepal.Length Sepal.Width Petal.Length Petal.Width Species# 1 5.1 3.5 1.4 0.2 setosa# 2 4.9 3.0 1.4 0.2 setosa# 3 4.7 3.2 1.3 0.2 setosa# 4 4.6 3.1 1.5 0.2 setosa# 5 5.0 3.6 1.4 0.2 setosa# 6 5.4 3.9 1.7 0.4 setosa

In this tutorial, we’ll use the iris flower data set as a basis.

The previous output of the RStudio console shows the structure of our example data. It consists of four numeric columns and a factor variable containing three different flower species.

If we want to use the functions of the patchwork package, we also have to install and load patchwork:

install.packages("patchwork") # Install & load patchwork packagelibrary("patchwork")

The patchwork package is used to combine plots created by the ggplot2 package. For that reason, we also have to install and load the ggplot2 package:

install.packages("ggplot2") # Install & load ggplot2 packagelibrary("ggplot2")

Now, we can plot our data in different types of ggplot2 plots as follows:

ggp1 <- ggplot(iris, # Create ggplot2 scatterplot aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point()ggp1 # Draw ggplot2 scatterplot

ggp2 <- ggplot(iris, # Create ggplot2 barchart aes(x = Species, y = Sepal.Width, fill = Species)) + geom_bar(stat = "identity")ggp2 # Draw ggplot2 barchart

ggp3 <- ggplot(iris, # Create ggplot2 boxplot aes(x = Species, y = Sepal.Width, col = Species)) + geom_boxplot()ggp3 # Draw ggplot2 boxplot

As shown in Figures 1, 2, and 3, we have created a scatterplot, a barplot, and a boxplot of the iris flower data set with the previously shown R code.

Example 1: Draw Composition of ggplot2 Plots Using patchwork Package

In this example, I’ll show how to draw a grid of plots using the patchwork package. Have a look at the following R code and the resulting graphic:

ggp_sbs <- (ggp1 + ggp2) / ggp3 # Create plot compositionggp_sbs # Draw plot composition

As shown in Figure 4, the previous R syntax created a plot composition of multiple ggplot2 graphs. The plot composition consists of several rows and columns.

If you want to learn more about how to arrange multiple ggplot2 plots using the patchwork package, you can have a look at this tutorial or at the following video on my YouTube channel:

Example 2: Print ggplot2 Plots On Top of Each Other Using patchwork Package

The following R programming syntax shows how to use the inset_element function of the patchwork package to draw different plots on top of each other. Consider the following R syntax:

ggp_top <- ggp1 + # Add plots on top of each other inset_element(ggp2, 0.01, 0.01, 0.7, 0.5) + inset_element(ggp3, 0.4, 0.6, 0.99, 0.99)ggp_top # Draw combined plots

As shown in Figure 5, the previous R programming code created a graphic containing three different plots. Our example plot ggp1 was used as background and the two plots ggp2 and ggp3 were added as inset to this plot.

If you want to learn more on how to add an inset to a plot, you may have a look here, or you may check out the following YouTube tutorial:

If you want to know how to add an image on top of a ggplot2 graph using the inset_element function, you might check out this tutorial or the following video:

In summary: You learned in this tutorial how to apply the functions of the patchwork package in R. Please let me know in the comments, if you have any additional questions.