# GGPlot Multiple Plots Made Ridiculuous Simple Using Patchwork R Package

#### GGPlot Multiple Plots Made Ridiculuous Simple Using Patchwork R Package

There are many R packages/functions for combining multiple ggplots into the same graphics. These include: gridExtra::grid.arrange() and cowplot::plot_grid.

Recently, Thomas Lin Pederson developed the patchwork R package to make very easy the creation of ggplot multiple plots

In this article, you will learn how to use the pachwork package for laying out multiple ggplots on a page.

Contents:

#### Related Book

GGPlot2 Essentials for Great Data Visualization in R

## Installation

At the date of writing this article, pathchwork was only available in developmental version from GitHub.

You can install patchwork from github with:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("thomasp85/patchwork")

Load the ggplot2 package and set the default theme to theme_bw() with the legend at the top of the plot:

library(ggplot2)
library(patchwork)
theme_set(
theme_bw() +
theme(legend.position = "top")
)

### Create some basic plots

The following R code creates 4 plots, including: box plot, dot plot, line plot and density plot.

# 0. Define custom color palette and prepare the data
my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# 1. Create a box plot (bp)
p <- ggplot(ToothGrowth, aes(x = dose, y = len))
bxp <- p + geom_boxplot(aes(color = dose)) +
scale_color_manual(values = my3cols)

# 2. Create a dot plot (dp)
dp <- p + geom_dotplot(aes(color = dose, fill = dose),
binaxis='y', stackdir='center') +
scale_color_manual(values = my3cols) +
scale_fill_manual(values = my3cols)

# 3. Create a line plot
lp <- ggplot(economics, aes(x = date, y = psavert)) +
geom_line(color = "#E46726")

# 4. Density plot
dens <- ggplot(iris, aes(Sepal.Length)) +
geom_density(aes(color = Species)) +
scale_color_manual(values = my3cols)

## Horizontal layouts

The usage of patchwork is simple: just add plots together!

bxp + dens

## Vertical layouts

Layouts can be specified by adding a plot_layout() call to the assemble. This lets you define the dimensions of the grid and how much space to allocate to the different rows and columns.

bxp + dens + plot_layout(ncol = 1)

## Specify the dimensions of each plot

• Change height
bxp + dens + plot_layout(ncol = 1, heights = c(1, 3))

• Change width
bxp + dens + plot_layout(ncol = 2, width = c(1, 2))

bxp + plot_spacer() + dens

## Nested layouts

You can make nested plots layout by wrapping part of the plots in parentheses - in these cases the layout is scoped to the different nesting levels.

lp + {
dens + {
bxp +
dp +
plot_layout(ncol = 1)
}
} +
plot_layout(ncol = 1)

patchwork provides some other operators that might be of interest: - will behave like + but put the left and right side in the same nesting level (as opposed to putting the right side into the left sides nesting level).

bxp + dp - lp + plot_layout(ncol = 1)

It can be seen that bxp + dens and lp is on the same level…

A note on semantics. If - is read as subtract its use makes little sense as we are not removing plots. Think of it as a hyphen instead…

Often you are interested in just putting plots besides or on top of each other. patchwork provides both | and / for horizontal and vertical layouts respectively. They can of course be combined for a very readable layout syntax:

(bxp | lp | dp) / dens

(bxp | lp ) / (dp | dens)

(bxp / lp / dp) | dens

There are two additional operators that are used for a slightly different purpose, namely to reduce code repetition. Consider the case where you want to change the theme for all plots in an assemble.

Instead of modifying all plots individually you can use & or * to add elements to all subplots. The two differ in that * will only affect the plots on the current nesting level:

(bxp + (dp + dens) + lp + plot_layout(ncol = 1)) * theme_gray()

whereas & will recurse into nested levels:

(bxp + (dp + dens) + lp + plot_layout(ncol = 1)) & theme_gray()

Note that parenthesis is required in the former case due to higher precedence of the * operator. The latter case is the most common so it has deserved the easiest use.

## Reference

Version: Français

• Antonio Canepa

Is there any way to label each plot (A, B, C) automatically, to create a publishable-ready figure?…

• Kassambara

It’s possible to use the option tag in the function labs() for adding label to each plot.

For example, kindly try this:

library(ggplot2)
library(patchwork)
p1 <- ggplot(mtcars) +
geom_point(aes(mpg, disp)) +
labs(tag = "A")
p2 <- ggplot(mtcars) +
geom_boxplot(aes(gear, disp, group = gear)) +
labs(tag = "B")
p1 + p2