Combining Multiple Plots

Learn how to combine multiple plots into a single visualization using R. This lecture covers essential techniques for arranging multiple plots using R.

Combining Multiple Plots

Introduction

Combining multiple plots into a single visualization allows you to compare and contrast different data sets and metrics effectively. In R, you can combine plots using various techniques, including base R, gridExtra, and patchwork. In this lecture, we will learn how to combine multiple plots using these methods.

Key Concepts

1. Why Combine Plots?

• Compare multiple data sets side-by-side.

• Show different aspects of the same data.

• Enhance the visual impact of your analysis.

2. Methods for Combining Plots

• Base R: Using par() and layout().

• gridExtra: Using grid.arrange().

• patchwork: Using the + operator.

Combining Plots in R

1. Combining Plots with Base R

You can use the par() function to combine plots in base R.

# Creating sample data

set.seed(123)

data1 <- rnorm(100)

data2 <- rnorm(100, mean = 5)



# Combining plots using par()

par(mfrow = c(1, 2)) # Arrange plots in 1 row and 2 columns

plot(data1, main = "Plot 1")

plot(data2, main = "Plot 2")

par(mfrow = c(1, 1)) # Reset to default

2. Combining Plots with gridExtra

The gridExtra package provides the grid.arrange() function for arranging multiple plots.

# Installing and loading gridExtra

install.packages("gridExtra")

library(gridExtra)



# Creating sample ggplot2 plots

library(ggplot2)

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")



# Combining plots using grid.arrange()

grid.arrange(p1, p2, ncol = 2)

# Plot result

library(gridExtra)

Warning: package 'gridExtra' was built under R version 4.3.2

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.3.3

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")

grid.arrange(p1, p2, ncol = 2)

3. Combining Plots with patchwork

The patchwork package allows you to combine ggplot2 plots using the + operator.

# Installing and loading patchwork

install.packages("patchwork")

library(patchwork)



# Creating sample ggplot2 plots

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")



# Combining plots using patchwork

p1 + p2

# Plot result

library(patchwork)

Warning: package 'patchwork' was built under R version 4.3.2

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")

p1 + p2

4. Customizing Layouts

You can customize the layout and arrangement of multiple plots using advanced features of gridExtra and patchwork.

Customizing Layout with gridExtra

# Customizing layout with grid.arrange()

grid.arrange(p1, p2, ncol = 1) # Arrange plots in 1 column

grid.arrange(p1, p2, ncol = 2, heights = c(1, 2)) # Custom heights

# Plot result

grid.arrange(p1, p2, ncol = 1)

grid.arrange(p1, p2, ncol = 2, heights = c(1, 2))

Customizing Layout with patchwork

# Customizing layout with patchwork

(p1 | p2) / (p1 | p2) # Arrange in a grid

# Plot result

(p1 | p2) / (p1 | p2)

Example: Comprehensive Combining Plots

Here’s a comprehensive example of combining multiple plots using different methods in R.

# Creating sample data

data1 <- rnorm(100)

data2 <- rnorm(100, mean = 5)



# Combining plots with base R

par(mfrow = c(1, 2))

plot(data1, main = "Plot 1")

plot(data2, main = "Plot 2")

par(mfrow = c(1, 1))



# Creating sample ggplot2 plots

library(ggplot2)

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")



# Combining plots with gridExtra

library(gridExtra)

grid.arrange(p1, p2, ncol = 2)



# Combining plots with patchwork

library(patchwork)

p1 + p2



# Customizing layout with gridExtra

grid.arrange(p1, p2, ncol = 1)

grid.arrange(p1, p2, ncol = 2, heights = c(1, 2))



# Customizing layout with patchwork

(p1 | p2) / (p1 | p2)

# Plot results

par(mfrow = c(1, 2))

plot(data1, main = "Plot 1")

plot(data2, main = "Plot 2")

par(mfrow = c(1, 1))



library(gridExtra)

library(ggplot2)

p1 <- ggplot(data.frame(x = data1), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 1")

p2 <- ggplot(data.frame(x = data2), aes(x = x)) + geom_histogram(binwidth = 0.5) + labs(title = "Histogram 2")

grid.arrange(p1, p2, ncol = 2)

library(patchwork)

p1 + p2

grid.arrange(p1, p2, ncol = 1)

grid.arrange(p1, p2, ncol = 2, heights = c(1, 2))

(p1 | p2) / (p1 | p2)

Summary

In this lecture, we covered how to combine multiple plots into a single visualization using R. We explored various techniques for combining plots using base R, gridExtra, and patchwork. Combining plots is essential for comparing data sets and enhancing the visual impact of your analysis.

Further Reading

For more detailed information, consider exploring the following resources:

• gridExtra package documentation

• patchwork package documentation

• ggplot2 documentation

Call to Action

If you found this lecture helpful, make sure to check out the other lectures in the R Graphs series. Happy plotting!