Arranging Multiple Plots with ggplot2

When working with data visualization in R using ggplot2, it is often necessary to arrange multiple plots side by side or in a grid layout. This can be particularly useful for comparing different aspects of the same dataset or for presenting multiple related visualizations together.

One common approach to arranging multiple plots is by using the grid.arrange function from the gridExtra package. This function allows you to combine multiple ggplot2 objects into a single plot, making it easy to arrange them in various layouts.

Here’s an example of how to use grid.arrange to place two plots side by side:

require(gridExtra)
library(ggplot2)

# Create two sample plots
plot1 <- qplot(1)
plot2 <- qplot(2)

# Arrange the plots side by side
grid.arrange(plot1, plot2, ncol = 2)

In this example, ncol = 2 specifies that we want to arrange the plots in two columns. You can adjust this parameter to achieve different layouts.

Another package that provides a convenient way to arrange multiple plots is cowplot. The plot_grid function from this package allows for more fine-grained control over the layout and also supports labeling each plot with letters (A, B, etc.), which is often required in academic publications.

Here’s an example of using plot_grid:

library(cowplot)
library(ggplot2)

# Create two sample plots
iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() + theme_bw()

iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.7) + theme_bw() +
  theme(legend.position = c(0.8, 0.8))

# Arrange the plots side by side with labels
p <- plot_grid(iris1, iris2, labels = "AUTO")
print(p)

In this example, labels = "AUTO" automatically assigns letters to each plot.

For those who prefer a more straightforward syntax, the patchwork package offers an elegant solution. It allows you to use the + operator to combine plots side by side and the / operator to stack them vertically.

Here’s how you can use patchwork:

library(patchwork)
library(ggplot2)

# Create two sample plots
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))

# Arrange the plots side by side
p1 + p2

# Or stack them vertically
p1 / p2

Lastly, for those looking for a more customizable solution or who prefer not to use additional packages beyond grid, you can define your own multiplot function. This approach requires working directly with the grid system in R but provides maximum flexibility.

Here’s an example of how to create and use such a multiplot function:

multiplot <- function(..., plotlist = NULL, cols) {
  require(grid)

  plots <- c(list(...), plotlist)
  numPlots <- length(plots)
  plotCols <- cols
  plotRows <- ceiling(numPlots / plotCols)

  grid.newpage()
  pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
  vplayout <- function(x, y) 
    viewport(layout.pos.row = x, layout.pos.col = y)

  for (i in 1:numPlots) {
    curRow <- ceiling(i / plotCols)
    curCol <- (i - 1) %% plotCols + 1
    print(plots[[i]], vp = vplayout(curRow, curCol))
  }
}

# Example usage:
plot1 <- qplot(1)
plot2 <- qplot(2)

multiplot(plot1, plot2, cols = 2)

Each of these methods has its own advantages and can be chosen based on the specific requirements of your project, such as the need for fine-grained control over the layout, support for labeling plots, or simplicity of syntax.