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Data Visualizations with ggplot2 in R

Multiple Plots with the Cowplot Package

Explore the cowplot package to combine multiple ggplot2 plots into a single layout. Learn to customize labels, arrange plots in grids, and use functions like ggdraw to add text and images. This lesson helps you present complex data comprehensively by creating clear multi-panel plots efficiently.

We'll cover the following...

Introduction to the cowplot package

Presenting information through multiple plots in a data visualization can provide a comprehensive and holistic view of complex data. Using multiple plots can help us quickly identify outliers and assist in an adequate representation of the underlying information.

In this context, let’s explore a powerful package called cowplot, which allows us to combine multiple ggplot2 plots into a single plot.

The cowplot package is a popular add-on package for ggplot2. It allows for easy alignment and spacing of multiple plots. Furthermore, it provides various options for customizing the appearance of the combined plot.

When compared to the patchwork package, managing the plot spacing and alignment becomes easier in the cowplot package, especially when working with different aspect ratios. More information about the cowplot package can be found here.

First, let’s load the cowplot package using the following command:

R
library(cowplot)

Next, we’ll set theme_bw() as the default theme for all the plots. We’ll define the color palette and use the theme() function to set the legend position to the bottom of the graph for all of our graphs created using the cowplot package.

R
colors <- c("#0b7a75","#1DD3B0","#5DA9E9","#00487C","#03045E")
theme_set(theme_bw() +
        theme(legend.position = "bottom", 
        plot.title = element_text(hjust = 0.5)))
  • Line 1: We use the c() function for creating a vector called color and store multiple colors or elements, each representing a hex color code.
  • Lines 2–3: We set the default theme to theme_bw(). We also change the default position of the legend by setting the legend.position argument to bottom inside the theme() function.
  • Line 4: We center align the plot title by setting the hjust argument as 0.5.

Plotting with cowplot

We’ll create four separate charts and store them in the chart1, chart2, chart3, and chart4 objects using the following code. This will make it easier for us to access and reproduce the plots as needed. We’ll also add a title to each plot for reference using the labs() function.

R
# Line chart 
df <- gapminder %>% filter(country %in% c("United States", "Canada","Mexico"))
chart1 <- ggplot(df) +
  geom_line(aes(x = year, y = gdpPercap, color = country),
        	linewidth = 1)+
  scale_color_manual(values = colors) + labs(title ="Line chart")
# Histogram
chart2 <- ggplot(diamonds) + 
  geom_histogram(aes(x = price), 
    color = "#5da9e9", fill ="#03045e", bins = 30) + labs(title ="Histogram")
# Boxplot
chart3 <- ggplot(housing) +
  geom_boxplot(aes(x = Type, y = Freq, fill = Infl)) +
    scale_fill_manual(values = colors) + labs(title ="Box plot")
# Violin plot
chart4 <- ggplot(warpbreaks, aes(x = tension, y = breaks, fill = wool)) +
  geom_violin(position = position_dodge(0.8), trim = FALSE) +
  geom_boxplot(position = position_dodge(0.8), color = "white", width = 0.1)+
  scale_fill_manual(values=colors) + labs(title ="Violin plot")
  • Line 2: We create a new df data frame for storing the filtered data of the gapminder dataset using the filter() function. The new df data frame will include only the rows where the country is United States, Canada, and
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