Two plots in one plot (ggplot2)

Introduction

In this post, we’ll explore a common problem in data visualization: combining multiple plots into a single plot. Specifically, we’ll discuss how to merge two plots created using ggplot2, a popular R package for creating static graphics. We’ll use the ggplot2 package to create two separate plots and then combine them into one cohesive graph.

Background

The problem arises when you have multiple plots that serve different purposes but share common data. For instance, you might want to visualize the average scores of groups within a country (Plot 1) alongside the country’s relative position compared to other countries (Plot 2). Plot 2 provides context for understanding Plot 1, but it has different x- and y-scales.

Data Preparation

To solve this problem, we first need to prepare our data. We have two datasets: within_scores containing group averages within a country and between_scores containing country-level means with relative positions.

# Load the ggplot2 library
library(ggplot2)

# Create a sample dataset for demonstration purposes
age_group <- c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49",
               "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89")
scores1 <- c(0.000, 0.000, -0.362, -0.546, -0.652, -1.588, -1.333, -1.365, -1.283, -1.394,
              -1.385, -1.622, -1.278, -1.502, -0.909)
within_scores <- data.frame(age_group, scores1)

scores2 <- c(-0.429, -0.950, -0.622, 0.000, -0.481, -0.778, -0.657, -1.017, -0.394, -0.546,
             -0.643, -0.844, -0.884, -0.575, -1.079, -0.717, -0.862, -0.481, -0.833, -0.779,
             -1.577, -0.419, -0.255, -0.707, -0.596, -0.283, -1.225, -0.375)
countries <- c("BE", "BG", "CH", "CZ", "DE", "DK", "EE", "ES", "FI", "FR", "GB", "GR", "HR",
               "HU", "IE", "IL", "LV", "NL", "NO", "PL", "PT", "RO", "RU", "SE", "SI", "SK", "TR",
               "UA")
between_scores <- data.frame(countries, scores2)

Plotting the Individual Plots

Let’s create two separate plots using ggplot2. We’ll use geom_point() and geom_line() to visualize the group averages and country-level means, respectively.

# Create a plot of within-country scores (age-groups)
g1 <- ggplot(within_scores, aes(x = age_group, y = scores1)) + 
  geom_point() + geom_line() +
  labs(title = "Belgium", x = NULL, y = NULL) +
  theme_bw() + ylim(-2, 2) +
  theme(text = element_text(size = 8), axis.text.x = element_text(angle = 50, hjust = 1))

# Create a plot of between-country scores (country-level means)
g2 <- ggplot(between_scores, aes(x = reorder(countries, -scores2), y = scores2)) + 
  geom_point() + geom_line() +
  labs(title = "Belgium", x = NULL, y = NULL) +
  theme_bw() + ylim(-2, 2) +
  theme(text = element_text(size = 8), axis.text.x = element_text(angle = 50, hjust = 1))

Merging the Plots

To merge these two plots into one cohesive graph, we can use ggplot2’s built-in functionality to create a new plot from multiple sources. We’ll create a single plot that combines both the within-country and between-country scores.

# Create a merged plot of within-country and between-country scores
merged_plot <- ggplot(data = rbind(within_scores, between_scores), aes(x = reorder(c("Belgium", "BE"), -scores2) if grepl("Belgium", countries) else reorder(countries, -scores2), y = reorder(c(scores1, scores2), -scores2))) +
  geom_point(position = position_jitter(width = 0.5, height = 0.5)) +
  geom_line() + 
  labs(title = "Merged Plot of Within-Country and Between-Country Scores", x = NULL, y = NULL) +
  theme_bw() + ylim(-2, 2) +
  theme(text = element_text(size = 8), axis.text.x = element_text(angle = 50, hjust = 1))

In this example, we’re using rbind() to combine the two datasets into a single data frame. We then reorder the x-axis values based on whether the country name matches “Belgium” or not.

Tips and Variations

Customize the plot: Use various ggplot2 functions like scale_x_continuous(), scale_y_continuous(), and theme() to customize the appearance of your plot.
Add labels and titles: Use labs() to add labels, titles, and legends to your plot.

Last modified on 2025-01-14