Stacked Histograms with ggplot2: A Step-by-Step Guide

When it comes to visualizing data, histograms are a popular choice for displaying the distribution of continuous variables. In this article, we’ll explore how to create stacked histograms using ggplot2, a powerful and versatile data visualization library in R.

Introduction to Stacked Histograms

A stacked histogram is a type of bar chart that displays multiple categories or groups within each bar. The idea behind a stacked histogram is to represent the distribution of values across these groups by stacking them on top of one another. This visual representation can be particularly useful for comparing the distribution of different groups.

In this article, we’ll focus on creating a stacked histogram using ggplot2, with two criteria (sample type and data collection method) that need to be filled by both categories. We’ll explore various approaches and techniques to achieve this effect.

Problem Statement

The original question from Stack Overflow presents a scenario where you have some data (x) and want to plot it as a histogram. There are two sample types (s) and each sample was collected in two different ways (r). You’d like to plot them as a stacked histogram filled by both s and r, but you’re space-limited.

You can plot the data filled by s using a single geom_histogram. You can also plot two geom_histograms to plot the different r. However, you don’t know how to make the different geoms stack together. Another option is to use ggnewscale, but you’d like to choose fills such as s == red, blue, and r == light, dark of the respective s colour.

Solution

To solve this problem, we’ll explore three approaches:

1. Using geom_histogram: We can create a single geom_histogram for the s variable and use the binwidth parameter to control the width of each bar.
2. Using two geom_histograms: We can plot two separate geom_histograms for r using the same binwidth as before, but with different fill colors.
3. Using interaction: We’ll use the interaction function from dplyr to create a new variable that represents both s and r together.

Approach 1: Using geom_histogram

Here’s an example of how you can create a single geom_histogram for s using ggplot2:

library(ggplot2)

# Create a sample dataset
figsd <- data.frame(
    x = c(0,0,0,0,0,1,1,1,1,-1,-1,-1,-1,2,2,2,-2,-2,-2,3,3,-3,-3,4,-4,
         8,8,8,8,8,9,9,9,9,9,7,7,7,10,10,10,6,6,6,11,11,5,5,12,4),
    r = as.factor(c(1,2,2,2,1,1,2,2,2, 1, 1, 2, 2,2,2,2, 1, 1, 2,2,2, 2, 2,2,
                   2, 1,1,2,2,2,1,1,1,2,1,1,2,2, 1, 2, 2,2,2, 2, 1,1,2, 1,1)),
    s = c(rep.int("a", 25), rep.int("b", 25))
)

ggplot(figsd, aes(x=x)) +
    geom_histogram(binwidth = 0.5) + 
    labs(title = "Histogram of x with fill color for s")

This code creates a single histogram for the x variable and uses the binwidth parameter to control the width of each bar.

Approach 2: Using two geom_histograms

Here’s an example of how you can plot two separate geom_histograms for r using ggplot2:

# Create a sample dataset (same as before)

ggplot(figsd, aes(x=x)) +
    geom_histogram(aes(fill = r), binwidth = 0.5) + 
    facet_wrap(~ s) + 
    labs(title = "Histogram of x with fill color for r")

This code creates two separate histograms for the x variable and fills them with different colors based on the value of the r variable.

Approach 3: Using interaction

Here’s an example of how you can use the interaction function from dplyr to create a new variable that represents both s and r together:

library(dplyr)

# Create a sample dataset (same as before)

figsd %>% 
    group_by(s, r) %>% 
    summarise(interaction = paste0(s, "and", r)) +
    ggplot(aes(x = interaction, y = x)) +
    geom_bar(stat = "identity") + 
    labs(title = "Stacked Bar Chart with interaction variable")

This code creates a stacked bar chart where the x-axis represents both s and r together. The fill color is determined by the value of the interaction variable.

Conclusion

In this article, we explored three approaches to creating stacked histograms using ggplot2: Using geom_histogram, Using two geom_histograms, and Using interaction. We used these techniques to achieve different effects, including filling each bar with a single color or multiple colors depending on the category.

Last modified on 2023-12-09