Understanding Stacked Bar Charts in ggplot2

Why do stacked bar charts not match values in tables?

In this article, we will explore why stacked bar charts from the ggplot package in R may not accurately represent the values of their corresponding data table. We’ll examine a reproducible example and discuss potential solutions to resolve this issue.

What is a Stacked Bar Chart?

A stacked bar chart is a visualization technique that displays multiple series of data as separate bars that stack on top of each other. This allows users to compare the relative magnitudes between different categories or series in a single chart.

ggplot2 and Stacked Bar Charts

The ggplot package, developed by Hadley Wickham, provides an elegant way to create various types of charts, including stacked bar charts. To create a stacked bar chart using ggplot, you need to:

• Create a data frame with the relevant variables (e.g., categorical variables for the x-axis and y-axis series).
• Use the geom_col() function to specify the geometry of the bars.
• Apply various aesthetic mappings (e.g., color, size) to customize the appearance of your chart.

The Issue

The problem arises when using ggplot with a grouped data frame. When we group by multiple variables and use mutate() or summarize() to calculate running totals, we create a situation where each row in the original data is duplicated for every level of the grouping variable.

This results in a chart that has bars longer than intended because ggplot stacks all the duplicated rows together. As a result, the relative proportions between different categories become distorted.

Solution

To resolve this issue, we need to replace the use of mutate() or summarize() with summarise() and ensure that our data frame only contains unique rows for each category in the grouping variable.

Here’s an example using the built-in mtcars dataset in R:

# Load necessary libraries
library(ggplot2)

# Create a grouped data frame (wrong approach)
data <- mtcars %>%
  group_by(cyl, gear) %>%
  mutate(total_wt = sum(hp))

# Try to create a stacked bar chart using the wrong data
ggplot(data, aes(x = factor(cyl), y = total_wt, fill = factor(gear))) +
  geom_col(position = "stack") +
  geom_text(aes(label = round(total_wt, 1)), position = position_dodge(width = 0.2))

# Create a new data frame with unique rows for each category
new_data <- mtcars %>%
  group_by(cyl, gear) %>%
  summarise(avg_hp = mean(hp), total_wt = sum(hp))

# Now create the correct stacked bar chart using the new data
ggplot(new_data, aes(x = factor(gear), y = avg_hp * total_wt / max(avg_hp * total_wt))) +
  geom_col(position = "stack") +
  geom_text(aes(label = round(total_wt / max(avg_hp * total_wt), 1)), position = position_dodge(width = 0.2))

Conclusion

Stacked bar charts can be a powerful tool for visualizing multiple series of data, but they can become distorted if not used correctly with grouped data frames.

By understanding how ggplot works and using alternative approaches to grouping and summarization, you can create accurate and informative stacked bar charts that effectively communicate your data insights.

Last modified on 2024-09-02