Creating a Heatmap-like Plot for Three Categorical Variables

In this article, we will explore how to create a heatmap-like plot for three categorical variables. The goal is to visualize the relationship between two categorical variables (in this case, color and shape) while incorporating a third variable as weight, in this case, size.

Introduction

Heatmaps are a popular data visualization tool used to display data as a matrix of colors. They can be particularly effective for showcasing relationships between categorical variables. However, when dealing with three categorical variables, creating a heatmap-like plot that effectively communicates the relationships between all three can be challenging.

In this article, we will explore how to create such a plot using R and the ggplot2 package.

Understanding the Data

To start, let’s take a closer look at our data frame. We have a data frame with four columns: Color (a categorical variable), Shape (a categorical variable), Size (a continuous variable), and Freq (the frequency of each combination).

Preparing the Data

To create our heatmap-like plot, we need to prepare our data by selecting only the cases where size is maximum for each color and shape. This will be done using the dplyr package.

First, let’s install and load the necessary packages:

install.packages("dplyr")
install.packages("ggplot2")

library(dplyr)
library(ggplot2)

Next, we’ll create a new data frame df_max that includes only the cases where size is maximum for each color and shape.

df_max <- df %>%
  group_by(Color, Shape) %>%
  slice(which.max(Freq))

Creating the Heatmap

Now that we have our prepared data, let’s create our heatmap-like plot using ggplot2. We’ll use the geom_tile() function to display a tile for each combination of color and shape. The size will be displayed as fill in the tile.

ggplot(df_max, aes(x = Color, y = Shape, fill = Size)) +
  geom_tile()

Explaining the Code

Let’s take a closer look at how the code works.

• We start by loading the necessary packages. The dplyr package is used for data manipulation and transformation, while the ggplot2 package provides the functionality for creating our heatmap-like plot.
• Next, we create a new data frame df_max using the group_by() function to group our data by color and shape, and then selecting only the cases where size is maximum for each combination using the slice() function. The which.max(Freq) expression selects the index of the row with the maximum frequency.
• In the ggplot() function, we map the Color variable to the x-axis, the Shape variable to the y-axis, and the Size variable to the fill in the tile using aes(). The geom_tile() function creates a layer of tiles for each combination of color and shape.
• Finally, we display our plot.

Example Use Cases

Our heatmap-like plot can be used to visualize relationships between three categorical variables. For example:

• In marketing analysis, this type of plot can help identify which colors are most associated with specific shapes among customers, allowing businesses to design more effective advertisements.
• In product development, it can aid in determining the most popular sizes for different products and use those to inform production decisions.

Conclusion

In this article, we explored how to create a heatmap-like plot for three categorical variables. We prepared our data using dplyr, and then used ggplot2 to create our visualization. By mapping color and shape to the x- and y-axes and size to fill in the tile, we effectively displayed relationships between all three variables.

By following these steps, you should be able to create your own heatmap-like plot for three categorical variables using R and ggplot2.

Last modified on 2024-02-29