Understanding Geom_density_ridges() Function in ggplot2

Introduction

The geom_density_ridges() function is a part of the ggplot2 library, which provides a variety of visualization tools for exploratory data analysis. One of its unique features is its ability to create a density plot with points on top, providing a detailed view of the distribution of values.

In this article, we will explore how to extend the geom_density_ridges() function to include an additional color layer based on a categorical variable.

Background

The iris dataset, often used in introductory statistics courses, consists of measurements for sepal and petal length and width of 50 flowers from each of three species of iris. The dataset is commonly used as a test case for visualization libraries like ggplot2.

When creating a density plot using geom_density_ridges(), we can specify the aesthetics for points (point_shape, point_size, etc.) but we cannot directly map these to categorical variables. However, by combining this function with another function from ggplot2 that creates a discrete color palette, we can achieve our desired outcome.

The Problem

The question arises when we need to differentiate between different categories in the data and assign them unique colors on top of the density plot created using geom_density_ridges(). In this case, we want to add an additional layer of color based on the categorical variable “cat.”

Solution Overview

To address the issue at hand, we can use ggplot2’s layering mechanism. By adding multiple geom_density_ridges() layers with different aesthetics, we can control the appearance of the points and create a separate color palette for each group.

Code Explanation

The solution involves creating two layers of points:

1. One layer uses the categorical variable “cat” as its aesthetic to map point colors.
2. The other layer omits this aesthetic, allowing it to inherit the alpha value from the base plot.

Here’s a code block demonstrating how to create these two separate layers:

library(ggplot2)
library(ggridges)

# Prepare iris dataset with an additional categorical variable "cat"
iris$cat <- factor(sample(1:5, size = nrow(iris), replace = TRUE))

ggplot() +
  geom_density_ridges(
    data = iris,
    aes(x = Sepal.Length, y = Species),
    alpha = 0.7
  ) +
  # First layer of points using the "cat" variable for point color
  geom_density_ridges(
    data = iris,
    aes(x = Sepal.Length, y = Species, point_color = cat),
    jittered_points = TRUE,
    linetype = 0,
    position = position_points_jitter(width = 0.05, height = 0),
    point_shape = '|', 
    point_size = 3, 
    point_alpha = 1, 
    alpha = 0
  ) +
  # Second layer of points without the "cat" variable for color mapping
  geom_density_ridges(
    data = iris,
    aes(x = Sepal.Length, y = Species),
    jittered_points = TRUE, 
    linetype = 0, 
    position = position_points_jitter(width = 0.05, height = 0), 
    point_shape = '|', 
    point_size = 3, 
    point_alpha = 1
  ) +
  # Select a color palette for the second layer using scale_color_viridis_d()
  scale_color_viridis_d(aesthetics = "point_color")

Additional Color Options

In this example, we’ve used the scale_color_viridis_d() function to map point colors from the categorical variable “cat” to a discrete color palette. You can modify this by uncommenting other color mapping functions like scale_discrete_manual(), which allows you to define your own set of discrete colors.

For instance:

# Use scale_discrete_manual() instead of scale_color_viridis_d()
scale_discrete_manual(aesthetics = "point_color",
                       values = c('black', 'red', 'grey', 'purple', 'blue'))

This can give you more control over the appearance and uniqueness of colors used across different groups.

Conclusion

The geom_density_ridges() function provides a powerful tool for visualizing data distributions, especially when combined with other ggplot2 functions that create color palettes. By utilizing layering and aesthetic mapping, we can extend its capabilities to include an additional color layer based on categorical variables.

Last modified on 2024-03-24