Ordering Factors in Each Facet of ggplot by Y-Axis Value

In this article, we’ll explore a common problem when visualizing data using the ggplot package from R. Specifically, we’ll look at how to order factors within each facet of a plot based on their values. We’ll also dive into some workarounds for issues that may arise and provide code examples to illustrate the concepts.

Background

The ggplot package is a popular data visualization tool in R that provides a powerful and flexible way to create high-quality, publication-ready graphics. However, one of its limitations is that it doesn’t natively support ordering factors within each facet of a plot based on their values.

The Problem

Suppose we have a dataset with multiple variables, including categorical and numerical columns. We want to create a plot with facets, where each facet represents a unique level of the categorical variable. However, we also want to order the x-axis (i.e., the categorical levels) based on the y-values within each facet.

A Simple Example

Let’s consider an example using the built-in letters, animals, and numbers datasets in R. We’ll create a plot with facets, where each facet represents a unique level of the animals variable.

library(dplyr)
library(ggplot2)

set.seed(33)
my_df <- data.frame(
  letters = c(letters[1:10], letters[6:15], letters[11:20]),
  animals = c(rep('sheep', 10), rep('cow', 10), rep('horse', 10)),
  numbers = rnorm(1:30)
)

ggplot(my_df, aes(x = letters, y = numbers)) + 
  geom_point() + 
  facet_wrap(~animals, ncol = 1, scales = 'free_x')

This code creates a plot with three facets, one for each level of the animals variable. However, we want to order the x-axis (i.e., the categorical levels) based on the y-values within each facet.

A Possible Solution

One possible solution is to create an interaction variable that combines the animal and letter columns, and then reorder this variable based on the numbers column. We’ll use the dplyr package to achieve this.

new_order <- my_df %>% 
  group_by(animals) %>% 
  do(data_frame(al = levels(reorder(interaction(.$animals, .$letters, drop = TRUE), .$numbers)))) %>% 
  pull(al)

my_df %>% 
  mutate(al = factor(interaction(animals, letters), levels = new_order)) %>% 
  ggplot(aes(x = al, y = numbers)) + 
    geom_point() + 
    facet_wrap(~ animals, ncol = 1, scales = 'free_x') + 
    scale_x_discrete(breaks = new_order, labels = gsub("^.*\\.", "", new_order))

This code creates an interaction variable al that combines the animal and letter columns. It then reorders this variable based on the numbers column using the reorder() function. Finally, it uses this reordered variable as the x-axis in the plot.

Issues with the Solution

While this solution works for small datasets, it has some limitations when dealing with larger datasets or more complex interactions. One issue is that the reorder() function may not work well with large datasets, leading to performance issues.

Another issue is that the interaction() function creates a new factor variable that combines two columns. However, this can lead to issues when working with categorical variables, as it may create unexpected interactions between variables.

Alternative Solutions

There are alternative solutions available that don’t rely on creating an interaction variable or reordering factors based on y-values. One approach is to use the reorder() function directly in the plot layer, without creating a new factor variable.

ggplot(my_df, aes(x = reorder(letters, numbers), y = numbers)) + 
  geom_point() + 
  facet_wrap(~ animals, ncol = 1, scales = 'free_x')

This code reorders the letters column based on the numbers column within each facet. However, this approach may not work well if there are multiple levels of the categorical variable.

Another alternative is to use the forcats package, which provides a more flexible and efficient way to handle categorical variables in R.

library(forcats)

my_df %>% 
  mutate(al = factor(letters)) %>% 
  ggplot(aes(x = al, y = numbers)) + 
    geom_point() + 
    facet_wrap(~ animals, ncol = 1, scales = 'free_x')

This code uses the forcats package to create a new categorical variable al that orders the letters column based on the numbers column within each facet.

Conclusion

In this article, we explored how to order factors within each facet of a plot in R using the ggplot package. We also discussed some workarounds for issues that may arise and provided code examples to illustrate the concepts. While there are alternative solutions available, creating an interaction variable or reordering factors based on y-values can be effective ways to achieve this goal. However, it’s essential to consider the limitations of these approaches and choose the best solution based on the specific requirements of your project.

Last modified on 2024-01-15