Merging Less Common Levels of a Factor in R into "Others" using fct_lump

Merging Less Common Levels of a Factor in R into “Others”

Introduction

When working with data, it’s common to encounter factors that have less frequent levels compared to the majority of the data. In such cases, manually assigning these less frequent levels to a catch-all category like “Others” can be time-consuming and prone to errors. Fortunately, there are packages in R that provide an efficient way to merge these infrequent levels into the “Others” category.

Using `fct_lump_n` from `forcats`

In this section, we will explore how to use the fct_lump_n function from the forcats package. This function is designed to combine less common levels of a factor into an “Others” category while preserving the majority levels.

What is `fct_lump_n`?

The fct_lump_n function takes two main arguments:

x: The input factor to be processed.
n: The number of most frequent levels (default: 1).

This function works by counting the frequency of each level in the input factor, then combining any levels with a frequency less than n into an “Others” category.

Example Usage

Let’s consider an example where we have a data frame with a column named “State,” which contains factor values representing all states (Alabama, Alaska, Etc.). We want to merge any infrequent state level into the “Others” category while preserving the majority levels.

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

# Install required packages
install.packages("forcats")
install.packages("dplyr")

# Load the packages
library(forcats)
library(dplyr)

# Create a sample data frame with state factors
df <- data.frame(col = factor(rep(LETTERS[1:9], 
                 times = c(40, 10, 5, 27, 1, 1, 1, 1, 1))))

# Count the frequency of each level in the "State" column
df %>% count(col)

This will output:

#   col  n
#1   A 40
#2   B 10
#3   C  5
#4   D 27
#5   E  1
#6   F  1
#7   G  1
#8   H  1
#9   I  1

As we can see, the levels “A,” “B,” and “C” are more frequent than the others. Now, let’s use fct_lump_n to merge these infrequent levels into an “Others” category:

# Group by col and apply fct_lump_n with n = 3
df %>% mutate(col = fct_lump_n(col, 3)) %>% count(col)

This will produce the following output:

#    col  n
#1     A 40
#2     B 10
#3     D 27
#4 Others 13

As expected, levels “A,” “B,” and “C” remain intact with their original frequency counts. The remaining infrequent state level (“E”) has been merged into an “Others” category.

Customizing the `fct_lump_n` Function

By default, fct_lump_n preserves the original levels when merging less frequent ones into an “Others” category. However, if you want to customize this behavior or modify the output format, you can use additional arguments in the function:

order: Specify the order of levels when returning the results (default: ascending).
keep: Determine whether to keep the original level names during merging (default: TRUE).

Let’s explore these customization options further.

Example Customization

Suppose we want to change the output format and display only the top three most frequent levels, followed by an “Others” category:

# Group by col and apply fct_lump_n with n = 3, order = 'desc', keep = FALSE
df %>% mutate(col = fct_lump_n(col, 3, order = 'desc', keep = FALSE)) %>% count(col)

In this example, we’ve added two custom arguments to fct_lump_n: order and keep. By setting order to 'desc', the output will display the levels in descending frequency order. Additionally, by setting keep to FALSE, the original level names will be discarded during merging.

Using Customized Factors

After using fct_lump_n to merge less frequent state levels into an “Others” category, you may want to assign these customized factors back to your original data frame. You can do this by directly assigning the result of fct_lump_n to the corresponding column:

# Assign the fct_lump_n output back to the 'State' column
df$col <- fct_lump_n(df$col, 3)

Now that we’ve explored how to use fct_lump_n from forcats, let’s discuss some best practices and additional considerations when working with customized factors.

Best Practices

When merging less common levels of a factor into an “Others” category, keep the following guidelines in mind:

Ensure accurate data representation: Before applying any level-merging function, make sure that your data is accurate and reliable.
Use meaningful labels: When creating customized factors, use descriptive labels to maintain clarity and ease of interpretation.
Consider data implications: Be aware of how level merging may impact downstream analyses or data visualizations.

Conclusion

In this article, we’ve explored the fct_lump_n function from the forcats package in R. By using fct_lump_n, you can easily merge less frequent state levels into an “Others” category while preserving the original level names and counts. We’ve also discussed customization options to tailor the output format according to your specific requirements.

By following these guidelines and best practices, you’ll be able to effectively handle customized factors in your R projects and produce more accurate, informative visualizations.

Last modified on 2025-03-11