How to Use the droplevels Function in R

The droplevels function in R is an invaluable function, specifically designed to drop unused levels in a factor or, more generally, in all factors appearing in a structured data object like data frames or lists. The usage of this function is crucial when dealing with categorical data, as unused levels can lead to inaccuracies in analyses and visualizations.

Basic Syntax of droplevels

Here is the basic syntax for the droplevels function:

droplevels(x, ...)

• x: The input object which could be a factor, data frame, or a list containing factors.
• ...: Additional arguments affecting the behavior of the function.

Why droplevels?

In R, categorical variables are typically stored as factors. Factors have levels which represent the different categories that the variable can take. When a factor variable is subsetted, some levels might not be represented in the data, but they still remain as levels of the factor variable. The droplevels function is used to remove these unused levels.

Basic Usage of droplevels

Let’s understand the basic usage of droplevels with a simple example:

fruits <- factor(c("apple", "banana", "cherry"))
subset_fruits <- fruits[fruits != "banana"]
print(levels(subset_fruits)) # Outputs "apple" "banana" "cherry"

cleaned_fruits <- droplevels(subset_fruits)
print(levels(cleaned_fruits)) # Outputs "apple" "cherry"

In this example, we first created a factor fruits with three levels. We then subsetted this factor to exclude “banana”. Although “banana” is not present in subset_fruits, it still remains as a level. Using droplevels, we successfully removed the unused level “banana” from cleaned_fruits.

Using droplevels with Data Frames

The droplevels function becomes especially relevant when working with data frames containing factor variables. Here’s how you can use droplevels on a data frame:

df <- data.frame(ID = c(1,2,3), Fruit = factor(c("apple", "banana", "cherry")))
subset_df <- df[df$Fruit != "banana",]
print(levels(subset_df$Fruit)) # Outputs "apple" "banana" "cherry"

cleaned_df <- droplevels(subset_df)
print(levels(cleaned_df$Fruit)) # Outputs "apple" "cherry"

In this scenario, even after subsetting the data frame df to exclude rows with “banana”, the level “banana” still exists in the subsetted data frame. Applying droplevels to the subsetted data frame effectively drops the unused level “banana”.

Impact on Modeling

Unused levels in factor variables can create problems in statistical modeling. Many modeling functions in R are designed to use the levels of a factor to define the possible outcomes or groups in the model. If unused levels are present, it can lead to inaccuracies or errors in the model.

For instance, when fitting a linear model, unused levels can result in incorrect degrees of freedom and p-values. Thus, using droplevels before modeling can help in preventing such inconsistencies and errors.

Use in Exploratory Data Analysis

During exploratory data analysis (EDA), understanding the distribution of categorical variables is critical. The presence of unused levels can misguide the analysis by suggesting categories that are not present in the dataset. Using droplevels ensures that visualizations and summary statistics generated during EDA accurately represent the available data.

Dropping Multiple Levels

In cases where a factor variable has multiple unused levels, droplevels can efficiently drop all of them in one go:

colors <- factor(c("red", "blue", "green", "yellow"))
subset_colors <- factor(colors[colors %in% c("red", "blue")])
cleaned_colors <- droplevels(subset_colors)
print(cleaned_colors) #output red  blue

Here, “green” and “yellow” are unused levels in subset_colors, and droplevels drops them both, leaving only “red” and “blue” as the levels in cleaned_colors.

Handling Nested Factors within Lists

When you have a list containing factor variables, each factor within the list needs to be individually addressed to remove unused levels. You can accomplish this by using the lapply() function to systematically apply droplevels() to each factor within the list. Here is a step-by-step guide:

# Step 1: Create a list containing factor variables
list_factors <- list(
  Fruit = factor(c("apple", "banana", "cherry")),
  Color = factor(c("red", "blue", "green"))
)

# Step 2: Subset or modify the list as needed
subset_list <- list(
  Fruit = list_factors$Fruit[list_factors$Fruit != "banana"],
  Color = list_factors$Color[list_factors$Color != "green"]
)

# Step 3: Apply droplevels() to each factor within the list
cleaned_list <- lapply(subset_list, function(x) if(is.factor(x)) droplevels(x) else x)
print(cleaned_list)

Explanation:

1. A list named list_factors containing factor variables is created.
2. The list is then modified or subsetted to exclude certain values, resulting in a subset_list.
3. Finally, the lapply() function is used to traverse the list and apply the droplevels() function to each factor within the list, yielding a cleaned_list with unused levels dropped.

By using lapply() combined with an anonymous function, this method ensures that droplevels() is properly applied to each factor within the list, providing a reliable approach to managing nested factor variables within list structures in R.

Using droplevels in Tidyverse

In the tidyverse ecosystem, the droplevels function can be integrated seamlessly with other tidyverse functions like filter and mutate:

library(tidyverse)

df <- tibble(ID = c(1,2,3), Fruit = factor(c("apple", "banana", "cherry")))

cleaned_df <- df %>%
  filter(Fruit != "banana") %>%
  droplevels()

This allows for efficient and readable data manipulation workflows.

Conclusion

The droplevels function in R is a fundamental tool for managing factor levels in structured data objects, crucial for accurate data analysis, visualization, and modeling. Its utility spans across various aspects of data analysis including data cleaning, preprocessing, and exploratory data analysis.

By meticulously removing unused levels in factors, whether they are standalone or within data frames or lists, droplevels aids in maintaining data integrity and ensuring that subsequent statistical analyses and visual representations are authentic and error-free.