R - Factors

R - Factors

Factors are a special data type in R used to handle categorical data. They are essential for statistical modeling and data visualization, as they allow you to define a fixed set of categories, known as levels. Factors can be created from both character and integer vectors and are particularly useful for columns with a limited number of unique values, such as "Male"/"Female", or "Low"/"Medium"/"High".

Factors are created using the factor() function, which takes a vector as its primary input. Understanding factors is crucial for both base R and modern data analysis workflows.

Important Note on Modern R: Since R version 4.0.0 (released in 2020), the default behavior of functions like data.frame() and read.csv() has changed. They no longer automatically convert character vectors into factors. The old default was stringsAsFactors = TRUE, but the new default is stringsAsFactors = FALSE. This means you now usually need to create factors explicitly, which gives you more control over your data.

Creating a Factor

Let’s create a factor from a character vector. Notice how the output displays the original data along with the unique Levels.

# Create a character vector
directions <- c("East", "West", "East", "North", "North", "East", "West")

# Check its class and if it's a factor
print(directions)
cat("Is it a factor? ", is.factor(directions), "\n")

# Apply the factor() function to convert it
factor_directions <- factor(directions)

# Check the result
print(factor_directions)
cat("Is it a factor now? ", is.factor(factor_directions), "\n")

When you run the code above, you get the following output:

[1] "East"  "West"  "East"  "North" "North" "East"  "West"
Is it a factor? FALSE

[1] East  West  East  North North East  West
Levels: East North West
Is it a factor now? TRUE

Factors in a Data Frame

As mentioned, modern R does not automatically create factors from strings. Let’s see this in action and then explicitly convert the column.

# Create vectors for our data frame
height <- c(132, 151, 162, 139, 166, 147, 122)
weight <- c(48, 49, 66, 53, 67, 52, 40)
gender <- c("male", "male", "female", "female", "male", "female", "male")

# Create the data frame
employee_data <- data.frame(height, weight, gender)
print(employee_data)

# Test if the gender column is a factor (it won't be by default)
cat("Is 'gender' a factor by default? ", is.factor(employee_data$gender), "\n")
cat("What is the class of 'gender'? ", class(employee_data$gender), "\n\n")

# Now, let's convert it to a factor
employee_data$gender <- factor(employee_data$gender)

# Check again
cat("Is 'gender' a factor now? ", is.factor(employee_data$gender), "\n")

# Print the gender column to see the levels
print(employee_data$gender)

Executing this code demonstrates the modern workflow:

  height weight gender
1    132     48   male
2    151     49   male
3    162     66 female
4    139     53 female
5    166     67   male
6    147     52 female
7    122     40   male
Is 'gender' a factor by default? FALSE
What is the class of 'gender'? character

Is 'gender' a factor now? TRUE
[1] male   male   female female male   female male
Levels: female male

Managing Factor Levels

The order of levels in a factor defaults to alphabetical. You can change this order, which is very important for visualizations and modeling (e.g., setting a baseline category).

Base R Approach

You can re-apply the factor() function with the levels argument to specify the desired order.

# Our original factor from before
directions <- c("East", "West", "East", "North")
factor_directions <- factor(directions)
cat("Original Factor Levels:\n")
print(factor_directions)

# Reorder the levels
ordered_factor <- factor(factor_directions, levels = c("North", "East", "West"))
cat("\nReordered Factor Levels:\n")
print(ordered_factor)

The output shows the new level ordering:

Original Factor Levels:
[1] East  West  East  North
Levels: East North West

Reordered Factor Levels:
[1] East  West  East  North
Levels: North East West

Modern Tidyverse Approach with forcats

The forcats package (part of the tidyverse) provides a suite of intuitive functions for working with factors. The fct_relevel() function is excellent for reordering.

# First, ensure you have tidyverse installed: install.packages("tidyverse")
library(forcats)

# Using the same factor
factor_directions <- factor(c("East", "West", "East", "North"))

# Use fct_relevel to bring "North" to the front
forcats_reordered <- fct_relevel(factor_directions, "North")

cat("Reordered with forcats:\n")
print(forcats_reordered)

# You can specify the full order as well
forcats_reordered_full <- fct_relevel(factor_directions, "North", "East", "West")
print(levels(forcats_reordered_full))

The forcats approach is often more readable and powerful for complex manipulations.

Reordered with forcats:
[1] East  West  East  North
Levels: North East West
[1] "North" "East"  "West"

Generating Factor Levels

The gl() function (Generate Levels) is a handy tool for creating balanced, repetitive factor structures, often used in designing experiments or creating sample data.

Syntax

gl(n, k, labels)

• n: an integer specifying the number of levels.
• k: an integer specifying the number of replications for each level.
• labels: a character vector providing labels for the factor levels.

Example

Let’s generate a factor with 3 levels, each repeated 4 times.

v <- gl(3, 4, labels = c("Tampa", "Seattle", "Boston"))
print(v)

The output clearly shows the generated factor:

 [1] Tampa   Tampa   Tampa   Tampa   Seattle Seattle Seattle Seattle Boston  Boston  Boston  Boston
Levels: Tampa Seattle Boston