How to Use case_when in dplyr for Conditional Mutations

Use case_when() in dplyr to create new columns based on multiple conditions. It works like an if-else ladder, checking each condition in order and assigning values accordingly. This helps you write clear and readable conditional logic inside mutate().

Syntax

The case_when() function takes a series of condition-value pairs separated by commas. Each condition is a logical test, and the value is what gets assigned if that condition is true. The syntax looks like this:

• condition1 ~ value1: If condition1 is true, assign value1.
• condition2 ~ value2: If condition2 is true, assign value2.
• ...
• TRUE ~ default_value: If none of the above conditions are true, assign default_value.

Use case_when() inside mutate() to add or modify columns in a data frame.

r

library(dplyr)

data <- tibble(x = c(1, 5, 10, 15))

result <- data %>%
  mutate(
    category = case_when(
      x < 5 ~ "small",
      x >= 5 & x < 10 ~ "medium",
      TRUE ~ "large"
    )
  )

Example

This example shows how to classify numbers into categories "small", "medium", or "large" using case_when(). It creates a new column category based on the value of x.

r

library(dplyr)

data <- tibble(x = c(2, 7, 12, 4, 9))

result <- data %>%
  mutate(
    category = case_when(
      x < 5 ~ "small",
      x >= 5 & x < 10 ~ "medium",
      TRUE ~ "large"
    )
  )

print(result)

Output

# A tibble: 5 × 2
      x category
  <dbl> <chr>   
1     2 small   
2     7 medium  
3    12 large   
4     4 small   
5     9 medium  

Common Pitfalls

Common mistakes when using case_when() include:

• Not covering all cases, which leads to NA values if no condition matches.
• Using overlapping conditions that cause unexpected matches.
• Forgetting to use TRUE ~ as a catch-all default case.

Always order conditions from most specific to least specific to avoid conflicts.

r

library(dplyr)

data <- tibble(score = c(85, 70, 55, 40))

# Wrong: Missing default case leads to NA
result_wrong <- data %>%
  mutate(
    grade = case_when(
      score >= 80 ~ "A",
      score >= 60 ~ "B"
      # Missing TRUE ~ ... leads to NA for scores < 60
    )
  )

# Right: Add default case
result_right <- data %>%
  mutate(
    grade = case_when(
      score >= 80 ~ "A",
      score >= 60 ~ "B",
      TRUE ~ "F"
    )
  )

print(result_wrong)
print(result_right)

Output

# A tibble: 4 × 2
  score grade
  <dbl> <chr>
1    85 A    
2    70 B    
3    55 <NA> 
4    40 <NA> 
# A tibble: 4 × 2
  score grade
  <dbl> <chr>
1    85 A    
2    70 B    
3    55 F    
4    40 F    

Quick Reference

Usage	Description
condition ~ value	Assign value if condition is TRUE
TRUE ~ default_value	Assign default if no conditions match
Use inside mutate()	Add or modify columns in a data frame
Order conditions carefully	From most specific to least specific
Avoid overlapping conditions	To prevent unexpected results

Key Takeaways

Use case_when() inside mutate() to create conditional columns in dplyr.

Always include a TRUE ~ default case to handle unmatched conditions.

Order your conditions from most specific to least specific to avoid conflicts.

case_when() returns NA if no condition matches and no default is set.

It simplifies complex if-else logic into clear, readable code.