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case_when() is a function in the dplyr package that can be used to create new columns from existing data by applying conditions to the values in existing columns. It takes in a series of conditions with corresponding values to assign to the new column. The syntax is case_when(conditions)~value, where conditions is a logical statement or series of logical statements and value is the corresponding value to be assigned if the conditions evaluate to TRUE. The case_when() function can be used to quickly create new columns with assigned values for different subsets of existing data.
The case_when() function from the package in R can be used to create new variables from existing variables.
This function uses the following basic syntax:
library(dplyr) df %>% mutate(new_var = case_when(var1 < 15 ~ 'low', var2 < 25 ~ 'med', TRUE ~ 'high'))
Note that TRUE is equivalent to an “else” statement.
The following examples show how to use this function in practice with the following data frame:
#create data frame df <- data.frame(player = c('AJ', 'Bob', 'Chad', 'Dan', 'Eric', 'Frank'), position = c('G', 'F', 'F', 'G', 'C', NA), points = c(12, 15, 19, 22, 32, NA), assists = c(5, 7, 7, 12, 11, NA)) #view data frame df player position points assists 1 AJ G 12 5 2 Bob F 15 7 3 Chad F 19 7 4 Dan G 22 12 5 Eric C 32 11 6 Frank NA NA NA
Example 1: Create New Variable from One Existing Variable
The following code shows how to create a new variable called quality whose values are derived from the points column:
df %>% mutate(quality = case_when(points > 20 ~ 'high', points > 15 ~ 'med', TRUE ~ 'low' )) player position points assists quality 1 AJ G 12 5 low 2 Bob F 15 7 low 3 Chad F 19 7 med 4 Dan G 22 12 high 5 Eric C 32 11 high 6 Frank NA NA NA low
Here is exactly how the case_when() function created the values for the new column:
- If the value in the points column is greater than 20, then the value in the quality column is “high”
- Else, if the value in the points column is greater than 15, then the value in the quality column is “med”
- Else, if the value in the points column is less than or equal to 15 (or a missing value like NA), then the value in the quality column is “low”
Example 2: Create New Variable from Multiple Variables
The following code shows how to create a new variable called quality whose values are derived from both the points and assists column:
df %>% mutate(quality = case_when(points > 15 & assists > 10 ~ 'great', points > 15 & assists > 5 ~ 'good', TRUE ~ 'average' )) player position points assists quality 1 AJ G 12 5 average 2 Bob F 15 7 average 3 Chad F 19 7 good 4 Dan G 22 12 great 5 Eric C 32 11 great 6 Frank NA NA NA average
Note that we can also use the is.na() function to explicitly assign strings to NA values:
df %>% mutate(quality = case_when(is.na(points) ~ 'missing', points > 15 & assists > 10 ~ 'great', points > 15 & assists > 5 ~ 'good', TRUE ~ 'average' )) player position points assists quality 1 AJ G 12 5 average 2 Bob F 15 7 average 3 Chad F 19 7 good 4 Dan G 22 12 great 5 Eric C 32 11 great 6 Frank NA NA NA missing