dplyr: How to Use mutate() with Multiple Conditions

You can use the following basic syntax in dplyr to use the mutate() function to create a new column based on multiple conditions:

library(dplyr)

df <- df%>% mutate(class = case_when((team == 'A' & points >= 20) ~ 'A_Good',
                                     (team == 'A' & points < 20) ~ 'A_Bad',
                                     (team == 'B' & points >= 20) ~ 'B_Good',
                                      TRUE ~ 'B_Bad'))

This particular syntax creates a new column called class that takes on the following values:

A_Good if team is equal to A and points is greater than or equal to 20.
A_Bad if team is equal to A and points is less than 20.
B_Good if team is equal to B and points is greater than or equal to 20.
B_Bad if none of the previous conditions are met.

The following example shows how to use this syntax in practice.

Example: Use mutate() in dplyr with Multiple Conditions

Suppose we have the following data frame in R that contains information about various basketball players:

#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'),
                 points=c(22, 30, 34, 19, 14, 12, 39, 15, 22, 25))

#view data frame
df

   team points
1     A     22
2     A     30
3     A     34
4     A     19
5     A     14
6     B     12
7     B     39
8     B     15
9     B     22
10    B     25

We can use the following syntax with the mutate() function to create a new column called class whose values are based on the values of the team and points columns:

library(dplyr)
#add new column based on values in team and points columns
df <- df%>% mutate(class = case_when((team == 'A' & points >= 20) ~ 'A_Good',
                                     (team == 'A' & points < 20) ~ 'A_Bad',
                                     (team == 'B' & points >= 20) ~ 'B_Good',
                                      TRUE ~ 'B_Bad'))

#view updated data frame
df

   team points  class
1     A     22 A_Good
2     A     30 A_Good
3     A     34 A_Good
4     A     19  A_Bad
5     A     14  A_Bad
6     B     12  B_Bad
7     B     39 B_Good
8     B     15  B_Bad
9     B     22 B_Good
10    B     25 B_Good

The new class column takes on values based on the values in the team and points columns.

For example, the first row had a value of A in the team column and a points value greater than or equal to 20, so it received a value of A_Good in the new class column.

Note that in this example we used the & symbol as an “AND” operator to check if two conditions were both met before assigning a value in the class column.

However, we could have used the | symbol as an “OR” operator to instead check if either one of two conditions were met before assigning a value in the class column.