You can use the following basic syntax to replace multiple values in a data frame in R using functions from the dplyr package:
library(dplyr) df %>% mutate(var1 = recode(var1, 'oldvalue1' = 'newvalue1', 'oldvalue2' = 'newvalue2'), var2 = recode(var2, 'oldvalue1' = 'newvalue1', 'oldvalue2' = 'newvalue2'))
The following example shows how to use this syntax in practice.
Example: Replace Multiple Values Using dplyr
Suppose we have the following data frame in R that contains information about various basketball players:
#create data frame df <- data.frame(conf=c('East', 'East', 'West', 'West', 'North'), position=c('Guard', 'Guard', 'Guard', 'Guard', 'Forward'), points=c(22, 25, 29, 13, 18)) #view data frame df conf position points 1 East Guard 22 2 East Guard 25 3 West Guard 29 4 West Guard 13 5 North Forward 18
Now suppose we would like to replace the following values in the data frame:
- ‘conf’ column:
- Replace ‘East’ with ‘E’
- Replace ‘West’ with ‘W’
- Replace ‘North’ with ‘N’
- ‘position’ column:
- Replace ‘Guard’ with ‘G’
- Replace ‘Forward’ with ‘F’
We can use the mutate() and recode() functions to do so:
library(dplyr) #replace multiple values in conf and position columns df %>% mutate(conf = recode(conf, 'East' = 'E', 'West' = 'W', 'North' = 'N'), position = recode(position, 'Guard' = 'G', 'Forward' = 'F')) conf position points 1 E G 22 2 E G 25 3 W G 29 4 W G 13 5 N F 18
Notice that each of the values in the ‘conf’ and ‘position’ columns have been replaced with specific values.
Also notice that the values in the ‘points’ column have remain unchanged.
The following tutorials explain how to perform other common tasks using dplyr:
How to Recode Values Using dplyr
How to Replace NA with Zero in dplyr
How to Filter Rows that Contain a Certain String Using dplyr