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Using the dplyr package, you can convert multiple columns to factor variables with the mutate_at() function. This function takes two arguments, a vector of column names to convert to factor variables and a function (e.g. as.factor) to apply to the specified columns. This is a convenient way to easily convert multiple columns to factor variables in one line of code.
You can use the following methods to convert multiple columns to factor using functions from the package:
Method 1: Convert Specific Columns to Factor
library(dplyr) df %>% mutate_at(c('col1', 'col2'), as.factor)
Method 2: Convert All Character Columns to Factor
library(dplyr) df %>% mutate_if(is.character, as.factor)
The following examples show how to use each method in practice.
Example 1: Convert Specific Columns to Factor
Suppose we have the following data frame in R:
#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'C', 'C', 'D'),
position=c('G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'),
starter=c('Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'N'),
points=c(12, 24, 25, 35, 30, 14, 19, 11))
#view structure of data frame
str(df)
'data.frame': 8 obs. of 4 variables:
$ team : chr "A" "A" "A" "B" ...
$ position: chr "G" "G" "F" "F" ...
$ starter : chr "Y" "Y" "Y" "N" ...
$ points : num 12 24 25 35 30 14 19 11 We can see that the team, position, and starter columns are characters while the points column is numeric.
To convert just the team and position columns to factors, we can use the following syntax:
library(dplyr) #convert team and position columns to factor df <- df %>% mutate_at(c('team', 'position'), as.factor) #view structure of updated data frame str(df) 'data.frame': 8 obs. of 4 variables: $ team : Factor w/ 4 levels "A","B","C","D": 1 1 1 2 2 3 3 4 $ position: Factor w/ 2 levels "F","G": 2 2 1 1 2 2 1 1 $ starter : chr "Y" "Y" "Y" "N" ... $ points : num 12 24 25 35 30 14 19 11
We can see that the team and position columns are now both factors.
Example 2: Convert All Character Columns to Factor
Suppose we have the following data frame in R:
#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'C', 'C', 'D'),
position=c('G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'),
starter=c('Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'N'),
points=c(12, 24, 25, 35, 30, 14, 19, 11))
#view structure of data frame
str(df)
'data.frame': 8 obs. of 4 variables:
$ team : chr "A" "A" "A" "B" ...
$ position: chr "G" "G" "F" "F" ...
$ starter : chr "Y" "Y" "Y" "N" ...
$ points : num 12 24 25 35 30 14 19 11 We can see that three of the columns in the data frame are character columns.
library(dplyr) #convert all character columns to factor df <- df %>% mutate_if(is.character, as.factor) #view structure of updated data frame str(df) 'data.frame': 8 obs. of 4 variables: $ team : Factor w/ 4 levels "A","B","C","D": 1 1 1 2 2 3 3 4 $ position: Factor w/ 2 levels "F","G": 2 2 1 1 2 2 1 1 $ starter : Factor w/ 2 levels "N","Y": 2 2 2 1 1 2 1 1 $ points : num 12 24 25 35 30 14 19 11
We can see that all of the character columns are now factors.
Note: Refer to the for a complete explanation of the mutate_at and mutate_if functions.
The following tutorials explain how to perform other common operations in R: