Often you may want to use the apply() function to apply a function to specific columns in a data frame in R.
However, the apply() function first forces all columns in a data frame to have the same object type before applying a function, which can sometimes have unintended consequences.
A better choice is the lapply() function, which uses the following basic syntax:
df[c('col1', 'col2')] col1', 'col2')], my_function)
This particular example applies the function my_function to only col1 and col2 in the data frame.
The following example shows how to use this syntax in practice.
Example: Apply Function to Specific Columns of Data Frame
Suppose we have the following data frame in R:
#create data frame df frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'), points=c(19, 22, 15, NA, 14, 25, 25, 25), rebounds=c(10, 6, 3, 7, 11, 13, 9, 12), assists=c(4, 4, 3, 6, 7, 5, 10, 8)) #view data frame df team points rebounds assists 1 A 19 10 4 2 A 22 6 4 3 A 15 3 3 4 A NA 7 6 5 B 14 11 7 6 B 25 13 5 7 B 25 9 10 8 B 25 12 8
Now suppose we define the following function that multiplies values by 2 and then adds 1:
#define function
my_function function(x) x*2 + 1
We can use the following lapply() function to apply this function only to the points and rebounds columns in the data frame:
#apply function to specific columns df[c('points', 'rebounds')] points', 'rebounds')], my_function) #view updated data frame df team points rebounds assists 1 A 39 21 4 2 A 45 13 4 3 A 31 7 3 4 A NA 15 6 5 B 29 23 7 6 B 51 27 5 7 B 51 19 10 8 B 51 25 8
From the output we can see that we multiplied each value in the points and rebounds columns by 2 and then added 1.
Also notice that the team and assists columns remained unchanged.
Additional Resources
The following tutorials explain how to perform other common tasks in R:
A Guide to apply(), lapply(), sapply(), and tapply() in R
How to Use the transform Function in R