I want to use the apply function on a dataframe, but only apply the function to the last 5 columns.

B<- by(wifi,(wifi$Room),FUN=function(y){apply(y, 2, A)})

This applies A to all the columns of y

B<- by(wifi,(wifi$Room),FUN=function(y){apply(y[4:9], 2, A)})

This applies A only to columns 4-9 of y, but the total return of B strips off the first 3 columns... I still want those, I just don't want A applied to them.

wifi[,1:3]+B 

also does not do what I expected/wanted.

2

6 Answers

lapply is probably a better choice than apply here, as apply first coerces your data.frame to an array which means all the columns must have the same type. Depending on your context, this could have unintended consequences.

The pattern is:

df[cols] <- lapply(df[cols], FUN)

The 'cols' vector can be variable names or indices. I prefer to use names whenever possible (it's robust to column reordering). So in your case this might be:

wifi[4:9] <- lapply(wifi[4:9], A)

An example of using column names:

wifi <- data.frame(A=1:4, B=runif(4), C=5:8)
wifi[c("B", "C")] <- lapply(wifi[c("B", "C")], function(x) -1 * x)

5

Using an example data.frame and example function (just +1 to all values)

A <- function(x) x + 1
wifi <- data.frame(replicate(9,1:4))
wifi
# X1 X2 X3 X4 X5 X6 X7 X8 X9
#1 1 1 1 1 1 1 1 1 1
#2 2 2 2 2 2 2 2 2 2
#3 3 3 3 3 3 3 3 3 3
#4 4 4 4 4 4 4 4 4 4
data.frame(wifi[1:3], apply(wifi[4:9],2, A) )
#or
cbind(wifi[1:3], apply(wifi[4:9],2, A) )
# X1 X2 X3 X4 X5 X6 X7 X8 X9
#1 1 1 1 2 2 2 2 2 2
#2 2 2 2 3 3 3 3 3 3
#3 3 3 3 4 4 4 4 4 4
#4 4 4 4 5 5 5 5 5 5

Or even:

data.frame(wifi[1:3], lapply(wifi[4:9], A) )
#or
cbind(wifi[1:3], lapply(wifi[4:9], A) )
# X1 X2 X3 X4 X5 X6 X7 X8 X9
#1 1 1 1 2 2 2 2 2 2
#2 2 2 2 3 3 3 3 3 3
#3 3 3 3 4 4 4 4 4 4
#4 4 4 4 5 5 5 5 5 5

5

This task is easily achieved with the dplyr package's across functionality.

Borrowing the data structure suggested by thelatemail:

A <- function(x) x + 1
wifi <- data.frame(replicate(9,1:4))

We can indicate the columns we wish to apply the function to either by index like this:

library(dplyr)
wifi %>% mutate(across(4:9, A))
# X1 X2 X3 X4 X5 X6 X7 X8 X9
#1 1 1 1 2 2 2 2 2 2
#2 2 2 2 3 3 3 3 3 3
#3 3 3 3 4 4 4 4 4 4
#4 4 4 4 5 5 5 5 5 5

Or by name:

wifi %>% mutate(across(X4:X9, A))
# X1 X2 X3 X4 X5 X6 X7 X8 X9
#1 1 1 1 2 2 2 2 2 2
#2 2 2 2 3 3 3 3 3 3
#3 3 3 3 4 4 4 4 4 4
#4 4 4 4 5 5 5 5 5 5

As mentioned, you simply want the standard R apply function applied to columns (MARGIN=2):

wifi[,4:9] <- apply(wifi[,4:9], MARGIN=2, FUN=A)

Or, for short:

wifi[,4:9] <- apply(wifi[,4:9], 2, A)

This updates columns 4:9 in-place using the A() function. Now, let's assume that na.rm is an argument to A(), which it probably should be. We can pass na.rm=T to remove NA values from the computation like so:

wifi[,4:9] <- apply(wifi[,4:9], MARGIN=2, FUN=A, na.rm=T)

The same is true for any other arguments you want to pass to your custom function.

The easiest way is to use the mutate function:

dataFunctionUsed <- data %>% mutate(columnToUseFunctionOn = function(oldColumn ...))

I think what you want is mapply. You could apply the function to all columns, and then just drop the columns you don't want. However, if you are applying different functions to different columns, it seems likely what you want is mutate, from the dplyr package.

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