R: Apply function on specific columns preserving the rest of the dataframe

问题

I'd like to learn how to apply functions on specific columns of my dataframe without "excluding" the other columns from my df. For example i'd like to multiply some specific columns by 1000 and leave the other ones as they are.

Using the sapply function for example like this:

    a<-as.data.frame(sapply(table.xy[,1], function(x){x*1000}))

I get new dataframes with the first column multiplied by 1000 but without the other columns that I didn't use in the operation. So my attempt was to do it like this:

    a<-as.data.frame(sapply(table.xy, function(x) if (colnames=="columnA") {x/1000} else {x}))

but this one didn't work.

My workaround was to give both dataframes another row with IDs and later on merge the old dataframe with the newly created to get a complete one. But I think there must be a better solution. Isn't it?

回答1:

If you only want to do a computation on one or a few columns you can use transform or simply do index it manually:

# with transfrom:
df <- data.frame(A = 1:10, B = 1:10)
df <- transform(df, A = A*1000)

# Manually:
df <- data.frame(A = 1:10, B = 1:10)
df$A <- df$A * 1000

回答2:

The following code will apply the desired function to the only the columns you specify. I'll create a simple data frame as a reproducible example.

(df <- data.frame(x = 1, y = 1:10, z=11:20))
(df <- cbind(df[1], apply(df[2:3],2, function(x){x*1000})))

Basically, use cbind() to select the columns you don't want the function to run on, then use apply() with desired functions on the target columns.

回答3:

In dplyr we would use mutate_at in which you can select or exclude (by preceding variable name with "-" minus sign) specific variables. You can just name a function

df <- df %>% mutate_at(vars(columnA), scale)

or create your own

df <- df %>% mutate_at(vars(columnA, columnC), function(x) {do this})

来源：https://stackoverflow.com/questions/13398001/r-apply-function-on-specific-columns-preserving-the-rest-of-the-dataframe

标签

dataframe

sapply