mutate columns after subsetting by value

Question

I have a large dataframe and want to standardise multiple columns while conditioning the mean and the standard deviation on values. Say I have the following example data:

set.seed(123)
df = data.frame("sample" = c(rep(1:2, each = 5)),
       "status" = c(0,1),
       "s1" = runif(10, -1, 1),
       "s2" = runif(10, -5, 5),
       "s3" = runif(10, -25, 25))

and want to standardise every s1-s3 while conditioning the mean and standard deviation to be status==0. If I should do this for say, s1 only I could do the following:

df = df %>% group_by(sample) %>%
  mutate(sd_s1 = (s1 - mean(s1[status==0])) / sd(s1[status==0]))

But my problem arises when I have to perform this operation on multiple columns. I tried writing a function to include with mutate_at:

standardize <- function(x) {
    return((x - mean(x[status==0]))/sd(x[status==0]))
}

df = df %>% group_by(sample) %>% 
  mutate_at(vars(s1:s3), standardize)

Which just creates Na values for s1-s3. I have tried to use the answer provided in: R - dplyr - mutate - use dynamic variable names, but cannot figure out how to do the subsetting.

Any help is greatly appreciated. Thanks!

Answer 1

We could just use

df %>%
  group_by(sample) %>% 
  mutate_at(vars(s1:s3), funs((.- mean(.[status == 0]))/sd(.[status == 0])))