Transmute over multiple columns in dplyr

Question

I have a dplyr question: How do I use transmute over each column without writing each column out by hand? I.e. is there something like transmute_each()?

I want to do the following: Using dplyr I want to get the z-score of each column for a MWE below:

tickers <- c(rep(1,10),rep(2,10))
df <- data.frame(cbind(tickers,rep(1:20),rep(2:21),rep(2:21),rep(4:23),rep(3:22)))
colnames(df) <- c("tickers","col1","col2","col3","col4","col5")
df %>%  group_by(tickers)

Is there a simple way to then use transmute to achieve the following:

for(i in 2:ncol(df)){
  df[,i] <- df[,i] - mean(df[,i])/sd(df[,i])
}

Many thanks

Answer 1

Now that there is a transmute_at() function (as of dplyr 0.7), you can do the following:

df %>% 
    group_by(tickers) %>% 
    transmute_at(.vars = vars(starts_with("col")),
                 .funs = funs(scale(.))) %>% 
    ungroup

Note that this uses the scale() function from base R, which by default converts a numeric vector into a z-score.

Also, the use of vars() in the .vars argument allows you to use all the helper functions that are available for dplyr's select(), such as one_of(), ends_with(), etc.

Finally, instead of writing funs(scale(.)) here, since you're using a simple function in the .funs argument, you can just write .funs = scale.

Answer 2

I solved this using the following:

df %>%  
   group_by(tickers) %>%  
   mutate_at(.funs = funs((. - mean(.))/sd(.)),
             .cols = vars(matches("col")))