dplyr rowwise summarise by column, grouped by name

Let's consider this simple dataset

set.seed(12345)
df <- data.frame(a1 = rnorm(5), a2 = rnorm(5), a3 = rnorm(5), 
                 b1 = rnorm(5), b2 = rnorm(5), b3 = rnorm(5),
                 c1 = rnorm(5), c2 = rnorm(5), c3 = rnorm(5))

Which looks like

          a1         a2         a3         b1         b2         b3        c1          c2         c3
1  0.5855288 -1.8179560 -0.1162478  0.8168998  0.7796219  1.8050975 0.8118732  0.49118828  1.1285108
2  0.7094660  0.6300986  1.8173120 -0.8863575  1.4557851 -0.4816474 2.1968335 -0.32408658 -2.3803581
3 -0.1093033 -0.2761841  0.3706279 -0.3315776 -0.6443284  0.6203798 2.0491903 -1.66205024 -1.0602656
4 -0.4534972 -0.2841597  0.5202165  1.1207127 -1.5531374  0.6121235 1.6324456  1.76773385  0.9371405
5  0.6058875 -0.9193220 -0.7505320  0.2987237 -1.5977095 -0.1623110 0.2542712  0.02580105  0.8544517

Now, I would like to get the mean of columns starting with a specific letter, specified in a vector.

So, for instance if I have

cols <- c("a", "c")

I'd like to output a dataframe with two columns (a and c) containing the mean of the a1/a2/a3 and c1/c2/c3 columns respectively.

            a          c
1 -0.449558319  0.8105241
2  1.052292204 -0.1692037
3 -0.004953185 -0.2243752
4 -0.072480153  1.4457733
5 -0.354655514  0.3781747

I've been playing around with starts_with and row_wise but I can't quite get the correct syntax.

Solution

select columns that starts_with a or c, then use split.default to split the columns, and apply rowMeans on each of the groups:

library(dplyr)
library(purrr)
select(df, starts_with(cols)) %>% 
  split.default(gsub("\\d", "", names(.))) %>% 
  map_dfc(rowMeans)

         a      c
1 -0.450    0.811
2  1.05    -0.169
3 -0.00495 -0.224
4 -0.0725   1.45 
5 -0.355    0.378

Note that the gsub part might need to be changed depending on the structure of your column names.