How to use map from purrr with dplyr::mutate to create multiple new columns based on column pairs

Question

I have to following issue using R. In short I want to create multiple new columns in a data frame based on calculations of different column pairs in the data frame.

Answer 1

in base R, all vectorized:

nms <- names(df)
df[paste0("sum_",unique(gsub("[1-9]","",nms)))] <-
  df[endsWith(nms,"1")] + df[endsWith(nms,"2")]

#   a1 b1 c1 a2 b2 c2 sum_a sum_b sum_c
# 1  1  4 10  9  3 15    10     7    25
# 2  2  5 11 10  4 16    12     9    27
# 3  3  6 12 11  5 17    14    11    29
# 4  4  7 13 12  6 18    16    13    31
# 5  5  8 14 13  7 19    18    15    33

Answer 2

df %>% 
  mutate(sum_a = pmap_dbl(select(., starts_with("a")), sum), 
         sum_b = pmap_dbl(select(., starts_with("b")), sum),
         sum_c = pmap_dbl(select(., starts_with("c")), sum))

  a1 b1 c1 a2 b2 c2 sum_a sum_b sum_c
1  1  4 10  9  3 15    10     7    25
2  2  5 11 10  4 16    12     9    27
3  3  6 12 11  5 17    14    11    29
4  4  7 13 12  6 18    16    13    31
5  5  8 14 13  7 19    18    15    33

EDIT:

In the case there are many columns, and you wish to apply it programmatically:

row_sums <- function(x) {
  transmute(df, !! paste0("sum_", quo_name(x)) := pmap_dbl(select(df, starts_with(x)), sum))
}

newdf <- map_dfc(letters[1:3], row_sums)
newdf

  sum_a sum_b sum_c
1    10     7    25
2    12     9    27
3    14    11    29
4    16    13    31
5    18    15    33

And if needed you can tack on the original variables with:

bind_cols(df, dfnew)

  a1 b1 c1 a2 b2 c2 sum_a sum_b sum_c
1  1  4 10  9  3 15    10     7    25
2  2  5 11 10  4 16    12     9    27
3  3  6 12 11  5 17    14    11    29
4  4  7 13 12  6 18    16    13    31
5  5  8 14 13  7 19    18    15    33