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

1) dplyr/tidyr Convert to long form, summarize and convert back to wide form:

library(dplyr)
library(tidyr)

DF %>%
  mutate(Row = 1:n()) %>%
  gather(colname, value, -Row) %>%
  group_by(g = gsub("\\d", "", colname), Row) %>%
  summarize(sum = sum(value)) %>%
  ungroup %>%
  mutate(g = paste("sum", g, sep = "_")) %>%
  spread(g, sum) %>%
  arrange(Row) %>%
  cbind(DF, .) %>%
  select(-Row)

giving:

  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  4  7 13 12  6 18    16    13    31
4  5  8 14 13  7 19    18    15    33

2) base using matrix multiplication

nms is a vector of column names without the digits and prefaced with sum_. u is a vector of the unique elements of it. Form a logical matrix using outer from that which when multiplied by DF gives the sums -- the logicals get converted to 0-1 when that is done. Finally bind it to the input.

nms <- gsub("(\\D+)\\d", "sum_\\1", names(DF))
u <- unique(nms)
sums <- as.matrix(DF) %*% outer(nms, setNames(u, u), "==")
cbind(DF, sums)

giving:

  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  4  7 13 12  6 18    16    13    31
4  5  8 14 13  7 19    18    15    33

3) base with tapply

Using nms from (2) apply tapply to each row:

cbind(DF, t(apply(DF, 1, tapply, nms, sum)))

giving:

  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  4  7 13 12  6 18    16    13    31
4  5  8 14 13  7 19    18    15    33

You may wish to replace nms with factor(nms, levels = unique(nms)) in the above expression if the names are not in ascending order.