Q:

Elementwise differences across multiple columns with dplyr

My goal is create new variables that are composed of differences, but iterating across multiple columns. In base, this is crazy easy:
iris[, 6:7] <- iris[, 1:2] - iris[, 3:4]

Is there a way to achieve this in dplyr, perhaps using mutate?
The following code subtracts the third column from the 1st and 2nd:
iris2 <- iris %>%
  mutate_at(1:2, funs(diffs = . - Petal.Length))

but what if I want to subtract the 3rd from the 1st and the 2nd from the 4th? 
I'm working with rather big data applications, so why not assume I'm trying to this across a table of 1000 columns -- a manual hack is not preferable ...

A:

Here's one way using dplyr::bind_cols and purrr::map2 that appears to be significantly faster than base at large numbers of columns. I don't know enough about profiling to guess at why, since it feels a little more convoluted than the other options. I am not sure that it is easy to do this with mutate_ verbs, though open to correction.
EDIT: added an option with dplyr::do, which is the "intended" way of doing computation that doesn't fit neatly inside a mutate function. The problem with mutate is that it expects to create exactly one column. I think other than using a map to construct individual mutate calls, which I cannot imagine will be faster, this is the best option.
library(tidyverse)
set.seed(4321)
df <- matrix(rnorm(1000000), ncol = 1000) %>%
  as_tibble()

microbenchmark::microbenchmark(
  base = df[, 1001:1500] <- df[, 1:500] - df[, 501:1000],
  base2 = df %>% magrittr::inset(, 1001:1500, .[, 1:500] - .[, 501:1000]),
  map =  df %>% bind_cols(map2(.x = .[, 1:500], .y = .[, 501:1000], .f = ~.x - .y)),
  nomap = df %>% bind_cols(.[, 1:500] - .[, 501:1000]),
  do = df %>% do(.[, 1:500] - .[, 501:1000])
)
#> Unit: milliseconds
#>   expr       min        lq      mean    median        uq       max neval
#>   base 32.928171 36.394238 39.362308 37.361149 39.454822 112.76356   100
#>  base2 33.302556 35.500491 38.888530 37.433863 40.207799  84.08674   100
#>    map  4.693637  5.139985  5.967655  5.468398  6.264793  12.20658   100
#>  nomap 23.061348 25.016053 28.598282 26.973913 29.574478  79.97451   100
#>     do 21.906042 23.460822 27.049262 25.135640 26.596373  80.01928   100
#>  cld
#>    c
#>    c
#>  a  
#>   b 
#>   b

Created on 2018-05-11 by the reprex package (v0.2.0).

