Performing dplyr mutate on subset of columns

tidyr::nest() understands the same selector syntax as dplyr::select(), so one approach would be to consolidate the columns of interest into a single column-of-dataframes, perform the necessary operations on that column-of-dataframes, and unnest to get back a flat data frame:

library( tidyverse )
dd %>% nest( X2, X4, .key="Slice" ) %>%
    mutate( evensum = map(Slice, rowSums),
           evenmean = map(Slice, rowMeans),
           evensd = map(Slice, pmap_dbl, lift_vd(sd)) ) %>%
    unnest
#   id       X1    X3    X5 evensum evenmean evensd    X2    X4
# 1 a     0.602 0.687 0.447   0.876    0.438 0.100  0.367 0.509
# 2 b     0.195 0.831 0.965   1.70     0.848 0.200  0.989 0.707
# 3 c     0.966 0.105 0.141   1.68     0.839 0.0333 0.815 0.862
# 4 d     0.651 0.646 0.777   1.10     0.548 0.416  0.254 0.842

Since data frames are basically lists, this approach is naturally suited for applying arbitrary functions (such as sd above) to arbitrary an set of columns using purrr::pmap() family of functions.

Side note: Since sd works on vectors, we use purrr::lift_vd to convert its interface to be suitable for pmap:

sd( c(0.367, 0.509) )        # 0.100
lift_vd(sd)( 0.367, .509 )   # 0.100

Am I missing something or would this work as expected:

cols <- paste0("X", c(2,4))
dd %>% mutate(evensum = rowSums(.[cols]), evenmean = rowMeans(.[cols]))
#  id        X1        X2        X3        X4        X5   evensum  evenmean
#1  a 0.6021140 0.3670719 0.6872308 0.5090904 0.4474437 0.8761623 0.4380811
#2  b 0.1950439 0.9888592 0.8314290 0.7066286 0.9646670 1.6954878 0.8477439
#3  c 0.9664587 0.8151934 0.1046694 0.8623137 0.1411871 1.6775071 0.8387535
#4  d 0.6509055 0.2539684 0.6461509 0.8417851 0.7767125 1.0957535 0.5478768

Or are you specifically looking for a custom function to do this?

---

Not exactly what you are looking for but if you want to do it inside a pipe you could use select explicitly inside mutate like this:

dd %>% mutate(xy = select(., num_range("X", c(2,4))) %>% rowSums)
#  id        X1        X2        X3        X4        X5        xy
#1  a 0.6021140 0.3670719 0.6872308 0.5090904 0.4474437 0.8761623
#2  b 0.1950439 0.9888592 0.8314290 0.7066286 0.9646670 1.6954878
#3  c 0.9664587 0.8151934 0.1046694 0.8623137 0.1411871 1.6775071
#4  d 0.6509055 0.2539684 0.6461509 0.8417851 0.7767125 1.0957535

However, it is a bit more complicated if you want to apply several functions. You could use a helper function along the lines of (..not thoroughly tested.. ):

f <- function(x, ...) {
  n <- nrow(x)
  x <- lapply(list(...), function(y) if (length(y) == 1L) rep(y, n) else y)
  matrix(unlist(x), nrow = n, byrow = FALSE)
}

And then apply it like this:

dd %>% mutate(xy = select(., num_range("X", c(2,4))) %>% f(., rowSums(.), max(.)))
#  id        X1        X2        X3        X4        X5      xy.1      xy.2
#1  a 0.6021140 0.3670719 0.6872308 0.5090904 0.4474437 0.8761623 0.9888592
#2  b 0.1950439 0.9888592 0.8314290 0.7066286 0.9646670 1.6954878 0.9888592
#3  c 0.9664587 0.8151934 0.1046694 0.8623137 0.1411871 1.6775071 0.9888592
#4  d 0.6509055 0.2539684 0.6461509 0.8417851 0.7767125 1.0957535 0.9888592

A number-of-columns agnostic approach using dplyr:

dd %>% 
  select(-id) %>% 
  mutate(evensum = rowSums(.[,1:length(.[1,])%%2==0]), 
         evenmean = rowMeans(.[,1:length(.[1,])%%2==0])) %>% 
  cbind(id=dd[,1],.)

  id        X1        X2        X3        X4        X5   evensum  evenmean
1  a 0.6021140 0.3670719 0.6872308 0.5090904 0.4474437 0.8761623 0.4380812
2  b 0.1950439 0.9888592 0.8314290 0.7066286 0.9646670 1.6954878 0.8477439
3  c 0.9664587 0.8151934 0.1046694 0.8623137 0.1411871 1.6775071 0.8387535
4  d 0.6509055 0.2539684 0.6461509 0.8417851 0.7767125 1.0957535 0.5478767