Mimicking a secondary tidy dots argument in an R function

I'm looking to create a function that accepts a list of (data frame) variables as one of its parameters. I've managed to get it working partially, but when I get to the group_by/count, things fall apart. How can I do this??

## Works
f1 <- function(dfr, ..., split = NULL) {
  dots <- rlang::enquos(...)
  split <- rlang::enquos(split)
  dfr %>%
    select(!!!dots, !!!split) %>%
    gather('type', 'score', -c(!!!split))
}

## does not work
f2 <- function(dfr, ..., split = NULL) {
  dots <- rlang::enquos(...)
  split <- rlang::enquos(split)
  dfr %>%
    select(!!!dots, !!!split) %>%
    gather('type', 'score', -c(!!!split))
    count(!!!split, type, score)
  }

I would want to do things like

mtcars %>% f2(drat:qsec)
mtcars %>% f2(drat:qsec, split = gear)
mtcars %>% f2(drat:qsec, split = c(gear, carb)) ## ??

These calls with f1() all work, but for f2 none of the commands work. They all end up with a Error in !split : invalid argument type. That f2(drat:qsec) doesn't (immediately) work without the split argument, I'm not too surprised about, but how to get the second and third comment working?

Solution

The issue with the second function (the missing pipe notwithstanding) is that count() (or rather group_by() which is called by count()) doesn't support tidyselect syntax so you can't pass it a list to be spliced like you can with select(), gather() etc. Instead, one option is to use group_by_at() and add_tally(). Here's a slightly modified version of the function:

library(dplyr)

f2 <- function(dfr, ..., split = NULL) {
  dfr %>%
    select(..., {{split}}) %>%
    gather('type', 'score', -{{split}}) %>%
    group_by_at(vars({{split}}, type, score)) %>% # could use `group_by_all()`
    add_tally()
}

mtcars %>% f2(drat:qsec)

# A tibble: 96 x 3
# Groups:   type, score [81]
   type  score     n
   <chr> <dbl> <int>
 1 drat   3.9      2
 2 drat   3.9      2
 3 drat   3.85     1
 4 drat   3.08     2
 5 drat   3.15     2
 6 drat   2.76     2
 7 drat   3.21     1
 8 drat   3.69     1
 9 drat   3.92     3
10 drat   3.92     3
# ... with 86 more rows

mtcars %>% f2(drat:qsec, split = c(gear, carb))

# A tibble: 96 x 5
# Groups:   gear, carb, type, score [89]
    gear  carb type  score     n
   <dbl> <dbl> <chr> <dbl> <int>
 1     4     4 drat   3.9      2
 2     4     4 drat   3.9      2
 3     4     1 drat   3.85     1
 4     3     1 drat   3.08     1
 5     3     2 drat   3.15     2
 6     3     1 drat   2.76     1
 7     3     4 drat   3.21     1
 8     4     2 drat   3.69     1
 9     4     2 drat   3.92     1
10     4     4 drat   3.92     2
# ... with 86 more rows