library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.5.1
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# It looks like nrow() is a tough choice to use as your example because it
# doesn't respect groups inside mutate():
mtcars %>%
head() %>%
group_by(cyl) %>%
mutate(n = nrow(.))
#> Warning: package 'bindrcpp' was built under R version 3.5.1
#> # A tibble: 6 x 12
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 6
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 6
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 6
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 6
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 6
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 6
# But if you look at the code for add_tally it has some good hints for how to
# get the functionality you want. Essentially you have to wrap your custom
# function inside of quo(). In this example you can provide an input column just
# like you were using a regular mutate call.
paste_unique <- function(df, col) {
col <-
enquo(col)
paste_unique <-
quo(paste(!!col, collapse = ", "))
out <- mutate(df,
n = !! paste_unique)
grouped_df(out, group_vars(df))
}
mtcars %>%
head() %>%
paste_unique(hp) %>%
select(cyl, n)
#> # A tibble: 6 x 2
#> cyl n
#> <dbl> <chr>
#> 1 6 110, 110, 93, 110, 175, 105
#> 2 6 110, 110, 93, 110, 175, 105
#> 3 4 110, 110, 93, 110, 175, 105
#> 4 6 110, 110, 93, 110, 175, 105
#> 5 8 110, 110, 93, 110, 175, 105
#> 6 6 110, 110, 93, 110, 175, 105
mtcars %>%
head() %>%
group_by(cyl) %>%
paste_unique(hp) %>%
select(cyl, n)
#> # A tibble: 6 x 2
#> # Groups: cyl [3]
#> cyl n
#> <dbl> <chr>
#> 1 6 110, 110, 110, 105
#> 2 6 110, 110, 110, 105
#> 3 4 93
#> 4 6 110, 110, 110, 105
#> 5 8 175
#> 6 6 110, 110, 110, 105Created on 2018-10-09 by the reprex package (v0.2.1)