My question is similar to this question but I need to apply a more complex function across columns and I can't figure out how to apply Lionel's suggested solution to a custom function with a scoped verb like filter_at() or a filter()+across() equivalent. It doesn't look like a "superstache"/{{{}}} operator has been introduced.
Here is a non-programmed example of what I want to do (doesn't use NSE):
library(dplyr)
library(magrittr)
foo <- tibble(group = c(1,1,2,2,3,3),
a = c(1,1,0,1,2,2),
b = c(1,1,2,2,0,1))
foo %>%
group_by(group) %>%
filter_at(vars(a,b), any_vars(n_distinct(.) != 1)) %>%
ungroup
#> # A tibble: 4 x 3
#> group a b
#> <dbl> <dbl> <dbl>
#> 1 2 0 2
#> 2 2 1 2
#> 3 3 2 0
#> 4 3 2 1
I haven't found an equivalent of this filter_at line with filter+across() yet, but since the new(ish) tidyeval functions predate dplyr 1.0 I assume that issue can be set aside. Here is my attempt to make a programmed version where the filtering variables are user-supplied with dots:
my_function <- function(data, ..., by) {
dots <- enquos(..., .named = TRUE)
helperfunc <- function(arg) {
return(any_vars(n_distinct(arg) != length(arg)))
}
dots <- lapply(dots, function(dot) call("helperfunc", dot))
data %>%
group_by({{ by }}) %>%
filter(!!!dots) %>%
ungroup
}
foo %>%
my_function(a, b, group)
#> Error: Problem with `filter()` input `..1`.
#> x Input `..1` is named.
#> i This usually means that you've used `=` instead of `==`.
#> i Did you mean `a == helperfunc(a)`?
I'd love if there were a way to just plug in an NSE operator inside the vars() argument in filter_at and not have to make all these extra calls (I assume this is what a {{{}}} function would do?)
Maybe I'm misunderstanding what the issue is, but the standard pattern of forwarding the dots seems to work fine here:
my_function <- function(data, ..., by) {
data %>%
group_by({{ by }}) %>%
filter_at(vars(...), any_vars(n_distinct(.) != 1)) %>%
ungroup
}
foo %>%
my_function( a, b, by=group ) # works