Why does across ignore grouping in its additional arguments?

[djrobust]

From a StackOverflow post.

I want to compute a weighted moving average across multiple columns, using the same weights for each column. The weighted moving average shall be computed per group.

In the example below, the grouping should make the weighted moving average "reset" every year, yielding missing values for the first two observations of each year.

Why does version 1 work, but version 2 does not?

library(tidyverse)

weighted.filter <- function(x, wt, filter, ...) {
  filter <- filter / sum(filter)
  stats::filter(x * wt, filter, ...) / stats::filter(wt, filter, ...)
}

# Version 1
economics %>%
  group_by(year = lubridate::year(date)) %>%
  arrange(date) %>%
  mutate(across(
    c(pce, psavert, uempmed),
    list("moving_average_weighted" = ~ weighted.filter(., wt = pop, filter = rep(1, 3), sides = 1))
  ))
#> # A tibble: 574 x 10
#> # Groups:   year [49]
#>    date         pce    pop psavert uempmed unemploy  year pce_moving_average_we…
#>    <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>                  <dbl>
#>  1 1967-07-01  507. 198712    12.6     4.5     2944  1967                    NA 
#>  2 1967-08-01  510. 198911    12.6     4.7     2945  1967                    NA 
#>  3 1967-09-01  516. 199113    11.9     4.6     2958  1967                   511.
#>  4 1967-10-01  512. 199311    12.9     4.9     3143  1967                   513.
#>  5 1967-11-01  517. 199498    12.8     4.7     3066  1967                   515.
#>  6 1967-12-01  525. 199657    11.8     4.8     3018  1967                   518.
#>  7 1968-01-01  531. 199808    11.7     5.1     2878  1968                    NA 
#>  8 1968-02-01  534. 199920    12.3     4.5     3001  1968                    NA 
#>  9 1968-03-01  544. 200056    11.7     4.1     2877  1968                   536.
#> 10 1968-04-01  544  200208    12.3     4.6     2709  1968                   541.
#> # … with 564 more rows, and 2 more variables:
#> #   psavert_moving_average_weighted <dbl>,
#> #   uempmed_moving_average_weighted <dbl>

# Version 2
economics %>%
  group_by(year = lubridate::year(date)) %>%
  arrange(date) %>%
  mutate(across(
    c(pce, psavert, uempmed),
    list("moving_average_weighted" = weighted.filter),
    wt = pop, filter = rep(1, 3), sides = 1
  ))
#> Error: Problem with `mutate()` input `..1`.
#> x Input `..1` can't be recycled to size 12.
#> ℹ Input `..1` is `(function (.cols = everything(), .fns = NULL, ..., .names = NULL) ...`.
#> ℹ Input `..1` must be size 12 or 1, not 6.
#> ℹ The error occurred in group 2: year = 1968.

^{Created on 2021-03-31 by the reprex package (v1.0.0)}

[romainfrancois]

Simpler reprex:

library(dplyr, warn.conflicts = FALSE)
f <- function(x, z) {
  x + z
}

data.frame(x = 1:2, y = 3:4, g = 1:2) %>% 
  group_by(g) %>% 
  summarise(across(x, f, z = y))
#> Error: Problem with `summarise()` input `..1`.
#> x object 'y' not found
#> ℹ Input `..1` is `across(x, f, z = y)`.
#> ℹ The error occurred in group 1: g = 1.

^{Created on 2021-04-01 by the reprex package (v0.3.0)}