These functions return information about the "current" group or "current"
variable, so only work inside specific contexts like summarise() and
mutate().
n()gives the current group size.cur_group()gives the group keys, a tibble with one row and one column for each grouping variable.cur_group_id()gives a unique numeric identifier for the current group.cur_group_rows()gives the row indices for the current group.cur_column()gives the name of the current column (inacross()only).
See group_data() for equivalent functions that return values for all
groups.
See pick() for a way to select a subset of columns using tidyselect syntax
while inside summarise() or mutate().
data.table
If you're familiar with data.table:
cur_group_id()<->.GRPcur_group()<->.BYcur_group_rows()<->.I
See pick() for an equivalent to .SD.
Examples
df <- tibble(
g = sample(rep(letters[1:3], 1:3)),
x = runif(6),
y = runif(6)
)
gf <- df |> group_by(g)
gf |> summarise(n = n())
#> # A tibble: 3 × 2
#> g n
#> <chr> <int>
#> 1 a 1
#> 2 b 2
#> 3 c 3
gf |> mutate(id = cur_group_id())
#> # A tibble: 6 × 4
#> # Groups: g [3]
#> g x y id
#> <chr> <dbl> <dbl> <int>
#> 1 a 0.721 0.648 1
#> 2 b 0.142 0.320 2
#> 3 c 0.549 0.308 3
#> 4 b 0.954 0.220 2
#> 5 c 0.585 0.369 3
#> 6 c 0.405 0.984 3
gf |> reframe(row = cur_group_rows())
#> # A tibble: 6 × 2
#> g row
#> <chr> <int>
#> 1 a 1
#> 2 b 2
#> 3 b 4
#> 4 c 3
#> 5 c 5
#> 6 c 6
gf |> summarise(data = list(cur_group()))
#> # A tibble: 3 × 2
#> g data
#> <chr> <list>
#> 1 a <tibble [1 × 1]>
#> 2 b <tibble [1 × 1]>
#> 3 c <tibble [1 × 1]>
gf |> mutate(across(everything(), ~ paste(cur_column(), round(.x, 2))))
#> # A tibble: 6 × 3
#> # Groups: g [3]
#> g x y
#> <chr> <chr> <chr>
#> 1 a x 0.72 y 0.65
#> 2 b x 0.14 y 0.32
#> 3 c x 0.55 y 0.31
#> 4 b x 0.95 y 0.22
#> 5 c x 0.59 y 0.37
#> 6 c x 0.4 y 0.98
