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()
<->.GRP
cur_group()
<->.BY
cur_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 c 0.873 0.0707 3
#> 2 b 0.00228 0.386 2
#> 3 a 0.474 0.961 1
#> 4 c 0.525 0.118 3
#> 5 c 0.517 0.323 3
#> 6 b 0.0880 0.445 2
gf %>% reframe(row = cur_group_rows())
#> # A tibble: 6 × 2
#> g row
#> <chr> <int>
#> 1 a 3
#> 2 b 2
#> 3 b 6
#> 4 c 1
#> 5 c 4
#> 6 c 5
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 c x 0.87 y 0.07
#> 2 b x 0 y 0.39
#> 3 a x 0.47 y 0.96
#> 4 c x 0.53 y 0.12
#> 5 c x 0.52 y 0.32
#> 6 b x 0.09 y 0.44