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 (in across() 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().
n()
cur_group()
cur_group_id()
cur_group_rows()
cur_column()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.
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 0.0940 0.216 3
#> 2 b 0.697 0.666 2
#> 3 c 0.406 0.204 3
#> 4 c 0.0656 0.549 3
#> 5 b 0.126 0.828 2
#> 6 a 0.937 0.128 1
gf %>% reframe(row = cur_group_rows())
#> # A tibble: 6 × 2
#> g row
#> <chr> <int>
#> 1 a 6
#> 2 b 2
#> 3 b 5
#> 4 c 1
#> 5 c 3
#> 6 c 4
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 0.09 y 0.22
#> 2 b x 0.7 y 0.67
#> 3 c x 0.41 y 0.2
#> 4 c x 0.07 y 0.55
#> 5 b x 0.13 y 0.83
#> 6 a x 0.94 y 0.13