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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_data() gives the current data for the current group (excluding grouping variables).

  • cur_data_all() gives the current data for the current group (including grouping variables)

  • 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.

Usage

n()

cur_data()

cur_data_all()

cur_group()

cur_group_id()

cur_group_rows()

cur_column()

data.table

If you're familiar with data.table:

  • cur_data() <-> .SD

  • cur_group_id() <-> .GRP

  • cur_group() <-> .BY

  • cur_group_rows() <-> .I

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     0.682  0.694     3
#> 2 b     0.140  0.151     2
#> 3 c     0.0559 0.957     3
#> 4 c     0.415  0.851     3
#> 5 a     0.158  0.263     1
#> 6 b     0.755  0.830     2
gf %>% summarise(row = cur_group_rows())
#> `summarise()` has grouped output by 'g'. You can override using the
#> `.groups` argument.
#> # A tibble: 6 × 2
#> # Groups:   g [3]
#>   g       row
#>   <chr> <int>
#> 1 a         5
#> 2 b         2
#> 3 b         6
#> 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 %>% summarise(data = list(cur_data()))
#> # A tibble: 3 × 2
#>   g     data            
#>   <chr> <list>          
#> 1 a     <tibble [1 × 2]>
#> 2 b     <tibble [2 × 2]>
#> 3 c     <tibble [3 × 2]>
gf %>% summarise(data = list(cur_data_all()))
#> # A tibble: 3 × 2
#>   g     data            
#>   <chr> <list>          
#> 1 a     <tibble [1 × 3]>
#> 2 b     <tibble [2 × 3]>
#> 3 c     <tibble [3 × 3]>

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 0.68 y 0.69
#> 2 b     x 0.14 y 0.15
#> 3 c     x 0.06 y 0.96
#> 4 c     x 0.42 y 0.85
#> 5 a     x 0.16 y 0.26
#> 6 b     x 0.76 y 0.83