Context dependent expressions

Source: R/context.R

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.

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 x 2
#>   g         n
#>   <chr> <int>
#> 1 a         1
#> 2 b         2
#> 3 c         3

gf %>% mutate(id = cur_group_id())

#> # A tibble: 6 x 4
#> # Groups:   g [3]
#>   g          x     y    id
#>   <chr>  <dbl> <dbl> <int>
#> 1 b     0.258  0.885     2
#> 2 b     0.712  0.827     2
#> 3 c     0.968  0.726     3
#> 4 c     0.661  0.367     3
#> 5 a     0.796  0.523     1
#> 6 c     0.0732 0.470     3
gf %>% summarise(row = cur_group_rows())

#> `summarise()` has grouped output by 'g'. You can override using the `.groups` argument.
#> # A tibble: 6 x 2
#> # Groups:   g [3]
#>   g       row
#>   <chr> <int>
#> 1 a         5
#> 2 b         1
#> 3 b         2
#> 4 c         3
#> 5 c         4
#> 6 c         6
gf %>% summarise(data = list(cur_group()))

#> # A tibble: 3 x 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 x 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 x 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 x 3
#> # Groups:   g [3]
#>   g     x      y     
#>   <chr> <chr>  <chr> 
#> 1 b     x 0.26 y 0.88
#> 2 b     x 0.71 y 0.83
#> 3 c     x 0.97 y 0.73
#> 4 c     x 0.66 y 0.37
#> 5 a     x 0.8  y 0.52
#> 6 c     x 0.07 y 0.47