context: Context dependent expressions

In dplyr: A Grammar of Data Manipulation

Description

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())

gf %>% mutate(id = cur_group_id())
gf %>% summarise(row = cur_group_rows())
gf %>% summarise(data = list(cur_group()))
gf %>% summarise(data = list(cur_data()))
gf %>% summarise(data = list(cur_data_all()))

gf %>% mutate(across(everything(), ~ paste(cur_column(), round(.x, 2))))

Example output

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  g         n
  <chr> <int>
1 a         1
2 b         2
3 c         3
# A tibble: 6 x 4
# Groups:   g [3]
  g          x     y    id
  <chr>  <dbl> <dbl> <int>
1 c     0.994  0.867     3
2 c     0.937  0.441     3
3 b     0.958  0.871     2
4 c     0.777  0.728     3
5 a     0.0312 0.820     1
6 b     0.985  0.632     2
`summarise()` regrouping output by 'g' (override with `.groups` argument)
# A tibble: 6 x 2
# Groups:   g [3]
  g       row
  <chr> <int>
1 a         5
2 b         3
3 b         6
4 c         1
5 c         2
6 c         4
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  g     data            
  <chr> <list>          
1 a     <tibble [1 × 1]>
2 b     <tibble [1 × 1]>
3 c     <tibble [1 × 1]>
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  g     data            
  <chr> <list>          
1 a     <tibble [1 × 2]>
2 b     <tibble [2 × 2]>
3 c     <tibble [3 × 2]>
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
  g     data            
  <chr> <list>          
1 a     <tibble [1 × 3]>
2 b     <tibble [2 × 3]>
3 c     <tibble [3 × 3]>
# A tibble: 6 x 3
# Groups:   g [3]
  g     x      y     
  <chr> <chr>  <chr> 
1 c     x 0.99 y 0.87
2 c     x 0.94 y 0.44
3 b     x 0.96 y 0.87
4 c     x 0.78 y 0.73
5 a     x 0.03 y 0.82
6 b     x 0.99 y 0.63

dplyr documentation built on June 19, 2021, 1:07 a.m.