Generate a unique identifier for consecutive combinations

consecutive_id() generates a unique identifier that increments every time a variable (or combination of variables) changes. Inspired by data.table::rleid().

Usage

consecutive_id(...)

Arguments

...

Unnamed vectors. If multiple vectors are supplied, then they should have the same length.

Value

A numeric vector the same length as the longest element of ....

Examples

consecutive_id(c(TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, NA, NA))
#> [1] 1 1 2 2 3 4 5 5
consecutive_id(c(1, 1, 1, 2, 1, 1, 2, 2))
#> [1] 1 1 1 2 3 3 4 4

df <- data.frame(x = c(0, 0, 1, 0), y = c(2, 2, 2, 2))
df |> group_by(x, y) |> summarise(n = n())
#> `summarise()` has regrouped the output.
#> ℹ Summaries were computed grouped by x and y.
#> ℹ Output is grouped by x.
#> ℹ Use `summarise(.groups = "drop_last")` to silence this message.
#> ℹ Use `summarise(.by = c(x, y))` for per-operation grouping
#>   (`?dplyr::dplyr_by`) instead.
#> # A tibble: 2 × 3
#> # Groups:   x [2]
#>       x     y     n
#>   <dbl> <dbl> <int>
#> 1     0     2     3
#> 2     1     2     1
df |> group_by(id = consecutive_id(x, y), x, y) |> summarise(n = n())
#> `summarise()` has regrouped the output.
#> ℹ Summaries were computed grouped by id, x, and y.
#> ℹ Output is grouped by id and x.
#> ℹ Use `summarise(.groups = "drop_last")` to silence this message.
#> ℹ Use `summarise(.by = c(id, x, y))` for per-operation grouping
#>   (`?dplyr::dplyr_by`) instead.
#> # A tibble: 3 × 4
#> # Groups:   id, x [3]
#>      id     x     y     n
#>   <int> <dbl> <dbl> <int>
#> 1     1     0     2     2
#> 2     2     1     2     1
#> 3     3     0     2     1