[R] Add Arrow methods slice_min(), slice_max()

[asfimport]

Implement slice_min() and slice_max() methods for ArrowTabular, Dataset, and arrow_dplyr_query objects.

These dplyr functions supersede the older dplyr function top_n() which I suppose we should also consider implementing a method for.

Reporter: Ian Cook / @ianmcook
Assignee: Neal Richardson / @nealrichardson

Related issues:

• [R] Add slice() method (is related to)
• [C++][Compute] Implement TopK/BottomK (depends upon)

PRs and other links:

• GitHub Pull Request #14361

_{Note: This issue was originally created as ARROW-13766. Please see the migration documentation for further details.}

[asfimport]

Dewey Dunnington / @paleolimbot:
Some example usage maybe useful for a test:

library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)

df <- tibble(a = rep(letters, 10), b = 1:260, c = 260:1)

df %>% slice_min(a, n = 5, with_ties = TRUE)
#> # A tibble: 10 × 3
#>    a         b     c
#>    <chr> <int> <int>
#>  1 a         1   260
#>  2 a        27   234
#>  3 a        53   208
#>  4 a        79   182
#>  5 a       105   156
#>  6 a       131   130
#>  7 a       157   104
#>  8 a       183    78
#>  9 a       209    52
#> 10 a       235    26
df %>% slice_min(a, n = 5, with_ties = FALSE)
#> # A tibble: 5 × 3
#>   a         b     c
#>   <chr> <int> <int>
#> 1 a         1   260
#> 2 a        27   234
#> 3 a        53   208
#> 4 a        79   182
#> 5 a       105   156

df %>% slice_min(c, n = 5)
#> # A tibble: 5 × 3
#>   a         b     c
#>   <chr> <int> <int>
#> 1 z       260     1
#> 2 y       259     2
#> 3 x       258     3
#> 4 w       257     4
#> 5 v       256     5
df %>% slice_min(c, prop = 5 / 260)
#> # A tibble: 5 × 3
#>   a         b     c
#>   <chr> <int> <int>
#> 1 z       260     1
#> 2 y       259     2
#> 3 x       258     3
#> 4 w       257     4
#> 5 v       256     5

[asfimport]

Dewey Dunnington / @paleolimbot:
Without ties this isn't bad:

library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)

df <- tibble(a = rep(letters, 10), b = 1:260, c = 260:1)

# slice_*() without ties is easier
record_batch(df) %>% 
  arrange(c) %>% head(5) %>%
  collect()
#> # A tibble: 5 × 3
#>   a         b     c
#>   <chr> <int> <int>
#> 1 z       260     1
#> 2 y       259     2
#> 3 x       258     3
#> 4 w       257     4
#> 5 v       256     5

record_batch(df) %>% 
  arrange(desc(c)) %>% head(5) %>%
  collect()
#> # A tibble: 5 × 3
#>   a         b     c
#>   <chr> <int> <int>
#> 1 a         1   260
#> 2 b         2   259
#> 3 c         3   258
#> 4 d         4   257
#> 5 e         5   256

With ties isn't too bad either (just needs a join):

library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)

df <- tibble(a = rep(letters, 10), b = 1:260, c = 260:1)

# slice_*() with ties needs a join
rb <-  record_batch(df)
rb %>% arrange(a) %>% select(a) %>% head(5) %>% distinct() %>% left_join(rb) %>% collect()
#> # A tibble: 10 × 3
#>    a         b     c
#>    <chr> <int> <int>
#>  1 a         1   260
#>  2 a        27   234
#>  3 a        53   208
#>  4 a        79   182
#>  5 a       105   156
#>  6 a       131   130
#>  7 a       157   104
#>  8 a       183    78
#>  9 a       209    52
#> 10 a       235    26

[asfimport]

Neal Richardson / @nealrichardson:
Issue resolved by pull request 14361
#14361