Suggestion: `slice_ends()`

[DesiQuintans]

I often want to know what discretising a continuous variable actually looks like in my data, so I often want to know what happens at the edges of level. Maybe this sort of thing is useful to enough other people that it's worth making a dedicated slice_ function?

library(dplyr)
#> 
#> 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

set.seed(12345)

x <- 
  data.frame(
    age = runif(50, min = 0, max = 100)
  ) %>% 
  mutate(
    age_group = 
      cut(
        age,
        breaks = c(0, 18, 26, 32, 50, 60, 75, 100),
        include.lowest = TRUE
      )
  )
  

x %>% 
  arrange(age) %>% 
  group_by(age_group) %>% 
  {
    bind_rows(
      slice_head(., n = 2),
      slice_tail(., n = 2)
    ) %>% 
    arrange(age)
  }
#> # A tibble: 24 × 2
#> # Groups:   age_group [6]
#>       age age_group
#>     <dbl> <fct>    
#>  1  0.114 [0,18]   
#>  2  0.599 [0,18]   
#>  3 16.6   [0,18]   
#>  4 17.9   [0,18]   
#>  5 18.8   (18,26]  
#>  6 22.6   (18,26]  
#>  7 22.6   (18,26]  
#>  8 26.0   (18,26]  
#>  9 32.1   (32,50]  
#> 10 32.5   (32,50]  
#> # ℹ 14 more rows

^{Created on 2024-12-09 with reprex v2.1.1}

[eutwt]

You can also use row_number() in slice()

x %>% 
  arrange(age) %>% 
  group_by(age_group) %>% 
  slice(head(row_number(), 2), tail(row_number(), 2))