How would you create a recursively nested tibble in R

Question

I trying to create what I'm calling a recursively nested tibble, a tibble with x rows that is nested such that each x row nests the following n rows of y. Here is an example of what I'm trying to achieve.

Given the tibble df:

df <- tibble(x = 5:1, y = 5:1)

I'm trying to produce a nested tibble that looks like this: (where data is the nested column)

The one approach I came up with was to use uncount to replicate the original tibble and then add an id column that represents each version of the tibble. Next, I grouped on the id column and then nested the tibble. From here create an index column for each group and filter out what I don't want. This may get me there, but its there must be a more elegant way to achieve this. I'd appreciate any thoughts or insights anyone has.

df <- tibble(x = 5:1, y = 5:1)
z <- uncount(df, 5, .id = "id")
z <- z %>% 
  arrange(id) %>% 
  select(id, x, y) %>% 
  group_by(id) %>% 
  mutate(n = row_number()-1)

Answer 1

Using purrr::map (or lapply) and tidyr::unnest you could do:

library(dplyr, warn = FALSE)
library(purrr)
library(tidyr)

df <- tibble(x = 5:1, y = 5:1)

n <- 3

df |>
  mutate(
    data = map(y, \(x) {
      y <- rev(seq_len(x - 1))
      tibble(y = y[seq_len(min(length(y), n))])
    }),
    .keep = "unused"
  ) |>
  unnest(data, keep_empty = TRUE)
#> # A tibble: 10 × 2
#>        x     y
#>    <int> <int>
#>  1     5     4
#>  2     5     3
#>  3     5     2
#>  4     4     3
#>  5     4     2
#>  6     4     1
#>  7     3     2
#>  8     3     1
#>  9     2     1
#> 10     1    NA