Take random sample of rows from dataframe with grouping variables

I have a dataframe with the following structure:

dat <- tibble(
  item_type  = rep(1:36, each = 6), 
  condition1 = rep(c("a", "b", "c"), times = 72), 
  condition2 = rep(c("y", "z"), each = 3, times = 36), 
) %>% 
  unite(unique, item_type, condition1, condition2, sep = "-", remove = F)

which looks like this:

# A tibble: 216 × 4
   unique item_type condition1 condition2
   <chr>      <int> <chr>      <chr>     
 1 1-a-y          1 a          y         
 2 1-b-y          1 b          y         
 3 1-c-y          1 c          y         
 4 1-a-z          1 a          z         
 5 1-b-z          1 b          z         
 6 1-c-z          1 c          z         
 7 2-a-y          2 a          y         
 8 2-b-y          2 b          y         
 9 2-c-y          2 c          y         
10 2-a-z          2 a          z    

I would like to take a random sample of 36 rows. The sample should include 6 repetitions of the condition1 by condition2 combinations without repeating item_type.

Using slice_sample() it seems I can get the subset I want...

set.seed(1)
dat %>% 
  slice_sample(n = 6, by = c("condition1", "condition2")) %>% 
  count(condition1, condition2)

  condition1 condition2     n
  <chr>      <chr>      <int>
1 a          y              6
2 a          z              6
3 b          y              6
4 b          z              6
5 c          y              6
6 c          z              6

But on closer inspection I see that item_type is repeated.

set.seed(1)
dat %>% 
  slice_sample(n = 6, by = c("condition1", "condition2")) %>% 
  count(item_type) %>% 
  arrange(desc(n))

# A tibble: 22 × 2
   item_type     n
       <int> <int>
 1        10     3
 2        34     3
 3         1     2
 4         6     2
 5         7     2
 6        15     2
 7        20     2
 8        21     2
 9        23     2
10        25     2
# … with 12 more rows

In other words, I would like only unique pulls overall from item_type.
Is it possible to get slice_sample() to do this?

EDIT
Adding second toy data example.

dat <- tibble(
  item_type  = rep(1:36, each = 3), 
  condition1 = rep(c("a", "b"), each = 54), 
  condition2 = rep(c("x", "y", "z"), times = 36), 
) %>% 
  unite(unique, item_type, condition1, condition2, sep = "-", remove = F)

Which looks like this:

# A tibble: 108 × 4
   unique item_type condition1 condition2
   <chr>      <int> <chr>      <chr>     
 1 1-a-x          1 a          x         
 2 1-a-y          1 a          y         
 3 1-a-z          1 a          z         
 4 2-a-x          2 a          x         
 5 2-a-y          2 a          y         
 6 2-a-z          2 a          z         
 7 3-a-x          3 a          x         
 8 3-a-y          3 a          y         
 9 3-a-z          3 a          z         
10 4-a-x          4 a          x    

Attempt to sample:

inner_join(
  dat, 
  distinct(dat,condition1, condition2) %>% 
    uncount(n()) %>% 
    mutate(item_type = sample(n()))
) 

Which produces a dataframe of length 20 with the following characteristics:

  condition1 condition2     n
  <chr>      <chr>      <int>
1 a          x              4
2 a          y              4
3 a          z              4
4 b          x              3
5 b          y              4
6 b          z              5

You could do this:

inner_join(
  dat, 
  distinct(dat,condition1, condition2) %>% 
    uncount(n()) %>% 
    mutate(item_type=sample(n())),
)

Output:

# A tibble: 36 × 4
   unique item_type condition1 condition2
   <chr>      <int> <chr>      <chr>     
 1 1-b-z          1 b          z         
 2 2-a-z          2 a          z         
 3 3-c-y          3 c          y         
 4 4-c-z          4 c          z         
 5 5-b-z          5 b          z         
 6 6-a-y          6 a          y         
 7 7-c-y          7 c          y         
 8 8-a-y          8 a          y         
 9 9-a-y          9 a          y         
10 10-c-z        10 c          z         
# … with 26 more rows

On the second dataset, you need to get the min/max range to sample:

inner_join(
  dat, 
  distinct(dat,condition1, condition2) %>% 
    uncount(n()) %>% 
    inner_join(dat %>% group_by(condition1, condition2) %>% summarize(imin = min(item_type), imax=max(item_type), .groups="drop")) %>% 
    group_by(condition1) %>% 
    mutate(item_type = sample(imin[1]:imax[1],size = n())) %>% 
    ungroup() %>% 
    select(-c(imin:imax))
)

Output:

# A tibble: 36 × 4
   unique item_type condition1 condition2
   <chr>      <int> <chr>      <chr>     
 1 1-a-y          1 a          y         
 2 2-a-z          2 a          z         
 3 3-a-z          3 a          z         
 4 4-a-y          4 a          y         
 5 5-a-z          5 a          z         
 6 6-a-y          6 a          y         
 7 7-a-x          7 a          x         
 8 8-a-z          8 a          z         
 9 9-a-y          9 a          y         
10 10-a-z        10 a          z         
# … with 26 more rows

Try

library(nplyr)
library(dplyr)
library(tidyr)
dat %>% 
  nest(data = -item_type) %>%
  nest_slice_sample(data, n = 1) %>%
  unnest(data)

-output

# A tibble: 36 × 4
   item_type unique condition1 condition2
       <int> <chr>  <chr>      <chr>     
 1         1 1-c-z  c          z         
 2         2 2-b-z  b          z         
 3         3 3-b-y  b          y         
 4         4 4-c-y  c          y         
 5         5 5-c-z  c          z         
 6         6 6-b-z  b          z         
 7         7 7-a-z  a          z         
 8         8 8-c-z  c          z         
 9         9 9-b-y  b          y         
10        10 10-a-y a          y         
# … with 26 more rows

Or perhaps we need

lst1 <- split(dat, dat[c("condition1", "condition2")], drop = TRUE)
lst2 <- vector('list', length(lst1))
item_type_rm <- numeric(0)
for(i in seq_along(lst1))
{
tmp <- lst1[[i]]
tmp1 <- tmp %>% 
   filter(!item_type %in% item_type_rm) %>%
   slice_sample(n = 6)
 item_type_rm <- c(item_type_rm, tmp1$item_type)
 lst2[[i]] <- tmp1
 

}

out <- bind_rows(lst2)
out
# A tibble: 36 × 4
   unique item_type condition1 condition2
   <chr>      <int> <chr>      <chr>     
 1 17-a-x        17 a          x         
 2 5-a-x          5 a          x         
 3 9-a-x          9 a          x         
 4 2-a-x          2 a          x         
 5 7-a-x          7 a          x         
 6 3-a-x          3 a          x         
 7 31-b-x        31 b          x         
 8 27-b-x        27 b          x         
 9 36-b-x        36 b          x         
10 19-b-x        19 b          x         
# … with 26 more rows
> out %>% count(item_type) %>% pull(n)
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1