group by and summarise a column into defined breaks

I have the following data:

(df <- data.frame(
  random_str = rep("ok", 30),
  value      = rnorm(30, mean=50, sd=25)
))

I want to run a few summary statistics, and can do so:

df %>% 
  group_by(random_str) %>% 
  summarise(max = max(value),
            min = min(value)
            # and so on.....
  )

But the value column I also want to split into breaks (e.g. 0-33, 34-66, 67-100).

I can do this manually:

df %>% 
  group_by(random_str) %>% 
  summarise(max = max(value),
            min = min(value),
            # and so on.....
            # .. more stuff
            # ..
            count1_33  = sum(value < 34),
            count34_66  = sum(value < 67 & value > 33),
            count67_100 = sum(value > 66)
  )

But is there a way to do these last three summaries automatically, in reality I have millions of rows of data and want 100s of equally space breaks.

set.seed(100)
df <- data.frame(
  random_str = rep("ok", 30),
  value      = rnorm(30, mean=50, sd=25))

library(dplyr)
library(tidyr)


df %>% 
  group_by(random_str) %>% 
  summarise(max = max(value),
            min = min(value)) %>% 
  bind_cols(., 
            df %>% 
              group_by(random_str, 
                       range = cut(value, 
                            breaks = seq(1, by = 33, 
                                    length.out = ceiling(max(value)/33) + 1))) %>% 
              summarise( n = n(), .groups = "drop") %>% 
              pivot_wider(-random_str, names_from = "range", values_from = "n"))

#> # A tibble: 1 x 7
#>   random_str   max   min `(1,34]` `(34,67]` `(67,100]` `(100,133]`
#>   <chr>      <dbl> <dbl>    <int>     <int>      <int>       <int>
#> 1 ok          108.  21.1        5        19          5           1

Update:

I am not sure if we can get cut to include empty ranges. For this, I used purrr::map_2 to provide the ranges "manually" and set their names (looped over each random_str). Then, I bind_rows the resulted dataframes (for each group), and finally will join it with summary stats.

set.seed(100)
df <- data.frame(
  random_str = c(rep("ok", 15), rep("not", 15)),
  value      = rnorm(30, mean=500, sd=250))

library(dplyr)
library(purrr)

mseq <- seq(0, 900, 100)

split(df, as.factor(df$random_str)) %>% 
  lapply(., function(mdf) {
             map2_dbl(.x = mseq, .y = mseq + 100, 
                      .f = ~nrow(subset(mdf, value > .x & value <= .y))) %>% 
                              set_names(., map2_chr(.x = mseq, .y = mseq + 100, 
                                        .f = ~paste0("(",.x, ",", .y, "]")))}) %>% 
  bind_rows(., .id = "random_str") %>% 
  right_join({df %>% 
                group_by(random_str) %>% 
                summarise(max = max(value),
                          min = min(value))}, .)
#> Joining, by = "random_str"
#> # A tibble: 2 x 13
#>   random_str   max   min `(0,100]` `(100,200]` `(200,300]` `(300,400]`
#>   <chr>      <dbl> <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
#> 1 not        1078.  211.         0           0           3           3
#> 2 ok          722.  294.         0           0           1           2
#> # ... with 6 more variables: (400,500] <dbl>, (500,600] <dbl>, (600,700] <dbl>,
#> #   (700,800] <dbl>, (800,900] <dbl>, (900,1000] <dbl>

^{Created on 2023-04-07 by the reprex package (v2.0.1)}