From list to data frame with tidyverse, selecting specific list elements

We can use imap and enframe to convert each element in the list to a data frame with name (row number) and value (the element name). We can then use reduce and full_join to join all data frames. Finally, we can select the columns we want. This approach does not need to specify a "magic" number.

library(tidyverse)

some_data$x %>% 
  as.numeric() %>% 
  hist(breaks = seq(from = 23, to = 24.6, by = 0.2),
       plot = FALSE) %>%
  imap(~enframe(.x, value = .y)) %>%
  reduce(full_join, by = "name") %>%
  select(bins = breaks, 
         frequency = counts)
# # A tibble: 9 x 2
#   bins frequency
#   <dbl>     <int>
# 1  23           3
# 2  23.2         9
# 3  23.4        20
# 4  23.6        23
# 5  23.8        19
# 6  24          16
# 7  24.2         7
# 8  24.4         3
# 9  24.6        NA

Part of the complicating factor is that the lists of a hist() object have different lengths:

library(tidyverse)

brks <- seq(from = 23, to = 24.6, by = 0.2)

hist_res <- some_data$x %>% 
  as.numeric() %>% 
  hist(breaks = brks,
       plot = FALSE)

lengths(hist_res)

  breaks   counts  density     mids    xname equidist 
       9        8        8        8        1        1 

OP commented that uneven lists is a main part of the question. We need to make a choice or rule to determine which list elements are selected for a data.frame. In this case, we can select the most frequent length using a combination of table(), which(), and base [. For this hist() example, I still include manually manipulating the breaks column in a mutate call:

l <- lengths(hist_res)
cols <- which(l == as.integer(names(table(l)))[which.max(table(l))])

hist_res%>%
  .[cols]%>%
  as_tibble()%>%
  mutate(brk_start = brks[-length(brks)],
         brk_end = brks[-1])

# A tibble: 8 x 5
  counts density  mids brk_start brk_end
   <int>   <dbl> <dbl>     <dbl>   <dbl>
1      3   0.15   23.1      23      23.2
2      9   0.45   23.3      23.2    23.4
3     20   1.000  23.5      23.4    23.6
4     23   1.15   23.7      23.6    23.8
5     19   0.95   23.9      23.8    24  
6     16   0.8    24.1      24      24.2
7      7   0.35   24.3      24.2    24.4
8      3   0.150  24.5      24.4    24.6