Transforming list to data frame with related data points in same row

I have a list that represents field of research information of a single publication. I want to combine the list to a data.frame such that each 2-digit code is stored in a "division" column and each 4-digit code in a "group" column. When the first two digits are shared, the division and group should be stored in the same row. I apologize for the bad title.

my_list <- list(
  list(id = "80067", name = "3403 Macromolecular and Materials Chemistry"),
  list(id = "80011", name = "40 Engineering"),
  list(id = "80005", name = "34 Chemical Sciences")
)

Desired output:

data.frame(division = c("40 Engineering", "34 Chemical Sciences"), 
           group = c(NA, "3403 Macromolecular and Materials Chemistry"))

First unlist your my_list into a vector, then enframe it into a two-column dataframe. filter to only include the name column, then assign group and prefix (for grouping into same row) by the patterns of digit. Finally reshape the structure from "long" to "wide".

library(tidyverse)

unlist(my_list) %>% 
  enframe() %>% 
  filter(name == "name") %>% 
  mutate(group = ifelse(str_count(value, "\\d") == 4, "group", "division"), 
         prefix = str_extract(value, "^\\d{2}"), .keep = "used") %>% 
  pivot_wider(names_from = group, values_from = value)

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Update: The above code can be simplified by a bit if we use bind_rows (inspired by @akrun's answer) at the beginning:

bind_rows(my_list) %>% 
  mutate(group = ifelse(str_count(name, "\\d") == 4, "group", "division"), 
         prefix = str_extract(name, "^\\d{2}"), .keep = "used") %>% 
  pivot_wider(names_from = group, values_from = name)

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Output

# A tibble: 2 × 3
  prefix group                                       division            
  <chr>  <chr>                                       <chr>               
1 34     3403 Macromolecular and Materials Chemistry 34 Chemical Sciences
2 40     NA                                          40 Engineering                                        40 Engineering 

We could use bind_rows to create a data.frame and then do a grouping by substring to extract the components

library(stringr)
library(dplyr)
 bind_rows(my_list) %>%
  group_by(grp = substr(name, 1, 2)) %>%
   summarise(group = str_extract(name, "^\\d{4}\\s+(.*)"), 
     division = name, .groups = "keep") %>% 
  filter(any(!is.na(group)) & !is.na(group) | n() == 1) %>% 
  ungroup %>% 
 select(division, group)

-output

# A tibble: 2 × 2
  division                                    group                                      
  <chr>                                       <chr>                                      
1 3403 Macromolecular and Materials Chemistry 3403 Macromolecular and Materials Chemistry
2 40 Engineering                              <NA>       

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