How to implement guide with `purrr::map()` returning unexpected results when using nested data frames in r
I'm relatively new to this, so bear with me. I've searched everywhere and can't find a clear answer. This might be a silly question, but I'm trying to apply a function to a nested data frame using `purrr::map()`, but the output isn't what I expect... I have a data frame with a column that contains other data frames, and I want to apply a summary function to each of those nested data frames. My data frame looks like this: ```r library(dplyr) library(purrr) # Sample data frame with nested data frames nested_df <- data.frame( id = 1:2, data = I(list(data.frame(value = c(1, 2, 3)), data.frame(value = c(4, 5, 6)))) ) ``` I want to calculate the mean of the `value` column in each nested data frame. Here's the code I'm using: ```r result <- nested_df %>% mutate(mean_value = map(data, ~ mean(.$value))) ``` However, I run into the following behavior: ``` behavior in `mutate()`: Column `mean_value` need to be converted to a list of length 2. ``` I thought that using `map()` inside `mutate()` would work, but the behavior suggests that the output isn't being handled properly. I've also tried using `map_df()` instead of `map()`, but it doesn't seem to be suitable for this situation since I want to keep the results in a list format. Any suggestions on how to resolve this scenario? I'm using R version 4.1.0 and the latest versions of `dplyr` and `purrr` as of October 2023. Am I missing something obvious? Am I missing something obvious? My development environment is Windows. Any help would be greatly appreciated!