implementing `purrr::map_dbl()` when applying a function to list columns in a nested data frame
Could someone explain I'm working with a scenario while trying to apply a function to a list-column in a nested data frame using `purrr::map_dbl()`..... I have a data frame where one of the columns contains lists of numeric vectors, and I want to calculate the mean of these vectors for each row. Here's a sample of my data frame: ```r library(dplyr) library(purrr) # Sample data frame nested_df <- tibble( id = 1:3, values = list(c(1, 2, 3), c(4, 5, NA), c(6, 7, 8)) ) ``` I attempted to use `map_dbl()` directly, but I'm running into an scenario with how to handle the `NA` values properly. Here’s the code I used: ```r nested_df <- nested_df %>% mutate(mean_value = map_dbl(values, ~ mean(.x, na.rm = TRUE))) ``` This code returns the expected output, but I noticed that when the lists contain only `NA` values, `map_dbl()` returns `NA` instead of the desired 0 (or any default value). I would like to modify the function to return 0 when the list is entirely `NA`. I tried adding a condition to handle this, but I keep running into type mismatch warnings. Here’s the version of the code that gives me the warning: ```r nested_df <- nested_df %>% mutate(mean_value = map_dbl(values, ~ ifelse(all(is.na(.x)), 0, mean(.x, na.rm = TRUE)))) ``` The warning I receive is: ``` Warning in ifelse(all(is.na(.x)), 0, mean(.x, na.rm = TRUE)): the condition has length > 1 and only the first element will be used ``` How can I resolve this scenario and ensure that I correctly handle cases where all values in the list are `NA` without working with warnings? Any insights or alternative approaches would be greatly appreciated! For reference, this is a production microservice. Is there a simpler solution I'm overlooking? I recently upgraded to R 3.10. Any pointers in the right direction?