implementing `purrr::map` and unexpected data frame row binding when applying functions in R
I'm getting frustrated with I'm migrating some code and I'm confused about I've searched everywhere and can't find a clear answer... I'm working with an scenario when trying to use `purrr::map` to apply a function across a list of data frames and subsequently bind the rows together using `dplyr::bind_rows`. The question arises when one of the data frames has missing columns compared to the others, which results in unexpected behavior. I have three data frames, `df1`, `df2`, and `df3`, where `df2` is missing a column that the other two have. Here’s the code I’m currently using: ```r library(dplyr) library(purrr) df1 <- data.frame(id = 1:3, value = c('A', 'B', 'C')) df2 <- data.frame(id = 4:5) # Missing 'value' column df3 <- data.frame(id = 6:8, value = c('D', 'E', 'F')) list_of_dfs <- list(df1, df2, df3) # Trying to apply a function that adds a new column result <- map(list_of_dfs, ~ .x %>% mutate(new_col = id * 2)) %>% bind_rows() ``` When I run this code, I get the following behavior: `behavior in bind_rows(!!!x): Argument 1 must be a data frame, not a NULL` It appears that `df2` is causing `bind_rows` to unexpected result because it has a missing column. I’ve tried using `safely()` from `purrr` to catch errors, but that doesn’t seem to resolve the scenario either. Is there a way to handle data frames with differing columns more gracefully in this situation? I want to ensure that the result still includes all columns but fills in `NA` where columns are missing. Any suggestions on how to modify my approach would be greatly appreciated! My development environment is Ubuntu. How would you solve this? This is part of a larger microservice I'm building. This is for a application running on macOS. I'd really appreciate any guidance on this. I recently upgraded to R latest. Any help would be greatly appreciated! Thanks, I really appreciate it!