Unexpected NA values when using purrr's map_df to combine list of data frames in R
I'm working on a project and hit a roadblock. I'm trying to combine a list of data frames using `purrr::map_df`, but I'm working with unexpected `NA` values in the resulting data frame. I have a list of three data frames, each with the same structure, but a few of them have missing values in a critical column that I need to retain for analysis. Here's the code I'm using: ```r library(purrr) # Sample list of data frames list_of_dfs <- list( data.frame(id = 1:3, value = c(10, 20, NA)), data.frame(id = 1:3, value = c(30, NA, 50)), data.frame(id = 1:3, value = c(NA, 60, 70)) ) combined_df <- map_df(list_of_dfs, ~ .x) ``` When I run this code, the `combined_df` ends up with a lot of `NA` values in the `value` column, which I wasn't expecting. The structure of each data frame is identical, and I thought `map_df` would handle this gracefully. I even checked if the data frames had different column names, but that’s not the case. I attempted to use `dplyr::bind_rows` as an alternative, but I faced the same scenario. Is there a way to combine these data frames without introducing unwanted `NA` values? Additionally, I am using R version 4.1.0 and the `purrr` package version 0.3.4. Any insights would be appreciated! Is there a better approach?