working with 'how to join with NA in both tables' scenarios using `dplyr::left_join` with custom NA handling in R
I'm deploying to production and I'm refactoring my project and I'm experimenting with This might be a silly question, but I'm trying to merge two data frames using `dplyr::left_join`, but I'm running into the behavior message: `behavior: want to join with NA in both tables`... My two data frames, `df1` and `df2`, look something like this: ```r # Sample data frames df1 <- data.frame(id = c(1, 2, 3, NA), value = c('A', 'B', 'C', 'D')) df2 <- data.frame(id = c(NA, 2, 3, 4), description = c('Desc1', 'Desc2', 'Desc3', 'Desc4')) ``` I want to join them on the `id` column, but I need to ensure that the NA values in `df1` and `df2` are handled in a way that they do not cause this behavior. I've tried using the `na.omit()` function on both data frames before the join, but it results in losing valuable rows with NA that I actually want to keep in the final output. Here's what I initially tried: ```r # Attempt to join without modifying NAs df_joined <- dplyr::left_join(df1, df2, by = 'id') ``` When running this, I got the aforementioned behavior. I also explored the option of replacing NAs with a placeholder value using `dplyr::mutate`, but that doesn't seem to be a viable solution given that the original contexts of the IDs should be preserved. I am using R version 4.3.1 and `dplyr` version 1.1.0. Is there a recommended approach to achieve this join while keeping the NA values intact, or is there an alternative solution that avoids this behavior? The goal is to retain as much data as possible after the merge. Thanks for any insights! For context: I'm using R on Windows. Am I missing something obvious? Thanks for any help you can provide! For reference, this is a production service. Could this be a known issue? Thanks for taking the time to read this! The stack includes R and several other technologies. Any suggestions would be helpful.