scenarios in R when attempting to join two data frames with `left_join()` from `dplyr` using non-matching keys
I'm optimizing some code but I'm working with an scenario when trying to perform a `left_join()` on two data frames in R using the `dplyr` package (version 1.0.7)... I have two data frames, `df1` and `df2`, where I want to merge them based on a common key. However, I'm receiving an unexpected output that includes many `NA` values for the columns from `df2` even though I believe the keys should match. Here’s a simplified version of my code: ```r library(dplyr) # Sample data frames df1 <- data.frame(id = c(1, 2, 3, 4), value = c('A', 'B', 'C', 'D')) df2 <- data.frame(id = c(2, 3, 5), desc = c('Beta', 'Gamma', 'Delta')) # Attempting to join df1 and df2 df_joined <- left_join(df1, df2, by = 'id') print(df_joined) ``` The output I get is: ``` id value desc 1 1 A <NA> 2 2 B Beta 3 3 C Gamma 4 4 D <NA> ``` As you can see, the first and last rows return `NA` for the `desc` column, which I expected since there are no matching keys in `df2` for those IDs. However, I am confused as to why the `left_join()` doesn’t return data for the IDs that do exist in `df1` but not in `df2`. I also checked for duplicates in `df2`, but all IDs are unique. I’ve tried using `inner_join()` as well, which gives me the expected rows, but that’s not what I need. Any insights on how I could troubleshoot this or improve my join operation? Am I missing something fundamental about how `left_join` behaves in this context? I'm working in a Debian environment. What would be the recommended way to handle this? This is happening in both development and production on Linux.