Unexpected NA values when merging data frames in R using base R's merge() function
I'm sure I'm missing something obvious here, but I'm having a hard time understanding I'm building a feature where Hey everyone, I'm running into an issue that's driving me crazy. I'm working with an scenario while merging two data frames using base R's `merge()` function. Despite both data frames having the same column names and data types, I'm working with unexpected NA values in the merged result. I've tried using both `all = TRUE` and `all.x = TRUE` parameters to see if that resolves the scenario, but I still get NA values where I expect matches. Here are the details: ```r # Sample data frames df1 <- data.frame(id = c(1, 2, 3), value = c('A', 'B', 'C')) df2 <- data.frame(id = c(2, 3, 4), value = c('D', 'E', 'F')) # Attempting to merge merged_df <- merge(df1, df2, by = 'id', all.x = TRUE) print(merged_df) ``` The output gives me: ``` id value.x value.y 1 1 A <NA> 2 2 B D 3 3 C E ``` I expected that the `id` column from `df1` with value `1` would show a NA in `value.y`, which it does, but the rows for `2` and `3` are not what I anticipated. I thought the `value` column should match based on `id`, but it seems like the column names might be causing confusion. I've checked that there are no leading or trailing spaces in the `id` column, and both are of type integer. Am I missing something? Is there a better way to handle merging that could avoid these NA values? Iām using R version 4.1.0. For context: I'm using R on Linux. Thanks in advance! My development environment is Ubuntu. Any ideas what could be causing this? My development environment is macOS. My development environment is Linux. What's the best practice here? My team is using R for this desktop app. Am I approaching this the right way?