Getting 'NA' values when merging datasets with differing row names in R using merge()
I've spent hours debugging this and I'm trying to merge two data frames in R using the `merge()` function, but I'm ending up with unexpected `NA` values in my resulting data frame..... Specifically, I have two data frames that I want to join based on a common column, but they also have row names that I'm not sure how to handle properly. Here's a simplified version of what I'm working with: ```r # Creating the first data frame df1 <- data.frame(ID = c(1, 2, 3), ValueA = c(10, 20, 30), row.names = c('a', 'b', 'c')) # Creating the second data frame # Note that the row names here are different df2 <- data.frame(ID = c(1, 2, 4), ValueB = c(100, 200, 400), row.names = c('x', 'y', 'z')) # Attempting to merge the two data frames result <- merge(df1, df2, by = 'ID') ``` After running the code, I get this output: ```r ID ValueA ValueB 1 1 10 100 2 2 20 200 3 3 30 NA ``` I expected that the merged data frame would only include the rows for IDs 1 and 2, but I didn't anticipate that ID 3 would appear with `NA` values for `ValueB`. I only want to keep rows that have matching IDs in both data frames without any `NA` values in the result. What am I missing? I've tried using the `all = FALSE` parameter, but it doesn't seem to resolve the issue. Any insights on how to achieve the correct merge without the `NA` values would be greatly appreciated! I'm using R version 4.1.0 and my understanding is that the default behavior of `merge()` should exclude non-matching rows. Is there a better way to handle this, or am I misunderstanding something in how `merge()` operates? Is there a better approach? I'm on Linux using the latest version of R. I'm open to any suggestions.