Preemptively use multiple = "all" in left_join()
#1
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR makes your package compatible with the next version of dplyr:
The join functions in dplyr (like
left_join()) now return a warning by default when a row inxmatches multiple rows iny. While this is typical SQL behavior, it is often unexpected during data analysis (many people don't even know it is possible), so we've decided to make this a warning. In dplyr 1.1.0, you silence this warning withmultiple = "all". In the meantime, we need to work around a broken test that was expecting a single warning.I've done this by preemptively adding
multiple = "all"to the join function, which doesn't do anything with CRAN dplyr but silences the warning in dev dplyr.I've also switched you to a snapshot test, which should be more robust for checking this kind of thing (i.e. it won't fail on CRAN if that section of code starts throwing >1 warning all of a sudden, but will fail in your CI and locally).
We actually plan to submit dplyr 1.1.0 today. We typically give you more time to make changes, but we sent out most of these PRs a month ago and missed yours because injurytools made it to CRAN in mid January. Sorry about that!