@brookslogan does this overlap #310 work?

Yes, this overlaps with #310, but while #310 would lend itself to something more general/proper in some cases, it's probably good to just do the mass-S3-implementation approach for now.

Something for select in particular: we can't just restore the old metadata as we may have dropped keys. We need to preserve the sort of stuff that goes on in [.epi_df and names<-.epi_df [degrading if we drop required columns, adjusting other_keys if we drop or rename them]. I realize now it may not be as simple as some of the other ones with just a dplyr_reconstruct or reclass whatever.

@dsweber2 I think the R/ folder changes still need to be pushed?

lol, whoops. I forgot to push the new file. I was trying to separate out the patch-style methods from the proper/more permanent ones.

Also, there's a ton of warnings which I don't think I caused, and the package has no styler, which means my commits may come with random formatting. Sorry about that.

[degrading if we drop required columns, adjusting other_keys if we drop or rename them]

Good point, I'll add some tests for this and adjust. Though the times I've actually seen select used in our code, it was for dropping a data-column.

Also, here or in a separate PR, it'd be nice to have a complete that doesn't drop epi_dfness?

I'd rather this not blossom into a weeks long project. I made an issue #391 for patching more generally; if any feel particularly important feel free to rearrange/add some.

Probably remove the list from the OP so it doesn't seem like this PR is aiming to check all those boxes.

So I added some tests for the cases you were talking about @lcbrooks, and it seems like whoever implemented dplyr_reconstruct covered all these bases, since it passed the tests without me changing the core code.

An edge case to fix or file off:

tibble::tibble(geo_value = 1, time_value = 1, age = 1, value = 1) %>%
  as_epi_df(additional_metadata = list(other_keys = "age")) %>%
  select(geo_value, time_value, age_group = age, value) %>%
  attr("metadata")
#> $geo_type
#> [1] "hhs"
#> 
#> $time_type
#> [1] "custom"
#> 
#> $as_of
#> [1] "2024-01-12 16:48:48 PST"
#> 
#> $other_keys
#> character(0)

Might be a regression from dplyr_reconstructing in names<-.epi_df. Taking a look.

Okay, I think I fixed names<-.epi-df. The problem is that it doesn't propagate through correctly via select. Now, based on ?dplyr_extending, you'd expect we could do something like:

select.epi_df <- function(.data, ...) {
  selected <- NextMethod(.data)
  might_decay <- reclass(selected, attr(selected, "metadata"))
  return(dplyr_reconstruct(might_decay, might_decay))
}

but that doesn't seem sufficient. It seems that select.data.frame's out <- set_names(out, names(loc)) just destroys our attributes when we are grouped, preventing us from seeing what was renamed. So we may need to do some eval_selecting ourselves.

[Hmmm, maybe the above isn't accurate. Looks like maybe I didn't account for grouped df's in names<- and that might be the problem. And implementation above does appear to work. Though a reimplementation without all the redirection and checks of dplyr is quite simple:

 select.epi_df <- function(.data, ...) {
   selection <- tidyselect::eval_select(expr(c(...)), .data)
   result <- .data[selection]
   names(result) <- names(selection)
   return (result)
 }

but I've opted to try to use dplyr's checks and any hidden behaviors.]

Just confirming, other_keys is now by default character(0) if I read this right? Otherwise seems good. Added some trivial annotations, and will then merge.

See also @brookslogan's comment on #186 about the character(0) default.