Error in unsplit() with tibbles

I get the sentiment, but this is really just bad coding (on my own part, I
suspect), so we might as well just fix it...

-pd

On 21 Nov 2020, at 17:42 , Marc Schwartz via R-devel <

r-devel at r-project.org> wrote:

On Nov 21, 2020, at 10:55 AM, Mario Annau <mario.annau at gmail.com>

wrote:

Hello,

using the `unsplit()` function with tibbles currently leads to the
following error:

mtcars_tb <- as_tibble(mtcars, rownames = NULL)
s <- split(mtcars_tb, mtcars_tb$gear)
unsplit(s, mtcars_tb$gear)

Error: Must subset rows with a valid subscript vector.
? Logical subscripts must match the size of the indexed input.
x Input has size 15 but subscript `rep(NA, len)` has size 32.
Run `rlang::last_error()` to see where the error occurred.

Tibble seems to (rightly) complain, that a logical vector has been used

for

subsetting which does not have the same length as the data.frame (rows).
Since `NA` is a logical value, the subset should be changed to
`NA_integer_` in `unsplit()`:

unsplit

function (value, f, drop = FALSE)
{
  len <- length(if (is.list(f)) f[[1L]] else f)
  if (is.data.frame(value[[1L]])) {
      x <- value[[1L]][rep(*NA_integer_*, len), , drop = FALSE]
      rownames(x) <- unsplit(lapply(value, rownames), f, drop = drop)
  }
  else x <- value[[1L]][rep(NA, len)]
  split(x, f, drop = drop) <- value
  x
}

Cheers,
Mario

Hi,

Perhaps I am missing something, but if you are using objects, like

tibbles, that are intended to be part of another environment, in this case
the tidyverse, why would you not use functions to manipulate these objects
that were specifically created in the other environment?

I don't use the tidyverse, but it seems to me that to expect base R

functions to work with objects not created in base R, is problematic, even
though, perhaps by coincidence, they may work without adverse effects, as
appears to be the case with split().

In other words, you should not, in reality, have had an a priori

expectation that split() would work with a tibble either.

Rather than modifying the base R functions, like unsplit(), as you are

suggesting, to be compatible with these third party objects, the burden
should either be on you to use relevant tidyverse functions, or on the
authors of the tidyverse to provide relevant class methods to provide that
functionality.

Regards,

Marc Schwartz

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Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
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