Hello,
I don't think it's a bug in ncol.
nrow and ncol are not generic, they are defined simply as
nrow <- function(x) dim(x)[1L]
ncol <- function(x) dim(x)[2L]
Checking:
nrow
#function (x)
#dim(x)[1L]
#<bytecode: 0x000001c3e2863c78>
#<environment: namespace:base>
ncol
#function (x)
#dim(x)[2L]
#<bytecode: 0x000001c3e1f8c030>
#<environment: namespace:base>
Since dim(table(letters)) returns a length 1 vector, nrow returns that
value and ncol's return is the expected behavior of subsetting past the
vector length.
dim(table(letters)) # length 1
# [1] 26
nrow(table(letters)) # works as expected
# [1] 26
ncol(table(letters)) # works as expected
# [1] NA
Hope this helps,
Rui Barradas
Às 04:19 de 26/08/2022, Michael Chirico via R-devel escreveu:
Does this somewhat contradict the requirement that dimensions are non-missing?
ncol(table(letters))
# [1] NA
On the other hand,
dim(table(letters))
# [1] 26
Maybe that's a bug for the ncol() method of table?
On Thu, Aug 25, 2022 at 2:55 AM Sebastian Meyer <seb.me...@fau.de> wrote:
Hmm. I think I haven't seen NA dims in base R before. R-lang says
The 'dim' attribute is used to implement arrays. The content of
the array is stored in a vector in column-major order and the 'dim'
attribute is a vector of integers specifying the respective extents of
the array. R ensures that the length of the vector is the product of
the lengths of the dimensions.
And R-intro says:
A dimension vector is a vector of non-negative integers.
I understand "vector of [non-negative] integers" as excluding NA_integer_.
Correspondingly:
a <- 1
dim(a) <- NA_integer_
Error in dim(a) <- NA_integer_ : the dims contain missing values
matrix(1, NA_integer_)
Error in matrix(1, NA_integer_) : invalid 'nrow' value (too large or NA)
array(1, NA_integer_)
Error in array(1, NA_integer_) : negative length vectors are not allowed
There must be many more examples where non-NULL dim() is assumed to
not contain missing values.
simplify2array() aligns with that specification and only needs to check
for numeric (non-NULL) dims at this point and not also !anyNA(c.dim).
So my take is that there is no bug.
Best regards,
Sebastian Meyer
Am 25.08.22 um 04:51 schrieb Michael Chirico via R-devel:
If any component of the dim() returned as c.dim here[1] are missing,
simplify2array() errors inscrutably (specifically, because the last &&
condition is missing):
Error in if (higher && length(c.dim <- unique(lapply(x, dim))) == 1 && :
missing value where TRUE/FALSE needed
At root here is that dim.tbl_lazy ({dbplyr} package method [2]) very
intentionally neglects to count the # of rows in the result -- the
whole point of a lazy table is to avoid calculating full queries
unless specifically requested, so the # of rows is left as missing,
i.e., there _is_ a quantity of rows, but the exact number is not
known.
That seems to me like a proper usage of NA, and hence this is a
simplify2array() bug, but I am curious other thoughts here before
attempting a patch.
[1]
https://github.com/r-devel/r-svn/blob/2d4f8c283d53ff2c98d92c7b77b11e652297742c/src/library/base/R/sapply.R#L46-L48
[2]
https://github.com/tidyverse/dbplyr/blob/36b146e36d6d9af215dc48e60862d4b807b9e606/R/tbl-lazy.R#L45-L47
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