A missing subscript is still preferable to a TRUE though because it
carries the meaning "take it all". A TRUE also achieves this but via
implicit recycling. For example x[ , , ] and x[TRUE, TRUE, TRUE]
achieve the same thing (if length(x) != 0) and are both no-ops but
the subsetting code gets a chance to immediately and easily detect
the former as a no-op whereas it will probably not be able to do it
so easily for the latter. So in this case it will most likely generate
a copy of 'x' and fill the new array by taking a full walk on it.
H.
On 06/08/2018 11:52 AM, Hadley Wickham wrote:
On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccbe...@ucsd.edu> wrote:
On Jun 8, 2018, at 10:37 AM, Hervé Pagès <hpa...@fredhutch.org> wrote:
Also the TRUEs cause problems if some dimensions are 0:
> matrix(raw(0), nrow=5, ncol=0)[1:3 , TRUE]
Error in matrix(raw(0), nrow = 5, ncol = 0)[1:3, TRUE] :
(subscript) logical subscript too long
OK. But this is easy enough to handle.
H.
On 06/08/2018 10:29 AM, Hadley Wickham wrote:
I suspect this will have suboptimal performance since the TRUEs will
get recycled. (Maybe there is, or could be, ALTREP, support for
recycling)
Hadley
AFAICS, it is not an issue. Taking
arr <- array(rnorm(2^22),c(2^10,4,4,4))
as a test case
and using a function that will either use the literal code `x[i,,,,drop=FALSE]'
or `eval(mc)':
subset_ROW4 <-
function(x, i, useLiteral=FALSE)
{
literal <- quote(x[i,,,,drop=FALSE])
mc <- quote(x[i])
nd <- max(1L, length(dim(x)))
mc[seq(4,length=nd-1L)] <- rep(TRUE, nd-1L)
mc[["drop"]] <- FALSE
if (useLiteral)
eval(literal)
else
eval(mc)
}
I get identical times with
system.time(for (i in 1:10000) subset_ROW4(arr,seq(1,length=10,by=100),TRUE))
and with
system.time(for (i in 1:10000) subset_ROW4(arr,seq(1,length=10,by=100),FALSE))
I think that's because you used a relatively low precision timing
mechnaism, and included the index generation in the timing. I see:
arr <- array(rnorm(2^22),c(2^10,4,4,4))
i <- seq(1,length = 10, by = 100)
bench::mark(
arr[i, TRUE, TRUE, TRUE],
arr[i, , , ]
)
#> # A tibble: 2 x 1
#> expression min mean median max n_gc
#> <chr> <bch:t> <bch:t> <bch:tm> <bch:tm> <dbl>
#> 1 arr[i, TRUE,… 7.4µs 10.9µs 10.66µs 1.22ms 2
#> 2 arr[i, , , ] 7.06µs 8.8µs 7.85µs 538.09µs 2
So not a huge difference, but it's there.
Hadley
--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M1-B514
P.O. Box 19024
Seattle, WA 98109-1024
E-mail: hpa...@fredhutch.org
Phone: (206) 667-5791
Fax: (206) 667-1319
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