[Rd] Subsetting the "ROW"s of an object

Hervé Pagès hp@ge@ @ending from fredhutch@org
Fri Jun 8 19:32:05 CEST 2018


On 06/08/2018 10:15 AM, Michael Lawrence wrote:
> There probably should be an abstraction for this. In S4Vectors, we
> have extractROWS().

FWIW the code in S4Vectors that does what your subset_ROW() does is:

 
https://github.com/Bioconductor/S4Vectors/blob/04cc9516af986b30445e99fd1337f13321b7b4f6/R/subsetting-utils.R#L466-L476

(This is the default "extractROWS" method.)

Except for the normalization of 'i', it does the same as your
subset_ROW(). I don't know how to do this without generating a call
with missing arguments.

H.

> 
> Michael
> 
> On Fri, Jun 8, 2018 at 8:45 AM, Hadley Wickham <h.wickham using gmail.com> wrote:
>> Hi all,
>>
>> Is there a better to way to subset the ROWs (in the sense of NROW) of
>> an vector, matrix, data frame or array than this?
>>
>> subset_ROW <- function(x, i) {
>>    nd <- length(dim(x))
>>    if (nd <= 1L) {
>>      x[i]
>>    } else {
>>      dims <- rep(list(quote(expr = )), nd - 1L)
>>      do.call(`[`, c(list(quote(x), quote(i)), dims, list(drop = FALSE)))
>>    }
>> }
>>
>> subset_ROW(1:10, 4:6)
>> #> [1] 4 5 6
>>
>> str(subset_ROW(array(1:10, c(10)), 2:4))
>> #>  int [1:3(1d)] 2 3 4
>> str(subset_ROW(array(1:10, c(10, 1)), 2:4))
>> #>  int [1:3, 1] 2 3 4
>> str(subset_ROW(array(1:10, c(5, 2)), 2:4))
>> #>  int [1:3, 1:2] 2 3 4 7 8 9
>> str(subset_ROW(array(1:10, c(10, 1, 1)), 2:4))
>> #>  int [1:3, 1, 1] 2 3 4
>>
>> subset_ROW(data.frame(x = 1:10, y = 10:1), 2:4)
>> #>   x y
>> #> 2 2 9
>> #> 3 3 8
>> #> 4 4 7
>>
>> It seems like there should be a way to do this that doesn't require
>> generating a call with missing arguments, but I can't think of it.
>>
>> Thanks!
>>
>> Hadley
>>
>> --
>> https://urldefense.proofpoint.com/v2/url?u=http-3A__hadley.nz&d=DwICAg&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=MF0DzYDiaYtcFXIyQwpQKs9lVbLNvdBBUubTv7BVAfM&s=GSpoAzc1Kn_BnTIkDh0HBFGKtRm-xFodxEPOejriC9Q&e=
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-- 
Hervé Pagès

Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
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