From ccberry @ending from uc@d@edu  Fri Jun  8 21:31:51 2018
From: ccberry @ending from uc@d@edu (Berry, Charles)
Date: Fri, 8 Jun 2018 19:31:51 +0000
Subject: [Rd] Subsetting the "ROW"s of an object
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> On Jun 8, 2018, at 11:52 AM, Hadley Wickham <h.wickham at gmail.com> wrote:
> 
> On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccberry at ucsd.edu> wrote:
>> 
>> 
>>> On Jun 8, 2018, at 10:37 AM, Herv? Pag?s <hpages at 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,?   7.4?s  10.9?s  10.66?s   1.22ms     2
> #> 2 arr[i, , , ]   7.06?s   8.8?s   7.85?s 538.09?s     2
> 
> So not a huge difference, but it's there.


Funny. I get similar results to yours above albeit with smaller differences. Usually < 5 percent.

But with subset_ROW4 I see no consistent difference.

In this example, it runs faster on average using `eval(mc)' to return the result:

> arr <- array(rnorm(2^22),c(2^10,4,4,4))
> i <- seq(1,length=10,by=100)
> bench::mark(subset_ROW4(arr,i,FALSE), subset_ROW4(arr,i,TRUE))[,1:8]
# A tibble: 2 x 8
  expression                      min     mean   median      max `itr/sec` mem_alloc  n_gc
  <chr>                      <bch:tm> <bch:tm> <bch:tm> <bch:tm>     <dbl> <bch:byt> <dbl>
1 subset_ROW4(arr, i, FALSE)   28.9?s   34.9?s   32.1?s   1.36ms    28686.    5.05KB     5
2 subset_ROW4(arr, i, TRUE)    28.9?s     35?s   32.4?s 875.11?s    28572.    5.05KB     5
>

And on subsequent reps the lead switches back and forth.


Chuck



[Rd] Subsetting the "ROW"s of an object
Berry, Charles