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

[Hervé Pagès]

The C code for subsetting doesn't need to recycle a logical subscript.
It only needs to walk on it and start again at the beginning of the
vector when it reaches the end. Not exactly the same as detecting the
"take everything along that dimension" situation though.
x[TRUE, TRUE, TRUE] triggers the full subsetting machinery when x[]
and x[ , , ] could (and should) easily avoid it.

H.

On 06/08/2018 01:49 PM, Hadley Wickham wrote:
> Hmmm, yes, there must be some special case in the C code to avoid
> recycling a length-1 logical vector:
> 
> dims <- c(4, 4, 4, 1e5)
> 
> arr <- array(rnorm(prod(dims)), dims)
> dim(arr)
> #> [1]      4      4      4 100000
> i <- c(1, 3)
> 
> bench::mark(
>    arr[i, TRUE, TRUE, TRUE],
>    arr[i, , , ]
> )[c("expression", "min", "mean", "max")]
> #> # A tibble: 2 x 4
> #>   expression                    min     mean      max
> #>   <chr>                    <bch:tm> <bch:tm> <bch:tm>
> #> 1 arr[i, TRUE, TRUE, TRUE]   41.8ms   43.6ms   46.5ms
> #> 2 arr[i, , , ]               41.7ms   43.1ms   46.3ms
> 
> 
> On Fri, Jun 8, 2018 at 12:31 PM, Berry, Charles <ccberry using ucsd.edu> wrote:
>>
>>
>>> On Jun 8, 2018, at 11:52 AM, Hadley Wickham <h.wickham using gmail.com> wrote:
>>>
>>> On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccberry using ucsd.edu> wrote:
>>>>
>>>>
>>>>> On Jun 8, 2018, at 10:37 AM, Hervé Pagès <hpages using 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.
>>
>>
>> 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)   28.9µs   34.9µs   32.1µs   1.36ms    28686.    5.05KB     5
>> 2 subset_ROW4(arr, i, TRUE)    28.9µs     35µs   32.4µs 875.11µs    28572.    5.05KB     5
>>>
>>
>> And on subsequent reps the lead switches back and forth.
>>
>>
>> Chuck
>>
> 
> 
> 

-- 
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: hpages using fredhutch.org
Phone:  (206) 667-5791
Fax:    (206) 667-1319