FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut
some corners compared to identical():
> microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE))
Unit: nanoseconds
expr min lq mean median uq max neval
identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100
isFALSE(FALSE) 713 761 1133.53 809.5 871.5 18619 100
> microbenchmark::microbenchmark(identical(TRUE, FALSE), isFALSE(TRUE))
Unit: nanoseconds
expr min lq mean median uq max neval
identical(TRUE, FALSE) 1009 1103.5 2228.20 1170.5 1357 14346 100
isFALSE(TRUE) 718 760.0 1298.98 798.0 898 17782 100
> microbenchmark::microbenchmark(identical("array", FALSE), isFALSE("array"))
Unit: nanoseconds
expr min lq mean median uq max neval
identical("array", FALSE) 975 1058.5 1257.95 1119.5 1250.0 9299 100
isFALSE("array") 409 433.5 658.76 446.0 476.5 9383 100
That could probably be used also is sapply(). The difference is that
isFALSE() is a bit more liberal than identical(x, FALSE), e.g.
> isFALSE(c(a = FALSE))
[1] TRUE
> identical(c(a = FALSE), FALSE)
[1] FALSE
Assuming the latter is not an issue, there are 69 places in base R
where isFALSE() could be used:
$ grep -E "identical[(][^,]+,[ ]*FALSE[)]" -r --include="*.R" | grep
-F "/R/" | wc
69 326 5472
and another 59 where isTRUE() can be used:
$ grep -E "identical[(][^,]+,[ ]*TRUE[)]" -r --include="*.R" | grep -F
"/R/" | wc
59 307 5021
/Henrik
On Tue, Mar 13, 2018 at 9:21 AM, Doran, Harold <hdo...@air.org> wrote:
> Quite possibly, and I’ll look into that. Aside from the work I was doing,
> however, I wonder if there is a way such that sapply could avoid the overhead
> of having to call the identical function to determine the conditional path.
>
>
>
> From: William Dunlap [mailto:wdun...@tibco.com]
> Sent: Tuesday, March 13, 2018 12:14 PM
> To: Doran, Harold <hdo...@air.org>
> Cc: Martin Morgan <martin.mor...@roswellpark.org>; r-help@r-project.org
> Subject: Re: [R] Possible Improvement to sapply
>
> Could your code use vapply instead of sapply? vapply forces you to declare
> the type and dimensions
> of FUN's output and stops if any call to FUN does not match the declaration.
> It can use much less
> memory and time than sapply because it fills in the output array as it goes
> instead of calling lapply()
> and seeing how it could be simplified.
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com<http://tibco.com>
>
> On Tue, Mar 13, 2018 at 7:06 AM, Doran, Harold
> <hdo...@air.org<mailto:hdo...@air.org>> wrote:
> Martin
>
> In terms of context of the actual problem, sapply is called millions of times
> because the work involves scoring individual students who took a test. A
> score for student A is generated and then student B and such and there are
> millions of students. The psychometric process of scoring students is complex
> and our code makes use of sapply many times for each student.
>
> The toy example used length just to illustrate, our actual code doesn't do
> that. But your point is well taken, there may be a very good counterexample
> why my proposal doesn't achieve the goal is a generalizable way.
>
>
>
> -----Original Message-----
> From: Martin Morgan
> [mailto:martin.mor...@roswellpark.org<mailto:martin.mor...@roswellpark.org>]
> Sent: Tuesday, March 13, 2018 9:43 AM
> To: Doran, Harold <hdo...@air.org<mailto:hdo...@air.org>>;
> 'r-help@r-project.org<mailto:r-help@r-project.org>'
> <r-help@r-project.org<mailto:r-help@r-project.org>>
> Subject: Re: [R] Possible Improvement to sapply
>
>
>
> On 03/13/2018 09:23 AM, Doran, Harold wrote:
>> While working with sapply, the documentation states that the simplify
>> argument will yield a vector, matrix etc "when possible". I was
>> curious how the code actually defined "as possible" and see this
>> within the function
>>
>> if (!identical(simplify, FALSE) && length(answer))
>>
>> This seems superfluous to me, in particular this part:
>>
>> !identical(simplify, FALSE)
>>
>> The preceding code could be reduced to
>>
>> if (simplify && length(answer))
>>
>> and it would not need to execute the call to identical in order to trigger
>> the conditional execution, which is known from the user's simplify = TRUE or
>> FALSE inputs. I *think* the extra call to identical is just unnecessary
>> overhead in this instance.
>>
>> Take for example, the following toy example code and benchmark results and a
>> small modification to sapply:
>>
>> myList <- list(a = rnorm(100), b = rnorm(100))
>>
>> answer <- lapply(X = myList, FUN = length) simplify = TRUE
>>
>> library(microbenchmark)
>>
>> mySapply <- function (X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE){
>> FUN <- match.fun(FUN)
>> answer <- lapply(X = X, FUN = FUN, ...)
>> if (USE.NAMES && is.character(X) && is.null(names(answer)))
>> names(answer) <- X
>> if (simplify && length(answer))
>> simplify2array(answer, higher = (simplify == "array"))
>> else answer
>> }
>>
>>
>>> microbenchmark(sapply(myList, length), times = 10000L)
>> Unit: microseconds
>> expr min lq mean median uq max neval
>> sapply(myList, length) 14.156 15.572 16.67603 15.926 16.634 650.46
>> 10000
>>> microbenchmark(mySapply(myList, length), times = 10000L)
>> Unit: microseconds
>> expr min lq mean median uq max
>> neval
>> mySapply(myList, length) 13.095 14.864 16.02964 15.218 15.573
>> 1671.804 10000
>>
>> My benchmark timings show a timing improvement with only that small change
>> made and it is seemingly nominal. In my actual work, the sapply function is
>> called millions of times and this additional overhead propagates to some
>> overall additional computing time.
>>
>> I have done some limited testing on various real data to verify that the
>> objects produced under both variants of the sapply (base R and my modified)
>> yield identical objects when simply is both TRUE or FALSE.
>>
>> Perhaps someone else sees a counterexample where my proposed fix does not
>> cause for sapply to behave as expected.
>>
>
> Check out ?sapply for possible values of `simplify=` to see why your proposal
> is not adequate.
>
> For your example, lengths() is an order of magnitude faster than sapply(.,
> length). This is a example of the advantages of vectorization (single call to
> an R function implemented in C) versus iteration (`for` loops but also the
> *apply family calling an R function many times).
> vapply() might also be relevant.
>
> Often performance improvements come from looking one layer up from where the
> problem occurs and re-thinking the algorithm. Why would one need to call
> sapply() millions of times, in a situation where this becomes rate-limiting?
> Can the algorithm be re-implemented to avoid this step?
>
> Martin Morgan
>
>> Harold
>>
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