# [Rd] wish list: generalized apply

Joshua Ulrich josh.m.ulrich at gmail.com
Fri Dec 9 16:31:34 CET 2016

```On Thu, Dec 8, 2016 at 3:59 PM, David Winsemius <dwinsemius at comcast.net> wrote:
>
>> On Dec 8, 2016, at 12:09 PM, John P. Nolan <jpnolan at american.edu> wrote:
>>
>> Dear All,
>>
>> I regularly want to "apply" some function to an array in a way that the arguments to the user function depend on the index on which the apply is working.  A simple example is:
>>
>> A <- array( runif(160), dim=c(5,4,8) )
>> x <- matrix( runif(32), nrow=4, ncol=8 )
>> b <- runif(8)
>> f1 <- function( A, x, b ) { sum( A %*% x ) + b }
>> result <- rep(0.0,8)
>> for (i in 1:8) {
>>  result[i] <- f1( A[,,i], x[,i] , b[i] )
>> }
>>
>> This works, but is slow.  I'd like to be able to do something like:
>>    generalized.apply( A, MARGIN=3, FUN=f1, list(x=x,MARGIN=2), list(b=b,MARGIN=1) ), where the lists tell generalized.apply to pass x[,i] and b[i] to FUN in addition to A[,,i].
>>
>> Does such a generalized.apply already exist somewhere?  While I can write a C function to do a particular case, it would be nice if there was a fast, general way to do this.
>
> I would have thought that this would achieve the same result:
>
> result <- sapply( seq_along(b) , function(i) { f1( A[,,i], x[,i] , b[i] )} )
>
> Or:
>
> result <- sapply( seq.int( dim(A) ) , function(i) { f1( A[,,i], x[,i] , b[i] )} )
>
> (I doubt it will be any faster, but if 'i' is large, parallelism might help. The inner function appears to be fairly efficient.)

You're right, it's slower.  Despite how often it's repeated that
"loops in R are slow", they're not *that* slow.  They're often faster
than the *apply functions, especially if they have been "compiled" by
compiler::cmpfun().

You really need to know *why* code is slow before trying to make it
faster.  I profiled an example that would have a loop with 1e6
iterations and 80%+ of the time was still spent inside f1().

set.seed(21)
nc <- 1e6
nr <- 10
A <- array( runif(5*nr*nc), dim=c(5,nr,nc) )
x <- matrix( runif(nr*nc), nrow=nr, ncol=nc )
b <- runif(nc)
f1 <- compiler::cmpfun(function( A, x, b ) { sum( A %*% x ) + b })
f2 <- compiler::cmpfun({
function(A, x, b, FUN) {
result <- numeric(length(b))
for (i in seq_along(b)) {
result[i] <- FUN( A[,,i], x[,i] , b[i] )
}
return(result)
}
})
Rprof(interval=0.01)
result <- f2(A,x,b,f1)
Rprof(NULL)
summaryRprof()

\$by.self
self.time self.pct total.time total.pct
"FUN"      4.29    84.28       4.76     93.52
"%*%"      0.47     9.23       0.47      9.23
"f2"       0.33     6.48       5.09    100.00

\$by.total
total.time total.pct self.time self.pct
"f2"        5.09    100.00      0.33     6.48
"FUN"       4.76     93.52      4.29    84.28
"%*%"       0.47      9.23      0.47     9.23

\$sample.interval
 0.01

\$sampling.time
 5.09

In this case, almost all the time is spent evaluating f1() ("FUN"),
even after calling compiler::cmpfun on f1() and on a function
containing the loop.  Making the looping construct faster is not going
to improve the performance of this code by a significant amount.
I.e., dropping to compiled code will only help if you avoid the R
function call, but then that's not a general solution...

> --
>
> David Winsemius
> Alameda, CA, USA
>
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> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel

--
Joshua Ulrich  |  about.me/joshuaulrich