You’re right, it sure does. My suggestion causes it to fail when simplify = ‘array’

From: William Dunlap [mailto:wdun...@tibco.com] Sent: Tuesday, March 13, 2018 12:11 PM To: Doran, Harold <hdo...@air.org> Cc: r-help@r-project.org Subject: Re: [R] Possible Improvement to sapply Wouldn't that change how simplify='array' is handled? > str(sapply(1:3, function(x)diag(x,5,2), simplify="array")) int [1:5, 1:2, 1:3] 1 0 0 0 0 0 1 0 0 0 ... > str(sapply(1:3, function(x)diag(x,5,2), simplify=TRUE)) int [1:10, 1:3] 1 0 0 0 0 0 1 0 0 0 ... > str(sapply(1:3, function(x)diag(x,5,2), simplify=FALSE)) List of 3 $ : int [1:5, 1:2] 1 0 0 0 0 0 1 0 0 0 $ : int [1:5, 1:2] 2 0 0 0 0 0 2 0 0 0 $ : int [1:5, 1:2] 3 0 0 0 0 0 3 0 0 0 Bill Dunlap TIBCO Software wdunlap tibco.com<http://tibco.com> On Tue, Mar 13, 2018 at 6:23 AM, Doran, Harold <hdo...@air.org<mailto:hdo...@air.org>> 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. Harold ______________________________________________ R-help@r-project.org<mailto:R-help@r-project.org> mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.