similar to: Converting an environment to a list: mget vs. as.list

Hi, S4 method dispatch can be very slow. Would it be reasonable to cache the most recent dispatch, anticipating the next invocation will be on the same type? This would be very helpful in loops. fun0 <- function(x) sapply(x, paste, collapse="+") fun1 <- function(x) { paste <- selectMethod(paste, class(x[[1]])) sapply(x, paste,

>>>>> Winston Chang <winstonchang1 at gmail.com> >>>>> on Fri, 23 Jan 2015 10:15:53 -0600 writes: > I think you can simplify a little by replacing this: > pkg %in% loadedNamespaces() > with this: > .getNamespace(pkg) almost: It would be !is.null(.getNamespace(pkg)) > Whereas getNamespace(pkg) will load the

(Apologies if this is better suited for R-help.) On my system (macOS Sierra, late 2014 MacBook Pro; R 3.4.1, Homebrew build), I found that it is faster to construct a function using eval(call("function", ...)) than using as.function(list(...)). Example: make_fn_1 <- function(a, b) eval(call("function", a, b), env = parent.frame()) make_fn_2 <- function(a, b)

Hi guys, I am working on some code for automatically translating recursive functions into looping functions to implemented tail-recursion optimisations. See https://github.com/mailund/tailr As a toy-example, consider the factorial function factorial <- function(n, acc = 1) { if (n <= 1) acc else factorial(n - 1, acc * n) } I can automatically translate this into the loop-version

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

Interestingly, the <<- operator is also a lot faster than using a namespace explicitly, and only slightly slower than using <- with local variables, see below. But, surely, both must at some point insert values in a given environment ? either the local one, for <-, or an enclosing one, for <<- ? so I guess I am asking if there is a more low-level assignment operation I can get my

Dear all, I am trying to benchmark code that occasionally prints on the screen and I want to suppress the printing. Is there an idiom for this? If I do sink(tempfile) microbenchmark(...) sink() then I'll be also measuring the costs of writing to tempfile. I could also sink to /dev/null, which is probably fast, but that is not portable. Is there a better solution? Is writing to a

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

I'm exploring latency overhead of parallel PSOCK workers and noticed that serializing/unserializing data back to the main R session is significantly slower on Linux than it is on Windows/MacOS with similar hardware. Is there a reason for this difference and is there a way to avoid the apparent additional Linux overhead? I attempted to isolate the behavior with a test that simply returns

Hi all, I think there's an unnecessary line in [.Date which has a considerable impact on performance when subsetting large dates: x <- Sys.Date() + 1:1e6 microbenchmark::microbenchmark(x[1]) #> Unit: microseconds #> expr min lq mean median uq max neval #> x[1] 920.651 1039.346 3624.833 2294.404 3786.881 41176.38 100 `[.Date` <- function(x, ...,

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 On Tue,

Following up on this attempt of implementing the tail-recursion optimisation ? now that I?ve finally had the chance to look at it again ? I find that non-local return implemented with callCC doesn?t actually incur much overhead once I do it more sensibly. I haven?t found a good way to handle parallel assignments that isn?t vastly slower than simply introducing extra variables, so I am going with

Hi, I'm using this mailing list for the first time and I hope this is the right one. I don't think that the following is a bug but it can be a performance issue. By my opinion, there is no need to filter by [w != 0] in last sum of weighted.mean.default method defined in src/library/stats/R/weighted.mean.R. There is no need to do it because you can always sum zero numbers and

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:wdunlap at tibco.com] Sent: Tuesday, March 13, 2018 12:14 PM To: Doran, Harold <HDoran at air.org> Cc: Martin Morgan <martin.morgan

On Mon, 2 Nov 2020 at 02:22, Simon Urbanek <simon.urbanek at r-project.org> wrote: > > It looks like R sockets on Linux could do with TCP_NODELAY -- without (status quo): How many network packets are generated with and without it? If there are many small writes and thus setting TCP_NODELAY causes many small packets to be sent, it might make more sense to set TCP_QUICKACK instead.

No clue, but see ?assign perhaps if you have not done so already. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 27, 2018 at 6:51 AM, Thomas Mailund <thomas.mailund at gmail.com> wrote: > Interestingly, the

Dear all I was thinking about efficient reading data into R and tried several ways to test if load(file.Rdata) or readRDS(file.rds) is faster. The files file.Rdata and file.rds contain the same data, the first created with save(d, ' file.Rdata', compress=F) and the second with saveRDS(d, ' file.rds', compress=F). First I used the function microbenchmark() and was a astonished

>>>>> Henrik Bengtsson <henrik.bengtsson at gmail.com> >>>>> on Tue, 13 Mar 2018 10:12:55 -0700 writes: > 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

Hi Duncan, On Mon, Mar 9, 2015 at 10:40 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 09/03/2015 11:07 AM, Hsiu-Khuern Tang wrote: >> >> On Mon, Mar 9, 2015 at 3:50 AM, Duncan Murdoch <murdoch.duncan at gmail.com> >> wrote: >> > On 08/03/2015 10:02 PM, Hsiu-Khuern Tang wrote: >> >> Hi, >> >> >> >> [This

> On Feb 11, 2018, at 7:48 AM, Thomas Mailund <thomas.mailund at gmail.com> wrote: > > Hi guys, > > I am working on some code for automatically translating recursive functions into looping functions to implemented tail-recursion optimisations. See https://github.com/mailund/tailr > > As a toy-example, consider the factorial function > > factorial <-