The large value for maximum time may be due to garbage collection, which happens periodically. E.g., try the following, where the unlist(as.list()) creates a lot of garbage. I get a very large time every 102 or 51 iterations and a moderately large time more often
mb <- microbenchmark::microbenchmark({ x <- as.list(sin(1:5e5)); x <- unlist(x) / cos(1:5e5) ; sum(x) }, times=1000) plot(mb$time) quantile(mb$time * 1e-6, c(0, .5, .75, .90, .95, .99, 1)) # 0% 50% 75% 90% 95% 99% 100% # 59.04446 82.15453 102.17522 180.36986 187.52667 233.42062 249.33970 diff(which(mb$time > quantile(mb$time, .99))) # [1] 102 51 102 102 102 102 102 102 51 diff(which(mb$time > quantile(mb$time, .95))) # [1] 6 41 4 47 4 40 7 4 47 4 33 14 4 47 4 47 4 47 4 47 4 47 4 6 41 #[26] 4 6 7 9 25 4 47 4 47 4 47 4 22 25 4 33 14 4 6 41 4 47 4 22 Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Aug 22, 2017 at 5:53 AM, <raphael.fel...@agroscope.admin.ch> wrote: > 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 about the > max value of the output. > > FIRST TEST: > > library(microbenchmark) > > microbenchmark( > + n <- readRDS('file.rds'), > + load('file.Rdata') > + ) > Unit: milliseconds > expr min lq > mean median uq > max neval > n <- readRDS(fl1) 106.5956 109.6457 237.3844 > 117.8956 141.9921 10934.162 100 > load(fl2) 295.0654 301.8162 > 335.6266 308.3757 319.6965 1915.706 > 100 > > It looks like the max value is an outlier. > > So I tried: > SECOND TEST: > > sapply(1:10, function(x) system.time(n <- readRDS('file.rds'))[3]) > elapsed elapsed elapsed elapsed > elapsed elapsed elapsed > elapsed elapsed elapsed > 10.50 0.11 0.11 > 0.11 0.10 0.11 > 0.11 0.11 0.12 > 0.12 > > sapply(1:10, function(x) system.time(load'flie.Rdata'))[3]) > elapsed elapsed elapsed elapsed > elapsed elapsed elapsed > elapsed elapsed elapsed > 1.86 0.29 0.31 > 0.30 0.30 0.31 > 0.30 0.29 0.31 > 0.30 > > Which confirmed my suspicion; the first time loading the data takes much > longer than the following times. I suspect that this has something to do > how the data is assigned and that R doesn't has to 'fully' read the data, > if it is read the second time. > > So the question remains, how can I make a realistic benchmark test? From > the first test I would conclude that reading the *.rds file is faster. But > this holds only for a large number of neval. If I set times = 1 then > reading the *.Rdata would be faster (as also indicated by the second test). > > Thanks for any help or comments. > > Kind regards > > Raphael > ------------------------------------------------------------ > ------------------------ > Raphael Felber, PhD > Scientific Officer, Climate & Air Pollution > > Federal Department of Economic Affairs, > Education and Research EAER > Agroscope > Research Division, Agroecology and Environment > > Reckenholzstrasse 191, CH-8046 Zürich > Phone +41 58 468 75 11 > Fax +41 58 468 72 01 > raphael.fel...@agroscope.admin.ch<mailto:raphael.fel...@agroscope.admin.ch > > > www.agroscope.ch<http://www.agroscope.ch/> > > > [[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. > [[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.