Many Many Many thanks! it is a demonstrative lesson. I need time to test all examples :) Thank you for your time and support. Happy and Healthy New Year
Ô__ c/ /'_;~~~~kmezhoud (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ http://bioinformatics.tn/ On Wed, Dec 31, 2014 at 2:38 PM, Martin Morgan <mtmor...@fredhutch.org> wrote: > On 12/31/2014 12:22 AM, Karim Mezhoud wrote: > >> Thanks, >> It seems for loop spends less time ;) >> >> with >> dim(DataFrame) >> [1] 338 70 >> >> For loop has >> user system elapsed >> 0.012 0.000 0.012 >> >> and apply has >> user system elapsed >> 0.020 0.000 0.021 >> > > The timings are so short that the answer in terms of speed is 'it does not > matter'. > > Here is a selection of approaches > > f0 <- function(df) { > for (i in seq_along(df)) > df[,i] <- as.numeric(df[,i]) > df > } > > f0a <- function(df) { > ## data.frame is a list-of-equal-length vectors; access each > ## column with "[[" > for (i in seq_along(df)) > df[[i]] <- as.numeric(df[[i]]) > df > } > > f0c <- compiler::cmpfun(f0) ## loops sometimes benefit from compilation > > f1 <- function(df) > as.data.frame(apply(df, 2, as.numeric)) > > f2 <- function(df) { > ## replace all columns of df with list-of-vectors > df[] <- lapply(df, as.numeric) > df > } > > f3 <- function(df) { > ## coerce to matrix to avoid the explicit loop, use mode<- to > ## change storage of elements > m <- as.matrix(df) > mode(m) <- "numeric" > as.data.frame(m) > } > > f4 <- function(df) { > ## if it's a matrix, why are we returning a data.frame? > m <- as.matrix(df) > mode(m) <- "numeric" > m > } > > f4a <- function(df) > ## unlist to single vector, coerce, then format as matrix > matrix(as.numeric(unlist(df, use.names=FALSE)), nrow(df), > dimnames=dimnames(df)) > > It's important to test that different methods return the same result > (perhaps allowing for differences in attributes such as row or column > names). The microbenchmark package repeats timings across multiple trials > (default 100 times). > > library(microbenchmark) > test <- function(df) { > stopifnot( > identical(f0(df), f0a(df)), > identical(f0(df), f0c(df)), > identical(f0(df), f1(df)), > identical(f0(df), f2(df)), > identical(f0(df), f3(df)), > identical(as.matrix(f0(df)), f4(df)), > all.equal(f4(df), f4a(df), check.attributes=FALSE)) > microbenchmark(f0(df), f0a(df), f1(df), f2(df), f3(df), f4(df), > f4a(df)) > } > > Here are some data sets > > m <- matrix(rnorm(338 * 70), 338) > df <- as.data.frame(m) > dfc <- as.data.frame(lapply(df, as.character), stringsAsFactors=FALSE) > dff <- as.data.frame(lapply(df, as.character)) > > and results > > > test(df) > Unit: microseconds > expr min lq mean median uq max neval > f0(df) 6208.956 6270.5500 6367.4138 6306.7110 6362.2225 7731.281 100 > f0a(df) 2917.973 2975.2090 3024.8623 3002.3805 3036.5365 3951.618 100 > f0c(df) 6078.399 6150.1085 6264.0998 6188.3690 6244.5725 7684.116 100 > f1(df) 2698.074 2743.2905 2821.8453 2769.3655 2805.5345 4033.229 100 > f2(df) 1989.057 2041.0685 2066.1830 2055.0020 2083.8545 2267.732 100 > f3(df) 1532.435 1572.9810 1609.7378 1597.6245 1624.2305 2003.584 100 > f4(df) 808.593 828.5445 852.2626 847.5355 864.6665 1180.977 100 > f4a(df) 422.657 437.2705 458.9845 455.2470 465.5815 695.443 100 > > test(dfc) > Unit: milliseconds > expr min lq mean median uq max neval > f0(df) 11.416532 11.647858 11.915287 11.767647 12.016276 14.239622 100 > f0a(df) 8.095709 8.211116 8.380638 8.289895 8.454948 9.529026 100 > f0c(df) 11.339293 11.577811 11.772087 11.702341 11.896729 12.674766 100 > f1(df) 8.227371 8.277147 8.422412 8.331403 8.490411 9.145499 100 > f2(df) 6.907888 7.010828 7.162529 7.147198 7.239048 7.763758 100 > f3(df) 6.608107 6.688232 6.845936 6.792066 6.892635 8.359274 100 > f4(df) 5.859482 5.939680 6.046976 5.993804 6.105388 6.968601 100 > f4a(df) 5.372214 5.460987 5.556687 5.521542 5.614482 6.107081 100 > > test(dff) > Error: identical(f0(df), f1(df)) is not TRUE > > Except when dealing with factors, the use of explicit loops is the > slowest. With factors, matrix-based methods coerce the level labels to > numeric, whereas vector-based methods coerce the underlying codes (level > values) of the factor; obviously great care needs to be taken. > > > f0(dff)[1:5, 1:5] > V1 V2 V3 V4 V5 > 1 150 232 294 88 56 > 2 159 8 89 59 10 > 3 132 171 40 205 119 > 4 214 273 26 262 216 > 5 281 49 255 31 233 > > f1(dff)[1:5, 1:5] > V1 V2 V3 V4 V5 > 1 -1.7092463 0.50234009 0.8492982 -0.5636901 -0.38545566 > 2 -2.3020854 -0.05580931 -0.5963673 -0.3671748 -0.09408031 > 3 -1.2915110 -2.46181533 -0.2470108 0.3301129 -1.06810225 > 4 0.3065989 0.89263099 -0.1717432 0.7721411 0.35856334 > 5 0.8795616 -0.43049898 0.4560515 -0.1722099 0.46125149 > > In terms of 'best practice', I would represent my data in the appropriate > data structure in the first place (as a matrix of appropriate type, rather > than data.frame, so the entire coercion is irrelevant). If faced with a > data.frame with specific columns to coerce I would use the approach > > cidx <- sapply(df, is.character) # index of columns to coerce > df[cidx] <- lapply(df[cidx], as.numeric) > > which seems to be reasonably correct, expressive, compact, and speedy. > > Martin Morgan > > > >> Ô__ >> c/ /'_;~~~~kmezhoud >> (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ >> http://bioinformatics.tn/ >> >> >> >> On Wed, Dec 31, 2014 at 8:54 AM, Berend Hasselman <b...@xs4all.nl> wrote: >> >> >>> On 31-12-2014, at 08:40, Karim Mezhoud <kmezh...@gmail.com> wrote: >>>> >>>> Hi All, >>>> I would like to choice between these two data frame convert. which is >>>> faster? >>>> >>>> for(i in 1:ncol(DataFrame)){ >>>> >>>> DataFrame[,i] <- as.numeric(DataFrame[,i]) >>>> } >>>> >>>> >>>> OR >>>> >>>> DataFrame <- as.data.frame(apply(DataFrame,2 ,function(x) >>>> as.numeric(x))) >>>> >>>> >>>> >>> Try it and use system.time. >>> >>> Berend >>> >>> Thanks >>>> Karim >>>> Ô__ >>>> c/ /'_;~~~~kmezhoud >>>> (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ >>>> http://bioinformatics.tn/ >>>> >>>> [[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. >> >> > > -- > Computational Biology / Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. > PO Box 19024 Seattle, WA 98109 > > Location: Arnold Building M1 B861 > Phone: (206) 667-2793 > [[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.
