Yes the last one this the best. But I need to test if returned data.frame is with factor or character: cidx <- sapply(df, is.factor) or cidx <- sapply(df, is.character) Thanks
Ô__ c/ /'_;~~~~kmezhoud (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ http://bioinformatics.tn/ On Wed, Dec 31, 2014 at 5:24 PM, Karim Mezhoud <kmezh...@gmail.com> wrote: > Concretely I request cbioportal through cgsdr package. > Depending of Cases and Genetic profiles I receive in general data.frame > with heterogeneous structure. The bad one if the returned data.frame is > composed by numeric and character columns. in this case numeric columns are > considered as factor. It is the case when I explore/extract information > from Clinical Data (Age, gender., tumor stage..). In this case I need to > convert only numeric column and not character ones. I am using > grep("[0-9]*.[0-9]*",df[,i])!=0 {fun to convert}. > > But this heterogeneity comes even with only supposed numeric data.frame > (gene expression). here an example > > > library(cgdsr) > GeneList <- c("DDR2", "HPGDS", "MS4A2","SSUH2","MLH1" ,"MSH2", "ATM" > ,"ATR", "MDC1" ,"PARP1") > cgds<-CGDS("http://www.cbioportal.org/public-portal/";) > > str(getProfileData(cgds,GeneList, > "stad_tcga_methylation_hm27","stad_tcga_methylation_hm27")) > > str(getProfileData(cgds,GeneList, > "stad_tcga_methylation_hm450","stad_tcga_methylation_hm450")) > > > With my computer I did not find the same structure (numeric vs factor). > > Also I need to preserve row and column names ;) > So I am working to resolve these details depending on data of cbioportal... > > Thank you > > > Ô__ > c/ /'_;~~~~kmezhoud > (*) \(*) ⴽⴰⵔⵉⵎ ⵎⴻⵣⵀⵓⴷ > http://bioinformatics.tn/ > > > > On Wed, Dec 31, 2014 at 4:37 PM, Karim Mezhoud <kmezh...@gmail.com> wrote: > >> 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.
