Hi Arun, Thank you very much for your suggestion.
While running some tests, I came across the following: # sample data n <- 2000 p <- 1000 x2 <- data.frame(variable = rep(paste0('x', 1:p), each = n), id = rep(paste0('p', 1:p), n), outcome = sample(0:2, n*p, TRUE), rate = runif(n*p, 0.5, 1)) str(x2) library(dplyr) library(tidyr) # Arun's suggestion system.time({wide1 <- x2%>% select(-rate) %>% mutate(variable=factor(variable, levels=unique(variable)),id=factor(id, levels=unique(id))) %>% spread(variable,outcome) colnames(wide1)[-1] <- paste("outcome",colnames(wide1)[-1],sep=".") }) # Error: C stack usage 18920219 is too close to the limit # Timing stopped at: 13.833 0.251 14.085 Do you happen to know what can be done to avoid this? Thank you. Best, Jorge.- On Mon, Jun 30, 2014 at 6:51 PM, arun <smartpink...@yahoo.com> wrote: > > > Hi Jorge, > > You may try: > library(dplyr) > library(tidyr) > > #Looks like this is faster than the other methods. > system.time({wide1 <- x2%>% > select(-rate) %>% > mutate(variable=factor(variable, > levels=unique(variable)),id=factor(id, levels=unique(id))) > %>% > spread(variable,outcome) > colnames(wide1)[-1] <- paste("outcome",colnames(wide1)[-1],sep=".") > }) > > #user system elapsed > # 0.006 0.00 0.006 > > > > system.time(wide <- reshape(x2[, -4], v.names = "outcome", idvar = "id", > timevar = "variable", direction = "wide")) > #user system elapsed > # 0.169 0.000 0.169 > > > > system.time({ > sel <- unique(x2$variable) > id <- unique(x2$id) > X <- matrix(NA, ncol = length(sel) + 1, nrow = length(id)) > X[, 1] <- id > colnames(X) <- c('id', sel) > r <- mclapply(seq_along(sel), function(i){ > out <- x2[x2$variable == sel[i], ][, 3] > }, mc.cores = 4) > X[, -1] <- do.call(rbind, r) > X > }) > > # user system elapsed > # 0.125 0.011 0.074 > > > wide2 <- wide1 > wide2$id <- as.character(wide2$id) > wide$id <- as.character(wide$id) > all.equal(wide, wide2, check.attributes=F) > #[1] TRUE > > A.K. > > > > On Sunday, June 29, 2014 11:48 PM, Jorge I Velez <jorgeivanve...@gmail.com> > wrote: > Dear R-help, > > I am working with some data stored as "filename.txt.gz" in my working > directory. > After reading the data in using read.table(), I can see that each of them > has four columns (variable, id, outcome, and rate) and the following > structure: > > # sample data > x2 <- data.frame(variable = rep(paste0('x', 1:100), each = 100), id = > rep(paste0('p', 1:100), 100), outcome = sample(0:2, 10000, TRUE), rate = > runif(10000, 0.5, 1)) > str(x2) > > Each variable, i.e., x1, x2,..., x100 is repeated as many times as the > number of unique IDs (100 in this example). What I would like to do is to > transform the data above > in a long format. I can do this by using > > # reshape > wide <- reshape(x2[, -4], v.names = "outcome", idvar = "id", > timevar = "variable", direction = "wide") > str(wide) > > # or a "hack" with mclapply: > > require(parallel) > sel <- as.character(unique(x2$variable)) > id <- as.character(unique(x2$id)) > X <- matrix(NA, ncol = length(sel) + 1, nrow = length(id)) > X[, 1] <- id > colnames(X) <- c('id', sel) > r <- mclapply(seq_along(sel), function(i){ > out <- x2[x2$variable == sel[i], ][, 3] > }, mc.cores = 4) > X[, -1] <- do.call(rbind, r) > X > > However, I was wondering if it is possible to come up with another solution > , hopefully faster than these > . Unfortunately, either one of these takes a very long time to process, > specially when the number of variables is very large > (> 250,000) and the number of ids is ~2000. > > I would very much appreciate your suggestions. At the end of this message > is my sessionInfo(). > > Thank you very much in advance. > > Best regards, > Jorge Velez.- > > > R> sessionInfo() > > R version 3.0.2 Patched (2013-12-11 r64449) > Platform: x86_64-apple-darwin10.8.0 (64-bit) > > locale: > [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8 > > attached base packages: > [1] graphics grDevices utils datasets parallel compiler stats > [8] methods base > > other attached packages: > [1] knitr_1.6.3 ggplot2_1.0.0 slidifyLibraries_0.3.1 > [4] slidify_0.3.52 > > loaded via a namespace (and not attached): > [1] colorspace_1.2-4 digest_0.6.4 evaluate_0.5.5 formatR_0.10 > [5] grid_3.0.2 gtable_0.1.2 markdown_0.7.1 MASS_7.3-33 > [9] munsell_0.4.2 plyr_1.8.1 proto_0.3-10 Rcpp_0.11.2 > [13] reshape2_1.4 scales_0.2.4 stringr_0.6.2 tools_3.0.2 > [17] whisker_0.4 yaml_2.1.13 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > 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 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.