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
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