Hi Greg,

Thanks, very encouraging: with my example, this is 10x more efficient than my loop:
utilisateur     système      écoulé
     13.819       5.510      20.204
utilisateur     système      écoulé
    156.206      44.859     202.150

In real life, I did some work on each file before doing rbind. I'll see if this work can be put in a custom-built function that would go into the lapply call you suggested.

Denis

Le 09-07-23 à 17:27, Greg Snow a écrit :

Try something like (untested):

mylist <- lapply(all.files, function(i) read.csv(i) )
mydf <- do.call('rbind', mylist)

If all the csv files are conformable that rbind works on them (if the loop method works then that should be the case) then this will read in each file, store the data frames as a list, then rbind them all together.

It seems that this should be faster than the loop, but testing will be needed to be sure.

Hope this helps,

--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111


-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Denis Chabot
Sent: Thursday, July 23, 2009 1:54 PM
To: list R
Subject: [R] alternative to rbind within a loop

Hi,

I often have to do this:

select a folder (directory) containing a few hundred data files in csv
format (up to 1000 files, in fact)

open each file, transform some character variables in date-tiime format

make into a dataframe (involves getting rid of a few variables I don't
need

concatenate to the master dataframe that will eventually contain the
data from all the files in the folder.

I use a loop going from 1 to the number of files. I have added a
command to print an incrementing number to the R console each time the
loop completes one iteration, to judge the speed of the process.

At the beginning, 3-4 files are processed each second. After a few
hundred iterations it slows down to about 1 file per second. Before I
reach the last file (898 in the case at hand), it has become much
slower, about 1 file every 2-3 seconds.

This progressive slowing down suggests the problem is linked to the
size of the growing "master" dataframe that rbind combines with each
new file.

In fact, the small script below confirms this as nothing at all
happens within the loop but rbind. You can cut the size of this
example not to waste to much of your time:


# create a dummy data.frame and copy it in a large number of csv files

test  <- file.path("test")

a <- 1:350
b <- rnorm(350,100,10)
c <- runif(350, 0, 100)
d <- month.name[runif(350,1,12)]

the.data <- data.frame(a,b,c,d)

for(i in 1:850){
        write.csv(the.data, file=paste(test, "/file_", i, ".csv",
sep=""))
}

# now lets make a single dataframe from all these csv files

all.files <- list.files(path=test,full.names=T,pattern=".csv")

new.data <- NULL

system.time({
        for(i in all.files){
        in.data <- read.csv(i)
        if (is.null(new.data)) {new.data = in.data} else {new.data =
rbind(new.data, in.data)}
        cat(paste(i, ", ", sep=""))
} # end for
}) # end system.time

utilisateur     système      écoulé
    156.206      44.859     202.150
This is with

sessionInfo()
R version 2.9.1 Patched (2009-07-16 r48939)
x86_64-apple-darwin9.7.0

locale:
fr_CA.UTF-8/fr_CA.UTF-8/C/C/fr_CA.UTF-8/fr_CA.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] doBy_3.7        chron_2.3-30    timeDate_290.84

loaded via a namespace (and not attached):
[1] cluster_1.12.0  grid_2.9.1      Hmisc_3.5-2     lattice_0.17-25
tools_2.9.1


Would it be better to somehow save all 850 files in one dataframe
each, and then rbind them all in a single operation?

Can I combine all my files without using a loop? I've never quite
mastered the "apply" family of functions but have not seen examples to
read files.

Thanks in advance,

Denis Chabot

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