If we add quote = FALSE to the write.csv statement its twice as fast
reading it in.
On 8/9/07, Michael Cassin <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I've been having similar experiences and haven't been able to
> substantially improve the efficiency using the guidance in the I/O
> Manual.
>
> Could anyone advise on how to improve the following scan()? It is not
> based on my real file, please assume that I do need to read in
> characters, and can't do any pre-processing of the file, etc.
>
> ## Create Sample File
> write.csv(matrix(as.character(1:1e6),ncol=10,byrow=TRUE),"big.csv",row.names=FALSE)
> q()
>
> **New Session**
> #R
> system("ls -l big.csv")
> system("free -m")
> big1<-matrix(scan("big.csv",sep=",",what=character(0),skip=1,n=1e6),ncol=10,byrow=TRUE)
> system("free -m")
>
> The file is approximately 9MB, but approximately 50-60MB is used to
> read it in.
>
> object.size(big1) is 56MB, or 56 bytes per string, which seems excessive.
>
> Regards, Mike
>
> Configuration info:
> > sessionInfo()
> R version 2.5.1 (2007-06-27)
> x86_64-redhat-linux-gnu
> locale:
> C
> attached base packages:
> [1] "stats" "graphics" "grDevices" "utils" "datasets" "methods"
> [7] "base"
>
> # uname -a
> Linux ***.com 2.6.9-023stab044.4-smp #1 SMP Thu May 24 17:20:37 MSD
> 2007 x86_64 x86_64 x86_64 GNU/Linux
>
>
>
> ====== Quoted Text ====
> From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
> Date: Tue, 26 Jun 2007 17:53:28 +0100 (BST)
>
>
>
>
> The R Data Import/Export Manual points out several ways in which you
> can use read.csv more efficiently.
>
> On Tue, 26 Jun 2007, ivo welch wrote:
>
> > dear R experts:
> >
> > I am of course no R experts, but use it regularly. I thought I would
> > share some experimentation with memory use. I run a linux machine
> > with about 4GB of memory, and R 2.5.0.
> >
> > upon startup, gc() reports
> >
> > used (Mb) gc trigger (Mb) max used (Mb)
> > Ncells 268755 14.4 407500 21.8 350000 18.7
> > Vcells 139137 1.1 786432 6.0 444750 3.4
> >
> > This is my baseline. linux 'top' reports 48MB as baseline. This
> > includes some of my own routines that are always loaded. Good..
> >
> >
> > Next, I created a s.csv file with 22 variables and 500,000
> > observations, taking up an uncompressed disk space of 115MB. The
> > resulting object.size() after a read.csv() is 84,002,712 bytes (80MB).
> >
> >> s= read.csv("s.csv");
> >> object.size(s);
> >
> > [1] 84002712
> >
> >
> > here is where things get more interesting. after the read.csv() is
> > finished, gc() reports
> >
> > used (Mb) gc trigger (Mb) max used (Mb)
> > Ncells 270505 14.5 8349948 446.0 11268682 601.9
> > Vcells 10639515 81.2 34345544 262.1 42834692 326.9
> >
> > I was a big surprised by this---R had 928MB intermittent memory in
> > use. More interestingly, this is also similar to what linux 'top'
> > reports as memory use of the R process (919MB, probably 1024 vs. 1000
> > B/MB), even after the read.csv() is finished and gc() has been run.
> > Nothing seems to have been released back to the OS.
> >
> > Now,
> >
> >> rm(s)
> >> gc()
> > used (Mb) gc trigger (Mb) max used (Mb)
> > Ncells 270541 14.5 6679958 356.8 11268755 601.9
> > Vcells 139481 1.1 27476536 209.7 42807620 326.6
> >
> > linux 'top' now reports 650MB of memory use (though R itself uses only
> > 15.6Mb). My guess is that It leaves the trigger memory of 567MB plus
> > the base 48MB.
> >
> >
> > There are two interesting observations for me here: first, to read a
> > .csv file, I need to have at least 10-15 times as much memory as the
> > file that I want to read---a lot more than the factor of 3-4 that I
> > had expected. The moral is that IF R can read a .csv file, one need
> > not worry too much about running into memory constraints lateron. {R
> > Developers---reducing read.csv's memory requirement a little would be
> > nice. of course, you have more than enough on your plate, already.}
> >
> > Second, memory is not returned fully to the OS. This is not
> > necessarily a bad thing, but good to know.
> >
> > Hope this helps...
> >
> > Sincerely,
> >
> > /iaw
> >
> > ______________________________________________
> > R-help_at_stat.math.ethz.ch 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.
> >
> --
> Brian D. Ripley, ripley_at_stats.ox.ac.uk
> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
> University of Oxford, Tel: +44 1865 272861 (self)
> 1 South Parks Road, +44 1865 272866 (PA)
> Oxford OX1 3TG, UK Fax: +44 1865 272595
>
> ______________________________________________
> R-help@stat.math.ethz.ch 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.
>
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