Thanks for looking, but my file has quotes.  It's also 400MB, and I don't
mind waiting, but don't have 6x the memory to read it in.

On 8/9/07, Gabor Grothendieck <ggrothendieck@gmail.com> wrote:
>
> If we add quote = FALSE to the write.csv statement its twice as fast
> reading it in.
>
> On 8/9/07, Michael Cassin <michael@cassin.name> 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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