By far, the cheapest and easiest solution (and the very first to try)
is to add more memory. The cost depends on what kind you need, but
here's for example 2 GB you can buy for only $150:
http://www.newegg.com/Product/Product.asp?Item=N82E16820144157
Project constraints?! If they don't want to spend a couple hundred USD
for memory, you're working on the wrong project (and/or for the wrong
organization). Buying more memory (say up to a few GB) is orders of
magnitude cheaper than the licenses for some proprietary software that
can get around memory constraints, and probably (much) cheaper than
the loss of productivity caused by the extra training and setup time
needed to try to implement an alternative solution (such as a
connection to a DBMS). And even if the extra memory needed for R were
as expensive as the license for a proprietary software, which choice
would be more reasonable?
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of mahesh r
> Sent: Wednesday, July 19, 2006 4:23 PM
> To: r-help at stat.math.ethz.ch
> Subject: Re: [R] how to use large data set ?
>
> Hi,
> I would like to extend to the query posted earlier on using large data
> bases. I am trying to use Rgdal to mine within the remote
> sensing imageries.
> I dont have problems bring the images within the R
> environment. But when I
> try to convert the images to a data.frame I receive an
> warning message from
> R saying "1: Reached total allocation of 510Mb: see
> help(memory.size)" and
> the process terminates. Due to project constarints I am given a very
> old 2.4Ghz computer with only 512 MB RAM. I think what R is currently
> doing is
> trying to store the results in the RAM and since the image
> size is very big
> (some 9 million pixels), I think it gets out of memory.
>
> My question is
> 1. Is there any possibility to dump the temporary variables
> in a temp folder
> within the hard disk (as many softwares do) instead of leting
> R store them
> in RAM
> 2. Could this be possible without creating a connection to a
> any back hand
> database like Oracle.
>
> Thanks,
>
> Mahesh
>
>
> On 7/19/06, Greg Snow <Greg.Snow at intermountainmail.org> wrote:
> >
> > You did not say what analysis you want to do, but many
> common analyses
> > can be done as special cases of regression models and you
> can use the
> > biglm package to do regression models.
> >
> > Here is an example that worked for me to get the mean and standard
> > deviation by day from an oracle database with over 23
> million rows (I
> > had previously set up 'edw' as an odbc connection to the
> database under
> > widows, any of the database connections packages should work for you
> > though):
> >
> > library(RODBC)
> > library(biglm)
> >
> > con <- odbcConnect('edw',uid='glsnow',pwd=pass)
> >
> > odbcQuery(con, "select ADMSN_WEEKDAY_CD, LOS_DYS from
> CM.CASEMIX_SMRY")
> >
> > t1 <- Sys.time()
> >
> > tmp <- sqlGetResults(con, max=100000)
> >
> > names(tmp) <- c("Day","LoS")
> > tmp$Day <- factor(tmp$Day, levels=as.character(1:7))
> > tmp <- na.omit(tmp)
> > tmp <- subset(tmp, LoS > 0)
> >
> > ff <- log(LoS) ~ Day
> >
> > fit <- biglm(ff, tmp)
> >
> > i <- nrow(tmp)
> > while( !is.null(nrow( tmp <- sqlGetResults(con, max=100000) ) ) ){
> > names(tmp) <- c("Day","LoS")
> > tmp$Day <- factor(tmp$Day, levels=as.character(1:7))
> > tmp <- na.omit(tmp)
> > tmp <- subset(tmp, LoS > 0)
> >
> > fit <- update(fit,tmp)
> >
> > i <- i + nrow(tmp)
> > cat(format(i,big.mark=',')," rows
processed\n")
> > }
> >
> > summary(fit)
> >
> > t2 <- Sys.time()
> >
> > t2-t1
> >
> > Hope this helps,
> >
> > --
> > Gregory (Greg) L. Snow Ph.D.
> > Statistical Data Center
> > Intermountain Healthcare
> > greg.snow at intermountainmail.org
> > (801) 408-8111
> >
> >
> > -----Original Message-----
> > From: r-help-bounces at stat.math.ethz.ch
> > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
> Yohan CHOUKROUN
> > Sent: Wednesday, July 19, 2006 9:42 AM
> > To: 'r-help at stat.math.ethz.ch'
> > Subject: [R] how to use large data set ?
> >
> > Hello R users,
> >
> >
> >
> > Sorry for my English, i'm French.
> >
> >
> >
> > I want to use a large dataset (3 millions of rows and 70 var) but I
> > don't know how to do because my computer crash quickly (P4
> 2.8Ghz, 1Go
> > ).
> >
> > I have also a bi Xeon with 2Go so I want to do computation on this
> > computer and show the results on mine. Both of them are on
> Windows XP...
> >
> >
> >
> > To do shortly I have:
> >
> >
> >
> > 1 server with a MySQL database
> >
> > 1computer
> >
> > and I want to use them with a large dataset.
> >
> >
> >
> > I'm trying to use RDCOM to connect the database and
> installing (but it's
> > hard for me..) Rpad.
> >
> >
> >
> > Is there another solutions ?
> >
> >
> >
> > Thanks in advance
> >
> >
> >
> >
> >
> > Yohan C.
> >
> >
> >
> >
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> ______________________________________________
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> PLEASE do read the posting guide
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