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@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@intermountainmail.org
> (801) 408-8111
>
>
> -----Original Message-----
> From: r-help-bounces@stat.math.ethz.ch
> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Yohan CHOUKROUN
> Sent: Wednesday, July 19, 2006 9:42 AM
> To: 'r-help@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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