[R] R for large data sets

[Fan]

AFAK, ROracle works only for R unix. 

RODBC works very well for R Windows, I'd like to know 
if there's any interests of ROracle for Windows users 
(ex. large data sets, faster, etc.) ?

Thanks for advice
--
Xiao Gang FAN

Ernesto Jardim a écrit :
> 
> Hi
> 
> I'm using some large datasets and I found the ROracle package to be of
> great help.
> 
> If you have the chance to create a database in Oracle or MySQL with one
> single table for your dataset, you can then use the ROracle package to
> access the dataset. I found several advantages on that.
> 
> I don't import the data into my environment. I use a small function (see
> below) to access the dataset and because the result is a data.frame you
> can use it as usually.
> 
> Your environment will not be to large and you'll have the ram memory
> less full.
> 
> It's easier to select subsets with SQL than S/R language.
> 
> Hope it helps
> 
> Regards
> 
> EJ
> 
> --//--
> 
> ora.fun <- function(){
> 
>         library(ROracle)
>         m <- dbManager("Oracle")
>         con <- dbConnect(m,user="user",password="password")
>         dat <- quickSQL(con,"select ...")
>         close(con)
>         unload(m)
>         dat
> 
> }
> 
> --//--
> 
> On Tue, 2002-01-15 at 19:43, Prof Brian Ripley wrote:
> > On Tue, 15 Jan 2002, wei, xiaoyan wrote:
> >
> > > As a part of our regular data analysis, I have to read in large data sets
> > > with six columns and about a million rows. In Splus, this usually take a
> > > couple of minutes. I just tried R, it seems take forever to use read.table()
> > > to read in the data frame! It did not help much even though I specified
> > > colClasses and nrows in read.table().
> > >
> > > How is R's ability to analyze large data sets? I used R on solaris 2.6 and I
> > > used all default compilation flags when building the R package. Will it help
> > > if I use some compilation flags with higher optimization level?
> >
> > It will help to use R-patched, since I guess you are using 1.4.0.
> > Also, look in the list archives, as I answered this more fully earlier
> > today.
> >
> > In either S-PLUS or R, scan would be a better choice for such a dataset.
> >
> > --
> > 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 272860 (secr)
> > Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> >
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