[R] memory problem in handling large dataset

[Søren Højsgaard]

An alternative could be to store data in a MySql database and then select a sample of the cases using the RODBC package.
Best
Søren

________________________________

Fra: r-help-bounces at stat.math.ethz.ch på vegne af Liaw, Andy
Sendt: to 27-10-2005 19:21
Til: 'Berton Gunter'; 'Weiwei Shi'; 'r-help'
Emne: Re: [R] memory problem in handling large dataset



If my calculation is correct (very doubtful, sometimes), that's

> 1.7e9 * (300 * 8 + 50 * 4) / 1024^3
[1] 4116.446

or over 4 terabytes, just to store the data in memory.

To sample rows and read that into R, Bert's suggestion of using connections,
perhaps along with seek() for skipping ahead, would be what I'd try.  I had
try to do such things in Python as a chance to learn that language, but I
found operationally it's easier to maintain the project by doing everything
in one language, namely R, if possible.

Andy


> From: Berton Gunter
>
> I think the general advice is that around 1/4 or 1/3 of your available
> memory is about the largest data set that R can handle -- and often
> considerably less depending upon what you do and how you do
> it (because R's
> semantics require explicitly copying objects rather than
> passing pointers).
> Fancy tricks using environments might enable you to do
> better, but that
> requires advice from a true guru, which I ain't.
>
> See ?connections, ?scan, ?seek  for reading in a file a chunk
> at a time from
> a connection, thus enabling you to sample one line of data
> from each chunk,
> say.
>
> I suppose you could do this directly with repeated calls to scan() or
> read.table() by skipping more and more lines at the beginning
> at each call,
> but I assume that is horridly inefficient and would take forever.
>
> HTH.
>
> -- Bert Gunter
> Genentech Non-Clinical Statistics
> South San Francisco, CA
> 
> "The business of the statistician is to catalyze the
> scientific learning
> process."  - George E. P. Box
> 
> 
>
> > -----Original Message-----
> > From: r-help-bounces at stat.math.ethz.ch
> > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Weiwei Shi
> > Sent: Thursday, October 27, 2005 9:28 AM
> > To: r-help
> > Subject: [R] memory problem in handling large dataset
> >
> > Dear Listers:
> > I have a question on handling large dataset. I searched
> R-Search and I
> > hope I can get more information as to my specific case.
> >
> > First, my dataset has 1.7 billion observations and 350 variables,
> > among which, 300 are float and 50 are integers.
> > My system has 8 G memory, 64bit CPU, linux box. (currently, we don't
> > plan to buy more memory).
> >
> > > R.version
> >          _
> > platform i686-redhat-linux-gnu
> > arch     i686
> > os       linux-gnu
> > system   i686, linux-gnu
> > status
> > major    2
> > minor    1.1
> > year     2005
> > month    06
> > day      20
> > language R
> >
> >
> > If I want to do some analysis for example like randomForest on a
> > dataset, how many max observations can I load to get the machine run
> > smoothly?
> >
> > After figuring out that number, I want to do some sampling
> first, but
> > I did not find read.table or scan can do this. I guess I can load it
> > into mysql and then use RMySQL do the sampling or use
> python to subset
> > the data first. My question is, is there a way I can subsample
> > directly from file just using R?
> >
> > Thanks,
> > --
> > Weiwei Shi, Ph.D
> >
> > "Did you always know?"
> > "No, I did not. But I believed..."
> > ---Matrix III
> >
> > ______________________________________________
> > 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
> >
>
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