[R] Suggestion for big files [was: Re: A comment about R:]
Prof Brian Ripley
ripley at stats.ox.ac.uk
Fri Jan 6 12:08:23 CET 2006
On Fri, 6 Jan 2006, Martin Maechler wrote:
>>>>>> "FrPi" == François Pinard <pinard at iro.umontreal.ca>
>>>>>> on Thu, 5 Jan 2006 22:41:21 -0500 writes:
> FrPi> [Brian Ripley]
> >> I rather thought that using a DBMS was standard practice in the
> >> R community for those using large datasets: it gets discussed rather
> >> often.
> FrPi> Indeed. (I tried RMySQL even before speaking of R to my co-workers.)
> >> Another possibility is to make use of the several DBMS interfaces already
> >> available for R. It is very easy to pull in a sample from one of those,
> >> and surely keeping such large data files as ASCII not good practice.
> FrPi> Selecting a sample is easy. Yet, I'm not aware of any
> FrPi> SQL device for easily selecting a _random_ sample of
> FrPi> the records of a given table. On the other hand, I'm
> FrPi> no SQL specialist, others might know better.
> FrPi> We do not have a need yet for samples where I work,
> FrPi> but if we ever need such, they will have to be random,
> FrPi> or else, I will always fear biases.
> >> One problem with Francois Pinard's suggestion (the credit has got lost)
> >> is that R's I/O is not line-oriented but stream-oriented. So selecting
> >> lines is not particularly easy in R.
> FrPi> I understand that you mean random access to lines,
> FrPi> instead of random selection of lines. Once again,
> FrPi> this chat comes out of reading someone else's problem,
> FrPi> this is not a problem I actually have. SPSS was not
> FrPi> randomly accessing lines, as data files could well be
> FrPi> hold on magnetic tapes, where random access is not
> FrPi> possible on average practice. SPSS reads (or was
> FrPi> reading) lines sequentially from beginning to end, and
> FrPi> the _random_ sample is built while the reading goes.
> FrPi> Suppose the file (or tape) holds N records (N is not
> FrPi> known in advance), from which we want a sample of M
> FrPi> records at most. If N <= M, then we use the whole
> FrPi> file, no sampling is possible nor necessary.
> FrPi> Otherwise, we first initialise M records with the
> FrPi> first M records of the file. Then, for each record in
> FrPi> the file after the M'th, the algorithm has to decide
> FrPi> if the record just read will be discarded or if it
> FrPi> will replace one of the M records already saved, and
> FrPi> in the latter case, which of those records will be
> FrPi> replaced. If the algorithm is carefully designed,
> FrPi> when the last (N'th) record of the file will have been
> FrPi> processed this way, we may then have M records
> FrPi> randomly selected from N records, in such a a way that
> FrPi> each of the N records had an equal probability to end
> FrPi> up in the selection of M records. I may seek out for
> FrPi> details if needed.
> FrPi> This is my suggestion, or in fact, more a thought that
> FrPi> a suggestion. It might represent something useful
> FrPi> either for flat ASCII files or even for a stream of
> FrPi> records coming out of a database, if those effectively
> FrPi> do not offer ready random sampling devices.
> FrPi> P.S. - In the (rather unlikely, I admit) case the gang
> FrPi> I'm part of would have the need described above, and
> FrPi> if I then dared implementing it myself, would it be welcome?
> I think this would be a very interesting tool and
> I'm also intrigued about the details of the algorithm you
> outline above.
It's called `reservoir sampling' and is described in my simulation book
and Knuth and elsewhere.
> If it would be made to work on all kind of read.table()-readable
> files, (i.e. of course including *.csv); that might be a valuable
> tool for all those -- and there are many -- for whom working
> with DBMs is too daunting initially.
It would be better (for the reasons I gave) to do this in a separate file
preprocessor: read.table reads from a connection not a file, of course.
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
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