[R] Suggestion for big files [was: Re: A comment about R:]

Martin Maechler maechler at stat.math.ethz.ch
Fri Jan 6 09:33:05 CET 2006

>>>>> "FrPi" == François 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.

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.

Martin Maechler, ETH Zurich

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