Use aggregate() for aggregation and use indexing or subset() for selection.
Alternately try the sqldf package: http://sqldf.googlecode.com
which allows one to perform SQL operations on data frames.
On Fri, Jun 6, 2008 at 6:12 AM, <[EMAIL PROTECTED]> wrote:
> Dear R experts,
>
> I am currently facing a tricky problem which I have read a lot about in
> the various R mailing lists without finding exactly what I need.
> I have a big data frame DF (about 2,000,000 rows) with 7 columns being
> variables and 1 being a measure (using reshape package nomeclature).
> There are no "duplicates" in it.
> Fot each of the variables I have some "rules" to apply, being COD_IN the
> value of the variable in the DF, COD_OUT the one to be transformed to;
> once obtained the "new codes" in the DF I have to aggregate the "new DF"
> (for example summing the measure).
> Usually the total transformation (merge+aggregate) really decreases the
> number of lines in the data frame, but sometimes it can grows depending
> on the rule. Just to give an idea, the first "rule" in v1 maps 820
> different values into 7 ones.
> Using SQL and a database this can be done in a very straightforward way
> (for example on the variable v1):
>
> Select COD_OUT, v2, v3, v4, v5, v6, v7, sum(measure)
> >From DF, RULE_v1
> Where v1=COD_IN
> Group by v2, v3,v4, v5, v6, v7
>
> So the first choice would be using a database; the second one would be
> splitting the data frame and then joining the results.
> Is there any other possibility to merge+aggregate caused by the merge ?
>
> Thank you in advance
>
> Angelo Linardi
>
>
>
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