Hi,

Yes dplyr syntax is quite equivalent to SQL, although it is faster.
Another alternative you could consider is to use *data.table* which has a
syntax very similar to the way you select subset within a data.frame and in
terms of performance is faster (a bit) than sqldf.

You can get some idea of how to work with it here:

http://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r

Regards,
Carlos Ortega
www.qualityexcellence.es





2014-05-06 11:12 GMT+02:00 Dr Eberhard Lisse <el@lisse.na>:

> Jeff
>
> It's in MySQL, at the moment roughly 1.8 GB, if I pull it into a
> dataframe it saves to 180MB. I work from the dataframe.
>
> But, it's not only a size issue it's also a speed issue and hence I
> don't care what I am going to use, as long as it is fast.
>
> sqldf is easy to understand for me but it takes ages.  If
> alternatives were roughly similar in speed I would remain with
> sqldf.
>
> dplyr sounds faster, and promising, but the intrinsic stuff is
> way beyond me (elderly Gynaecologist) on the learning curve...
>
> el
>
> on 2014-05-06, 09:41 Jeff Newmiller said the following:
> > In what format is this "growing" data stored?  CSV? SQL? Log
> > textfile?  You say you don't want to use sqldf, but you haven't
> > said what you do want to use.
>
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>



-- 
Saludos,
Carlos Ortega
www.qualityexcellence.es

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