> z <- ddply(past, c("GEO_CNTRY_NAME","PROD_SEG_NAME"),
> function(x) summary(lm(VAL~fy,x))$r.squared)
>
> But when ave is not exactly doing what I need. Above code runs under a
> minute for my data set where as ave runs over 8 mins.
It's hard to know without a reproducible example, but I doubt that
ddply is the bottleneck in that code. I'd suspect that it's the linear
model fitting that takes the majority of the time
Hadley
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
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