Thank you for suggestions,
apparently data.table is much quicker than ddply and it's fantastic to use.
I forgot to mention in my topic I'm looking for alternative in R 2.10
version as on my platform our server runs older version of software which
only support R up to version of R-2.10 (upgrade is currently impossible
because of all applications that run on it)
Ave function does not suffice as what I'm trying to do is:
z1 <- past[, c(summary(lm(VAL~fy))$r.squared)
, by = list(GEO_CNTRY_NAME,PROD_SEG_NAME)]
z <- ddply(past, c("GEO_CNTRY_NAME","PROD_SEG_NAME"),
function(x) summary(lm(VAL~fy,x))$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.
Any ideas/help is greatly appreciated.
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