On 08/26/2011 09:14 AM, AdamMarczak wrote:
> Thank you all for suggestions, they were great and informative.
> I will surely use data.tables in future when our server will be upgraded for
> now this is solution that I used. This solution performs exactly same task
> and produces exact same results at ddply.
>
>
> s <- split(past, paste(past$"CNTRY_NAME",past$"SEG_NAME"))
> R2 <- lapply(s, function(x) return(list(x$"CNTRY_NAME"[1], x$"SEG_NAME[1],
> summary(lm(VAL~fy,x))$r.squared)));
> R2 <- data.frame(do.call(rbind, R2))
> R2[,1] <- unlist(R2[,1]); R2[,2] <- unlist(R2[,2]); R2[,3] <-
> unlist(R2[,3]);
> colnames(R2)[1:3] <- c("CNTRY_NAME","SEG_NAME","V1")
> R2<-R2[order(R2$CNTRY_NAME,R2$SEG_NAME),]
Is it much faster than ddply? And why not use data.table? You do not
need a new server to benefit from the speed gain.
Paul
>
> Above lines produce exactly same result as ddply in the exactly same fashion
> allow quick replacement of ddply without any further rebuild of the code
> (sorting is just precaution).
>
> Best regards,
> Adam.
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/ddply-from-plyr-package-any-alternatives-tp3765936p3770352.html
> Sent from the R help mailing list archive at Nabble.com.
>
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--
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Global Climate Division
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