Try timing this to see if its any faster:
> lev <- levels(wer$Predictor)
> out <- outer(wer$Predictor, lev, "==")
> colnames(out) <- lev
> aggregate(out, wer[1:2], sum)
Name Type A B
1 1 a 1 0
2 2 b 1 0
3 3 c 1 0
4 4 d 1 1
5 5 e 1 1
On Tue, Dec 2, 2008 at 11:52 PM, Daren Tan <[EMAIL PROTECTED]> wrote:
>
> Hi,
>
> I am casting a dataframe from long to wide format. The same codes that works
> for a smaller dataframe would take a long time (more than two hours and still
> running) for a longer dataframe of 2495227 rows and ten different predictors.
> How to make it more efficient ?
>
> wer <- data.frame(Name=c(1:5, 4:5), Type=c(letters[1:5], letters[4:5]),
> Predictor=c("A", "A", "A", "A", "A", "B", "B"))
>> wer
> Name Type Predictor
> 1 1 a A
> 2 2 b A
> 3 3 c A
> 4 4 d A
> 5 5 e A
> 6 4 d B
> 7 5 e B
>
> wer.melt <- melt(wer, id.var=c("Name", "Type"))
>
> cast(wer.melt, Name + Type ~ value, length, fill=0)
> Name Type A B
> 1 1 a 1 0
> 2 2 b 1 0
> 3 3 c 1 0
> 4 4 d 1 1
> 5 5 e 1 1
>
>> sessionInfo()
> R version 2.7.0 (2008-04-22)
> x86_64-unknown-linux-gnu
> locale:
> LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
> other attached packages:
> [1] reshape_0.8.0
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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