Speeding up casting a dataframe from long to wide format

lev <- levels(wer$Predictor)
out <- outer(wer$Predictor, lev, "==")
colnames(out) <- lev
aggregate(out, wer[1:2], sum)

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