interestingly, in my case, the opposite seems to be the case. data frames
seem faster than matrices when it comes to "by" computation (which is where
most of my calculations are in):
### here is my data frame and some information about it
> dim(rets.subset)
[1] 132508 3
> names(rets.subset)
[1] "PERMNO" "RET" "mdate"
> length(unique(as.factor(rets.subset$PERMNO)))
[1] 6832
> length((as.factor(rets.subset$PERMNO)))
[1] 132508
### calculation using data frame
> system.time( { by( rets.subset, as.factor(rets.subset$PERMNO), mean) } )
user system elapsed
3.295 2.798 6.095
### same as matrix
> m=as.matrix(rets.subset)
> system.time( { a=by( m, as.factor(m[,1]), mean) } )
user system elapsed
5.371 5.557 10.928
PS: Any speed suggestions are appreciated. This is "experimenting time" for
me.
> One note: if you're worried about speed, it almost always makes sense to
use matrices rather than dataframes. If you've got mixed types this is
tedious and error-prone (each type needs to be in a separate matrix), but if
your data is all numeric, it's very simple, and will make things a lot
faster.
>
> Duncan Murdoch
>
--
Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com)
CV Starr Professor of Economics (Finance), Brown University
http://welch.econ.brown.edu/
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