Or try data.table 1.4 on r-forge, its grouping is faster than aggregate :
agg datatable
X10 0.012 0.008
X100 0.020 0.008
X1000 0.172 0.020
X10000 1.164 0.144
X1e.05 9.397 1.180
install.packages("data.table", repos="http://R-Forge.R-project.org";)
require(data.table)
dt = as.data.table(df)
t3 <- system.time(zz3 <- dt[, list(sumflt=sum(fltval), sumint=sum
(intval)), by=id])
Matthew
On Thu, 15 Apr 2010 13:09:17 +0000, hadley wickham wrote:
> On Thu, Apr 15, 2010 at 1:16 AM, Chuck <vijay.n...@gmail.com> wrote:
>> Depending on the size of the dataframe and the operations you are
>> trying to perform, aggregate or ddply may be better. In the function
>> below, df has the same structure as your dataframe.
>
> Current version of plyr:
>
> agg ddply
> X10 0.005 0.007
> X100 0.007 0.026
> X1000 0.086 0.248
> X10000 0.577 3.136
> X1e.05 4.493 44.147
>
> Development version of plyr:
>
> agg ddply
> X10 0.003 0.005
> X100 0.007 0.007
> X1000 0.042 0.044
> X10000 0.410 0.443
> X1e.05 4.479 4.237
>
> So there are some big speed improvements in the works.
>
> Hadley
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