On May 17, 2013, at 11:48 AM, Chirag Maru wrote:
> I have following data for which I need to calculate the weighted aggregate
> value of the parameter at each time.
>
> Date,Parameter,Weight
> 2012-01-31,90,200
> 2012-01-31,80,400
> 2012-01-31,70,500
> 2012-01-31,60,800
> 2012-02-29,120,220
> 2012-02-29,110,410
> 2012-02-29,75,520
> 2012-02-29,65,840
> 2012-03-31,115,210
> 2012-03-31,100,405
> 2012-03-31,70,500
> 2012-03-31,60,800
>
> So for the above sample the solution looks like:
>
> Date,Weighted Parameter
> 2012-01-31,70
> 2012-02-29,82.96482412
> 2012-03-31,77.10182768
>
> Could I potentially use tapply / aggregate for this? Would like to avoid a
> for loop if possible.
> by(dat, dat[1], FUN=function(d) weighted.mean(d[["Parameter"]],
> w=d[["Weight"]]) )
Date: 2012-01-31
[1] 70
------------------------------------------------------------
Date: 2012-02-29
[1] 82.96482
------------------------------------------------------------
Date: 2012-03-31
[1] 77.10183
It's a bit of a shame that there is no as.data.frame.by function. You can
create a dataframe from the by object with as.data.frame.table with the only
defect in the naming of the second column
as.data.frame.table(by(dat, dat[1], FUN=function(d)
weighted.mean(d[["Parameter"]], w=d[["Weight"]]) ))
Date Freq
1 2012-01-31 70.00000
2 2012-02-29 82.96482
3 2012-03-31 77.10183
setNames(as.data.frame.table(by(dat, dat[1], FUN=function(d)
weighted.mean(d[["Parameter"]], w=d[["Weight"]]) )),
c("Dts", "wtdmeans"))
Dts wtdmeans
1 2012-01-31 70.00000
2 2012-02-29 82.96482
3 2012-03-31 77.10183
--
David Winsemius
Alameda, CA, USA
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