[R] time-series aggregation of information

[arun]

Hi,
May be this helps:
dat<- read.table(text="
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
",sep=",",header=TRUE,stringsAsFactors=FALSE)
library(plyr)
 ddply(dat,.(Date), summarize, wtdmeans=weighted.mean(Parameter,Weight))
#        Date wtdmeans
#1 2012-01-31 70.00000
#2 2012-02-29 82.96482
#3 2012-03-31 77.10183
A.K.




----- Original Message -----
From: Chirag Maru <chirag.maru at ironfinancial.com>
To: "r-help at R-project.org" <r-help at r-project.org>
Cc: 
Sent: Friday, May 17, 2013 2:48 PM
Subject: [R] time-series aggregation of information

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.

Thank you!




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