[R] A complicated 'aggregate'

[Josh Quigley]

Hi,

I have a financial (zoo) time series with prices and volumes (although I can
get the coredata as a matrix). Due to the data-source some indices have
multiple observations. I want to aggregate these according to a weighted
average.

11:00:01	34	1000
11:00:01	35	 500
11:00:01	35	1000
11:00:02	34	 500
11:00:02	35	 500

should become

11:00:01	34.6	2500
11:00:02	34.5	1000

I currently do this using a loop, and the result is abysmally slow:


f <- function(x)
{
  retval <- c(0, 0);
  x <- coredata(x);

  retval[2] <- sum(x[,2]);
  retval[1] <- sum(x[,1] * x[,2]) / retval[2];
  retval;
}

#ts is a zoo timeseries
uniqueTimes <- unique(index(ts))
tmpMat <- NULL
for(i in 1:length(uniqueTimes))
{
    tmpMat <- rbind(tmpMat, f(ts[uniqueTimes[i]]));
}

ts.agg <- zooreg(tmpMat, order.by=uniqueTimes);


I'm sure the above can be done with aggregate or tapply or by or something,
but I haven't managed to get those to work.

Any suggestions greatly appreciated!

Cheers,

Josh Quigley.