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.
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
