Hi,
I'm removing non-unique time indices in a zoo time series by means of
aggregate. The time series is bivariate, and the row to be kept only depends
on the maximum of one of the two columns. Here's an example:
x <- zoo(rbind( c(1,1), c(1.1, 0.9), c(1.1, 1.1), c(1,1) ),
order.by=c(1,1,2,2))
The eventual aggregated result should be
1 1.1 0.9
2 1.1 1.1
that is, in each slice of the underlying data (a slice being all rows with
the same time stamp), we take the row that has maximum value in the first
column. (For the moment, let's not worry about several rows within the same
slice having the same maximum value in the first column.)
I have tried subsetting x by
slices <- aggregate(x[,1], by=identity, FUN=which.max)
but ended up with something as ugly as:
T <- length( unique(time(x)) )
result <- zoo( matrix(NA, ncol=2, nrow=T), order.by=unique(time(x)) )
for(t in seq(length.out=T))
{
result[t,] <- x[ time(x)==time(slices[t]) ][coredata(slices[t]),]
}
There must be a better way of doing this -- maybe using tapply or the plyr
package, but possibly something much simpler. Any pointers are very welcome.
Thanks, Jo
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