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copied!<p>I don't believe that there's any way to do this built into zoo or chron, but you can create your own function by using a little math. Here you go:</p> <pre><code>trunc.minutes <- function (x, n.minutes) { if (!inherits(x, "times")) x <- as.chron(x) x <- as.numeric(x) sec <- round(24 * 3600 * abs(x - floor(x))) h <- (sec%/%(n.minutes*60))/(60/n.minutes) hour <- as.integer(h) minutes <- (h %% hour) * 60 chron(dates=chron::dates(x), times=times(paste(hour, minutes, "00", sep=":"))) } </code></pre> <p>Here's an example of the usage:</p> <pre><code>dts <- chron::dates(c("02/27/92", "02/27/92", "01/14/92", "02/28/92", "02/01/92")) tms <- times(c("23:03:20", "23:29:56", "01:03:30", "18:21:03", "16:56:26")) x <- chron(dates = dts, times = tms) # original dates x [1] (02/27/92 23:03:20) (02/27/92 22:29:56) (01/14/92 01:03:30) [4] (02/28/92 18:21:03) (02/01/92 16:56:26) trunc.minutes(x, 15) # new dates at 15 minute intervals [1] (02/27/92 23:00:00) (02/27/92 22:15:00) (01/14/92 01:00:00) [4] (02/28/92 18:15:00) (02/01/92 16:45:00) trunc.minutes(x, 30) # new dates at 30 minute intervals [1] (02/27/92 23:00:00) (02/27/92 22:00:00) (01/14/92 01:00:00) [4] (02/28/92 18:00:00) (02/01/92 16:30:00) </code></pre> <p>Lastly, you can now use this function to aggregate the data:</p> <pre><code>ts.zoo <- zoo(rnorm(5), x) # a zoo time series </code></pre> <p>Or just use these new dates for aggregation (see how it rolls up the second example since there are two values in that window):</p> <pre><code>> aggregate(ts.zoo, trunc.minutes(x, 15), mean) (01/14/92 01:00:00) (02/01/92 16:45:00) (02/27/92 23:00:00) (02/27/92 23:15:00) -0.6738659 -0.4844803 0.7968155 -1.3571121 (02/28/92 18:15:00) 0.7625861 > aggregate(ts.zoo, trunc.minutes(x, 30), mean) (01/14/92 01:00:00) (02/01/92 16:30:00) (02/27/92 23:00:00) (02/28/92 18:00:00) -0.6738659 -0.4844803 -0.2801483 0.7625861 </code></pre>

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