r - What is an efficient method for partitioning and aggregating intervals from timestamped rows in a data frame?

Question

From a data frame with timestamped rows (strptime results), what is the best method for aggregating statistics for intervals?

Intervals could be an hour, a day, etc.

There's the aggregate function, but that doesn't help with assigning each row to an interval. I'm planning on adding a column to the data frame that denotes interval and using that with aggregate, but if there's a better solution it'd be great to hear it.

Thanks for any pointers!

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Example Data

Five rows with timestamps divided into 15-minute intervals starting at 03:00.

Interval 1

• "2010-01-13 03:02:38 UTC"
• "2010-01-13 03:08:14 UTC"
• "2010-01-13 03:14:52 UTC"

Interval 2

• "2010-01-13 03:20:42 UTC"
• "2010-01-13 03:22:19 UTC"

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Conclusion

Using a time series package such as xts should be the solution; however I had no success using them and winded up using cut. As I presently only need to plot histograms, with rows grouped by interval, this was enough.

cut is used liked so:

interv <- function(x, start, period, num.intervals) {
  return(cut(x, as.POSIXlt(start)+0:num.intervals*period))
}

Answer 1

Use a time series package. The xts package has functions designed specifically to do that. Or look at the aggregate and rollapply functions in the zoo package.

The rmetrics ebook has a useful discussion, including a performance comparison of the various packages: https://www.rmetrics.org/files/freepdf/TimeSeriesFAQ.pdf

Edit: Look at my answer to this question. Basically you need to truncate every timestamp into a specific interval and then do the aggregation using those new truncated timestamps as your grouping vector.