Regular analysis over irregular time series

Question

I have an irregular time series (`xts` in `R`) that I want to apply some time-windowing to. For example, given a time series like the following, I want to compute things like how many observations there are in each discrete 3-hour window, starting from `2009-09-22 00:00:00`: ``` library(lubridate) s <- xts(c("OK", "Fail", "Service", "OK", "Service", "OK"), ymd_hms(c("2009-09-22 07:43:30", "2009-10-01 03:50:30", "2009-10-01 08:45:00", "2009-10-01 09:48:15", "2009-11-11 10:30:30", "2009-11-11 11:12:45"))) ``` I apparently can't use `period.apply()` or `split()` to do it, because those will omit periods with no observations, and I can't give it a starting time. My desired output for the simple counting problem (though, of course, my real tasks are more complicated with each segment!) would be something like this if I aggregated 3 days at a time: ``` 2009-09-22 1 2009-09-25 0 2009-09-28 0 2009-10-01 3 2009-10-04 0 2009-10-07 0 2009-10-10 0 2009-10-13 0 2009-10-16 0 2009-10-19 0 2009-10-22…

Accepted Answer

Use `align.time` to put the index of `s` into the periods you're interested in. Then use `period.apply` to find the length of each 3-hour window. Then merge it with an empty xts object that has all the index values you want.

```
# align index into 3-hour blocks
a <- align.time(s, n=60*60*3)
# find the number of obs in each block
count <- period.apply(a, endpoints(a, "hours", 3), length)
# create an empty xts object with the desired index
e <- xts(,seq(start(a),end(a),by="3 hours"))
# merge the counts with the empty object and fill with zeros
out <- merge(e,count,fill=0)
```

Regular analysis over irregular time series

Solution

Problem