Rolling window over irregular time series

Question

I have an irregular time series of events (posts) using `xts`, and I want to calculate the number of events that occur over a rolling weekly window (or biweekly, or 3 day, etc). The data looks like this: ``` postid 2010-08-04 22:28:07 867 2010-08-04 23:31:12 891 2010-08-04 23:58:05 901 2010-08-05 08:35:50 991 2010-08-05 13:28:02 1085 2010-08-05 14:14:47 1114 2010-08-05 14:21:46 1117 2010-08-05 15:46:24 1151 2010-08-05 16:25:29 1174 2010-08-05 23:19:29 1268 2010-08-06 12:15:42 1384 2010-08-06 15:22:06 1403 2010-08-07 10:25:49 1550 2010-08-07 18:58:16 1596 2010-08-07 21:15:44 1608 ``` which should produce something like ``` nposts 2010-08-05 00:00:00 10 2010-08-06 00:00:00 9 2010-08-07 00:00:00 5 ``` for a 2-day window. I have looked into `rollapply`, `apply.rolling` from `PerformanceAnalytics`, etc, and they all assume regular time series data. I tried changing all of the times to just the day the the post occurred and using something like `ddply` to group on each day, which gets me cl…

Accepted Answer

Here's a solution using xts:

```
x <- structure(c(867L, 891L, 901L, 991L, 1085L, 1114L, 1117L, 1151L, 
  1174L, 1268L, 1384L, 1403L, 1550L, 1596L, 1608L), .Dim = c(15L, 1L),
  index = structure(c(1280960887, 1280964672, 1280966285, 
  1280997350, 1281014882, 1281017687, 1281018106, 1281023184, 1281025529, 
  1281050369, 1281096942, 1281108126, 1281176749, 1281207496, 1281215744),
  tzone = "", tclass = c("POSIXct", "POSIXt")), class = c("xts", "zoo"),
  .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct", "POSIXt"),
  .indexTZ = "", tzone = "")
# first count the number of observations each day
xd <- apply.daily(x, length)
# now sum the counts over a 2-day rolling window
x2d <- rollapply(xd, 2, sum)
# align times at the end of the period (if you want)
y <- align.time(x2d, n=60*60*24)  # n is in seconds
```

Rolling window over irregular time series

Solution

Problem