Sliding time intervals for time series data in R

Question

I am trying to extract interesting statistics for an irregular time series data set, but coming up short on finding the right tools for the job. The tools for manipulating

Answer 1

As of version v1.9.8 (on CRAN 25 Nov 2016), data.table has gained the ability to aggregate in a non-equi join which can be used to apply a rolling function on a sliding time window of an irregular time series.

For demonstration and verification, a smaller dataset is used.

library(data.table)   # development version 1.11.9 used

# create small dataset
set.seed(0)
nSamples    <- 10
vecDT       <- rexp(nSamples, 3)
vecTimes    <- cumsum(c(0,vecDT))
vecVals     <- 0:nSamples
vec         <- data.table(vecTimes, vecVals)
vec

      vecTimes vecVals
 1: 0.00000000       0
 2: 0.06134553       1
 3: 0.10991444       2
 4: 0.15651286       3
 5: 0.30186907       4
 6: 1.26685858       5
 7: 1.67671260       6
 8: 1.85660688       7
 9: 2.17546271       8
10: 2.22447804       9
11: 2.68805641      10

# define window size in seconds 
win_sec = 0.3

# aggregate in sliding window by a non-equi join
vec[.(t = vecTimes, upper = vecTimes + win_sec, lower = vecTimes - win_sec), 
    on = .(vecTimes < upper, vecTimes > lower), 
    .(t, .N, sliding_mean = mean(vecVals)), by = .EACHI]

     vecTimes     vecTimes          t N sliding_mean
 1: 0.3000000 -0.300000000 0.00000000 4          1.5
 2: 0.3613455 -0.238654473 0.06134553 5          2.0
 3: 0.4099144 -0.190085564 0.10991444 5          2.0
 4: 0.4565129 -0.143487143 0.15651286 5          2.0
 5: 0.6018691  0.001869065 0.30186907 4          2.5
 6: 1.5668586  0.966858578 1.26685858 1          5.0
 7: 1.9767126  1.376712596 1.67671260 2          6.5
 8: 2.1566069  1.556606875 1.85660688 2          6.5
 9: 2.4754627  1.875462707 2.17546271 2          8.5
10: 2.5244780  1.924478037 2.22447804 2          8.5
11: 2.9880564  2.388056413 2.68805641 1         10.0

The first two columns show the upper and lower bounds of the time intervall, resp., t is the original vecTimes, and N denotes the number of data points included in the calculation of the sliding mean.