Rolling Mean/standard deviation with conditions

Question

I have a bit of a question about computing the Rolling Mean/standard deviation based on conditions. To be honest it is more of a syntax question, but since I think it is slo

Answer 1

There now also is a rolling mean function within data.table itself, please see github disscussion for details. The implementation is really straightforward.

DT[, rollmean := data.table::frollmean(x, n = 3, fill = 0, align = "right"), 
by = .(stock)]

A quick benchmarking of the two, shows that the data.table version is a bit quicker (most of the time).

library(microbenchmark)

microbenchmark(a = DT[, rollmean := data.table::frollmean(x, n = 3, fill = 0, align = "right"), 
                      by = .(stock)]
               , b = DT[, rollmean := rollmean(x, k = 3, fill = 0, align = "right"),
                            by = .(stock)]
, times = 100L

)

Unit: milliseconds
expr    min      lq     mean  median     uq     max neval cld
   a 1.5695 1.66605 2.329675 1.79340 2.1980 39.3750   100  a 
   b 2.6711 2.82105 3.660617 2.99725 4.3577 20.3178   100   b