time series - R data.table sliding window

Question

What is the best (fastest) way to implement a sliding window function with the data.table package?

I'm trying to calculate a rolling median but have multiple rows per date (due to 2 additional factors), which I think means that the zoo rollapply function wouldn't work. Here is an example using a naive for loop:

library(data.table)
df <- data.frame(
  id=30000,
  date=rep(as.IDate(as.IDate("2012-01-01")+0:29, origin="1970-01-01"), each=1000),
  factor1=rep(1:5, each=200),
  factor2=1:5,
  value=rnorm(30, 100, 10)
)

dt = data.table(df)
setkeyv(dt, c("date", "factor1", "factor2"))

get_window <- function(date, factor1, factor2) {
  criteria <- data.table(
    date=as.IDate((date - 7):(date - 1), origin="1970-01-01"),
    factor1=as.integer(factor1),
    factor2=as.integer(factor2)
  )
  return(dt[criteria][, value])
}

output <- data.table(unique(dt[, list(date, factor1, factor2)]))[, window_median:=as.numeric(NA)]

for(i in nrow(output):1) {
  print(i)
  output[i, window_median:=median(get_window(date, factor1, factor2))]
}

Answer 1

data.table doesn't have any special features for rolling windows, currently. Further detail here in my answer to another similar question here :

Is there a fast way to run a rolling regression inside data.table?

Rolling median is interesting. It would need a specialized function to do efficiently (same link as in earlier comment) :

Rolling median algorithm in C

The data.table solutions in the question and answers here are all very inefficient, relative to a proper specialized rollingmedian function (which isn't available for R afaik).