R data.table sliding window
data.table, r, sliding-window, time-series
Solution
`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).
Problem
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))] } ```