Coding in R - how to do rolling window without a for-loop? For-loop too slow-CodePudding

I understand for-loops are slow in R, and the suite of apply() functions are designed to be used instead (in many cases).

However, I can't figure out how to use those functions in my situation, and advice would be greatly appreciated.

I have a list/vector of values (let's say length=10,000) and at every point, starting at the 21st value, I need to take the standard deviation of the trailing 20 values. So at 21st, I take SD of 1st-21st . At 22nd value, I take SD(2:22) and so on.

So you see I have a rolling window where I need to take the SD() of the previous 20 indices. Is there any way to accomplish this faster, without a for-loop?

CodePudding user response：

I found a solution to my question.

The zoo package has a function called "rollapply" which does exactly that: uses apply() on a rolling window basis.

CodePudding user response：

library(microbenchmark)
library(ggplot2)

# dummy vector
x <- sample(1:100, 50, replace=T)

# parameter
y <- 20               # length of each vector
z <- length(x) - y    # final starting index

# benchmark
xx <-
microbenchmark(lapply = {a <- lapply( 1:z, \(i) sd(x[i:(i y)]) )}
               , loop = {
                           b <- list()
                            for (i in 1:z)
                            {
                              b[[i]] <- sd(x[i:(i y)])
                            }
                           }
               , times = 30
               )

# plot
autoplot(xx)

It would appear while lapply has the speed advantage of a smaller vector, a loop should be used with longer vectors.

EDIT* Results vary from run to run with a vector of length 1e4. It would appear other larger factors are at play. I would maintain, however, loops are not slow per se as long as they are not applied incorrectly (iterating over rows).