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

Question

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?

Answer 1

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.

Answer 2

library(microbenchmark)
library(ggplot2)

# dummy vector
c <- 50
x <- sample(1:100, c, 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 <- vector("list", z)
                            for (i in 1:z)
                            {
                              b[[i]] <- sd(x[i:(i+y)])
                              }
                           }
               , times = 30
               )

# plot
autoplot(xx) +
  ggtitle(paste('vector of size', c))

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

I would maintain, however, loops are not slow per se as long as they are not applied incorrectly (iterating over rows).