Calculating moving average

I'm trying to use R to calculate the moving average over a series of values in a matrix. There doesn't seem to be a built-in function in R that will allow me to calculate moving averages. Do any packages provide one? Or do I need to write my own?

Or you can simply calculate it using filter, here's the function I use:

ma <- function(x, n = 5){filter(x, rep(1 / n, n), sides = 2)}

If you use dplyr, be careful to specify stats::filter in the function above.

• Rolling Means/Maximums/Medians in the zoo package (rollmean)
• MovingAverages in TTR
• ma in forecast

Using cumsum should be sufficient and efficient. Assuming you have a vector x and you want a running sum of n numbers

cx <- c(0,cumsum(x))
rsum <- (cx[(n+1):length(cx)] - cx[1:(length(cx) - n)]) / n

As pointed out in the comments by @mzuther, this assumes that there are no NAs in the data. to deal with those would require dividing each window by the number of non-NA values. Here's one way of doing that, incorporating the comment from @Ricardo Cruz:

cx <- c(0, cumsum(ifelse(is.na(x), 0, x)))
cn <- c(0, cumsum(ifelse(is.na(x), 0, 1)))
rx <- cx[(n+1):length(cx)] - cx[1:(length(cx) - n)]
rn <- cn[(n+1):length(cx)] - cn[1:(length(cx) - n)]
rsum <- rx / rn

This still has the issue that if all the values in the window are NAs then there will be a division by zero error.

In data.table 1.12.0 new frollmean function has been added to compute fast and exact rolling mean carefully handling NA, NaN and +Inf, -Inf values.

As there is no reproducible example in the question there is not much more to address here.

You can find more info about ?frollmean in manual, also available online at ?frollmean.

Examples from manual below:

library(data.table)
d = as.data.table(list(1:6/2, 3:8/4))

# rollmean of single vector and single window
frollmean(d[, V1], 3)

# multiple columns at once
frollmean(d, 3)

# multiple windows at once
frollmean(d[, .(V1)], c(3, 4))

# multiple columns and multiple windows at once
frollmean(d, c(3, 4))

## three above are embarrassingly parallel using openmp

The caTools package has very fast rolling mean/min/max/sd and few other functions. I've only worked with runmean and runsd and they are the fastest of any of the other packages mentioned to date.