Calculating moving average

Question

I\'m trying to use R to calculate the moving average over a series of values in a matrix. The normal R mailing list search hasn\'t been very helpful though. There doesn\'t s

Answer 1

In fact RcppRoll is very good.

The code posted by cantdutchthis must be corrected in the fourth line to the window be fixed:

ma <- function(arr, n=15){
  res = arr
  for(i in n:length(arr)){
    res[i] = mean(arr[(i-n+1):i])
  }
  res
}

Another way, which handles missings, is given here.

A third way, improving cantdutchthis code to calculate partial averages or not, follows:

  ma <- function(x, n=2,parcial=TRUE){
  res = x #set the first values

  if (parcial==TRUE){
    for(i in 1:length(x)){
      t<-max(i-n+1,1)
      res[i] = mean(x[t:i])
    }
    res

  }else{
    for(i in 1:length(x)){
      t<-max(i-n+1,1)
      res[i] = mean(x[t:i])
    }
    res[-c(seq(1,n-1,1))] #remove the n-1 first,i.e., res[c(-3,-4,...)]
  }
}

Answer 2

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.

Answer 3

EDIT: took great joy in adding the side parameter, for a moving average (or sum, or ...) of e.g. the past 7 days of a Date vector.

---

For people just wanting to calculate this themselves, it's nothing more than:

# x = vector with numeric data
# w = window length
y <- numeric(length = length(x))

for (i in seq_len(length(x))) {
  ind <- c((i - floor(w / 2)):(i + floor(w / 2)))
  ind <- ind[ind %in% seq_len(length(x))]
  y[i] <- mean(x[ind])
}

y

But it gets fun to make it independent of mean(), so you can calculate any 'moving' function!

# our working horse:
moving_fn <- function(x, w, fun, ...) {
  # x = vector with numeric data
  # w = window length
  # fun = function to apply
  # side = side to take, (c)entre, (l)eft or (r)ight
  # ... = parameters passed on to 'fun'
  y <- numeric(length(x))
  for (i in seq_len(length(x))) {
    if (side %in% c("c", "centre", "center")) {
      ind <- c((i - floor(w / 2)):(i + floor(w / 2)))
    } else if (side %in% c("l", "left")) {
      ind <- c((i - floor(w) + 1):i)
    } else if (side %in% c("r", "right")) {
      ind <- c(i:(i + floor(w) - 1))
    } else {
      stop("'side' must be one of 'centre', 'left', 'right'", call. = FALSE)
    }
    ind <- ind[ind %in% seq_len(length(x))]
    y[i] <- fun(x[ind], ...)
  }
  y
}

# and now any variation you can think of!
moving_average <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = mean, side = side, na.rm = na.rm)
}

moving_sum <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = sum, side = side, na.rm = na.rm)
}

moving_maximum <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = max, side = side, na.rm = na.rm)
}

moving_median <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = median, side = side, na.rm = na.rm)
}

moving_Q1 <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = quantile, side = side, na.rm = na.rm, 0.25)
}

moving_Q3 <- function(x, w = 5, side = "centre", na.rm = FALSE) {
  moving_fn(x = x, w = w, fun = quantile, side = side, na.rm = na.rm, 0.75)
}

Answer 4

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.