Which is 'right' versus 'left' rolling mean in R?

Question

If I want to calculate the previous n mean years with a lag of the current year, how would I accomplish this? Is it as simple as a "right" rolling mean window? Or is it a "left" rolling mean window? I'm not sure which window to use here.

Sample Data

set.seed(1234)
dat <- data.frame(year = c(1990:2010), 
                  x = rnorm(21))
dat$x_lag1 <- lag(dat$x, 1)

Answer 1

It may be easier to think in terms of offsets. If you want a window of 3 then

• align = "right" corresponds to using a window based on offsets of -2, -1, 0, i.e. point before prior, prior and current point. The current point is the rightmost end of the window. Note that rollapplyr with an r on the end is the same as specifying align = "right"
• align = "center" corresponds to using a window based on offsets of -1, 0, 1, i.e. prior point current point and next point. The current point is the center of the window. This is the default for align= .
• align = "left" corresponds to using a window based on offsets of 0, 1, 2 i.e. current point, next point and point after that. The current point is the leftmost point of the window.

rollapply allows one to use the align= specification or offset notation. To use the latter for width specify a list containing a single vector defining the offsets. (The actual specification of width is to specify a vector of widths, one for each element of the input or a list of offset vectors; however, in both cases they recycle so the usual case of specifying a single scalar width or a list containing a single offset vector are specifical cases.)

window ending in current point

Below we use align= to take the mean of a window of 3 ending in the current point and also use offsets as an alternative. We show both data frames and zoo objects.

We have omitted fill=NA for the zoo objects since they automatically align anyways so it is typically unnecessary to use it.

library(zoo)

r1 <- transform(dat, roll = rollapplyr(x, 3, mean, fill = NA))

r2 <- transform(dat, roll = rollapply(x, list(seq(-2, 0)), mean, fill = NA))

all.equal(r1, r2)
## [1] TRUE

z <- read.zoo(dat, FUN = identity)
r3 <- rollapplyr(z, 3, mean)

r4 <- rollmeanr(z, 3)

r5 <- rollapply(z, list(seq(-2, 0)), mean) # z from above

all.equal(r3, r4, r5)
## [1] TRUE

window ending in prior point

If you want the 3 prior points, i.e. offsets -3, -2, -1, i.e. not the current point but the 3 points prior to that, then the following would work. Note that lag in the last line requires a time series and should not be used with plain vectors.

# r6 is data frame
r6 <- transform(dat, roll = rollapply(x, list(-seq(3)), mean, fill = NA))

# r7, r8, r9 are zoo objects

r7 <- rollapply(z, list(-seq(3)), mean) # z from above

r8 <- stats::lag(rollapplyr(z, 3, mean), -1)

r9 <- stats::lag(rollmeanr(z, 3), -1)

all.equal(r7, r8, r9)
## [1] TRUE