# rollapply

##### Apply Rolling Functions

A generic function for applying a function to rolling margins of an array.

##### Usage

```
rollapply(data, …)
# S3 method for ts
rollapply(data, …)
# S3 method for zoo
rollapply(data, width, FUN, …, by = 1, by.column = TRUE,
fill = if (na.pad) NA, na.pad = FALSE, partial = FALSE,
align = c("center", "left", "right"), coredata = TRUE)
# S3 method for default
rollapply(data, …)
rollapplyr(…, align = "right")
```

##### Arguments

- data
the data to be used (representing a series of observations).

- width
numeric vector or list. In the simplest case this is an integer specifying the window width (in numbers of observations) which is aligned to the original sample according to the

`align`

argument. Alternatively,`width`

can be a list regarded as offsets compared to the current time, see below for details.- FUN
the function to be applied.

- …
optional arguments to

`FUN`

.- by
calculate FUN at every

`by`

-th time point rather than every point.`by`

is only used if`width`

is length 1 and either a plain scalar or a list.- by.column
logical. If

`TRUE`

,`FUN`

is applied to each column separately.- fill
a three-component vector or list (recycled otherwise) providing filling values at the left/within/to the right of the data range. See the

`fill`

argument of`na.fill`

for details.- na.pad
deprecated. Use

`fill = NA`

instead of`na.pad = TRUE`

.- partial
logical or numeric. If

`FALSE`

(default) then`FUN`

is only applied when all indexes of the rolling window are within the observed time range. If`TRUE`

, then the subset of indexes that are in range are passed to`FUN`

. A numeric argument to`partial`

can be used to determin the minimal window size for partial computations. See below for more details.- align
specifyies whether the index of the result should be left- or right-aligned or centered (default) compared to the rolling window of observations. This argument is only used if

`width`

represents widths.- coredata
logical. Should only the

`coredata(data)`

be passed to every`width`

window? If set to`FALSE`

the full zoo series is used.

##### Details

If `width`

is a plain numeric vector its elements are regarded as widths
to be interpreted in conjunction with `align`

whereas if `width`

is a list
its components are regarded as offsets. In the above cases if the length of
`width`

is 1 then `width`

is recycled for every `by`

-th point.
If `width`

is a list its components represent integer offsets such that
the i-th component of the list refers to time points at positions
`i + width[[i]]`

. If any of these points are below 1 or above the
length of `index(data)`

then `FUN`

is not evaluated for that
point unless `partial = TRUE`

and in that case only the valid
points are passed.

The rolling function can also be applied to partial windows by setting `partial = TRUE`

For example, if `width = 3, align = "right"`

then for the first point
just that point is passed to `FUN`

since the two points to its
left are out of range. For the same example, if `partial = FALSE`

then `FUN`

is not
invoked at all for the first two points. If `partial`

is a numeric then it
specifies the minimum number of offsets that must be within range. Negative
`partial`

is interpreted as `FALSE`

.

If `width`

is a scalar then `partial = TRUE`

and `fill = NA`

are
mutually exclusive but if offsets are specified for the `width`

and 0 is not
among the offsets then the output will be shorter than the input even
if `partial = TRUE`

is specified. In that case it may still be useful
to specify `fill`

in addition to `partial`

.

If `FUN`

is `mean`

, `max`

or `median`

and `by.column`

is
`TRUE`

and width is a plain scalar and there are no other arguments
then special purpose code is used to enhance performance.
Also in the case of `mean`

such special purpose code is only invoked if the
`data`

argument has no `NA`

values.
See `rollmean`

, `rollmax`

and `rollmedian`

for more details.

Currently, there are methods for `"zoo"`

and `"ts"`

series
and `"default"`

method for ordinary vectors and matrices.

`rollapplyr`

is a wrapper around `rollapply`

that uses a default
of `align = "right"`

.

If `data`

is of length 0, `data`

is returned unmodified.

##### Value

A object of the same class as `data`

with the results of the rolling function.

##### See Also

##### Examples

```
# NOT RUN {
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
## rolling mean
z <- zoo(11:15, as.Date(31:35))
rollapply(z, 2, mean)
## non-overlapping means
z2 <- zoo(rnorm(6))
rollapply(z2, 3, mean, by = 3) # means of nonoverlapping groups of 3
aggregate(z2, c(3,3,3,6,6,6), mean) # same
## optimized vs. customized versions
rollapply(z2, 3, mean) # uses rollmean which is optimized for mean
rollmean(z2, 3) # same
rollapply(z2, 3, (mean)) # does not use rollmean
## rolling regression:
## set up multivariate zoo series with
## number of UK driver deaths and lags 1 and 12
seat <- as.zoo(log(UKDriverDeaths))
time(seat) <- as.yearmon(time(seat))
seat <- merge(y = seat, y1 = lag(seat, k = -1),
y12 = lag(seat, k = -12), all = FALSE)
## run a rolling regression with a 3-year time window
## (similar to a SARIMA(1,0,0)(1,0,0)_12 fitted by OLS)
rr <- rollapply(seat, width = 36,
FUN = function(z) coef(lm(y ~ y1 + y12, data = as.data.frame(z))),
by.column = FALSE, align = "right")
## plot the changes in coefficients
## showing the shifts after the oil crisis in Oct 1973
## and after the seatbelt legislation change in Jan 1983
plot(rr)
## rolling mean by time window (e.g., 3 days) rather than
## by number of observations (e.g., when these are unequally spaced):
#
## - test data
tt <- as.Date("2000-01-01") + c(1, 2, 5, 6, 7, 8, 10)
z <- zoo(seq_along(tt), tt)
## - fill it out to a daily series, zm, using NAs
## using a zero width zoo series g on a grid
g <- zoo(, seq(start(z), end(z), "day"))
zm <- merge(z, g)
## - 3-day rolling mean
rollapply(zm, 3, mean, na.rm = TRUE, fill = NA)
##
## - without expansion to regular grid: find interval widths
## that encompass the previous 3 days for each Date
w <- seq_along(tt) - findInterval(tt - 3, tt)
## a solution to computing the widths 'w' that is easier to read but slower
## w <- sapply(tt, function(x) sum(tt >= x - 2 & tt <= x))
##
## - rolling sum from 3-day windows
## without vs. with expansion to regular grid
rollapplyr(z, w, sum)
rollapplyr(zm, 3, sum, partial = TRUE, na.rm = TRUE)
## rolling weekly sums (with some missing dates)
z <- zoo(1:11, as.Date("2016-03-09") + c(0:7, 9:10, 12))
weeksum <- function(z) sum(z[time(z) > max(time(z)) - 7])
zs <- rollapplyr(z, 7, weeksum, fill = NA, coredata = FALSE)
merge(value = z, weeksum = zs)
## replicate cumsum with either 'partial' or vector width 'k'
cumsum(1:10)
rollapplyr(1:10, 10, sum, partial = TRUE)
rollapplyr(1:10, 1:10, sum)
## different values of rule argument
z <- zoo(c(NA, NA, 2, 3, 4, 5, NA))
rollapply(z, 3, sum, na.rm = TRUE)
rollapply(z, 3, sum, na.rm = TRUE, fill = NULL)
rollapply(z, 3, sum, na.rm = TRUE, fill = NA)
rollapply(z, 3, sum, na.rm = TRUE, partial = TRUE)
# this will exclude time points 1 and 2
# It corresonds to align = "right", width = 3
rollapply(zoo(1:8), list(seq(-2, 0)), sum)
# but this will include points 1 and 2
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 1)
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 0)
# so will this
rollapply(zoo(1:8), list(seq(-2, 0)), sum, fill = NA)
# by = 3, align = "right"
L <- rep(list(NULL), 8)
L[seq(3, 8, 3)] <- list(seq(-2, 0))
str(L)
rollapply(zoo(1:8), L, sum)
rollapply(zoo(1:8), list(0:2), sum, fill = 1:3)
rollapply(zoo(1:8), list(0:2), sum, fill = 3)
L2 <- rep(list(-(2:0)), 10)
L2[5] <- list(NULL)
str(L2)
rollapply(zoo(1:10), L2, sum, fill = "extend")
rollapply(zoo(1:10), L2, sum, fill = list("extend", NULL))
rollapply(zoo(1:10), L2, sum, fill = list("extend", NA))
rollapply(zoo(1:10), L2, sum, fill = NA)
rollapply(zoo(1:10), L2, sum, fill = 1:3)
rollapply(zoo(1:10), L2, sum, partial = TRUE)
rollapply(zoo(1:10), L2, sum, partial = TRUE, fill = 99)
rollapply(zoo(1:10), list(-1), sum, partial = 0)
rollapply(zoo(1:10), list(-1), sum, partial = TRUE)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, rowSums, by.column = FALSE)
# these two are the same
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, sum)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, colSums, by.column = FALSE)
# these two are the same
rollapply(zoo(1:6), 2, sum, by = 2, align = "right")
aggregate(zoo(1:6), c(2, 2, 4, 4, 6, 6), sum)
# these two are the same
rollapply(zoo(1:3), list(-1), c)
lag(zoo(1:3), -1)
# these two are the same
rollapply(zoo(1:3), list(1), c)
lag(zoo(1:3))
# these two are the same
rollapply(zoo(1:5), list(c(-1, 0, 1)), sum)
rollapply(zoo(1:5), 3, sum)
# these two are the same
rollapply(zoo(1:5), list(0:2), sum)
rollapply(zoo(1:5), 3, sum, align = "left")
# these two are the same
rollapply(zoo(1:5), list(-(2:0)), sum)
rollapply(zoo(1:5), 3, sum, align = "right")
# these two are the same
rollapply(zoo(1:6), list(NULL, NULL, -(2:0)), sum)
rollapply(zoo(1:6), 3, sum, by = 3, align = "right")
# these two are the same
rollapply(zoo(1:5), list(c(-1, 1)), sum)
rollapply(zoo(1:5), 3, function(x) sum(x[-2]))
# these two are the same
rollapply(1:5, 3, rev)
embed(1:5, 3)
# these four are the same
x <- 1:6
rollapply(c(0, 0, x), 3, sum, align = "right") - x
rollapply(x, 3, sum, partial = TRUE, align = "right") - x
rollapply(x, 3, function(x) sum(x[-3]), partial = TRUE, align = "right")
rollapply(x, list(-(2:1)), sum, partial = 0)
# same as Matlab's buffer(x, n, p) for valid non-negative p
# See http://www.mathworks.com/help/toolbox/signal/buffer.html
x <- 1:30; n <- 7; p <- 3
t(rollapply(c(rep(0, p), x, rep(0, n-p)), n, by = n-p, c))
# these three are the same
y <- 10 * seq(8); k <- 4; d <- 2
# 1
# from http://ucfagls.wordpress.com/2011/06/14/embedding-a-time-series-with-time-delay-in-r-part-ii/
Embed <- function(x, m, d = 1, indices = FALSE, as.embed = TRUE) {
n <- length(x) - (m-1)*d
X <- seq_along(x)
if(n <= 0)
stop("Insufficient observations for the requested embedding")
out <- matrix(rep(X[seq_len(n)], m), ncol = m)
out[,-1] <- out[,-1, drop = FALSE] +
rep(seq_len(m - 1) * d, each = nrow(out))
if(as.embed)
out <- out[, rev(seq_len(ncol(out)))]
if(!indices)
out <- matrix(x[out], ncol = m)
out
}
Embed(y, k, d)
# 2
rollapply(y, list(-d * seq(0, k-1)), c)
# 3
rollapply(y, d*k-1, function(x) x[d * seq(k-1, 0) + 1])
## mimic convolve() using rollapplyr()
A <- 1:4
B <- 5:8
## convolve(..., type = "open")
cross <- function(x) x
# }
# NOT RUN {
<!-- %*% tail(B, length(x)) -->
# }
# NOT RUN {
rollapplyr(c(A, 0*B[-1]), length(B), cross, partial = TRUE)
convolve(A, B, type = "open")
# convolve(..., type = "filter")
rollapplyr(A, length(B), cross)
convolve(A, B, type = "filter")
# }
```

*Documentation reproduced from package zoo, version 1.8-8, License: GPL-2 | GPL-3*