rollapply {zoo}  R Documentation 
Apply Rolling Functions
Description
A generic function for applying a function to rolling margins of an array.
Usage
rollapply(data, ...)
## S3 method for class 'ts'
rollapply(data, ...)
## S3 method for class '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)
## Default S3 method:
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 
FUN 
the function to be applied. 
... 
optional arguments to 
by 
calculate FUN at every 
by.column 
logical. If 
fill 
a threecomponent vector or list (recycled otherwise) providing
filling values at the left/within/to the right of the data range.
See the 
na.pad 
deprecated. Use 
partial 
logical or numeric. If 
align 
specifyies whether the index of the result
should be left or rightaligned or centered (default) compared
to the rolling window of observations. This argument is only used if

coredata 
logical. Should only the 
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 ith 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
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
## rolling mean
z < zoo(11:15, as.Date(31:35))
rollapply(z, 2, mean)
## nonoverlapping 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 3year 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("20000101") + 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)
##  3day 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 3day 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("20160309") + 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 corresponds 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 nonnegative 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, np)), n, by = np, c))
# these three are the same
y < 10 * seq(8); k < 4; d < 2
# 1
# from http://ucfagls.wordpress.com/2011/06/14/embeddingatimeserieswithtimedelayinrpartii/
Embed < function(x, m, d = 1, indices = FALSE, as.embed = TRUE) {
n < length(x)  (m1)*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, k1)), c)
# 3
rollapply(y, d*k1, function(x) x[d * seq(k1, 0) + 1])
## mimic convolve() using rollapplyr()
A < 1:4
B < 5:8
## convolve(..., type = "open")
cross < function(x) x
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")
# weighted sum including partials near ends, keeping
## alignment with wts correct
points < zoo(cbind(lon = c(11.8300715, 11.8296697,
11.8268708, 11.8267236, 11.8249612, 11.8251062),
lat = c(48.1099048, 48.10884, 48.1067431, 48.1066077,
48.1037673, 48.103318),
dist = c(46.8463805878941, 33.4921440879536, 10.6101735030534,
18.6085009578724, 6.97253109610173, 9.8912817449265)))
mysmooth < function(z, wts = c(0.3, 0.4, 0.3)) {
notna < !is.na(z)
sum(z[notna] * wts[notna]) / sum(wts[notna])
}
points2 < points
points2[, 1:2] < rollapply(rbind(NA, coredata(points)[, 1:2], NA), 3, mysmooth)
points2