Fast rolling functions to calculate aggregates on sliding windows. Function name and arguments are experimental.
frollmean(x, n, fill=NA, algo=c("fast", "exact"),
align=c("right", "left", "center"), na.rm=FALSE, hasNA=NA, adaptive=FALSE)
frollsum(x, n, fill=NA, algo=c("fast","exact"),
align=c("right", "left", "center"), na.rm=FALSE, hasNA=NA, adaptive=FALSE)
frollapply(x, n, FUN, ..., fill=NA, align=c("right", "left", "center"))A list except when the input is a vector and
length(n)==1 in which case a vector is returned.
Vector, data.frame or data.table of integer, numeric or logical columns over which to calculate the windowed aggregations. May also be a list, in which case the rolling function is applied to each of its elements.
Integer vector giving rolling window size(s). This is the total number of included values. Adaptive rolling functions also accept a list of integer vectors.
Numeric; value to pad by. Defaults to NA.
Character, default "fast". When set to "exact", a slower (but more accurate) algorithm is used. It
suffers less from floating point rounding errors by performing an extra pass, and carefully handles all non-finite values.
It will use multiple cores where available. See Details for more information.
Character, specifying the "alignment" of the rolling window, defaulting to "right". "right" covers preceding rows (the window ends on the current value); "left" covers following rows (the window starts on the current value); "center" is halfway in between (the window is centered on the current value, biased towards "left" when n is even).
Logical, default FALSE. Should missing values be removed when
calculating window? For details on handling other non-finite values, see Details.
Logical. If it is known that x contains NA
then setting this to TRUE will speed up calculation. Defaults to NA.
Logical, default FALSE. Should the rolling function be calculated adaptively? See Details below.
The function to be applied to the rolling window; see Details for restrictions.
Extra arguments passed to FUN in frollapply.
froll* functions accept vectors, lists, data.frames or
data.tables. They always return a list except when the input is a
vector and length(n)==1, in which case a vector
is returned, for convenience. Thus, rolling functions can be used
conveniently within data.table syntax.
Argument n allows multiple values to apply rolling functions on
multiple window sizes. If adaptive=TRUE, then n must be a list.
Each list element must be integer vector of window sizes corresponding
to every single observation in each column; see Examples.
When algo="fast" an "on-line" algorithm is used, and
all of NaN, +Inf, -Inf are treated as NA.
Setting algo="exact" will make rolling functions to use a more
computationally-intensive algorithm that suffers less from floating point
rounding error (the same consideration applies to mean).
algo="exact" also handles NaN, +Inf, -Inf consistently to
base R. In case of some functions (like mean), it will additionally
make extra pass to perform floating point error correction. Error
corrections might not be truly exact on some platforms (like Windows)
when using multiple threads.
Adaptive rolling functions are a special case where each observation has its own corresponding rolling window width. Due to the logic of adaptive rolling functions, the following restrictions apply:
align only "right".
if list of vectors is passed to x, then all
vectors within it must have equal length.
When multiple columns or multiple windows width are provided, then they
are run in parallel. The exception is for algo="exact", which runs in
parallel already.
frollapply computes rolling aggregate on arbitrary R functions.
The input x (first argument) to the function FUN
is coerced to numeric beforehand and FUN
has to return a scalar numeric value. Checks for that are made only
during the first iteration when FUN is evaluated. Edge cases can be
found in examples below. Any R function is supported, but it is not optimized
using our own C implementation -- hence, for example, using frollapply
to compute a rolling average is inefficient. It is also always single-threaded
because there is no thread-safe API to R's C eval. Nevertheless we've
seen the computation speed up vis-a-vis versions implemented in base R.
shift, 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 calls above will use multiple cores when available
# partial window using adaptive rolling function
an = function(n, len) c(seq.int(n), rep(n, len-n))
n = an(3, nrow(d))
frollmean(d, n, adaptive=TRUE)
# frollsum
frollsum(d, 3:4)
# frollapply
frollapply(d, 3:4, sum)
f = function(x, ...) if (sum(x, ...)>5) min(x, ...) else max(x, ...)
frollapply(d, 3:4, f, na.rm=TRUE)
# performance vs exactness
set.seed(108)
x = sample(c(rnorm(1e3, 1e6, 5e5), 5e9, 5e-9))
n = 15
ma = function(x, n, na.rm=FALSE) {
ans = rep(NA_real_, nx<-length(x))
for (i in n:nx) ans[i] = mean(x[(i-n+1):i], na.rm=na.rm)
ans
}
fastma = function(x, n, na.rm) {
if (!missing(na.rm)) stop("NAs are unsupported, wrongly propagated by cumsum")
cs = cumsum(x)
scs = shift(cs, n)
scs[n] = 0
as.double((cs-scs)/n)
}
system.time(ans1<-ma(x, n))
system.time(ans2<-fastma(x, n))
system.time(ans3<-frollmean(x, n))
system.time(ans4<-frollmean(x, n, algo="exact"))
system.time(ans5<-frollapply(x, n, mean))
anserr = list(
fastma = ans2-ans1,
froll_fast = ans3-ans1,
froll_exact = ans4-ans1,
frollapply = ans5-ans1
)
errs = sapply(lapply(anserr, abs), sum, na.rm=TRUE)
sapply(errs, format, scientific=FALSE) # roundoff
# frollapply corner cases
f = function(x) head(x, 2) ## FUN returns non length 1
try(frollapply(1:5, 3, f))
f = function(x) { ## FUN sometimes returns non length 1
n = length(x)
# length 1 will be returned only for first iteration where we check length
if (n==x[n]) x[1L] else range(x) # range(x)[2L] is silently ignored!
}
frollapply(1:5, 3, f)
options(datatable.verbose=TRUE)
x = c(1,2,1,1,1,2,3,2)
frollapply(x, 3, uniqueN) ## FUN returns integer
numUniqueN = function(x) as.numeric(uniqueN(x))
frollapply(x, 3, numUniqueN)
x = c(1,2,1,1,NA,2,NA,2)
frollapply(x, 3, anyNA) ## FUN returns logical
as.logical(frollapply(x, 3, anyNA))
options(datatable.verbose=FALSE)
f = function(x) { ## FUN returns character
if (sum(x)>5) "big" else "small"
}
try(frollapply(1:5, 3, f))
f = function(x) { ## FUN is not type-stable
n = length(x)
# double type will be returned only for first iteration where we check type
if (n==x[n]) 1 else NA # NA logical turns into garbage without coercion to double
}
try(frollapply(1:5, 3, f))
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