# roll

##### Rolling functions

Fast rolling functions to calculate aggregates on sliding window. Function name and arguments are experimental.

- Keywords
- data

##### Usage

```
frollmean(x, n, fill=NA, algo=c("fast", "exact"), align=c("right",
"left", "center"), na.rm=FALSE, hasNA=NA, adaptive=FALSE,
verbose=getOption("datatable.verbose"))
```

##### Arguments

- x
vector, list, data.frame or data.table of numeric columns.

- n
integer vector, for adaptive rolling function also list of integer vectors, rolling window size.

- fill
numeric, value to pad by. Defaults to

`NA`

.- algo
character, default

`"fast"`

. When set to`"exact"`

, then slower algorithm is used. It suffers less from floating point rounding error, performs extra pass to adjust rounding error correction and carefully handles all non-finite values. If available it will use multiple cores. See details for more information.- align
character, define if rolling window covers preceding rows (

`"right"`

), following rows (`"left"`

) or centered (`"center"`

). Defaults to`"right"`

.- na.rm
logical. Should missing values be removed when calculating window? Defaults to

`FALSE`

. For details on handling other non-finite values, see details below.- hasNA
logical. If it is known that

`x`

contains`NA`

then setting to`TRUE`

will speed up. Defaults to`NA`

.- adaptive
logical, should adaptive rolling function be calculated, default

`FALSE`

. See details below.- verbose
logical, default

`getOption("datatable.verbose")`

,`TRUE`

turns on status and information messages to the console, it also disable parallel processing.

##### Details

`froll*`

functions accepts 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 it expects a list.
Each list element must be integer vector of window sizes corresponding
to every single observation in each column.

When `algo="fast"`

is used then any `NaN, +Inf, -Inf`

is
treated as `NA`

.
Setting `algo="exact"`

will make rolling functions to use
compute-intensive algorithm that suffers less from floating point
rounding error. It will additionally make extra pass to perform floating
point error correction. It also handles `NaN, +Inf, -Inf`

consistently to base R.

Adaptive rolling functions are special cases where for each single observation has own corresponding rolling window width. Due to the logic of adaptive rolling functions, following restrictions apply:

`align`

only`"right"`

.if list of vectors is passed to

`x`

, then all list vectors must have equal length.

When multiple columns or multiple windows width are provided, then they
are run in parallel. Eventually nested parallelism occurs when
`algo="exact"`

, see examples.

##### Value

A list except when the input is a `vector`

and
`length(n)==1`

in which case a `vector`

is returned.

##### Note

Users coming from most popular package for rolling functions
`zoo`

might expect following differences in `data.table`

implementation.

rolling function will always return result of the same length as input.

`fill`

defaults to`NA`

.`fill`

accepts only constant values. It does not support for*na.locf*or other functions.`align`

defaults to`"right"`

.`na.rm`

is respected, and other functions are not needed when input contains`NA`

.integers are always coerced to double.

when

`adaptive=FALSE`

(default), then`n`

must be a numeric vector. List is not accepted.when

`adaptive=TRUE`

, then`n`

must be vector of length equal to`nrow(x)`

, or list of such vectors.`partial`

window feature is not supported, although it can be accomplished by using`adaptive=TRUE`

, see examples.

Be aware that rolling functions operates on the physical order of input. If the intent is to roll values in a vector by a logical window, for example an hour, or a day, one has to ensure that there are no gaps in input. For details see issue #3241.

##### References

##### See Also

##### Examples

```
# NOT RUN {
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)
# 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, algo="exact")) # parallel using openmp again
system.time(ans4<-frollmean(x, n))
anserr = list(
froll_exact_f = ans4-ans1,
froll_exact_t = ans3-ans1,
fastma = ans2-ans1
)
errs = sapply(lapply(anserr, abs), sum, na.rm=TRUE)
sapply(errs, format, scientific=FALSE) # roundoff
# }
```

*Documentation reproduced from package data.table, version 1.12.0, License: MPL-2.0 | file LICENSE*