froll.RdFast rolling functions to calculate aggregates on sliding window. 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"))
| x | vector, list, data.frame or data.table of numeric or logical columns. |
|---|---|
| n | integer vector, for adaptive rolling function also list of integer vectors, rolling window size. |
| fill | numeric, value to pad by. Defaults to |
| algo | character, default |
| align | character, define if rolling window covers preceding rows
( |
| na.rm | logical. Should missing values be removed when
calculating window? Defaults to |
| hasNA | logical. If it is known that |
| adaptive | logical, should adaptive rolling function be
calculated, default |
| FUN | the function to be applied in rolling fashion; see Details for restrictions |
| ... | extra arguments passed to |
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" then on-line algorithm is used, also
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 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 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. Except for the 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.
A list except when the input is a vector and
length(n)==1 in which case a vector is returned.
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 and logical 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.
d = as.data.table(list(1:6/2, 3:8/4)) # rollmean of single vector and single window frollmean(d[, V1], 3)#> [1] NA NA 1.0 1.5 2.0 2.5# multiple columns at once frollmean(d, 3)#> [[1]] #> [1] NA NA 1.0 1.5 2.0 2.5 #> #> [[2]] #> [1] NA NA 1.00 1.25 1.50 1.75 #>#> [[1]] #> [1] NA NA 1.0 1.5 2.0 2.5 #> #> [[2]] #> [1] NA NA NA 1.25 1.75 2.25 #>#> [[1]] #> [1] NA NA 1.0 1.5 2.0 2.5 #> #> [[2]] #> [1] NA NA NA 1.25 1.75 2.25 #> #> [[3]] #> [1] NA NA 1.00 1.25 1.50 1.75 #> #> [[4]] #> [1] NA NA NA 1.125 1.375 1.625 #>## 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)#> [[1]] #> [1] 0.50 0.75 1.00 1.50 2.00 2.50 #> #> [[2]] #> [1] 0.750 0.875 1.000 1.250 1.500 1.750 #># frollsum frollsum(d, 3:4)#> [[1]] #> [1] NA NA 3.0 4.5 6.0 7.5 #> #> [[2]] #> [1] NA NA NA 5 7 9 #> #> [[3]] #> [1] NA NA 3.00 3.75 4.50 5.25 #> #> [[4]] #> [1] NA NA NA 4.5 5.5 6.5 #># frollapply frollapply(d, 3:4, sum)#> [[1]] #> [1] NA NA 3.0 4.5 6.0 7.5 #> #> [[2]] #> [1] NA NA NA 5 7 9 #> #> [[3]] #> [1] NA NA 3.00 3.75 4.50 5.25 #> #> [[4]] #> [1] NA NA NA 4.5 5.5 6.5 #>f = function(x, ...) if (sum(x, ...)>5) min(x, ...) else max(x, ...) frollapply(d, 3:4, f, na.rm=TRUE)#> [[1]] #> [1] NA NA 1.5 2.0 1.5 2.0 #> #> [[2]] #> [1] NA NA NA 2.0 1.0 1.5 #> #> [[3]] #> [1] NA NA 1.25 1.50 1.75 1.50 #> #> [[4]] #> [1] NA NA NA 1.50 1.00 1.25 #># 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))#> user system elapsed #> 0.004 0.000 0.004#> user system elapsed #> 0.000 0.000 0.001#> user system elapsed #> 0 0 0#> user system elapsed #> 0 0 0#> user system elapsed #> 0.003 0.000 0.003anserr = 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#> fastma froll_fast froll_exact frollapply #> "0.00001287466" "0.00000001833541" "0" "0"# frollapply corner cases f = function(x) head(x, 2) ## FUN returns non length 1 try(frollapply(1:5, 3, f))#> Error in frollapply(1:5, 3, f) : #> frollapply: results from provided FUN are not length 1f = 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)#> [1] NA NA 1 2 3options(datatable.verbose=TRUE) x = c(1,2,1,1,1,2,3,2) frollapply(x, 3, uniqueN) ## FUN returns integer#> frollapplyR: allocating memory for results 1x1 #> forder.c received 3 rows and 1 columns #> frollapply: results from provided FUN are not of type double, coercion from integer or logical will be applied on each iteration #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> frollapply: took 0.000s #> frollapplyR: processing of 1 column(s) and 1 window(s) took 0.000s#> [1] NA NA 2 2 1 2 3 2#> frollapplyR: allocating memory for results 1x1 #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> forder.c received 3 rows and 1 columns #> frollapply: took 0.000s #> frollapplyR: processing of 1 column(s) and 1 window(s) took 0.000s#> [1] NA NA 2 2 1 2 3 2#> frollapplyR: allocating memory for results 1x1 #> frollapply: results from provided FUN are not of type double, coercion from integer or logical will be applied on each iteration #> frollapply: took 0.000s #> frollapplyR: processing of 1 column(s) and 1 window(s) took 0.000s#> [1] NA NA 0 0 1 1 1 1#> frollapplyR: allocating memory for results 1x1 #> frollapply: results from provided FUN are not of type double, coercion from integer or logical will be applied on each iteration #> frollapply: took 0.000s #> frollapplyR: processing of 1 column(s) and 1 window(s) took 0.000s#> [1] NA NA FALSE FALSE TRUE TRUE TRUE TRUEoptions(datatable.verbose=FALSE) f = function(x) { ## FUN returns character if (sum(x)>5) "big" else "small" } try(frollapply(1:5, 3, f))#> Error in frollapply(1:5, 3, f) : #> frollapply: results from provided FUN are not of type doublef = 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))#> Error in frollapply(1:5, 3, f) : #> REAL() can only be applied to a 'numeric', not a 'logical'