Fast 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"))

Arguments

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 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.

FUN

the function to be applied in rolling fashion; see Details for restrictions

...

extra arguments passed to FUN in frollapply.

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" 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.

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 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.

See also

shift, data.table

References

Round-off error

Examples

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 #>
# multiple windows at once frollmean(d[, .(V1)], c(3, 4))
#> [[1]] #> [1] NA NA 1.0 1.5 2.0 2.5 #> #> [[2]] #> [1] NA NA NA 1.25 1.75 2.25 #>
# multiple columns and multiple windows at once frollmean(d, c(3, 4))
#> [[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 #> 0.006 0.000 0.018
system.time(ans2<-fastma(x, n))
#> user system elapsed #> 0 0 0
system.time(ans3<-frollmean(x, n))
#> user system elapsed #> 0.000 0.000 0.001
system.time(ans4<-frollmean(x, n, algo="exact"))
#> user system elapsed #> 0 0 0
system.time(ans5<-frollapply(x, n, mean))
#> user system elapsed #> 0.004 0.000 0.004
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
#> 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 1
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)
#> [1] NA NA 1 2 3
options(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
numUniqueN = function(x) as.numeric(uniqueN(x)) frollapply(x, 3, numUniqueN)
#> 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
x = c(1,2,1,1,NA,2,NA,2) frollapply(x, 3, anyNA) ## FUN returns logical
#> 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
as.logical(frollapply(x, 3, anyNA))
#> 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 TRUE
options(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 double
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))
#> Error in frollapply(1:5, 3, f) : #> REAL() can only be applied to a 'numeric', not a 'logical'