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RcppRoll

Source: R/RcppRoll.R
RcppRoll-exports.Rd

Efficient windowed / rolling operations. Each function here applies an operation over a moving window of size n, with (customizable) weights specified through weights.

Usage

roll_mean(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_meanr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_meanl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_median(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_medianr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_medianl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_min(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_minr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_minl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_max(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_maxr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_maxl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_prod(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_prodr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_prodl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_sum(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_sumr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_suml(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_sd(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_sdr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_sdl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

roll_var(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = numeric(0),
  partial = FALSE,
  align = c("center", "left", "right"),
  normalize = TRUE,
  na.rm = FALSE
)

roll_varr(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "right",
  normalize = TRUE,
  na.rm = FALSE
)

roll_varl(
  x,
  n = 1L,
  weights = NULL,
  by = 1L,
  fill = NA,
  partial = FALSE,
  align = "left",
  normalize = TRUE,
  na.rm = FALSE
)

Arguments

x

A numeric vector or a numeric matrix.

n

A positive integer scalar giving the window size. Ignored when weights is non-NULL.

weights

A non-empty vector of length n, giving the weights for each element within a window. If NULL, we take unit weights of width n. For an even window with uniform weights, roll_median() returns the lower of the two central values; the unweighted median instead averages those values. Variance and standard deviation require finite, non-negative frequency weights.

by

A positive integer scalar. Calculate at every by-th point rather than every point.

fill

Either an empty vector (no fill), or a vector (recycled to) length 3 giving left, center and right fills.

partial

A single non-missing logical. Compute windows at the edges of x over however many elements are in range, rather than filling them? Cannot be combined with weights, and fill does not apply.

align

Align windows on the "left", "center" or "right".

normalize

A single non-missing logical. Normalize window weights, such that they sum to n? Normalized weights must be finite and have a finite, non-zero sum.

na.rm

A single non-missing logical. Remove missing values?

Details

The functions postfixed with l and r are convenience wrappers that set left / right alignment of the windowed operations.