roll_sum: Fast by-group rolling functions

In timeplyr: Fast Tidy Tools for Date and Date-Time Manipulation

Fast by-group rolling functions

Description

An efficient method for rolling sum, mean and growth rate for many groups.

Usage

roll_sum(
  x,
  window = Inf,
  g = NULL,
  partial = TRUE,
  weights = NULL,
  na.rm = TRUE,
  ...
)

roll_mean(
  x,
  window = Inf,
  g = NULL,
  partial = TRUE,
  weights = NULL,
  na.rm = TRUE,
  ...
)

roll_geometric_mean(
  x,
  window = Inf,
  g = NULL,
  partial = TRUE,
  weights = NULL,
  na.rm = TRUE,
  ...
)

roll_harmonic_mean(
  x,
  window = Inf,
  g = NULL,
  partial = TRUE,
  weights = NULL,
  na.rm = TRUE,
  ...
)

roll_growth_rate(
  x,
  window = Inf,
  g = NULL,
  partial = TRUE,
  na.rm = FALSE,
  log = FALSE,
  inf_fill = NULL
)

Arguments

x	Numeric vector, data frame, or list.
window	Rolling window size, default is Inf.
g	Grouping object passed directly to collapse::GRP(). This can for example be a vector or data frame.
partial	Should calculations be done using partial windows? Default is TRUE.
weights	Importance weights. Must be the same length as x. Currently, no normalisation of weights occurs.
na.rm	Should missing values be removed for the calculation? The default is TRUE.
...	Additional arguments passed to data.table::frollmean and data.table::frollsum.
log	For roll_growth_rate: If TRUE then growth rates are calculated on the log-scale.
inf_fill	For roll_growth_rate: Numeric value to replace Inf values with. Default behaviour is to keep Inf values.

Details

roll_sum and roll_mean support parallel computations when x is a data frame of multiple columns.
roll_geometric_mean and roll_harmonic_mean are convenience functions that utilise roll_mean.
roll_growth_rate calculates the rate of percentage change per unit time on a rolling basis.

Value

A numeric vector the same length as x when x is a vector, or a list when x is a data.frame.

See Also

time_roll_mean

Examples

library(timeplyr)

x <- 1:10
roll_sum(x) # Simple rolling total
roll_mean(x) # Simple moving average
roll_sum(x, window = 3)
roll_mean(x, window = 3)
roll_sum(x, window = 3, partial = FALSE)
roll_mean(x, window = 3, partial = FALSE)

# Plot of expected value of 'coin toss' over many flips
set.seed(42)
x <- sample(c(1, 0), 10^3, replace = TRUE)
ev <- roll_mean(x)
plot(ev)
abline(h = 0.5, lty = 2)

all.equal(roll_sum(iris$Sepal.Length, g = iris$Species),
          ave(iris$Sepal.Length, iris$Species, FUN = cumsum))
# The below is run using parallel computations where applicable
roll_sum(iris[, 1:4], window = 7, g = iris$Species)

  library(data.table)
  library(bench)
  df <- data.table(g = sample.int(10^4, 10^5, TRUE),
                   x = rnorm(10^5))
  mark(e1 = df[, mean := frollmean(x, n = 7,
                                   align = "right", na.rm = FALSE), by = "g"]$mean,
       e2 = df[, mean := roll_mean(x, window = 7, g = get("g"),
                                   partial = FALSE, na.rm = FALSE)]$mean)

timeplyr documentation built on Sept. 12, 2024, 7:37 a.m.