mean2: Fast averaging over subset of vector elements

Description

Computes the sample mean of all or a subset of values.

Usage

mean2(x, idxs = NULL, na.rm = FALSE, refine = TRUE, ...)
meanOver(...)

Arguments

A numeric or logical vector of length N.

idxs

A vector indicating subset of elements to operate over. If NULL, no subsetting is done.

na.rm

If TRUE, missing values are skipped, otherwise not.

refine

If TRUE and x is numeric, then extra effort is used to calculate the average with greater numerical precision, otherwise not.

...

Not used.

Value

Returns a numeric scalar.

Details

mean2(x, idxs) gives equivalent results as mean(x[idxs]), but is faster and more memory efficient since it avoids the actual subsetting which requires copying of elements and garbage collection thereof.

If x is numeric and refine = TRUE, then a two-pass scan is used to calculate the average. The first scan calculates the total sum and divides by the number of (non-missing) values. In the second scan, this average is refined by adding the residuals towards the first average. The mean() uses this approach. mean2(..., refine = FALSE) is almost twice as fast as mean2(..., refine = TRUE).

Examples

Run this code

# NOT RUN {
x <- 1:10
n <- length(x)

idxs <- seq(from = 1, to = n, by = 2)
s1 <- mean(x[idxs])                     # 25
s2 <- mean2(x, idxs = idxs)             # 25
stopifnot(identical(s1, s2))

idxs <- seq(from = n, to = 1, by = -2)
s1 <- mean(x[idxs])                     # 25
s2 <- mean2(x, idxs = idxs)             # 25
stopifnot(identical(s1, s2))

s1 <- mean(x)                           # 55
s2 <- mean2(x)                          # 55
stopifnot(identical(s1, s2))
# }