Vector aggregate.

Description

This function is somewhat similar to tapply, but is designed for use in conjunction with id. It is simpler in that it only accepts a single grouping vector (use id if you have more) and uses vapply internally, using the .default value as the template.

Usage

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vaggregate(.value, .group, .fun, ..., .default = NULL, .n = nlevels(.group))

Arguments

.value

vector of values to aggregate

.group

grouping vector

.fun

aggregation function

...

other arguments passed on to .fun

.default

default value used for missing groups. This argument is also used as the template for function output.

.n

total number of groups

Details

vaggregate should be faster than tapply in most situations because it avoids making a copy of the data.

Examples

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# Some examples of use borrowed from ?tapply
n <- 17; fac <- factor(rep(1:3, length.out = n), levels = 1:5)
table(fac)
vaggregate(1:n, fac, sum)
vaggregate(1:n, fac, sum, .default = NA_integer_)
vaggregate(1:n, fac, range)
vaggregate(1:n, fac, range, .default = c(NA, NA) + 0)
vaggregate(1:n, fac, quantile)
# Unlike tapply, vaggregate does not support multi-d output:
tapply(warpbreaks$breaks, warpbreaks[,-1], sum)
vaggregate(warpbreaks$breaks, id(warpbreaks[,-1]), sum)

# But it is about 10x faster
x <- rnorm(1e6)
y1 <- sample.int(10, 1e6, replace = TRUE)
system.time(tapply(x, y1, mean))
system.time(vaggregate(x, y1, mean))

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