ageutils (version 0.0.8)

reaggregate_counts: Reaggregate age counts

Description

reaggregate_counts() converts counts over one interval range to another with optional weighting by a known population.

Usage

reaggregate_counts(...)
# S3 method for default
reaggregate_counts(
  bounds,
  counts,
  new_bounds,
  ...,
  population_bounds = NULL,
  population_weights = NULL
)

Value

A data frame with 4 entries; interval, lower_bound, upper_bound and a corresponding count.

Arguments

...

Further arguments passed to or from other methods.

bounds

[numeric]

The current boundaries in (strictly) increasing order.

These correspond to the left hand side of the intervals (e.g. the closed side of [x, y).

Double values are coerced to integer prior to categorisation.

counts

[numeric]

Vector of counts corresponding to the intervals defined by bounds.

new_bounds

[numeric]

The desired boundaries in (strictly) increasing order.

population_bounds

[numeric]

Interval boundaries for a known population weighting given by the population_weights argument.

population_weights

[numeric]

Population weightings corresponding to population_bounds.

Used to weight the output across the desired intervals.

If NULL (default), counts are divided proportional to the interval sizes.

Examples

# Reaggregating some data obtained from the 2021 UK census
head(pop_dat)

# Each row of the data is for the same region so we can drop some columns
# `age_category` and `value` columns
dat <- subset(pop_dat, select = c(age_category, value))

# Add the lower bounds to the data
dat <- transform(
    dat,
    lower_bound = as.integer(sub("\\[([0-9]+), .+)", "\\1", age_category))
)

# Now recategorise to the desired age intervals
with(
    dat,
    reaggregate_counts(
        bounds = lower_bound,
        counts = value,
        new_bounds = c(0, 1, 5, 15, 25, 45, 65)
    )
)