as_age_groups: Categorise continuous age into age groups

In CDU-data-science-team/nottshcMethods: Helper functions for working with NHS healthcare data by the Data Science Team of the Nottinghamshire Healthcare NHS Foundation Trust

Categorise continuous age into age groups

Description

Categorise continuous age into age groups

Usage

as_age_groups(
  var,
  min = 0,
  max = 120,
  by = 10,
  grouping_method = c("user_defined", "ons_1", "ons_2", "nhs_survey")
)

Arguments

var	Name of variable or vector
min	Numeric, specifying the minimum age of the first age group. Age values lower than this will be returned as missing values (NA)
max	Numeric, specifying the upper end of the last age group
by	Numeric, increment of the age categories
grouping_method	String, specifying the method to be used for grouping age into categories. Details about the different methods are available here: "user_defined", "ons_1", "ons_2", "nhs_survey" ... TODO

Examples

# Example using a vector:
set.seed(123)
age <- sample(1:100, 100, replace = TRUE)
as_age_groups(age)

# Example using a data frame
tibble::tibble(age = sample(1:115, 100, replace = TRUE)) %>%
  dplyr::mutate(age_groups = as_age_groups(age, min = 10, max = 100))

CDU-data-science-team/nottshcMethods documentation built on March 19, 2023, 11:54 a.m.