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233 lines
9.6 KiB
R
Executable File
233 lines
9.6 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# CITE AS #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# doi:10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Age in Years of Individuals
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#'
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#' Calculates age in years based on a reference date, which is the system date at default.
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#' @param x date(s), [character] (vectors) will be coerced with [as.POSIXlt()]
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#' @param reference reference date(s) (defaults to today), [character] (vectors) will be coerced with [as.POSIXlt()]
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#' @param exact a [logical] to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of [year-to-date](https://en.wikipedia.org/wiki/Year-to-date) (YTD) of `x` by the number of days in the year of `reference` (either 365 or 366).
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#' @param na.rm a [logical] to indicate whether missing values should be removed
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#' @param ... arguments passed on to [as.POSIXlt()], such as `origin`
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#' @details Ages below 0 will be returned as `NA` with a warning. Ages above 120 will only give a warning.
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#'
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#' This function vectorises over both `x` and `reference`, meaning that either can have a length of 1 while the other argument has a larger length.
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#' @return An [integer] (no decimals) if `exact = FALSE`, a [double] (with decimals) otherwise
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#' @seealso To split ages into groups, use the [age_groups()] function.
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#' @export
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#' @examples
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#' # 10 random pre-Y2K birth dates
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#' df <- data.frame(birth_date = as.Date("2000-01-01") - runif(10) * 25000)
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#'
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#' # add ages
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#' df$age <- age(df$birth_date)
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#'
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#' # add exact ages
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#' df$age_exact <- age(df$birth_date, exact = TRUE)
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#'
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#' # add age at millenium switch
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#' df$age_at_y2k <- age(df$birth_date, "2000-01-01")
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#'
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#' df
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age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
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meet_criteria(x, allow_class = c("character", "Date", "POSIXt"))
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meet_criteria(reference, allow_class = c("character", "Date", "POSIXt"))
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meet_criteria(exact, allow_class = "logical", has_length = 1)
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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if (length(x) != length(reference)) {
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if (length(x) == 1) {
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x <- rep(x, length(reference))
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} else if (length(reference) == 1) {
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reference <- rep(reference, length(x))
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} else {
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stop_("`x` and `reference` must be of same length, or `reference` must be of length 1.")
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}
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}
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x <- as.POSIXlt(x, ...)
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reference <- as.POSIXlt(reference, ...)
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# from https://stackoverflow.com/a/25450756/4575331
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years_gap <- reference$year - x$year
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ages <- ifelse(reference$mon < x$mon | (reference$mon == x$mon & reference$mday < x$mday),
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as.integer(years_gap - 1),
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as.integer(years_gap)
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)
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# add decimals
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if (exact == TRUE) {
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# get dates of `x` when `x` would have the year of `reference`
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x_in_reference_year <- as.POSIXlt(paste0(
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format(as.Date(reference), "%Y"),
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format(as.Date(x), "-%m-%d")
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),
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format = "%Y-%m-%d"
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)
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# get differences in days
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n_days_x_rest <- as.double(difftime(as.Date(reference),
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as.Date(x_in_reference_year),
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units = "days"
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))
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# get numbers of days the years of `reference` has for a reliable denominator
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n_days_reference_year <- as.POSIXlt(paste0(format(as.Date(reference), "%Y"), "-12-31"),
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format = "%Y-%m-%d"
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)$yday + 1
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# add decimal parts of year
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mod <- n_days_x_rest / n_days_reference_year
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# negative mods are cases where `x_in_reference_year` > `reference` - so 'add' a year
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mod[!is.na(mod) & mod < 0] <- mod[!is.na(mod) & mod < 0] + 1
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# and finally add to ages
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ages <- ages + mod
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}
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if (any(ages < 0, na.rm = TRUE)) {
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ages[!is.na(ages) & ages < 0] <- NA
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warning_("in `age()`: NAs introduced for ages below 0.")
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}
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if (any(ages > 120, na.rm = TRUE)) {
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warning_("in `age()`: some ages are above 120.")
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}
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if (isTRUE(na.rm)) {
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ages <- ages[!is.na(ages)]
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}
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if (exact == TRUE) {
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as.double(ages)
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} else {
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as.integer(ages)
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}
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}
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#' Split Ages into Age Groups
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#'
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#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis.
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#' @param x age, e.g. calculated with [age()]
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#' @param split_at values to split `x` at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See *Details*.
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#' @param na.rm a [logical] to indicate whether missing values should be removed
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#' @details To split ages, the input for the `split_at` argument can be:
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#'
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#' * A [numeric] vector. A value of e.g. `c(10, 20)` will split `x` on 0-9, 10-19 and 20+. A value of only `50` will split `x` on 0-49 and 50+.
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#' The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
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#' * A character:
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#' - `"children"` or `"kids"`, equivalent of: `c(0, 1, 2, 4, 6, 13, 18)`. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.
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#' - `"elderly"` or `"seniors"`, equivalent of: `c(65, 75, 85)`. This will split on 0-64, 65-74, 75-84, 85+.
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#' - `"fives"`, equivalent of: `1:20 * 5`. This will split on 0-4, 5-9, ..., 95-99, 100+.
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#' - `"tens"`, equivalent of: `1:10 * 10`. This will split on 0-9, 10-19, ..., 90-99, 100+.
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#' @return Ordered [factor]
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#' @seealso To determine ages, based on one or more reference dates, use the [age()] function.
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#' @export
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#' @examples
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#' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
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#'
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#' # split into 0-49 and 50+
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#' age_groups(ages, 50)
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#'
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#' # split into 0-19, 20-49 and 50+
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#' age_groups(ages, c(20, 50))
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#'
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#' # split into groups of ten years
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#' age_groups(ages, 1:10 * 10)
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#' age_groups(ages, split_at = "tens")
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#'
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#' # split into groups of five years
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#' age_groups(ages, 1:20 * 5)
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#' age_groups(ages, split_at = "fives")
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#'
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#' # split specifically for children
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#' age_groups(ages, c(1, 2, 4, 6, 13, 18))
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#' age_groups(ages, "children")
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#'
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#' \donttest{
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#' # resistance of ciprofloxacin per age group
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#' if (require("dplyr")) {
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#' example_isolates %>%
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#' filter_first_isolate() %>%
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#' filter(mo == as.mo("Escherichia coli")) %>%
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#' group_by(age_group = age_groups(age)) %>%
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#' select(age_group, CIP) %>%
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#' ggplot_rsi(
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#' x = "age_group",
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#' minimum = 0,
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#' x.title = "Age Group",
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#' title = "Ciprofloxacin resistance per age group"
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#' )
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#' }
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#' }
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age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
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meet_criteria(x, allow_class = c("numeric", "integer"), is_positive_or_zero = TRUE, is_finite = TRUE)
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meet_criteria(split_at, allow_class = c("numeric", "integer", "character"), is_positive_or_zero = TRUE, is_finite = TRUE)
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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if (any(x < 0, na.rm = TRUE)) {
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x[x < 0] <- NA
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warning_("in `age_groups()`: NAs introduced for ages below 0.")
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}
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if (is.character(split_at)) {
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split_at <- split_at[1L]
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if (split_at %like% "^(child|kid|junior)") {
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split_at <- c(0, 1, 2, 4, 6, 13, 18)
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} else if (split_at %like% "^(elder|senior)") {
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split_at <- c(65, 75, 85)
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} else if (split_at %like% "^five") {
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split_at <- 1:20 * 5
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} else if (split_at %like% "^ten") {
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split_at <- 1:10 * 10
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}
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}
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split_at <- sort(unique(as.integer(split_at)))
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if (!split_at[1] == 0) {
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# add base number 0
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split_at <- c(0, split_at)
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}
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split_at <- split_at[!is.na(split_at)]
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stop_if(length(split_at) == 1, "invalid value for `split_at`") # only 0 is available
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# turn input values to 'split_at' indices
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y <- x
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lbls <- split_at
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for (i in seq_len(length(split_at))) {
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y[x >= split_at[i]] <- i
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# create labels
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lbls[i - 1] <- paste0(unique(c(split_at[i - 1], split_at[i] - 1)), collapse = "-")
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}
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# last category
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lbls[length(lbls)] <- paste0(split_at[length(split_at)], "+")
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agegroups <- factor(lbls[y], levels = lbls, ordered = TRUE)
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if (isTRUE(na.rm)) {
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agegroups <- agegroups[!is.na(agegroups)]
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}
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agegroups
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}
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