AMR/R/age.R

# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Analysis                              #
#                                                                      #
# SOURCE                                                               #
# https://gitlab.com/msberends/AMR                                     #
#                                                                      #
# LICENCE                                                              #
# (c) 2018-2020 Berends MS, Luz CF et al.                              #
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
#                                                                      #
# We created this package for both routine data analysis and academic  #
# research and it was publicly released in the hope that it will be    #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
# Visit our website for more info: https://msberends.gitlab.io/AMR.    #
# ==================================================================== #

#' Age in years of individuals
#'
#' Calculates age in years based on a reference date, which is the sytem date at default.
#' @inheritSection lifecycle Stable lifecycle
#' @param x date(s), will be coerced with [as.POSIXlt()]
#' @param reference reference date(s) (defaults to today), will be coerced with [as.POSIXlt()] and cannot be lower than `x`
#' @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).
#' @param na.rm a logical to indicate whether missing values should be removed
#' @return An integer (no decimals) if `exact = FALSE`, a double (with decimals) otherwise
#' @seealso To split ages into groups, use the [age_groups()] function.
#' @importFrom dplyr if_else
#' @inheritSection AMR Read more on our website!
#' @export
#' @examples
#' # 10 random birth dates
#' df <- data.frame(birth_date = Sys.Date() - runif(10) * 25000)
#' # add ages
#' df$age <- age(df$birth_date)
#' # add exact ages
#' df$age_exact <- age(df$birth_date, exact = TRUE)
#'
#' df
age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) {
  if (length(x) != length(reference)) {
    if (length(reference) == 1) {
      reference <- rep(reference, length(x))
    } else {
      stop("`x` and `reference` must be of same length, or `reference` must be of length 1.")
    }
  }
  x <- as.POSIXlt(x)
  reference <- as.POSIXlt(reference)

  # from https://stackoverflow.com/a/25450756/4575331
  years_gap <- reference$year - x$year
  ages <- if_else(reference$mon < x$mon | (reference$mon == x$mon & reference$mday < x$mday),
                  as.integer(years_gap - 1),
                  as.integer(years_gap))

  # add decimals
  if (exact == TRUE) {
    # get dates of `x` when `x` would have the year of `reference`
    x_in_reference_year <- as.POSIXlt(paste0(format(reference, "%Y"), format(x, "-%m-%d")))
    # get differences in days
    n_days_x_rest <- as.double(difftime(reference, x_in_reference_year, units = "days"))
    # get numbers of days the years of `reference` has for a reliable denominator
    n_days_reference_year <- as.POSIXlt(paste0(format(reference, "%Y"), "-12-31"))$yday + 1
    # add decimal parts of year
    mod <- n_days_x_rest / n_days_reference_year
    # negative mods are cases where `x_in_reference_year` > `reference` - so 'add' a year
    mod[mod < 0] <- 1 + mod[mod < 0]
    # and finally add to ages
    ages <- ages + mod
  }

  if (any(ages < 0, na.rm = TRUE)) {
    ages[ages < 0] <- NA
    warning("NAs introduced for ages below 0.")
  }
  if (any(ages > 120, na.rm = TRUE)) {
    warning("Some ages are above 120.")
  }
  
  if (isTRUE(na.rm)) {
    ages <- ages[!is.na(ages)]
  }

  ages
}

#' Split ages into age groups
#'
#' Split ages into age groups defined by the `split` parameter. This allows for easier demographic (antimicrobial resistance) analysis.
#' @inheritSection lifecycle Stable lifecycle
#' @param x age, e.g. calculated with [age()]
#' @param split_at values to split `x` at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.
#' @param na.rm a logical to indicate whether missing values should be removed
#' @details To split ages, the input can be:
#' 
#' * A numeric vector. A vector of e.g. `c(10, 20)` will split on 0-9, 10-19 and 20+. A value of only `50` will split on 0-49 and 50+.
#'   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+).
#' * A character:
#'   - `"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+.
#'   - `"elderly"` or `"seniors"`, equivalent of: `c(65, 75, 85)`. This will split on 0-64, 65-74, 75-84, 85+.
#'   - `"fives"`, equivalent of: `1:20 * 5`. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.
#'   - `"tens"`, equivalent of: `1:10 * 10`. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.
#' @return Ordered [`factor`]
#' @seealso To determine ages, based on one or more reference dates, use the [age()] function.
#' @export
#' @inheritSection AMR Read more on our website!
#' @examples
#' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
#'
#' # split into 0-49 and 50+
#' age_groups(ages, 50)
#'
#' # split into 0-19, 20-49 and 50+
#' age_groups(ages, c(20, 50))
#'
#' # split into groups of ten years
#' age_groups(ages, 1:10 * 10)
#' age_groups(ages, split_at = "tens")
#'
#' # split into groups of five years
#' age_groups(ages, 1:20 * 5)
#' age_groups(ages, split_at = "fives")
#'
#' # split specifically for children
#' age_groups(ages, "children")
#' # same:
#' age_groups(ages, c(1, 2, 4, 6, 13, 17))
#'
#' \dontrun{
#' # resistance of ciprofloxacine per age group
#' library(dplyr)
#' example_isolates %>%
#'   filter_first_isolate() %>%
#'   filter(mo == as.mo("E. coli")) %>%
#'   group_by(age_group = age_groups(age)) %>%
#'   select(age_group, CIP) %>%
#'   ggplot_rsi(x = "age_group")
#' }
age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
  if (!is.numeric(x)) {
    stop("`x` and must be numeric, not a ", paste0(class(x), collapse = "/"), ".")
  }
  if (any(x < 0, na.rm = TRUE)) {
    x[x < 0] <- NA
    warning("NAs introduced for ages below 0.")
  }
  if (is.character(split_at)) {
    split_at <- split_at[1L]
    if (split_at %like% "^(child|kid|junior)") {
      split_at <- c(0, 1, 2, 4, 6, 13, 18)
    } else if (split_at %like% "^(elder|senior)") {
      split_at <-  c(65, 75, 85)
    } else if (split_at %like% "^five") {
      split_at <- 1:20 * 5
    } else if (split_at %like% "^ten") {
      split_at <- 1:10 * 10
    }
  }
  split_at <- sort(unique(as.integer(split_at)))
  if (!split_at[1] == 0) {
    # add base number 0
    split_at <- c(0, split_at)
  }
  split_at <- split_at[!is.na(split_at)]
  if (length(split_at) == 1) {
    # only 0 is available
    stop("invalid value for `split_at`.")
  }

  # turn input values to 'split_at' indices
  y <- x
  labs <- split_at
  for (i in seq_len(length(split_at))) {
    y[x >= split_at[i]] <- i
    # create labels
    labs[i - 1] <- paste0(unique(c(split_at[i - 1], split_at[i] - 1)), collapse = "-")
  }

  # last category
  labs[length(labs)] <- paste0(split_at[length(split_at)], "+")

  agegroups <- factor(labs[y], levels = labs, ordered = TRUE)
  
  if (isTRUE(na.rm)) {
    agegroups <- agegroups[!is.na(agegroups)]
  }
  
  agegroups
}