R/random_mctq.R

In gipsousp/mctq: Tools to Process the Munich ChronoType Questionnaire (MCTQ)

#' Build a random MCTQ case
#'
#' @description
#'
#' `r lifecycle::badge("maturing")`
#'
#' `random_mctq` builds a fictional Munich ChronoType Questionnaire (MCTQ) case
#' composed of MCTQ basic/measurable variables.
#'
#' This function is __for testing and learning purposes only__. Please don't
#' misuse it.
#'
#' @details
#'
#' The case structure (variable names and classes) are the same as the datasets
#' provided by the `mctq` package. See [`?std_mctq`][mctq::std_mctq],
#' [`?micro_mctq`][mctq::micro_mctq] and [`?shift_mctq`][mctq::shift_mctq] to
#' learn more.
#'
#' ## Requirements
#'
#' This function requires the [`stats`][stats::stats-package] package. This
#' won't be an issue for most people since the package comes with a standard R
#' installation.
#'
#' If you don't have the [`stats`][stats::stats-package] package, you can
#' install it with `install.packages("stats")`.
#'
#' ## Cases
#'
#' Random standard and micro MCTQ cases were created with the general
#' population in mind. The data was set to resemble the distribution parameters
#' shown in Roenneberg, Wirz-Justice, & Merrow (2003).
#'
#' MCTQ\eqn{^{Shift}}{ Shift} random cases were created based on the shift
#' configuration from "Study Site 1" shown in Vetter, Juda, & Roenneberg (2012).
#' The data was set to resemble the distribution parameters shown in Juda,
#' Vetter, & Roenneberg (2013).
#'
#' You can see more about the distribution parameters used
#' [here](https://github.com/ropensci/mctq/blob/main/data-raw/random_mctq.R).
#'
#' @param model A string indicating the data model to return. Valid values are:
#'   `"standard"`, "`shift"`, and `"micro"` (default: `"standard"`).
#'
#' @return A named [`list`][base::list()] with elements representing each MCTQ
#'   basic/measurable variable of the model indicated in the `model` argument.
#'
#' @template references_f
#' @family utility functions
#' @importFrom lubridate %within%
#' @export
#'
#' @examples
#' \dontrun{
#' random_mctq("standard")
#' random_mctq("micro")
#' random_mctq("shift")
#' }
random_mctq <- function(model = "standard") {
  checkmate::assert_choice(model, c("std", "standard", "shift", "micro"))
  require_pkg("stats")

  if (model %in% c("std", "standard")) {
    random_std_mctq()
  } else if (model == "micro") {
    random_micro_mctq()
  } else if (model == "shift") {
    random_shift_mctq()
  }
}

random_std_mctq <- function() {
  # R CMD Check variable bindings fix (see: https://bit.ly/3z24hbU)
  # nolint start: object_usage_linter.
  work <- wd <- NULL
  bt_w <- sprep_w <- slat_w <- se_w <- si_w <- alarm_w <- NULL
  wake_before_w <- le_w <- NULL
  bt_f <- sprep_f <- slat_f <- se_f <- si_f <- alarm_f <- NULL
  reasons_f <- reasons_why_f <- le_f <- NULL
  # nolint end

  # Set values -----

  by <- as.numeric(lubridate::dminutes(5))
  envir <- environment()

  # Create `work` and `wd` -----

  work <- sample(c(TRUE, FALSE), 1, prob = c(10, 1))
  wd <- sample(0:7, 1, prob = c(1, rep(2, 4), 10, 2, 1))
  if (work == FALSE) wd <- as.integer(0)

  # Create `bt_w` and `bt_f` -----

  ## `bt_w` for workdays:
  ## min = ~ `so_w_min` - 01:00; max = ~ `so_w_max` - 01:00;
  ## mean = ~ `so_w_mean` - 01:00; sd = ~ `so_w_sd`

  ## `bt_f` for work-free days:
  ## min = ~ `so_f_min` - 01:00; max = ~ `so_f_max` - 02:00;
  ## mean = ~ `so_f_mean` - 02:00; sd = ~ `so_f_sd`

  values <- list(
    bt_w = list(
      name = "bt_w",
      min = hms::parse_hm("20:00"),
      max = hms::parse_hm("01:00"),
      mean = hms::parse_hm("22:30"),
      sd = hms::parse_hm("01:00")
    ),
    bt_f = list(
      name = "bt_f",
      min = hms::parse_hm("19:15"),
      max = hms::parse_hm("03:25"),
      mean = hms::parse_hm("22:50"),
      sd = hms::parse_hm("01:30")
    )
  )

  lapply(values, sampler_2, by = by, envir = envir)

  # Create `sprep_w` and `sprep_f` -----

  values <- list(
    sprep_w = list(
      name = "sprep_w",
      min = bt_w,
      max = bt_w + hms::parse_hm("01:00"),
      mean = bt_w + hms::parse_hm("00:30"),
      sd = hms::parse_hm("00:30")
    ),
    sprep_f = list(
      name = "sprep_f",
      min = bt_f,
      max = bt_f + hms::parse_hm("02:00"),
      mean = bt_f + hms::parse_hm("01:00"),
      sd = hms::parse_hm("01:00")
    )
  )

  lapply(values, sampler_3, y = "bt", by = by, envir = envir)

  # Create `slat_w` and `slat_f` -----

  values <- list(
    slat_w = list(
      name = "slat_w",
      min = hms::parse_hm("00:05"),
      max = hms::parse_hm("01:00"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:15")
    ),
    slat_f = list(
      name = "slat_f",
      min = hms::parse_hm("00:05"),
      max = hms::parse_hm("02:00"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:15")
    )
  )

  lapply(values, sampler_4, by = by, envir = envir)

  # Create `se_w` and `se_f` -----

  ## `se_w` for workdays:
  ## min = `sprep_w` + `slat_w` + ~ `sd_w_min`;
  ## max = `sprep_w` + `slat_w` + ~ `sd_w_max`;
  ## mean = `sprep_w` + `slat_w` + ~ `sd_w_mean`; sd = ~ `sd_w_sd`

  ## `se_f` for work-free days:
  ## min = `sprep_f` + `slat_f` + ~ `sd_f_min`;
  ## max = `sprep_f` + `slat_f` + ~ `sd_f_max`;
  ## mean = `sprep_f` + `slat_f` + ~ `sd_f_mean`; sd = ~ `sd_f_sd`

  values <- list(
    se_w = list(
      name = "se_w",
      min = sprep_w + slat_w + hms::parse_hm("03:55"),
      max = sprep_w + slat_w + hms::parse_hm("10:50"),
      mean = sprep_w + slat_w + hms::parse_hm("07:20"),
      sd = hms::parse_hm("01:10")
    ),
    se_f = list(
      name = "se_f",
      min = sprep_f + slat_f + hms::parse_hm("03:50"),
      max = sprep_f + slat_f + hms::parse_hm("13:05"),
      mean = sprep_f + slat_f + hms::parse_hm("08:25"),
      sd = hms::parse_hm("01:30")
    )
  )

  lapply(values, sampler_3, y = "bt", by = by, envir = envir)

  # Create si_w` and `si_f` -----

  values <- list(
    si_w = list(
      name = "si_w",
      min = hms::parse_hm("00:05"),
      max = hms::parse_hm("02:00"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:15")
    ),
    si_f = list(
      name = "si_f",
      min = hms::parse_hm("00:05"),
      max = hms::parse_hm("02:00"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:15")
    )
  )

  lapply(values, sampler_4, by = by, envir = envir)

  # Create `le_w` and `le_f` -----

  values <- list(
    le_w = list(
      name = "le_w",
      min = hms::parse_hm("00:00"),
      max = hms::parse_hm("04:00"),
      mean = hms::parse_hm("01:30"),
      sd = hms::parse_hm("01:00")
    ),
    le_f = list(
      name = "le_f",
      min = hms::parse_hm("00:00"),
      max = hms::parse_hm("06:00"),
      mean = hms::parse_hm("02:00"),
      sd = hms::parse_hm("01:00")
    )
  )

  lapply(values, sampler_4, by = by, envir = envir)

  # Create `alarm_w` and `wake_before_f` -----

  alarm_w <- sample(c(TRUE, FALSE), 1, prob = c(10, 1))
  wake_before_w <- sample(c(TRUE, FALSE), 1, prob = c(1, 10))
  if (isFALSE(alarm_w)) wake_before_w <- as.logical(NA)

  # Create `alarm_f`, `reasons_f`, and `reasons_why_f` -----

  alarm_f <- sample(c(TRUE, FALSE), 1, prob = c(1, 10))
  reasons_f <- sample(c(TRUE, FALSE), 1, prob = c(1, 5))
  reasons_why_f <- sample(c("Child(ren)/pet(s)", "Hobbies"), 1)
  if (isFALSE(reasons_f)) reasons_why_f <- as.character(NA)

  # Create and return output -----

  list(
    work = work,
    wd = as.integer(wd),

    bt_w = bt_w,
    sprep_w = sprep_w,
    slat_w = slat_w,
    se_w = se_w,
    si_w = si_w,
    alarm_w = alarm_w,
    wake_before_w = wake_before_w,
    le_w = le_w,

    bt_f = bt_f,
    sprep_f = sprep_f,
    slat_f = slat_f,
    se_f = se_f,
    si_f = si_f,
    alarm_f = alarm_f,
    reasons_f = reasons_f,
    reasons_why_f = reasons_why_f,
    le_f = le_f
  )
}

random_micro_mctq <- function() {
  # R CMD Check variable bindings fix (see: https://bit.ly/3z24hbU)
  # nolint start: object_usage_linter.
  shift_work <- wd <- so_w <- se_w <- so_f <- se_f <- NULL
  # nolint end

  # Set values -----

  by <- as.numeric(lubridate::dminutes(5))
  envir <- environment()

  # Create `shift_work` and `wd` -----

  shift_work <- sample(c(TRUE, FALSE), 1, prob = c(1, 15))
  wd <- sample(0:7, 1, prob = c(1, rep(2, 4), 10, 2, 1))

  # Create `so_w` and so_f` -----

  ## `so` values for workdays:
  ## min = ~ `so_w_min`; max = ~ `so_w_max`; mean = ~ `so_w_mean`;
  ## sd = ~ `so_w_sd`

  ## `so` values for work-free days:
  ## min = ~ `so_f_min`; max = ~ `se_f_min`; mean = ~ `so_f_mean`;
  ## sd = ~ `so_f_sd`

  values <- list(
    so_w = list(
      name = "so_w",
      min = hms::parse_hm("21:00"),
      max = hms::parse_hm("02:00"),
      mean = hms::parse_hm("23:30"),
      sd = hms::parse_hm("01:00")
    ),
    so_f = list(
      name = "so_f",
      min = hms::parse_hm("20:15"),
      max = hms::parse_hm("05:25"),
      mean = hms::parse_hm("00:50"),
      sd = hms::parse_hm("01:30")
    )
  )

  lapply(values, sampler_2, by = by, envir = envir)

  # Create `se_w` and `se_f` -----

  ## `se` values for workdays:
  ## min = `so_w` + ~ `sd_w_min`; max = `so_w` + ~ `sd_w_max`;
  ## mean = `so_w` + ~ `sd_w_mean`; sd = ~ `sd_w_sd`

  ## `se` for work-free days:
  ## min = `so_f` + ~ `sd_f_min`; max = `so_f` + ~ `sd_f_max`;
  ## mean = `so_f` + ~ `sd_f_mean`; sd = ~ `sd_f_sd`

  values <- list(
    se_w = list(
      name = "se_w",
      min = so_w + hms::parse_hm("03:55"),
      max = so_w + hms::parse_hm("10:50"),
      mean = so_w + hms::parse_hm("07:20"),
      sd = hms::parse_hm("01:10")
    ),
    se_f = list(
      name = "se_f",
      min = so_f + hms::parse_hm("03:50"),
      max = so_f + hms::parse_hm("13:05"),
      mean = so_f + hms::parse_hm("08:25"),
      sd = hms::parse_hm("01:30")
    )
  )

  lapply(values, sampler_3, y = "so", by = by, envir = envir)

  # Create and return output -----

  list(
    shift_work = shift_work,
    wd = as.integer(wd),

    so_w = so_w,
    se_w = se_w,

    so_f = so_f,
    se_f = se_f
  )
}

random_shift_mctq <- function(n_w = c(n_w_m = 6, n_w_e = 4, n_w_n = 6),
                              n_f = c(n_f_m = 2, n_f_e = 2, n_f_n = 8)) {
  # Check arguments -----

  checkmate::assert_integerish(n_w, lower = 0, any.missing = FALSE, len = 3)
  checkmate::assert_integerish(n_f, lower = 0, any.missing = FALSE, len = 3)

  # R CMD Check variable bindings fix (see: https://bit.ly/3z24hbU)
  # nolint start: object_usage_linter.
  n_w_m <- bt_w_m <- sprep_w_m <- slat_w_m <- se_w_m <- alarm_w_m <- NULL
  tgu_w_m <- nap_w_m <- napo_w_m <- nape_w_m <- reasons_w_m <- NULL
  reasons_why_w_m <- NULL
  n_f_m <- bt_f_m <- sprep_f_m <- slat_f_m <- se_f_m <- alarm_f_m <- NULL
  tgu_f_m <- nap_f_m <- napo_f_m <- nape_f_m <- reasons_f_m <- NULL
  reasons_why_f_m <- NULL
  n_w_e <- bt_w_e <- sprep_w_e <- slat_w_e <- se_w_e <- alarm_w_e <- NULL
  tgu_w_e <- nap_w_e <- napo_w_e <- nape_w_e <- reasons_w_e <- NULL
  reasons_why_w_e <- NULL
  n_f_e <- bt_f_e <- sprep_f_e <- slat_f_e <- se_f_e <- alarm_f_e <- NULL
  tgu_f_e <- nap_f_e <- napo_f_e <- nape_f_e <- reasons_f_e <- NULL
  reasons_why_f_e <- NULL
  n_w_n <- bt_w_n <- sprep_w_n <- slat_w_n <- se_w_n <- alarm_w_n <- NULL
  tgu_w_n <- nap_w_n <- napo_w_n <- nape_w_n <- reasons_w_n <- NULL
  reasons_why_w_n <- NULL
  n_f_n <- bt_f_n <- sprep_f_n <- slat_f_n <- se_f_n <- alarm_f_n <- NULL
  tgu_f_n <- nap_f_n <- napo_f_n <- nape_f_n <- reasons_f_n <- NULL
  reasons_why_f_n <- NULL
  # nolint end

  # Set values -----

  by <- as.numeric(lubridate::dminutes(5))
  envir <- environment()

  # Create `sprep_w_*` and `sprep_f_*` -----

  values <- list(
    sprep_w_m = list(
      name = "sprep_w_m",
      min = hms::parse_hm("19:15"),
      max = hms::parse_hm("00:40"), # Adjusted
      mean = hms::parse_hm("22:30"),
      sd = hms::parse_hm("01:05")
    ),
    sprep_w_e = list(
      name = "sprep_w_e",
      min = hms::parse_hm("21:40"),
      max = hms::parse_hm("02:35"), # Adjusted
      mean = hms::parse_hm("00:40"),
      sd = hms::parse_hm("01:00")
    ),
    sprep_w_n = list(
      name = "sprep_w_n",
      min = hms::parse_hm("06:30"), # Adjusted
      max = hms::parse_hm("10:10"),
      mean = hms::parse_hm("07:20"),
      sd = hms::parse_hm("01:00")
    ),
    sprep_f_m = list(
      name = "sprep_f_m",
      min = hms::parse_hm("19:50"),
      max = hms::parse_hm("03:45"),
      mean = hms::parse_hm("23:45"),
      sd = hms::parse_hm("01:20")
    ),
    sprep_f_e = list(
      name = "sprep_f_e",
      min = hms::parse_hm("19:50"),
      max = hms::parse_hm("04:30"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("01:25")
    ),
    sprep_f_n = list(
      name = "sprep_f_n",
      min = hms::parse_hm("17:45"),
      max = hms::parse_hm("07:35"),
      mean = hms::parse_hm("00:40"),
      sd = hms::parse_hm("02:20")
    )
  )

  lapply(values, sampler_2, by = by, envir = envir)

  # Create `bt_w_*` and `bt_f_*` -----

  min <- as.numeric(hms::parse_hm("00:00"))
  max <- as.numeric(hms::parse_hm("02:00"))
  prob <- stats::dnorm(seq(min, max, by),
                       mean = as.numeric(hms::parse_hm("00:30")),
                       sd = as.numeric(hms::parse_hm("00:30")))

  for (i in c("bt_w_m", "bt_w_e", "bt_w_n", "bt_f_m", "bt_f_e", "bt_f_n")) {
    sample <- sample_time(min = min, max = max, by = by, prob = prob)
    name <- paste0("sprep_", str_extract_(i, "._.$"))

    assign(
      i,
      hms::hms(as.numeric(sum_time(
        get(name), - sample, cycle = lubridate::ddays()
      )))
    )
  }

  # Create `slat_w_*` and `slat_f_*` -----

  values <- list(
    slat_w_m = list(
      name = "slat_w_m",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:30"), # Adjusted
      mean = hms::parse_hm("00:20"),
      sd = hms::parse_hm("00:25")
    ),
    slat_w_e = list(
      name = "slat_w_e",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:40"), # Adjusted
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:20")
    ),
    slat_w_n = list(
      name = "slat_w_n",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("01:10"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:20")
    ),
    slat_f_m = list(
      name = "slat_f_m",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("01:00"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:15")
    ),
    slat_f_e = list(
      name = "slat_f_e",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("01:05"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:15")
    ),
    slat_f_n = list(
      name = "slat_f_n",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("02:15"),
      mean = hms::parse_hm("00:25"),
      sd = hms::parse_hm("00:35")
    )
  )

  lapply(values, sampler_4, by = by, envir = envir)

  # Create `se_w_*` and `se_f_*` -----

  ## Transition times: 06:00, 14:00, and 22:00

  ## Bear in mind:
  ## `se_w_min` must be greater or equal than `sprep_max + slat_max` +
  ##  `sd_w_min`
  ## `se_w_max` + `tgu_w_max` must be less or equal than the
  ## `transition time` - `00:30`
  ## `sd` must be equal or greater than `max` - `min`

  ## `se` for workdays (if the assumptions above are true):
  ## min = `sprep_w` + `slat_w` + ~ `sd_w_min`;
  ## max = ~ `se_w`; mean = ~ `se_w`; sd = ~ `se_sd`

  ## `se` for work-free days:
  ## min = `sprep_f` + `slat_f` + ~ `sd_f_min`;
  ## max = `sprep_f` + `slat_f` + ~ `sd_f_max`
  ## mean = `sprep_f` + `slat_f` + ~ `sd_f_mean`; sd = ~ `sd_sd`

  values <- list(
    # se_w_m_min >= sprep_w_m_max + slat_w_m_max + sd_w_m_min
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("00:40"), hms::parse_hm("00:30"),
    # `sd_w_m_min`)))
    # se_w_m_max + tgu_w_m_max <= 06:00 - 00:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("05:00"), hms::parse_hm("00:30"))))
    se_w_m = list(
      name = "se_w_m",
      min = hms::hms(as.numeric(sum_time(
        sprep_w_m, slat_w_m, hms::parse_hm("02:05"), cycle = lubridate::ddays()
      ))),
      max = hms::parse_hm("05:00"), # Adjusted
      mean = hms::parse_hm("04:35"),
      sd = hms::parse_hm("00:35")
    ),
    # se_w_e_min >= sprep_w_e_max + slat_w_e_max + sd_w_e_min
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("02:35"), hms::parse_hm("00:40"),
    # `sd_w_e_min`)))
    # se_w_e_max + tgu_w_e_max) <= 14:00 - 00:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("12:25"), hms::parse_hm("00:50"))))
    se_w_e = list(
      name = "se_w_e",
      min = hms::hms(as.numeric(sum_time(
        sprep_w_e, slat_w_e, hms::parse_hm("03:40"), cycle = lubridate::ddays()
      ))),
      max = hms::parse_hm("12:25"),
      mean = hms::parse_hm("08:25"),
      sd = hms::parse_hm("01:20")
    ),
    # se_w_n_min >= sprep_w_n_max + slat_w_n_max + sd_w_n_min
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("10:10"), hms::parse_hm("01:10"),
    # `sd_w_n_min`)))
    # se_w_n_max + tgu_w_n_max <= 22:00 - 00:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("18:05"), hms::parse_hm("01:00"))))
    se_w_n = list(
      name = "se_w_n",
      min = hms::hms(as.numeric(sum_time(
        sprep_w_n, slat_w_n, hms::parse_hm("02:00"), # Adj.
        cycle = lubridate::ddays()
      ))),
      max = hms::parse_hm("18:05"),
      mean = hms::parse_hm("13:30"),
      sd = hms::parse_hm("01:30")
    ),
    se_f_m = list(
      name = "se_f_m",
      min = sprep_f_m + slat_f_m + hms::parse_hm("03:20"),
      max = sprep_f_m + slat_f_m + hms::parse_hm("12:45"),
      mean = sprep_f_m + slat_f_m + hms::parse_hm("08:05"),
      sd = hms::parse_hm("01:35")
    ),
    se_f_e = list(
      name = "se_f_e",
      min = sprep_f_e + slat_f_e + hms::parse_hm("04:15"),
      max = sprep_f_e + slat_f_e + hms::parse_hm("12:00"),
      mean = sprep_f_e + slat_f_e + hms::parse_hm("08:10"),
      sd = hms::parse_hm("01:20")
    ),
    se_f_n = list(
      name = "se_f_n",
      min = sprep_f_n + slat_f_n + hms::parse_hm("02:15"),
      max = sprep_f_n + slat_f_n + hms::parse_hm("13:35"),
      mean = sprep_f_n + slat_f_n + hms::parse_hm("07:55"),
      sd = hms::parse_hm("01:55")
    )
  )

  lapply(values, sampler_2, by = by, envir = envir)

  # Create `tgu_w_*` and `tgu_f_*` -----

  values <- list(
    tgu_w_m = list(
      name = "tgu_w_m",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:30"), # Adjusted
      mean = hms::parse_hm("00:05"),
      sd = hms::parse_hm("00:10")
    ),
    tgu_w_e = list(
      name = "tgu_w_e",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:50"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:15")
    ),
    tgu_w_n = list(
      name = "tgu_w_n",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("01:00"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:15")
    ),
    tgu_f_m = list(
      name = "tgu_f_m",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("01:25"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:25")
    ),
    tgu_f_e = list(
      name = "tgu_f_e",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:45"),
      mean = hms::parse_hm("00:10"),
      sd = hms::parse_hm("00:10")
    ),
    tgu_f_n = list(
      name = "tgu_f_n",
      min = hms::parse_hm("00:05"), # Adjusted
      max = hms::parse_hm("00:55"),
      mean = hms::parse_hm("00:15"),
      sd = hms::parse_hm("00:15")
    )
  )

  lapply(values, sampler_4, by = by, envir = envir)

  # Create `napo_w_*` and `napo_f_*` -----

  ## Transition times: 06:00, 14:00, and 22:00

  ## Bear in mind:
  ## `napo_w_min` must be greater or equal than the `transition time` +
  ## `01:30`
  ## `napo_f_min` must be greater or equal than `se_f_max + tgu_f_max` +
  ## `01:30` or `sprep_f_max` + `slat_f_max` + `sd_f_max` + tgu_f_max` + 01:30
  ## `napo_max` must be less or equal than `bt_min` - `01:30`
  ## `sd` must be equal or greater than `max` - `min`

  values <- list(
    # napo_w_m_min >= 06:00 + 01:30
    # napo_w_m_max <= bt_w_m_min - 01:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("19:15"), - hms::parse_hm("01:30"))))
    napo_w_m = list(
      name = "napo_w_m",
      min = hms::parse_hm("07:30"),
      max = hms::parse_hm("17:45"),
      mean = interval_mean(hms::parse_hm("09:00"), hms::parse_hm("17:45")),
      sd = hms::parse_hm("01:30")
    ),
    # napo_w_e_min >= 14:00 + 01:30
    # napo_w_e_max <= bt_w_e_min - 01:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("21:40"), - hms::parse_hm("01:30"))))
    napo_w_e = list(
      name = "napo_w_e",
      min = hms::parse_hm("15:30"),
      max = hms::parse_hm("20:10"),
      mean = interval_mean(hms::parse_hm("15:30"), hms::parse_hm("20:10")),
      sd = hms::parse_hm("01:30")
    ),
    # napo_w_n_min >= 22:00 + 01:30
    # napo_w_n_max <= bt_w_n_min - 01:30
    # hms::hms(as.numeric(
    # sum_time(hms::parse_hm("06:30"), - hms::parse_hm("01:30"))))
    napo_w_n = list(
      name = "napo_w_n",
      min = hms::parse_hm("23:30"),
      max = hms::parse_hm("05:00"),
      mean = interval_mean(hms::parse_hm("23:30"), hms::parse_hm("05:00")),
      sd = hms::parse_hm("01:30")
    ),
    napo_f_m = list(
      name = "napo_f_m",
      min = hms::hms(as.numeric(sum_time(
        se_f_m, tgu_f_m, hms::parse_hm("01:30")
      ))),
      max = hms::hms(as.numeric(sum_time(bt_f_m, - hms::parse_hm("01:30")))),
      mean = interval_mean(hms::as_hms(se_f_m + tgu_f_m), bt_f_m),
      sd = hms::parse_hm("01:30")
    ),
    napo_f_e = list(
      name = "napo_f_e",
      min = hms::hms(as.numeric(sum_time(
        se_f_e, tgu_f_e, hms::parse_hm("01:30")
      ))),
      max = hms::hms(as.numeric(sum_time(bt_f_e, - hms::parse_hm("01:30")))),
      mean = interval_mean(hms::as_hms(se_f_e + tgu_f_e), bt_f_e),
      sd = hms::parse_hm("01:30")
    ),
    napo_f_n = list(
      name = "napo_f_n",
      min = hms::hms(as.numeric(sum_time(
        se_f_n, tgu_f_n, hms::parse_hm("01:30")
      ))),
      max = hms::hms(as.numeric(sum_time(bt_f_n, - hms::parse_hm("01:30")))),
      mean = interval_mean(hms::as_hms(se_f_n + tgu_f_n), bt_f_n),
      sd = hms::parse_hm("01:30")
    )
  )

  lapply(values, sampler_1, by = by, envir = envir)

  # Create `nape_w_*` and `nape_f_*` -----

  min <- as.numeric(hms::parse_hm("00:10"))
  max <- as.numeric(hms::parse_hm("01:00"))
  prob <- stats::dnorm(
    seq(min, max, by),
    mean = as.numeric(hms::parse_hm("00:30")),
    sd = as.numeric(hms::parse_hm("00:15"))
  )

  for (i in c("nape_w_m", "nape_w_e", "nape_w_n", "nape_f_m", "nape_f_e",
              "nape_f_n")) {
    name <- get(paste0("napo_", str_extract_(i, "._.$")))
    sample <- sample_time(min = min, max = max, by = by, prob = prob)

    assign(
      i,
      hms::hms(as.numeric(sum_time(name, sample, cycle = lubridate::ddays())))
    )
  }

  # Create `nap_w_*` and `nap_f_*` -----

  for (i in c("nap_w_m", "nap_w_e", "nap_w_n", "nap_f_m", "nap_f_e",
              "nap_f_n")) {
    assign(i, sample(c(TRUE, FALSE), 1, prob = c(1, 1)))

    if (isFALSE(get(i))) {
      name <- paste0("napo_", str_extract_(i, "._.$"))
      assign(name, hms::as_hms(NA))
      name <- paste0("nape_", str_extract_(i, "._.$"))
      assign(name, hms::as_hms(NA))
    }
  }

  # Create `alarm_w_*` and `alarm_f_*` -----

  alarm_w_m <- sample(c(TRUE, FALSE), 1, prob = c(10, 1))
  alarm_w_e <- sample(c(TRUE, FALSE), 1, prob = c(5, 1))
  alarm_w_n <- sample(c(TRUE, FALSE), 1, prob = c(1, 1))

  for (i in c("alarm_f_m", "alarm_f_e", "alarm_f_n")) {
    assign(i, sample(c(TRUE, FALSE), 1, prob = c(1, 10)))
  }

  # Create `reasons_w_*`, `reasons_f_*`, `reasons_why_w`, and
  # `reasons_why_f` -----

  for (i in c("reasons_why_w_m", "reasons_why_w_e", "reasons_why_w_n",
              "reasons_why_f_m", "reasons_why_f_e", "reasons_why_f_n")) {
    assign(i, sample(c("Child(ren)/pet(s)", "Hobbies"), 1))
  }

  for (i in c("reasons_w_m", "reasons_w_e", "reasons_w_n",
              "reasons_f_m", "reasons_f_e", "reasons_f_n")) {
    assign(i, sample(c(TRUE, FALSE), 1, prob = c(1, 5)))

    if (isFALSE(get(i))) {
      name <- paste0("reasons_why_", str_extract_(i, "._.$"))
      assign(name, as.character(NA))
    }
  }

  # Create and return output -----

  list(
    n_w_m = as.integer(n_w[1]),
    bt_w_m = bt_w_m,
    sprep_w_m = sprep_w_m,
    slat_w_m = slat_w_m,
    se_w_m = se_w_m,
    tgu_w_m = tgu_w_m,
    alarm_w_m = alarm_w_m,
    reasons_w_m = reasons_w_m,
    reasons_why_w_m = reasons_why_w_m,
    nap_w_m = nap_w_m,
    napo_w_m = napo_w_m,
    nape_w_m = nape_w_m,

    n_f_m = as.integer(n_f[1]),
    bt_f_m = bt_f_m,
    sprep_f_m = sprep_f_m,
    slat_f_m = slat_f_m,
    se_f_m = se_f_m,
    tgu_f_m = tgu_f_m,
    alarm_f_m = alarm_f_m,
    reasons_f_m = reasons_f_m,
    reasons_why_f_m = reasons_why_f_m,
    nap_f_m = nap_f_m,
    napo_f_m = napo_f_m,
    nape_f_m = nape_f_m,

    n_w_e = as.integer(n_w[2]),
    bt_w_e = bt_w_e,
    sprep_w_e = sprep_w_e,
    slat_w_e = slat_w_e,
    se_w_e = se_w_e,
    tgu_w_e = tgu_w_e,
    alarm_w_e = alarm_w_e,
    reasons_w_e = reasons_w_e,
    reasons_why_w_e = reasons_why_w_e,
    nap_w_e = nap_w_e,
    napo_w_e = napo_w_e,
    nape_w_e = nape_w_e,

    n_f_e = as.integer(n_f[2]),
    bt_f_e = bt_f_e,
    sprep_f_e = sprep_f_e,
    slat_f_e = slat_f_e,
    se_f_e = se_f_e,
    tgu_f_e = tgu_f_e,
    alarm_f_e = alarm_f_e,
    reasons_f_e = reasons_f_e,
    reasons_why_f_e = reasons_why_f_e,
    nap_f_e = nap_f_e,
    napo_f_e = napo_f_e,
    nape_f_e = nape_f_e,

    n_w_n = as.integer(n_w[3]),
    bt_w_n = bt_w_n,
    sprep_w_n = sprep_w_n,
    slat_w_n = slat_w_n,
    se_w_n = se_w_n,
    tgu_w_n = tgu_w_n,
    alarm_w_n = alarm_w_n,
    reasons_w_n = reasons_w_n,
    reasons_why_w_n = reasons_why_w_n,
    nap_w_n = nap_w_n,
    napo_w_n = napo_w_n,
    nape_w_n = nape_w_n,

    n_f_n = as.integer(n_f[3]),
    bt_f_n = bt_f_n,
    sprep_f_n = sprep_f_n,
    slat_f_n = slat_f_n,
    se_f_n = se_f_n,
    tgu_f_n = tgu_f_n,
    alarm_f_n = alarm_f_n,
    reasons_f_n = reasons_f_n,
    reasons_why_f_n = reasons_why_f_n,
    nap_f_n = nap_f_n,
    napo_f_n = napo_f_n,
    nape_f_n = nape_f_n
  )
}

normalize <- function(min, max, mean, ambiguity = 24) {
  classes <- c("Duration", "difftime", "hms")

  checkmate::assert_multi_class(min, classes)
  assert_length_one(min)
  checkmate::assert_multi_class(max, classes)
  assert_length_one(max)
  checkmate::assert_multi_class(mean, classes)
  assert_length_one(mean)
  checkmate::assert_choice(ambiguity, c(0, 24, NA))

  min <- cycle_time(hms::as_hms(as.numeric(min)), cycle = lubridate::ddays())
  max <- cycle_time(hms::as_hms(as.numeric(max)), cycle = lubridate::ddays())
  mean <- cycle_time(hms::as_hms(as.numeric(mean)), cycle = lubridate::ddays())

  interval <- assign_date(min, max, ambiguity = ambiguity)
  min <- hms::as_hms(lubridate::int_start(interval))
  max <- hms::as_hms(as.numeric(min) + as.numeric(interval))

  check_1 <- lubridate::as_datetime(mean, tz = "UTC") %within% interval
  check_2 <- lubridate::as_datetime(
    as.numeric(mean + lubridate::ddays()), tz = "UTC"
  )
  check_2 <- check_2 %within% interval

  if (check_1) {
    list(min = min, max = max, mean = hms::hms(as.numeric(mean)))
  } else if (check_2) {
    mean <- hms::hms(as.numeric(mean + lubridate::ddays()))
    list(min = min, max = max, mean = mean)
  } else {
    cli::cli_abort(paste0(
      "'mean' cannot be found within the interval between 'min' ",
      "and 'max'."
    ))
  }

}

sample_time <- function(min = hms::parse_hms("00:00:00"),
                        max = hms::parse_hms("23:59:59"),
                        by = lubridate::dminutes(5), size = 1,
                        replace = FALSE, prob = NULL) {
  classes <- c("Duration", "Period", "hms", "integer", "numeric")

  checkmate::assert_multi_class(min, classes)
  assert_length_one(min)
  checkmate::assert_multi_class(max, classes)
  assert_length_one(max)
  checkmate::assert_multi_class(by, classes)
  assert_length_one(by)
  checkmate::assert_flag(replace)
  checkmate::assert_number(size, lower = 0)
  checkmate::assert_numeric(prob, null.ok = TRUE)

  min <- as.numeric(min)
  max <- as.numeric(max)
  by <- as.numeric(by)

  if (size > length(seq(min, max, by)) && isFALSE(replace)) {
    cli::cli_abort(paste0(
      "You cannot take a sample larger than the population ",
      "when 'replace = FALSE'."
    ))
  }

  sample <- sample(
    seq(min, max, by), size = size, replace = replace, prob = prob
  )

  hms::hms(sample)
}

sampler_1 <- function(x, by, envir) {
  classes <- c("character", "numeric", "Duration", "difftime", "hms")
  names <- c("name", "min", "max", "mean", "sd")

  checkmate::assert_list(x)
  checkmate::assert_names(names(x), identical.to = names)
  lapply(x, checkmate::assert_multi_class, classes = classes)

  classes <- c("numeric", "Duration", "difftime", "hms")
  checkmate::assert_multi_class(by, classes)
  checkmate::assert_environment(envir)

  normalize <- normalize(x$min, x$max, x$mean)
  list2env(lapply(normalize, as.numeric), envir = environment())

  sd <- as.numeric(x$sd)
  by <- as.numeric(by)
  prob <- stats::dnorm(seq(min, max, by), mean = mean, sd = sd)
  sample <- cycle_time(
    sample_time(min = min, max = max, by = by, prob = prob),
    cycle = lubridate::ddays()
  )

  assign(x$name, sample, envir = envir)
}

sampler_2 <- function(x, by, envir) {
  classes <- c("character", "numeric", "Duration", "difftime", "hms")
  names <- c("name", "min", "max", "mean", "sd")

  checkmate::assert_list(x)
  checkmate::assert_names(names(x), identical.to = names)
  lapply(x, checkmate::assert_multi_class, classes = classes)

  classes <- c("numeric", "Duration", "difftime", "hms")
  checkmate::assert_multi_class(by, classes)
  checkmate::assert_environment(envir)

  normalize <- normalize(x$min, x$max, x$mean)
  list2env(lapply(normalize, as.numeric), envir = environment())

  sd <- as.numeric(x$sd)
  by <- as.numeric(by)
  prob <- stats::dnorm(seq(min, max, by), mean = mean, sd = sd)
  sample <- cycle_time(
    sample_time(min = min, max = max, by = by, prob = prob),
    cycle = lubridate::ddays()
  )

  assign(x$name, sample, envir = envir)

  if (grepl("_f$|_f_", x$name, perl = TRUE)) {
    work <- get(sub("_f", "_w", x$name), envir = envir)

    for (i in seq(3)) { # Bias
      free <- get(x$name, envir = envir)

      check <- shush(shorter_int(free, work))
      check <- lubridate::int_end(check)
      if (hms::as_hms(check) == free) break

      sample <- cycle_time(
        sample_time(min = min, max = max, by = by, prob = prob),
        cycle = lubridate::ddays()
      )

      assign(x$name, sample, envir = envir)
    }
  }
}

sampler_3 <- function(x, y, by, envir) {
  classes <- c("character", "numeric", "Duration", "difftime", "hms")
  names <- c("name", "min", "max", "mean", "sd")

  checkmate::assert_list(x)
  checkmate::assert_names(names(x), identical.to = names)
  lapply(x, checkmate::assert_multi_class, classes = classes)
  checkmate::assert_string(y, min.chars = 1)

  classes <- c("numeric", "Duration", "difftime", "hms")
  checkmate::assert_multi_class(by, classes)
  checkmate::assert_environment(envir)

  normalize <- normalize(x$min, x$max, x$mean)
  list2env(lapply(normalize, as.numeric), envir = environment())

  sd <- as.numeric(x$sd)
  by <- as.numeric(by)
  prob <- stats::dnorm(seq(min, max, by), mean = mean, sd = sd)
  sample <- cycle_time(
    sample_time(min = min, max = max, by = by, prob = prob),
    cycle = lubridate::ddays()
  )

  assign(x$name, sample, envir = envir)

  if (grepl("_f$|_f_", x$name, perl = TRUE)) {
    x_work <- get(sub("_f", "_w", x$name), envir = envir)
    y_work <- get(paste0(y, "_w"), envir = envir)
    y_free <- get(paste0(y, "_f"), envir = envir)

    for (i in seq(3)) { # Bias
      x_free <- get(x$name, envir = envir)

      check_w <- shush(hms::hms(shorter_int(x_work, y_work)))
      check_f <- shush(hms::hms(shorter_int(x_free, y_free)))
      if (check_f >= check_w) break

      sample <- cycle_time(
        sample_time(min = min, max = max, by = by, prob = prob),
        cycle = lubridate::ddays()
      )

      assign(x$name, sample, envir = envir)
    }
  }
}

sampler_4 <- function(x, by, envir) {
  classes <- c("character", "numeric", "Duration", "difftime", "hms")
  names <- c("name", "min", "max", "mean", "sd")

  checkmate::assert_list(x)
  checkmate::assert_names(names(x), identical.to = names)
  lapply(x, checkmate::assert_multi_class, classes = classes)

  classes <- c("numeric", "Duration", "difftime", "hms")
  checkmate::assert_multi_class(by, classes)
  checkmate::assert_environment(envir)

  min <- as.numeric(x$min)
  max <- as.numeric(x$max)
  mean <- as.numeric(x$mean)
  sd <- as.numeric(x$sd)
  by <- as.numeric(by)
  prob <- stats::dnorm(seq(min, max, by), mean = mean, sd = sd)
  sample <-
    sample_time(min = min, max = max, by = by, prob = prob) |>
    lubridate::as.duration()
  assign(x$name, sample, envir = envir)

  if (grepl("_f$|_f_", x$name, perl = TRUE)) {
    work <- get(sub("_f", "_w", x$name), envir = envir)

    for (j in seq(3)) { # Bias
      free <- get(x$name, envir = envir)
      if (free >= work) break

      sample <-
        sample_time(min = min, max = max, by = by, prob = prob) |>
        lubridate::as.duration()

      assign(x$name, sample, envir = envir)
    }
  }
}