R/random-tidy-gamma.R

In spsanderson/TidyDensity: Functions for Tidy Analysis and Generation of Random Data

#' Tidy Randomly Generated Gamma Distribution Tibble
#'
#' @family Continuous Distribution
#' @family Gamma
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @seealso \url{https://www.statology.org/fit-gamma-distribution-to-dataset-in-r/}
#' @seealso \url{https://en.wikipedia.org/wiki/Gamma_distribution}
#'
#' @details This function uses the underlying `stats::rgamma()`, and its underlying
#' `p`, `d`, and `q` functions. For more information please see [stats::rgamma()]
#'
#' @description This function will generate `n` random points from a gamma
#' distribution with a user provided, `.shape`, `.scale`, and number of
#' random simulations to be produced. The function returns a tibble with the
#' simulation number column the x column which corresponds to the n randomly
#' generated points, the `d_`, `p_` and `q_` data points as well.
#'
#' The data is returned un-grouped.
#'
#' The columns that are output are:
#'
#' -  `sim_number` The current simulation number.
#' -  `x` The current value of `n` for the current simulation.
#' -  `y` The randomly generated data point.
#' -  `dx` The `x` value from the [stats::density()] function.
#' -  `dy` The `y` value from the [stats::density()] function.
#' -  `p` The values from the resulting p_ function of the distribution family.
#' -  `q` The values from the resulting q_ function of the distribution family.
#'
#' @param .n The number of randomly generated points you want.
#' @param .shape This is strictly 0 to infinity.
#' @param .scale The standard deviation of the randomly generated data. This is
#' strictly from 0 to infinity.
#' @param .num_sims The number of randomly generated simulations you want.
#' @param .return_tibble A logical value indicating whether to return the result
#' as a tibble. Default is TRUE.
#'
#' @examples
#' tidy_gamma()
#'
#' @return
#' A tibble of randomly generated data.
#'
#' @name tidy_gamma
NULL

#' @export
#' @rdname tidy_gamma

tidy_gamma <- function(.n = 50, .shape = 1, .scale = 0.3, .num_sims = 1,
                       .return_tibble = TRUE) {

  # Tidyeval ----
  n <- as.integer(.n)
  num_sims <- as.integer(.num_sims)
  shp <- .shape
  scle <- .scale
  ret_tbl <- as.logical(.return_tibble)

  # Checks ----
  if (!is.integer(n) | n < 0) {
    rlang::abort(
      "The parameters '.n' must be of class integer. Please pass a whole
            number like 50 or 100. It must be greater than 0."
    )
  }

  if (!is.integer(num_sims) | num_sims < 0) {
    rlang::abort(
      "The parameter `.num_sims' must be of class integer. Please pass a
            whole number like 50 or 100. It must be greater than 0."
    )
  }

  if (!is.numeric(shp) | shp < 0) {
    rlang::abort(
      "The parameters of '.shape' and '.scale' must be of class numeric.
            Please pass a numer like 1 or 1.1 etc. and must be greater than 0."
    )
  }

  if (!is.numeric(scle) | scle < 0) {
    rlang::abort(
      "The parameters of '.shape' and '.scale' must be of class numeric.
            Please pass a numer like 1 or 1.1 etc."
    )
  }

  x <- seq(1, num_sims, 1)

  qs <- seq(0, 1, (1 / (n - 1)))
  ps <- qs

  # Create a data.table with one row per simulation
  df <- data.table::CJ(sim_number = factor(1:num_sims), x = 1:n)

  # Group the data by sim_number and add columns for x and y
  df[, y := stats::rgamma(n = .N, shape = shp, scale = scle)]

  # Compute the density of the y values and add columns for dx and dy
  df[, c("dx", "dy") := density(y, n = n)[c("x", "y")], by = sim_number]

  # Compute the p-values for the y values and add a column for p
  df[, p := stats::pgamma(y, shape = shp, scale = scle)]

  # Compute the q-values for the p-values and add a column for q
  df[, p := stats::qgamma(p, shape = shp, scale = scle)]

  if(.return_tibble){
    df <- dplyr::as_tibble(df)
  } else {
    data.table::setkey(df, NULL)
  }

  # Create a tibble of the parameter grid
  param_grid <- dplyr::tibble(.shape, .scale)

  # Attach descriptive attributes to tibble
  attr(df, "distribution_family_type") <- "continuous"
  attr(df, ".shape") <- .shape
  attr(df, ".scale") <- .scale
  attr(df, ".n") <- .n
  attr(df, ".num_sims") <- .num_sims
  attr(df, ".ret_tbl") <- .return_tibble
  attr(df, "tibble_type") <- "tidy_gamma"
  attr(df, "ps") <- ps
  attr(df, "qs") <- qs
  attr(df, "param_grid") <- param_grid
  attr(df, "param_grid_txt") <- paste0(
    "c(",
    paste(param_grid[, names(param_grid)], collapse = ", "),
    ")"
  )
  attr(df, "dist_with_params") <- paste0(
    "Gamma",
    " ",
    paste0(
      "c(",
      paste(param_grid[, names(param_grid)], collapse = ", "),
      ")"
    )
  )

  # Return final result as function output
  return(df)
}