R/vec-kurtosis.R

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

#' Compute Kurtosis of a Vector
#'
#' @family Vector Function
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @family Statistic
#' @family Vector Function
#'
#' @details
#' A function to return the kurtosis of a vector.
#'
#' @seealso \url{https://en.wikipedia.org/wiki/Kurtosis}
#'
#' @description
#' This function takes in a vector as it's input and will return the kurtosis
#' of that vector. The length of this vector must be at least four numbers. The
#' kurtosis explains the sharpness of the peak of a distribution of data.
#'
#' `((1/n) * sum(x - mu})^4) / ((()1/n) * sum(x - mu)^2)^2`
#'
#' @param .x A numeric vector of length four or more.
#'
#' @examples
#' tidy_kurtosis_vec(rnorm(100, 3, 2))
#'
#' @return
#' The kurtosis of a vector
#'
#' @export
#'

tidy_kurtosis_vec <- function(.x) {

  # Tidyeval ----
  x_term <- .x

  # Checks ----
  if (!is.numeric(x_term)) {
    stop(call. = FALSE, ".x must be a numeric vector.")
  }

  if (length(x_term) < 4) {
    stop(call. = FALSE, ".x must be a numeric vector of 4 or more.")
  }

  # Calculate
  n <- length(x_term)
  mu <- mean(x_term, na.rm = TRUE)
  n_diff <- (x_term - mu)^4
  nu <- (1 / n * sum(n_diff))
  d_diff <- (x_term - mu)^2
  de <- (1 / n * sum(d_diff))^2
  k <- nu / de
  return(k)
  print(k)
}