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 `# ==================================================================== #` `# TITLE #` `# Antimicrobial Resistance (AMR) Data Analysis for R #` `# #` `# SOURCE #` `# https://github.com/msberends/AMR #` `# #` `# LICENCE #` `# (c) 2018-2022 Berends MS, Luz CF et al. #` `# Developed at the University of Groningen, the Netherlands, in #` `# collaboration with non-profit organisations Certe Medical #` `# Diagnostics & Advice, and University Medical Center Groningen. # ` `# #` `# 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 the full manual and a complete tutorial about #` `# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #` `# ==================================================================== #` `#' Skewness of the Sample` `#'` `#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.` `#'` `#' When negative ('left-skewed'): the left tail is longer; the mass of the distribution is concentrated on the right of a histogram. When positive ('right-skewed'): the right tail is longer; the mass of the distribution is concentrated on the left of a histogram. A normal distribution has a skewness of 0.` `#' @inheritSection lifecycle Stable Lifecycle` `#' @param x a vector of values, a [matrix] or a [data.frame]` `#' @param na.rm a [logical] value indicating whether `NA` values should be stripped before the computation proceeds` `#' @seealso [kurtosis()]` `#' @rdname skewness` `#' @inheritSection AMR Read more on Our Website!` `#' @export` `skewness <- function(x, na.rm = FALSE) {` ` meet_criteria(na.rm, allow_class = "logical", has_length = 1)` ` UseMethod("skewness")` `}` `#' @method skewness default` `#' @rdname skewness` `#' @export` `skewness.default <- function(x, na.rm = FALSE) {` ` meet_criteria(na.rm, allow_class = "logical", has_length = 1)` ` x <- as.vector(x)` ` if (na.rm == TRUE) {` ` x <- x[!is.na(x)]` ` }` ` n <- length(x)` ` (sum((x - mean(x))^3) / n) / (sum((x - mean(x)) ^ 2) / n) ^ (3 / 2)` `}` `#' @method skewness matrix` `#' @rdname skewness` `#' @export` `skewness.matrix <- function(x, na.rm = FALSE) {` ` meet_criteria(na.rm, allow_class = "logical", has_length = 1)` ` apply(x, 2, skewness.default, na.rm = na.rm)` `}` `#' @method skewness data.frame` `#' @rdname skewness` `#' @export` `skewness.data.frame <- function(x, na.rm = FALSE) {` ` meet_criteria(na.rm, allow_class = "logical", has_length = 1)` ` vapply(FUN.VALUE = double(1), x, skewness.default, na.rm = na.rm)` ```} ``` ``` ```