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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)
}