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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/ #
# ==================================================================== #
#' Principal Component Analysis (for AMR)
#'
#' Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] containing [numeric] columns
#' @param ... columns of `x` to be selected for PCA, can be unquoted since it supports quasiquotation.
#' @inheritParams stats::prcomp
#' @details The [pca()] function takes a [data.frame] as input and performs the actual PCA with the \R function [prcomp()].
#'
#' The result of the [pca()] function is a [prcomp] object, with an additional attribute `non_numeric_cols` which is a vector with the column names of all columns that do not contain [numeric] values. These are probably the groups and labels, and will be used by [ggplot_pca()].
#' @return An object of classes [pca] and [prcomp]
#' @importFrom stats prcomp
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' # `example_isolates` is a data set available in the AMR package.
#' # See ?example_isolates.
#'
#' \donttest{
#' if (require("dplyr")) {
#' # calculate the resistance per group first
#' resistance_data <- example_isolates %>%
#' group_by(order = mo_order(mo), # group on anything, like order
#' genus = mo_genus(mo)) %>% # and genus as we do here;
#' summarise_if(is.rsi, resistance) # then get resistance of all drugs
#'
#' # now conduct PCA for certain antimicrobial agents
#' pca_result <- resistance_data %>%
#' pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
#'
#' pca_result
#' summary(pca_result)
#' biplot(pca_result)
#' ggplot_pca(pca_result) # a new and convenient plot function
#' }
#' }
pca <- function(x,
...,
retx = TRUE,
center = TRUE,
scale. = TRUE,
tol = NULL,
rank. = NULL) {
meet_criteria(x, allow_class = "data.frame")
meet_criteria(retx, allow_class = "logical", has_length = 1)
meet_criteria(center, allow_class = "logical", has_length = 1)
meet_criteria(scale., allow_class = "logical", has_length = 1)
meet_criteria(tol, allow_class = "numeric", has_length = 1, allow_NULL = TRUE)
meet_criteria(rank., allow_class = "numeric", has_length = 1, allow_NULL = TRUE)
# unset data.table, tibble, etc.
# also removes groups made by dplyr::group_by
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.bak <- x
# defuse R expressions, this replaces rlang::enquos()
dots <- substitute(list(...))
if (length(dots) > 1) {
new_list <- list(0)
for (i in seq_len(length(dots) - 1)) {
new_list[[i]] <- tryCatch(eval(dots[[i + 1]], envir = x),
error = function(e) stop(e$message, call. = FALSE))
if (length(new_list[[i]]) == 1) {
if (is.character(new_list[[i]]) & new_list[[i]] %in% colnames(x)) {
# this is to support quoted variables: df %pm>% pca("mycol1", "mycol2")
new_list[[i]] <- x[, new_list[[i]]]
} else {
# remove item - it's an argument like `center`
new_list[[i]] <- NULL
}
}
}
x <- as.data.frame(new_list, stringsAsFactors = FALSE)
if (any(vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y)))) {
warning_("in `pca()`: be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. See Examples in ?pca.", call = FALSE)
}
# set column names
tryCatch(colnames(x) <- as.character(dots)[2:length(dots)],
error = function(e) warning("column names could not be set"))
# keep only numeric columns
x <- x[, vapply(FUN.VALUE = logical(1), x, function(y) is.numeric(y))]
# bind the data set with the non-numeric columns
x <- cbind(x.bak[, vapply(FUN.VALUE = logical(1), x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
}
x <- pm_ungroup(x) # would otherwise select the grouping vars
x <- x[rowSums(is.na(x)) == 0, ] # remove columns containing NAs
pca_data <- x[, which(vapply(FUN.VALUE = logical(1), x, function(x) is.numeric(x)))]
message_("Columns selected for PCA: ", vector_and(font_bold(colnames(pca_data), collapse = NULL), quotes = TRUE),
". Total observations available: ", nrow(pca_data), ".")
if (getRversion() < "3.4.0") {
# stats::prcomp prior to 3.4.0 does not have the 'rank.' argument
pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol)
} else {
pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol, rank. = rank.)
}
groups <- x[, vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
rownames(groups) <- NULL
attr(pca_model, "non_numeric_cols") <- groups
class(pca_model) <- c("pca", class(pca_model))
pca_model
}
#' @method print pca
#' @export
#' @noRd
print.pca <- function(x, ...) {
a <- attributes(x)$non_numeric_cols
if (!is.null(a)) {
print_pca_group(a)
class(x) <- class(x)[class(x) != "pca"]
}
print(x, ...)
}
#' @method summary pca
#' @export
#' @noRd
summary.pca <- function(object, ...) {
a <- attributes(object)$non_numeric_cols
if (!is.null(a)) {
print_pca_group(a)
class(object) <- class(object)[class(object) != "pca"]
}
summary(object, ...)
}
print_pca_group <- function(a) {
grps <- sort(unique(a[, 1, drop = TRUE]))
cat("Groups (n=", length(grps), ", named as '", colnames(a)[1], "'):\n", sep = "")
print(grps)
cat("\n")
}