AMR/R/pca.R

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R
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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# #
# 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 more info: https://msberends.gitlab.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 Maturing lifecycle
#' @param x a [data.frame] containing numeric columns
#' @param ... columns of `x` to be selected for PCA
#' @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
#' @importFrom dplyr ungroup %>% filter_all all_vars
#' @importFrom rlang enquos eval_tidy
#' @export
#' @examples
#' # `example_isolates` is a dataset available in the AMR package.
#' # See ?example_isolates.
#'
#' # calculate the resistance per group first
#' library(dplyr)
#' 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) {
if (!is.data.frame(x)) {
stop("this function only takes a data.frame as input")
}
# unset data.table, tibble, etc.
# also removes groups made by dplyr::group_by
x <- as.data.frame(x, stringsAsFactors = FALSE)
x.bak <- x
user_exprs <- enquos(...)
if (length(user_exprs) > 0) {
new_list <- list(0)
for (i in seq_len(length(user_exprs))) {
new_list[[i]] <- tryCatch(eval_tidy(user_exprs[[i]], data = x),
error = function(e) stop(e$message, call. = FALSE))
if (length(new_list[[i]]) == 1) {
if (i == 1) {
# only for first item:
if (is.character(new_list[[i]]) & new_list[[i]] %in% colnames(x)) {
# this is to support: df %>% pca("mycol")
new_list[[i]] <- x[, new_list[[i]]]
}
} else {
# remove item - it's a parameter like `center`
new_list[[i]] <- NULL
}
}
}
x <- as.data.frame(new_list, stringsAsFactors = FALSE)
if (any(sapply(x, function(y) !is.numeric(y)))) {
warning("Be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. Please see Examples in ?pca.")
}
# set column names
tryCatch(colnames(x) <- sapply(user_exprs, function(y) as_label(y)),
error = function(e) warning("column names could not be set"))
# keep only numeric columns
x <- x[, sapply(x, function(y) is.numeric(y))]
# bind the data set with the non-numeric columns
x <- cbind(x.bak[, sapply(x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
}
x <- x %>%
ungroup() %>% # would otherwise select the grouping vars
filter_all(all_vars(!is.na(.)))
pca_data <- x[, which(sapply(x, function(x) is.numeric(x)))]
message(blue(paste0("NOTE: Columns selected for PCA: ", paste0(bold(colnames(pca_data)), collapse = "/"),
".\n Total observations available: ", nrow(pca_data), ".")))
pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol, rank. = rank.)
attr(pca_model, "non_numeric_cols") <- x[, sapply(x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
class(pca_model) <- c("pca", class(pca_model))
pca_model
}