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styled, unit test fix

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dr. M.S. (Matthijs) Berends
2022-08-28 10:31:50 +02:00
parent 4cb1db4554
commit 4d050aef7c
147 changed files with 10897 additions and 8169 deletions
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68
R/pca.R
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@ -9,7 +9,7 @@
# (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. #
# 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 #
@ -24,42 +24,44 @@
# ==================================================================== #
#' 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.
#' @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
#' @examples
#' @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;
#' filter(n() >= 30) %>% # filter on only 30 results per group
#' summarise_if(is.rsi, resistance) # then get resistance of all drugs
#'
#' # 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;
#' filter(n() >= 30) %>% # filter on only 30 results per group
#' 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 <- resistance_data %>%
#' pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
#'
#' pca_result
#' summary(pca_result)
#'
#'
#' # old base R plotting method:
#' biplot(pca_result)
#' # new ggplot2 plotting method using this package:
#' ggplot_pca(pca_result)
#'
#'
#' if (require("ggplot2")) {
#' ggplot_pca(pca_result) +
#' scale_colour_viridis_d() +
@ -70,7 +72,7 @@
pca <- function(x,
...,
retx = TRUE,
center = TRUE,
center = TRUE,
scale. = TRUE,
tol = NULL,
rank. = NULL) {
@ -80,19 +82,20 @@ pca <- function(x,
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$message, call. = FALSE))
error = function(e) stop(e$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")
@ -103,30 +106,33 @@ pca <- function(x,
}
}
}
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"))
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)), drop = FALSE]
# 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 <- 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))), drop = FALSE]
message_("Columns selected for PCA: ", vector_and(font_bold(colnames(pca_data), collapse = NULL), quotes = TRUE),
". Total observations available: ", nrow(pca_data), ".")
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)