mirror of https://github.com/msberends/AMR.git
350 lines
15 KiB
R
350 lines
15 KiB
R
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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#' PCA biplot with `ggplot2`
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#'
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#' This function is to produce a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
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#' @inheritSection lifecycle Maturing lifecycle
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#' @param x an object returned by [pca()], [prcomp()] or [princomp()]
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#' @inheritParams stats::biplot.prcomp
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#' @param labels an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].
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#' @param labels_textsize the size of the text used for the labels
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#' @param labels_text_placement adjustment factor the placement of the variable names (`>=1` means further away from the arrow head)
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#' @param groups an optional vector of groups for the labels, with the same length as `labels`. If set, the points and labels will be coloured according to these groups. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].
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#' @param ellipse a logical to indicate whether a normal data ellipse should be drawn for each group (set with `groups`)
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#' @param ellipse_prob statistical size of the ellipse in normal probability
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#' @param ellipse_size the size of the ellipse line
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#' @param ellipse_alpha the alpha (transparency) of the ellipse line
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#' @param points_alpha the alpha (transparency) of the points
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#' @param arrows a logical to indicate whether arrows should be drawn
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#' @param arrows_textsize the size of the text for variable names
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#' @param arrows_colour the colour of the arrow and their text
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#' @param arrows_size the size (thickness) of the arrow lines
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#' @param arrows_textsize the size of the text at the end of the arrows
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#' @param arrows_alpha the alpha (transparency) of the arrows and their text
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#' @param base_textsize the text size for all plot elements except the labels and arrows
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#' @param ... Parameters passed on to functions
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#' @source The [ggplot_pca()] function is based on the [ggbiplot()] function from the `ggbiplot` package by Vince Vu, as found on GitHub: <https://github.com/vqv/ggbiplot> (retrieved: 2 March 2020, their latest commit: [`7325e88`](https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9); 12 February 2015).
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#'
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#' As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:
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#' 1. Rewritten code to remove the dependency on packages `plyr`, `scales` and `grid`
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#' 2. Parametrised more options, like arrow and ellipse settings
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#' 3. Added total amount of explained variance as a caption in the plot
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#' 4. Cleaned all syntax based on the `lintr` package
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#' 5. Updated documentation
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#' @details The default colours for labels and points is set with [scale_colour_viridis_d()], but these can be changed by adding another scale for colour, like [scale_colour_brewer()].
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#' @rdname ggplot_pca
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#' @export
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#' @examples
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#' # `example_isolates` is a dataset available in the AMR package.
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#' # See ?example_isolates.
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#'
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#' # See ?pca for more info about Principal Component Analysis (PCA).
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#' library(dplyr)
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#' pca_model <- example_isolates %>%
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#' filter(mo_genus(mo) == "Staphylococcus") %>%
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#' group_by(species = mo_shortname(mo)) %>%
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#' summarise_if (is.rsi, resistance) %>%
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#' pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY)
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#'
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#' # old
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#' biplot(pca_model)
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#'
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#' # new
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#' ggplot_pca(pca_model)
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ggplot_pca <- function(x,
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choices = 1:2,
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scale = TRUE,
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labels = NULL,
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labels_textsize = 3,
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labels_text_placement = 1.5,
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groups = NULL,
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ellipse = FALSE,
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ellipse_prob = 0.68,
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ellipse_size = 0.5,
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ellipse_alpha = 0.25,
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points_size = 2,
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points_alpha = 0.25,
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arrows = TRUE,
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arrows_colour = "darkblue",
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arrows_size = 0.5,
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arrows_textsize = 3,
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arrows_alpha = 0.75,
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base_textsize = 10,
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...) {
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stopifnot_installed_package("ggplot2")
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calculations <- pca_calculations(pca_model = x,
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groups = groups,
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groups_missing = missing(groups),
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labels = labels,
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labels_missing = missing(labels),
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choices = choices,
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scale = scale,
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ellipse_prob = ellipse_prob,
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labels_text_placement = labels_text_placement)
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nobs.factor <- calculations$nobs.factor
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d <- calculations$d
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u <- calculations$u
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v <- calculations$v
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choices <- calculations$choices
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df.u <- calculations$df.u
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df.v <- calculations$df.v
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r <- calculations$r
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ell <- calculations$ell
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groups <- calculations$groups
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group_name <- calculations$group_name
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labels <- calculations$labels
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stopifnot(length(choices) == 2)
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# Append the proportion of explained variance to the axis labels
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if ((1 - as.integer(scale)) == 0) {
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u.axis.labs <- paste("Standardised PC", choices, sep = "")
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} else {
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u.axis.labs <- paste("PC", choices, sep = "")
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}
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u.axis.labs <- paste(u.axis.labs,
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paste0("\n(explained var: ",
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percentage(x$sdev[choices] ^ 2 / sum(x$sdev ^ 2)), ")"))
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# Score Labels
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if (!is.null(labels)) {
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df.u$labels <- labels
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}
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# Grouping variable
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if (!is.null(groups)) {
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df.u$groups <- groups
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}
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# Base plot
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g <- ggplot2::ggplot(data = df.u,
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ggplot2::aes(x = xvar, y = yvar)) +
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ggplot2::xlab(u.axis.labs[1]) +
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ggplot2::ylab(u.axis.labs[2]) +
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ggplot2::expand_limits(x = c(-1.15, 1.15),
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y = c(-1.15, 1.15))
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# Draw either labels or points
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if (!is.null(df.u$labels)) {
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if (!is.null(df.u$groups)) {
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g <- g +
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ggplot2::geom_point(ggplot2::aes(colour = groups),
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alpha = points_alpha,
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size = points_size) +
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ggplot2::geom_text(ggplot2::aes(label = labels, colour = groups),
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nudge_y = -0.05,
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size = labels_textsize) +
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ggplot2::scale_colour_viridis_d() +
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ggplot2::labs(colour = group_name)
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} else {
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g <- g +
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ggplot2::geom_point(alpha = points_alpha,
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size = points_size) +
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ggplot2::geom_text(ggplot2::aes(label = labels),
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nudge_y = -0.05,
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size = labels_textsize)
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}
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} else {
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if (!is.null(df.u$groups)) {
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g <- g +
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ggplot2::geom_point(ggplot2::aes(colour = groups),
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alpha = points_alpha,
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size = points_size) +
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ggplot2::scale_colour_viridis_d() +
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ggplot2::labs(colour = group_name)
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} else {
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g <- g + ggplot2::geom_point(alpha = points_alpha,
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size = points_size)
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}
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}
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# Overlay a concentration ellipse if there are groups
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if (!is.null(df.u$groups) & isTRUE(ellipse)) {
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g <- g +
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ggplot2::geom_path(data = ell,
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ggplot2::aes(colour = groups, group = groups),
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size = ellipse_size,
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alpha = points_alpha)
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}
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# Label the variable axes
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if (arrows == TRUE) {
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g <- g +
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ggplot2::geom_segment(data = df.v,
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ggplot2::aes(x = 0, y = 0, xend = xvar, yend = yvar),
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arrow = ggplot2::arrow(length = ggplot2::unit(0.5, "picas"),
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angle = 20,
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ends = "last",
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type = "open"),
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colour = arrows_colour,
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size = arrows_size,
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alpha = arrows_alpha) +
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ggplot2::geom_text(data = df.v,
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ggplot2::aes(label = varname, x = xvar, y = yvar, angle = angle, hjust = hjust),
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colour = arrows_colour,
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size = arrows_textsize,
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alpha = arrows_alpha)
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}
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# Add caption label about total explained variance
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g <- g + ggplot2::labs(caption = paste0("Total explained variance: ",
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percentage(sum(x$sdev[choices] ^ 2 / sum(x$sdev ^ 2)))))
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# mark-up nicely
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g <- g + ggplot2::theme_minimal(base_size = base_textsize) +
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ggplot2::theme(panel.grid.major = ggplot2::element_line(colour = "grey85"),
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panel.grid.minor = ggplot2::element_blank(),
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# centre title and subtitle
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plot.title = ggplot2::element_text(hjust = 0.5),
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plot.subtitle = ggplot2::element_text(hjust = 0.5))
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g
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}
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#' @importFrom dplyr bind_rows
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pca_calculations <- function(pca_model,
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groups = NULL,
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groups_missing = TRUE,
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labels = NULL,
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labels_missing = TRUE,
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choices = 1:2,
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scale = 1,
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ellipse_prob = 0.68,
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labels_text_placement = 1.5) {
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non_numeric_cols <- attributes(pca_model)$non_numeric_cols
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if (groups_missing) {
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groups <- tryCatch(non_numeric_cols[[1]],
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error = function(e) NULL)
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group_name <- tryCatch(colnames(non_numeric_cols[1]),
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error = function(e) NULL)
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}
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if (labels_missing) {
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labels <- tryCatch(non_numeric_cols[[2]],
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error = function(e) NULL)
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}
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if (!is.null(groups) & is.null(labels)) {
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# turn them around
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labels <- groups
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groups <- NULL
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group_name <- NULL
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}
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# Recover the SVD
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if (inherits(pca_model, "prcomp")) {
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nobs.factor <- sqrt(nrow(pca_model$x) - 1)
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d <- pca_model$sdev
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u <- sweep(pca_model$x, 2, 1 / (d * nobs.factor), FUN = "*")
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v <- pca_model$rotation
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} else if (inherits(pca_model, "princomp")) {
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nobs.factor <- sqrt(pca_model$n.obs)
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d <- pca_model$sdev
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u <- sweep(pca_model$scores, 2, 1 / (d * nobs.factor), FUN = "*")
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v <- pca_model$loadings
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} else if (inherits(pca_model, "PCA")) {
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nobs.factor <- sqrt(nrow(pca_model$call$X))
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d <- unlist(sqrt(pca_model$eig)[1])
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u <- sweep(pca_model$ind$coord, 2, 1 / (d * nobs.factor), FUN = "*")
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v <- sweep(pca_model$var$coord, 2, sqrt(pca_model$eig[seq_len(ncol(pca_model$var$coord)), 1]), FUN = "/")
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} else if (inherits(pca_model, "lda")) {
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nobs.factor <- sqrt(pca_model$N)
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d <- pca_model$svd
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u <- predict(pca_model)$x / nobs.factor
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v <- pca_model$scaling
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d.total <- sum(d ^ 2)
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} else {
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stop("Expected a object of class prcomp, princomp, PCA, or lda")
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}
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# Scores
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choices <- pmin(choices, ncol(u))
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obs.scale <- 1 - as.integer(scale)
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df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*"))
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# Directions
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v <- sweep(v, 2, d ^ as.integer(scale), FUN = "*")
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df.v <- as.data.frame(v[, choices])
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names(df.u) <- c("xvar", "yvar")
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names(df.v) <- names(df.u)
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df.u <- df.u * nobs.factor
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# Scale the radius of the correlation circle so that it corresponds to
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# a data ellipse for the standardized PC scores
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circle_prob <- 0.69
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r <- sqrt(qchisq(circle_prob, df = 2)) * prod(colMeans(df.u ^ 2)) ^ (0.25)
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# Scale directions
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v.scale <- rowSums(v ^ 2)
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df.v <- r * df.v / sqrt(max(v.scale))
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# Grouping variable
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if (!is.null(groups)) {
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df.u$groups <- groups
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}
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df.v$varname <- rownames(v)
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# Variables for text label placement
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df.v$angle <- with(df.v, (180 / pi) * atan(yvar / xvar))
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df.v$hjust <- with(df.v, (1 - labels_text_placement * sign(xvar)) / 2)
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if (!is.null(df.u$groups)) {
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theta <<- c(seq(-pi, pi, length = 50), seq(pi, -pi, length = 50))
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circle <<- cbind(cos(theta), sin(theta))
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ell <- bind_rows(
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sapply(unique(df.u$groups), function(g, df = df.u) {
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x <- df[which(df$groups == g), , drop = FALSE]
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if (nrow(x) <= 2) {
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return(NULL)
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}
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sigma <- var(cbind(x$xvar, x$yvar))
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mu <- c(mean(x$xvar), mean(x$yvar))
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ed <- sqrt(qchisq(ellipse_prob, df = 2))
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data.frame(sweep(circle %*% chol(sigma) * ed, 2, mu, FUN = "+"),
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groups = x$groups[1])
|
||
|
|
}))
|
||
|
|
names(ell)[1:2] <- c("xvar", "yvar")
|
||
|
|
} else {
|
||
|
|
ell <- NULL
|
||
|
|
}
|
||
|
|
|
||
|
|
list(nobs.factor = nobs.factor,
|
||
|
|
d = d,
|
||
|
|
u = u,
|
||
|
|
v = v,
|
||
|
|
choices = choices,
|
||
|
|
df.u = df.u,
|
||
|
|
df.v = df.v,
|
||
|
|
r = r,
|
||
|
|
ell = ell,
|
||
|
|
groups = groups,
|
||
|
|
group_name = group_name,
|
||
|
|
labels = labels
|
||
|
|
)
|
||
|
|
|
||
|
|
}
|