# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 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 analysis: https://msberends.github.io/AMR/ # # ==================================================================== # #' PCA biplot with `ggplot2` #' #' Produces 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. #' @inheritSection lifecycle Maturing lifecycle #' @param x an object returned by [pca()], [prcomp()] or [princomp()] #' @inheritParams stats::biplot.prcomp #' @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()]. #' @param labels_textsize the size of the text used for the labels #' @param labels_text_placement adjustment factor the placement of the variable names (`>=1` means further away from the arrow head) #' @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()]. #' @param ellipse a logical to indicate whether a normal data ellipse should be drawn for each group (set with `groups`) #' @param ellipse_prob statistical size of the ellipse in normal probability #' @param ellipse_size the size of the ellipse line #' @param ellipse_alpha the alpha (transparency) of the ellipse line #' @param points_size the size of the points #' @param points_alpha the alpha (transparency) of the points #' @param arrows a logical to indicate whether arrows should be drawn #' @param arrows_textsize the size of the text for variable names #' @param arrows_colour the colour of the arrow and their text #' @param arrows_size the size (thickness) of the arrow lines #' @param arrows_textsize the size of the text at the end of the arrows #' @param arrows_textangled a logical whether the text at the end of the arrows should be angled #' @param arrows_alpha the alpha (transparency) of the arrows and their text #' @param base_textsize the text size for all plot elements except the labels and arrows #' @param ... Parameters passed on to functions #' @source The [ggplot_pca()] function is based on the `ggbiplot()` function from the `ggbiplot` package by Vince Vu, as found on GitHub: (retrieved: 2 March 2020, their latest commit: [`7325e88`](https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9); 12 February 2015). #' #' As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were: #' 1. Rewritten code to remove the dependency on packages `plyr`, `scales` and `grid` #' 2. Parametrised more options, like arrow and ellipse settings #' 3. Hardened all input possibilities by defining the exact type of user input for every parameter #' 4. Added total amount of explained variance as a caption in the plot #' 5. Cleaned all syntax based on the `lintr` package, fixed grammatical errors and added integrity checks #' 6. Updated documentation #' @details The colours for labels and points can be changed by adding another scale layer for colour, like `scale_colour_viridis_d()` or `scale_colour_brewer()`. #' @rdname ggplot_pca #' @export #' @examples #' # `example_isolates` is a dataset available in the AMR package. #' # See ?example_isolates. #' #' # See ?pca for more info about Principal Component Analysis (PCA). #' if (require("dplyr")) { #' pca_model <- example_isolates %>% #' filter(mo_genus(mo) == "Staphylococcus") %>% #' group_by(species = mo_shortname(mo)) %>% #' summarise_if (is.rsi, resistance) %>% #' pca(FLC, AMC, CXM, GEN, TOB, TMP, SXT, CIP, TEC, TCY, ERY) #' #' # old (base R) #' biplot(pca_model) #' #' # new #' ggplot_pca(pca_model) #' #' if (require("ggplot2")) { #' ggplot_pca(pca_model) + #' scale_colour_viridis_d() + #' labs(title = "Title here") #' } #' } ggplot_pca <- function(x, choices = 1:2, scale = 1, pc.biplot = TRUE, labels = NULL, labels_textsize = 3, labels_text_placement = 1.5, groups = NULL, ellipse = TRUE, ellipse_prob = 0.68, ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2, points_alpha = 0.25, arrows = TRUE, arrows_colour = "darkblue", arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE, arrows_alpha = 0.75, base_textsize = 10, ...) { stop_ifnot_installed("ggplot2") meet_criteria(x, allow_class = c("prcomp", "princomp", "PCA", "lda")) meet_criteria(choices, allow_class = c("numeric", "integer"), has_length = 2) meet_criteria(scale, allow_class = c("numeric", "integer", "logical"), has_length = 1) meet_criteria(pc.biplot, allow_class = "logical", has_length = 1) meet_criteria(labels, allow_class = "character", allow_NULL = TRUE) meet_criteria(labels_textsize, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(labels_text_placement, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(groups, allow_class = "character", allow_NULL = TRUE) meet_criteria(ellipse, allow_class = "logical", has_length = 1) meet_criteria(ellipse_prob, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(ellipse_size, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(ellipse_alpha, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(points_size, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(points_alpha, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(arrows, allow_class = "logical", has_length = 1) meet_criteria(arrows_colour, allow_class = "character", has_length = 1) meet_criteria(arrows_size, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(arrows_textsize, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(arrows_textangled, allow_class = "logical", has_length = 1) meet_criteria(arrows_alpha, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(base_textsize, allow_class = c("numeric", "integer"), has_length = 1) calculations <- pca_calculations(pca_model = x, groups = groups, groups_missing = missing(groups), labels = labels, labels_missing = missing(labels), choices = choices, scale = scale, pc.biplot = pc.biplot, ellipse_prob = ellipse_prob, labels_text_placement = labels_text_placement) choices <- calculations$choices df.u <- calculations$df.u df.v <- calculations$df.v ell <- calculations$ell groups <- calculations$groups group_name <- calculations$group_name labels <- calculations$labels # Append the proportion of explained variance to the axis labels if ((1 - as.integer(scale)) == 0) { u.axis.labs <- paste0("Standardised PC", choices) } else { u.axis.labs <- paste0("PC", choices) } u.axis.labs <- paste0(u.axis.labs, paste0("\n(explained var: ", percentage(x$sdev[choices] ^ 2 / sum(x$sdev ^ 2)), ")")) # Score Labels if (!is.null(labels)) { df.u$labels <- labels } # Grouping variable if (!is.null(groups)) { df.u$groups <- groups } # Base plot g <- ggplot2::ggplot(data = df.u, ggplot2::aes(x = xvar, y = yvar)) + ggplot2::xlab(u.axis.labs[1]) + ggplot2::ylab(u.axis.labs[2]) + ggplot2::expand_limits(x = c(-1.15, 1.15), y = c(-1.15, 1.15)) # Draw either labels or points if (!is.null(df.u$labels)) { if (!is.null(df.u$groups)) { g <- g + ggplot2::geom_point(ggplot2::aes(colour = groups), alpha = points_alpha, size = points_size) + ggplot2::geom_text(ggplot2::aes(label = labels, colour = groups), nudge_y = -0.05, size = labels_textsize) + ggplot2::labs(colour = group_name) } else { g <- g + ggplot2::geom_point(alpha = points_alpha, size = points_size) + ggplot2::geom_text(ggplot2::aes(label = labels), nudge_y = -0.05, size = labels_textsize) } } else { if (!is.null(df.u$groups)) { g <- g + ggplot2::geom_point(ggplot2::aes(colour = groups), alpha = points_alpha, size = points_size) + ggplot2::labs(colour = group_name) } else { g <- g + ggplot2::geom_point(alpha = points_alpha, size = points_size) } } # Overlay a concentration ellipse if there are groups if (!is.null(df.u$groups) & !is.null(ell) & isTRUE(ellipse)) { g <- g + ggplot2::geom_path(data = ell, ggplot2::aes(colour = groups, group = groups), size = ellipse_size, alpha = points_alpha) } # Label the variable axes if (arrows == TRUE) { g <- g + ggplot2::geom_segment(data = df.v, ggplot2::aes(x = 0, y = 0, xend = xvar, yend = yvar), arrow = ggplot2::arrow(length = ggplot2::unit(0.5, "picas"), angle = 20, ends = "last", type = "open"), colour = arrows_colour, size = arrows_size, alpha = arrows_alpha) if (arrows_textangled == TRUE) { g <- g + ggplot2::geom_text(data = df.v, ggplot2::aes(label = varname, x = xvar, y = yvar, angle = angle, hjust = hjust), colour = arrows_colour, size = arrows_textsize, alpha = arrows_alpha) } else { g <- g + ggplot2::geom_text(data = df.v, ggplot2::aes(label = varname, x = xvar, y = yvar, hjust = hjust), colour = arrows_colour, size = arrows_textsize, alpha = arrows_alpha) } } # Add caption label about total explained variance g <- g + ggplot2::labs(caption = paste0("Total explained variance: ", percentage(sum(x$sdev[choices] ^ 2 / sum(x$sdev ^ 2))))) # mark-up nicely g <- g + ggplot2::theme_minimal(base_size = base_textsize) + ggplot2::theme(panel.grid.major = ggplot2::element_line(colour = "grey85"), panel.grid.minor = ggplot2::element_blank(), # centre title and subtitle plot.title = ggplot2::element_text(hjust = 0.5), plot.subtitle = ggplot2::element_text(hjust = 0.5)) g } #' @importFrom stats qchisq var pca_calculations <- function(pca_model, groups = NULL, groups_missing = TRUE, labels = NULL, labels_missing = TRUE, choices = 1:2, scale = 1, pc.biplot = TRUE, ellipse_prob = 0.68, labels_text_placement = 1.5) { non_numeric_cols <- attributes(pca_model)$non_numeric_cols if (groups_missing) { groups <- tryCatch(non_numeric_cols[[1]], error = function(e) NULL) group_name <- tryCatch(colnames(non_numeric_cols[1]), error = function(e) NULL) } if (labels_missing) { labels <- tryCatch(non_numeric_cols[[2]], error = function(e) NULL) } if (!is.null(groups) & is.null(labels)) { # turn them around labels <- groups groups <- NULL group_name <- NULL } # Recover the SVD if (inherits(pca_model, "prcomp")) { nobs.factor <- sqrt(nrow(pca_model$x) - 1) d <- pca_model$sdev u <- sweep(pca_model$x, 2, 1 / (d * nobs.factor), FUN = "*") v <- pca_model$rotation } else if (inherits(pca_model, "princomp")) { nobs.factor <- sqrt(pca_model$n.obs) d <- pca_model$sdev u <- sweep(pca_model$scores, 2, 1 / (d * nobs.factor), FUN = "*") v <- pca_model$loadings } else if (inherits(pca_model, "PCA")) { nobs.factor <- sqrt(nrow(pca_model$call$X)) d <- unlist(sqrt(pca_model$eig)[1]) u <- sweep(pca_model$ind$coord, 2, 1 / (d * nobs.factor), FUN = "*") v <- sweep(pca_model$var$coord, 2, sqrt(pca_model$eig[seq_len(ncol(pca_model$var$coord)), 1]), FUN = "/") } else if (inherits(pca_model, "lda")) { nobs.factor <- sqrt(pca_model$N) d <- pca_model$svd u <- predict(pca_model)$x / nobs.factor v <- pca_model$scaling d.total <- sum(d ^ 2) } else { stop("Expected an object of class prcomp, princomp, PCA, or lda") } # Scores choices <- pmin(choices, ncol(u)) obs.scale <- 1 - as.integer(scale) df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*")) # Directions v <- sweep(v, 2, d ^ as.integer(scale), FUN = "*") df.v <- as.data.frame(v[, choices]) names(df.u) <- c("xvar", "yvar") names(df.v) <- names(df.u) if (isTRUE(pc.biplot)) { df.u <- df.u * nobs.factor } # Scale the radius of the correlation circle so that it corresponds to # a data ellipse for the standardized PC scores circle_prob <- 0.69 r <- sqrt(qchisq(circle_prob, df = 2)) * prod(colMeans(df.u ^ 2)) ^ (0.25) # Scale directions v.scale <- rowSums(v ^ 2) df.v <- r * df.v / sqrt(max(v.scale)) # Grouping variable if (!is.null(groups)) { df.u$groups <- groups } df.v$varname <- rownames(v) # Variables for text label placement df.v$angle <- with(df.v, (180 / pi) * atan(yvar / xvar)) df.v$hjust <- with(df.v, (1 - labels_text_placement * sign(xvar)) / 2) if (!is.null(df.u$groups)) { theta <- c(seq(-pi, pi, length = 50), seq(pi, -pi, length = 50)) circle <- cbind(cos(theta), sin(theta)) df.groups <- lapply(unique(df.u$groups), function(g, df = df.u) { x <- df[which(df$groups == g), , drop = FALSE] if (nrow(x) <= 2) { return(data.frame(X1 = numeric(0), X2 = numeric(0), groups = character(0))) } sigma <- var(cbind(x$xvar, x$yvar)) mu <- c(mean(x$xvar), mean(x$yvar)) ed <- sqrt(qchisq(ellipse_prob, df = 2)) data.frame(sweep(circle %*% chol(sigma) * ed, MARGIN = 2, STATS = mu, FUN = "+"), groups = x$groups[1], stringsAsFactors = FALSE) }) ell <- do.call(rbind, df.groups) if (NROW(ell) == 0) { ell <- NULL } else { names(ell)[1:2] <- c("xvar", "yvar") } } else { ell <- NULL } list(choices = choices, df.u = df.u, df.v = df.v, ell = ell, groups = groups, group_name = group_name, labels = labels ) }