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# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
# 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/ #
# ==================================================================== #
#' 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.
#' @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 ... arguments 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: <https://github.com/vqv/ggbiplot> (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 argument
#' 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, such as `scale_colour_viridis_d()` and `scale_colour_brewer()`.
#' @rdname ggplot_pca
#' @export
#' @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
#'
#' # now conduct PCA for certain antimicrobial drugs
#' pca_result <- resistance_data %>%
#' pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
#'
#' summary(pca_result)
#'
#' # old base R plotting method:
#' biplot(pca_result)
#'
#' # new ggplot2 plotting method using this package:
#' if (require("ggplot2")) {
#' ggplot_pca(pca_result)
#'
#' # still extendible with any ggplot2 function
#' ggplot_pca(pca_result) +
#' 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, is_positive = TRUE, is_finite = TRUE)
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, is_positive = TRUE, is_finite = TRUE)
meet_criteria(labels_text_placement, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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, is_positive = TRUE, is_finite = TRUE)
meet_criteria(ellipse_size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(ellipse_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(points_size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(points_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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, is_positive = TRUE, is_finite = TRUE)
meet_criteria(arrows_textsize, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(arrows_textangled, allow_class = "logical", has_length = 1)
meet_criteria(arrows_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(base_textsize, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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
} 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 = "*"),
stringsAsFactors = FALSE
)
# Directions
v <- sweep(v, 2, d^as.integer(scale), FUN = "*")
df.v <- as.data.frame(v[, choices],
stringsAsFactors = FALSE
)
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$groups == g), , drop = FALSE]
if (nrow(x) <= 2) {
return(data.frame(
X1 = numeric(0),
X2 = numeric(0),
groups = character(0),
stringsAsFactors = FALSE
))
}
sigma <- var(cbind(x$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
)
}
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