Nothing
R/plotVar.R
In diversityForest: Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling
Defines functions
plotVarBoxplot
plotVarDensity
plotVar
Documented in
plotVar
# -------------------------------------------------------------------------------
# This file is part of 'diversityForest'.
#
# 'diversityForest' is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# 'diversityForest' is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with 'diversityForest'. If not, see <http://www.gnu.org/licenses/>.
#
# NOTE: 'diversityForest' is a fork of the popular R package 'ranger', written by Marvin N. Wright.
# Most R and C++ code is identical with that of 'ranger'. The package 'diversityForest'
# was written by taking the original 'ranger' code and making any
# changes necessary to implement diversity forests.
#
# -------------------------------------------------------------------------------
##' This function allows to visualise the (estimated) distributions of a variable \code{x} for each of the categories of a categorical variable \code{y}.
##' This allows to study the dependency structure of \code{y} on \code{x}.
##' Two types of visualisations are available: density plots and boxplots.
##'
##' See the 'Details' section of \code{\link{plotMcl}}.
##'
##' @title Plot of the (estimated) dependency structure of a variable \code{x} on a categorical variable \code{y}
##' @param x Metric variable or ordered categorical variable that has at least as many unique values as \code{y}
##' @param y Factor variable with at least three categories.
##' @param plot_type Plot type, one of the following: "both" (the default), "density", "boxplot". If "density", a \code{"density"} plot is produced, if "boxplot", a \code{"boxplot"} is produced, and if "both", both a \code{"density"} plot and a \code{"boxplot"} are produced. See the 'Details' section of \code{\link{plotMcl}} for details.
##' @param x_label Optional. The label of the x-axis.
##' @param y_label Optional. The label (heading) of the legend that differentiates the categories of \code{y}.
##' @param plot_title Optional. The title of the plot.
##' @param plotit This states whether the plots are actually plotted or merely returned as \code{ggplot} objects. Default is \code{TRUE}.
##' @return A list returned invisibly containing:
##' \itemize{
##' \item Only the element \code{dens_pl} if \code{plot_type = "density"};
##' \item Only the element \code{boxplot_pl} if \code{plot_type = "boxplot"};
##' \item The elements \code{dens_pl}, \code{boxplot_pl}, and \code{combined_pl} if \code{plot_type = "both"}.
##' }
##' All returned plots are \code{ggplot2} objects, with \code{combined_pl} being a \code{patchwork} object.
##' @examples
##' \dontrun{
##'
##' ## Load package:
##'
##' library("diversityForest")
##'
##'
##'
##' ## Load the "ctg" data set:
##'
##' data(ctg)
##'
##'
##' ## Set seed to make results reproducible (this is necessary because
##' ## the rug plot produced by 'plotVar' does not show all observations, but
##' ## only a random subset of 1000 observations):
##'
##' set.seed(1234)
##'
##'
##' ## Using a "density" plot and a "boxplot", visualise the (estimated)
##' ## distributions of the variable "Mean" for each of the categories of the
##' # variable "Tendency":
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency)
##'
##'
##' ## Re-create this plot with labels:
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency, x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'")
##'
##'
##' ## Re-create this plot, but only show the "density" plot:
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency, plot_type = "density",
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'")
##'
##'
##' ## Use ggplot2 and RColorBrewer functionalities to change the line colors and
##' ## the labels of the categories of "Tendency":
##'
##' library("ggplot2")
##' library("RColorBrewer")
##' p <- plotVar(x = ctg$Mean, y = ctg$Tendency, plot_type = "density",
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'",
##' plotit = FALSE)$dens_pl +
##' scale_color_manual(values = brewer.pal(n = 3, name = "Set2"),
##' labels = c("left asymmetric", "symmetric",
##' "right asymmetric")) +
##' scale_linetype_manual(values = rep(1, 3),
##' labels = c("left asymmetric", "symmetric",
##' "right asymmetric"))
##'
##' p
##'
##' ## # Save as PDF:
##' ## ggsave(file="mypathtofolder/FigureXY1.pdf", width=10, height=7)
##'
##'
##'
##' ## Further customizations:
##'
##' # Create plot without plotting it:
##'
##' plotobj <- plotVar(x = ctg$Mean, y = ctg$Tendency,
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plotit = FALSE)
##'
##'
##' # Customize the density plot:
##'
##' dens_pl <- plotobj$dens_pl + theme(legend.position = "inside",
##' legend.position.inside = c(0.25, 0.9),
##' legend.title = element_text(size = 16),
##' legend.text = element_text(size = 12),
##' axis.title = element_text(size=16),
##' axis.text = element_text(size=12)) +
##' ylab("(Scaled) density")
##'
##'
##' # Customize the boxplot:
##'
##' boxplot_pl <- plotobj$boxplot_pl +
##' theme(axis.text.x = element_text(color = "transparent"),
##' axis.ticks.x = element_line(color = "transparent"),
##' axis.title = element_text(size=16),
##' axis.text = element_text(size=12))
##'
##'
##' # Create a title with increased font size:
##'
##' library("grid")
##' title_grob <- textGrob(
##' "Title of the combined plot",
##' gp = gpar(fontsize = 18)
##' )
##'
##'
##' # Arrange plots with title:
##'
##' library("gridExtra")
##' p <- arrangeGrob(
##' dens_pl, boxplot_pl,
##' top = title_grob,
##' nrow = 1
##' )
##' p
##'
##' ## # Save as PDF:
##' ## ggsave(file="mypathtofolder/FigureXY2.pdf", p, width=16, height=7)
##'
##' }
##'
##' @author Roman Hornung
##' @references
##' \itemize{
##' \item Hornung, R. (2022). Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <\doi{10.1007/s42979-021-00920-1}>.
##' }
##' @seealso \code{\link{plotMcl}}, \code{\link{plot.multifor}}
##' @encoding UTF-8
##' @importFrom ggplot2 ggplot aes geom_line geom_rug theme_bw theme labs ggtitle xlab ylab scale_x_continuous scale_y_continuous scale_color_manual scale_linetype_manual geom_boxplot element_text
##' @export
plotVar <- function(x, y, plot_type=c("both", "density", "boxplot")[1], x_label="", y_label="", plot_title="", plotit=TRUE) {
# If plot_type=="density", create a density plot:
if (plot_type=="density") {
dens_pl <- plotVarDensity(x=x, y=y, x_label=x_label, y_label=y_label, plot_title=plot_title)$p
if (plotit) {
print(dens_pl)
}
res <- list(dens_pl=dens_pl)
invisible(res)
} else if (plot_type=="boxplot") {
# If plot_type=="boxplot", create a boxplot:
boxplot_pl <- plotVarBoxplot(x=x, y=y, x_label=x_label, y_label=y_label, plot_title=plot_title)
if (plotit) {
print(boxplot_pl)
}
res <- list(boxplot_pl=boxplot_pl)
invisible(res)
} else if (plot_type=="both") {
# If plot_type=="both", create both a density plot a boxplot:
# Create the density plot:
dens_res <- plotVarDensity(x=x, y=y, x_label=x_label, y_label=y_label, plot_title="")
dens_pl <- dens_res$p
boxplot_pl <- plotVarBoxplot(x=x, y=y, x_label=x_label, y_label=y_label, plot_title="", plotres=dens_res$plotres)
# Add the boxplot using the same colors and line types as the density plot (through 'plotres=dens_res$plotres'):
combined_pl <- patchwork::wrap_plots(dens_pl, boxplot_pl, ncol = 2)
combined_pl <- combined_pl +
patchwork::plot_annotation(
title = plot_title,
theme = ggplot2::theme(
plot.title = ggplot2::element_text(hjust = 0.5)
)
)
if (plotit) {
print(combined_pl)
}
res <- list(combined_pl=combined_pl, dens_pl=dens_pl, boxplot_pl=boxplot_pl)
invisible(res)
}
}
plotVarDensity <- function(x, y, x_label="", y_label="", plot_title="") {
classtab <- table(y)
# The densities are plotted only for classes with at least two observations:
levels_to_keep <- names(classtab[classtab >= 2])
filterbool <- y %in% levels_to_keep
x <- x[filterbool]
y <- y[filterbool]
if (length(unique(x)) < length(unique(y)))
stop("The number of unique covariate values must be at least as large as the number of classes.")
allclasses <- levels(y)[levels(y) %in% unique(y)]
classtab <- classtab[classtab >= 2]
classprob <- classtab/sum(classtab)
# The maximum number of different colors used. If the number of classes is larger
# than this, the different classes are differentiated visually using both
# colors and line types:
nmax <- min(c(length(allclasses), 7))
colors <- scales::hue_pal()(nmax)
if (length(allclasses) == nmax) {
colorsvec <- colors
linetypesvec <- rep("solid", length=length(colorsvec))
} else {
colorsvec <- rep(colors, length=length(allclasses))
linetypesvec <- rep(c("solid", "longdash", "dotdash"), each=nmax)[1:length(colorsvec)]
linetypesvec <- c(linetypesvec, rep("dotdash", times=length(colorsvec) - length(linetypesvec)))
}
# Create a density plot for a numeric covariate:
if (inherits(x, "numeric")) {
denstemps <- list()
for(i in seq(along=allclasses)) {
xtemp <- x[y==allclasses[i]]
denstemp <- density(xtemp)
denstemp <- data.frame(x=denstemp$x, y=denstemp$y)
# The density values are scaled by the class sizes:
denstemp$y <- denstemp$y*classprob[i]
denstemps[[i]] <- denstemp
}
plotdata <- do.call("rbind", denstemps)
plotdata$class <- factor(rep(allclasses, times=sapply(denstemps, nrow)), levels=allclasses)
pointdata <- data.frame(x=x, class=y)
pointdata$class <- droplevels(pointdata$class)
# If there are more than 1000 observations, the rug plot on the lower margin
# only shows a random subset of 1000 observations:
if (nrow(pointdata) > 1000) {
pointdata <- pointdata[sample(1:nrow(pointdata), size=1000),]
}
p <- ggplot(plotdata, aes(x=.data$x, color=.data$class, linetype=.data$class)) + theme_bw() + geom_line(aes(y=.data$y)) +
scale_color_manual(values=colorsvec) + scale_linetype_manual(values = linetypesvec) +
ylab("(scaled) density") + geom_rug(data=pointdata, sides="b")
}
# Create a density plot for a factor covariate:
if (inherits(x, "ordered") || inherits(x, "factor")) {
if (inherits(x, "factor"))
warning("The plot is likely not meaningful because the variable is an unordered factor..")
x_levels <- levels(x)[levels(x) %in% unique(x)]
# For plotting, the factor variable is transformed to a continuous variable:
x <- as.numeric(x)
denstemps <- list()
for(i in seq(along=allclasses)) {
xtemp <- x[y==allclasses[i]]
denstemp <- density(xtemp)
denstemp <- data.frame(x=denstemp$x, y=denstemp$y)
denstemp$y <- denstemp$y*classprob[i]
denstemps[[i]] <- denstemp
}
plotdata <- do.call("rbind", denstemps)
plotdata$class <- factor(rep(allclasses, times=sapply(denstemps, nrow)), levels=allclasses)
if (x_label=="")
xlabadd <- theme(axis.title.x=element_blank())
else
xlabadd <- xlab(x_label)
x_unique_sorted <- sort(unique(x))
p <- ggplot(plotdata, aes(x=.data$x, y=.data$y, color=.data$class, linetype=.data$class)) + theme_bw() + geom_line() +
scale_color_manual(values=colorsvec) + scale_linetype_manual(values = linetypesvec) +
# The labels of the categories of the covariate are added to the x-axis:
scale_x_continuous(breaks=x_unique_sorted, labels=x_levels) +
ylab("density") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
}
# Add labels to the plot if provided:
if (x_label=="")
p <- p + theme(axis.title.x=element_blank())
else
p <- p + xlab(x_label)
if (y_label!="")
p <- p + labs(colour=y_label, linetype=y_label)
if (plot_title!="")
p <- p + ggtitle(plot_title)
# The information on the colors and linetypes of the classes are returned too
# because these are required by "plotVarBoxplot" in cases in which both the
# densities and the boxplots are plotted:
plotres <- list(allclasses=allclasses, colorsvec=colorsvec, linetypesvec=linetypesvec)
reslist <- list(p=p, plotres=plotres)
return(reslist)
}
plotVarBoxplot <- function(x, y, x_label="", y_label="", plot_title="", plotres=NULL) {
# Create a boxplot for a numeric covariate:
if (inherits(x, "numeric")) {
plotdata <- data.frame(x=x, y=y)
# If no information on the colors and line types of the boxplots is provided
# (usually that returned by "plotVarDensity") boxplots with black lines are generated:
if (is.null(plotres))
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x)) + theme_bw() + geom_boxplot()
else {
# If information on the colors and line types of the boxplots is provided
# boxplots with the specified colors and line types are generated:
classes_dens <- plotres$allclasses
colorsvec_dens <- plotres$colorsvec
linetypesvec_dens <- plotres$linetypesvec
classtab <- table(y)
classes_present <- names(classtab[classtab >= 1])
colorsvec <- linetypesvec <- rep("", length(classes_present))
colorsvec[classes_present %in% classes_dens] <- colorsvec_dens
linetypesvec[classes_present %in% classes_dens] <- linetypesvec_dens
# Classes for which no colors or line types are provided are depicted
# in grey:
colorsvec[colorsvec==""] <- "grey"
linetypesvec[linetypesvec==""] <- "solid"
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x, color=.data$y, linetype=.data$y)) + theme_bw() + geom_boxplot() +
scale_color_manual(values=colorsvec) +
scale_linetype_manual(values=linetypesvec) + theme(legend.position = "none")
}
}
# Create a density plot for a factor covariate:
if (inherits(x, "ordered") || inherits(x, "factor")) {
if (inherits(x, "factor"))
warning("The plot is likely not meaningful because the variable is an unordered factor.")
x_levels <- levels(x)[levels(x) %in% unique(x)]
# For plotting, the factor variable is transformed to a continuous variable:
x <- as.numeric(x)
plotdata <- data.frame(x=x, y=y)
x_unique_sorted <- sort(unique(x))
if (is.null(plotres))
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x)) + theme_bw() + geom_boxplot() +
scale_y_continuous(breaks=x_unique_sorted, labels=x_levels)
else {
classes_dens <- plotres$allclasses
colorsvec_dens <- plotres$colorsvec
linetypesvec_dens <- plotres$linetypesvec
classtab <- table(y)
classes_present <- names(classtab[classtab >= 1])
colorsvec <- linetypesvec <- rep("", length(classes_present))
colorsvec[classes_present %in% classes_dens] <- colorsvec_dens
linetypesvec[classes_present %in% classes_dens] <- linetypesvec_dens
colorsvec[colorsvec==""] <- "grey"
linetypesvec[linetypesvec==""] <- "solid"
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x, color=.data$y, linetype=.data$y)) + theme_bw() + geom_boxplot() +
# The labels of the categories of the covariate are added to the x-axis:
scale_y_continuous(breaks=x_unique_sorted, labels=x_levels) +
scale_color_manual(values=colorsvec) +
scale_linetype_manual(values=linetypesvec) + theme(legend.position = "none")
}
}
# Add labels to the plot if provided:
if (x_label=="")
p <- p + theme(axis.title.x=element_blank())
else
p <- p + ylab(x_label)
if (y_label=="")
p <- p + xlab("class")
else
p <- p + xlab(y_label)
if (plot_title!="")
p <- p + ggtitle(plot_title)
p
}
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Defines functions plotVarBoxplot plotVarDensity plotVar
Documented in plotVar
# -------------------------------------------------------------------------------
# This file is part of 'diversityForest'.
#
# 'diversityForest' is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# 'diversityForest' is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with 'diversityForest'. If not, see <http://www.gnu.org/licenses/>.
#
# NOTE: 'diversityForest' is a fork of the popular R package 'ranger', written by Marvin N. Wright.
# Most R and C++ code is identical with that of 'ranger'. The package 'diversityForest'
# was written by taking the original 'ranger' code and making any
# changes necessary to implement diversity forests.
#
# -------------------------------------------------------------------------------
##' This function allows to visualise the (estimated) distributions of a variable \code{x} for each of the categories of a categorical variable \code{y}.
##' This allows to study the dependency structure of \code{y} on \code{x}.
##' Two types of visualisations are available: density plots and boxplots.
##'
##' See the 'Details' section of \code{\link{plotMcl}}.
##'
##' @title Plot of the (estimated) dependency structure of a variable \code{x} on a categorical variable \code{y}
##' @param x Metric variable or ordered categorical variable that has at least as many unique values as \code{y}
##' @param y Factor variable with at least three categories.
##' @param plot_type Plot type, one of the following: "both" (the default), "density", "boxplot". If "density", a \code{"density"} plot is produced, if "boxplot", a \code{"boxplot"} is produced, and if "both", both a \code{"density"} plot and a \code{"boxplot"} are produced. See the 'Details' section of \code{\link{plotMcl}} for details.
##' @param x_label Optional. The label of the x-axis.
##' @param y_label Optional. The label (heading) of the legend that differentiates the categories of \code{y}.
##' @param plot_title Optional. The title of the plot.
##' @param plotit This states whether the plots are actually plotted or merely returned as \code{ggplot} objects. Default is \code{TRUE}.
##' @return A list returned invisibly containing:
##' \itemize{
##' \item Only the element \code{dens_pl} if \code{plot_type = "density"};
##' \item Only the element \code{boxplot_pl} if \code{plot_type = "boxplot"};
##' \item The elements \code{dens_pl}, \code{boxplot_pl}, and \code{combined_pl} if \code{plot_type = "both"}.
##' }
##' All returned plots are \code{ggplot2} objects, with \code{combined_pl} being a \code{patchwork} object.
##' @examples
##' \dontrun{
##'
##' ## Load package:
##'
##' library("diversityForest")
##'
##'
##'
##' ## Load the "ctg" data set:
##'
##' data(ctg)
##'
##'
##' ## Set seed to make results reproducible (this is necessary because
##' ## the rug plot produced by 'plotVar' does not show all observations, but
##' ## only a random subset of 1000 observations):
##'
##' set.seed(1234)
##'
##'
##' ## Using a "density" plot and a "boxplot", visualise the (estimated)
##' ## distributions of the variable "Mean" for each of the categories of the
##' # variable "Tendency":
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency)
##'
##'
##' ## Re-create this plot with labels:
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency, x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'")
##'
##'
##' ## Re-create this plot, but only show the "density" plot:
##'
##' plotVar(x = ctg$Mean, y = ctg$Tendency, plot_type = "density",
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'")
##'
##'
##' ## Use ggplot2 and RColorBrewer functionalities to change the line colors and
##' ## the labels of the categories of "Tendency":
##'
##' library("ggplot2")
##' library("RColorBrewer")
##' p <- plotVar(x = ctg$Mean, y = ctg$Tendency, plot_type = "density",
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plot_title = "Relationship between 'Mean' and 'Tendency'",
##' plotit = FALSE)$dens_pl +
##' scale_color_manual(values = brewer.pal(n = 3, name = "Set2"),
##' labels = c("left asymmetric", "symmetric",
##' "right asymmetric")) +
##' scale_linetype_manual(values = rep(1, 3),
##' labels = c("left asymmetric", "symmetric",
##' "right asymmetric"))
##'
##' p
##'
##' ## # Save as PDF:
##' ## ggsave(file="mypathtofolder/FigureXY1.pdf", width=10, height=7)
##'
##'
##'
##' ## Further customizations:
##'
##' # Create plot without plotting it:
##'
##' plotobj <- plotVar(x = ctg$Mean, y = ctg$Tendency,
##' x_label = "Mean of the histogram ('Mean')",
##' y_label = "Histogram tendency ('Tendency')",
##' plotit = FALSE)
##'
##'
##' # Customize the density plot:
##'
##' dens_pl <- plotobj$dens_pl + theme(legend.position = "inside",
##' legend.position.inside = c(0.25, 0.9),
##' legend.title = element_text(size = 16),
##' legend.text = element_text(size = 12),
##' axis.title = element_text(size=16),
##' axis.text = element_text(size=12)) +
##' ylab("(Scaled) density")
##'
##'
##' # Customize the boxplot:
##'
##' boxplot_pl <- plotobj$boxplot_pl +
##' theme(axis.text.x = element_text(color = "transparent"),
##' axis.ticks.x = element_line(color = "transparent"),
##' axis.title = element_text(size=16),
##' axis.text = element_text(size=12))
##'
##'
##' # Create a title with increased font size:
##'
##' library("grid")
##' title_grob <- textGrob(
##' "Title of the combined plot",
##' gp = gpar(fontsize = 18)
##' )
##'
##'
##' # Arrange plots with title:
##'
##' library("gridExtra")
##' p <- arrangeGrob(
##' dens_pl, boxplot_pl,
##' top = title_grob,
##' nrow = 1
##' )
##' p
##'
##' ## # Save as PDF:
##' ## ggsave(file="mypathtofolder/FigureXY2.pdf", p, width=16, height=7)
##'
##' }
##'
##' @author Roman Hornung
##' @references
##' \itemize{
##' \item Hornung, R. (2022). Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <\doi{10.1007/s42979-021-00920-1}>.
##' }
##' @seealso \code{\link{plotMcl}}, \code{\link{plot.multifor}}
##' @encoding UTF-8
##' @importFrom ggplot2 ggplot aes geom_line geom_rug theme_bw theme labs ggtitle xlab ylab scale_x_continuous scale_y_continuous scale_color_manual scale_linetype_manual geom_boxplot element_text
##' @export
plotVar <- function(x, y, plot_type=c("both", "density", "boxplot")[1], x_label="", y_label="", plot_title="", plotit=TRUE) {
# If plot_type=="density", create a density plot:
if (plot_type=="density") {
dens_pl <- plotVarDensity(x=x, y=y, x_label=x_label, y_label=y_label, plot_title=plot_title)$p
if (plotit) {
print(dens_pl)
}
res <- list(dens_pl=dens_pl)
invisible(res)
} else if (plot_type=="boxplot") {
# If plot_type=="boxplot", create a boxplot:
boxplot_pl <- plotVarBoxplot(x=x, y=y, x_label=x_label, y_label=y_label, plot_title=plot_title)
if (plotit) {
print(boxplot_pl)
}
res <- list(boxplot_pl=boxplot_pl)
invisible(res)
} else if (plot_type=="both") {
# If plot_type=="both", create both a density plot a boxplot:
# Create the density plot:
dens_res <- plotVarDensity(x=x, y=y, x_label=x_label, y_label=y_label, plot_title="")
dens_pl <- dens_res$p
boxplot_pl <- plotVarBoxplot(x=x, y=y, x_label=x_label, y_label=y_label, plot_title="", plotres=dens_res$plotres)
# Add the boxplot using the same colors and line types as the density plot (through 'plotres=dens_res$plotres'):
combined_pl <- patchwork::wrap_plots(dens_pl, boxplot_pl, ncol = 2)
combined_pl <- combined_pl +
patchwork::plot_annotation(
title = plot_title,
theme = ggplot2::theme(
plot.title = ggplot2::element_text(hjust = 0.5)
)
)
if (plotit) {
print(combined_pl)
}
res <- list(combined_pl=combined_pl, dens_pl=dens_pl, boxplot_pl=boxplot_pl)
invisible(res)
}
}
plotVarDensity <- function(x, y, x_label="", y_label="", plot_title="") {
classtab <- table(y)
# The densities are plotted only for classes with at least two observations:
levels_to_keep <- names(classtab[classtab >= 2])
filterbool <- y %in% levels_to_keep
x <- x[filterbool]
y <- y[filterbool]
if (length(unique(x)) < length(unique(y)))
stop("The number of unique covariate values must be at least as large as the number of classes.")
allclasses <- levels(y)[levels(y) %in% unique(y)]
classtab <- classtab[classtab >= 2]
classprob <- classtab/sum(classtab)
# The maximum number of different colors used. If the number of classes is larger
# than this, the different classes are differentiated visually using both
# colors and line types:
nmax <- min(c(length(allclasses), 7))
colors <- scales::hue_pal()(nmax)
if (length(allclasses) == nmax) {
colorsvec <- colors
linetypesvec <- rep("solid", length=length(colorsvec))
} else {
colorsvec <- rep(colors, length=length(allclasses))
linetypesvec <- rep(c("solid", "longdash", "dotdash"), each=nmax)[1:length(colorsvec)]
linetypesvec <- c(linetypesvec, rep("dotdash", times=length(colorsvec) - length(linetypesvec)))
}
# Create a density plot for a numeric covariate:
if (inherits(x, "numeric")) {
denstemps <- list()
for(i in seq(along=allclasses)) {
xtemp <- x[y==allclasses[i]]
denstemp <- density(xtemp)
denstemp <- data.frame(x=denstemp$x, y=denstemp$y)
# The density values are scaled by the class sizes:
denstemp$y <- denstemp$y*classprob[i]
denstemps[[i]] <- denstemp
}
plotdata <- do.call("rbind", denstemps)
plotdata$class <- factor(rep(allclasses, times=sapply(denstemps, nrow)), levels=allclasses)
pointdata <- data.frame(x=x, class=y)
pointdata$class <- droplevels(pointdata$class)
# If there are more than 1000 observations, the rug plot on the lower margin
# only shows a random subset of 1000 observations:
if (nrow(pointdata) > 1000) {
pointdata <- pointdata[sample(1:nrow(pointdata), size=1000),]
}
p <- ggplot(plotdata, aes(x=.data$x, color=.data$class, linetype=.data$class)) + theme_bw() + geom_line(aes(y=.data$y)) +
scale_color_manual(values=colorsvec) + scale_linetype_manual(values = linetypesvec) +
ylab("(scaled) density") + geom_rug(data=pointdata, sides="b")
}
# Create a density plot for a factor covariate:
if (inherits(x, "ordered") || inherits(x, "factor")) {
if (inherits(x, "factor"))
warning("The plot is likely not meaningful because the variable is an unordered factor..")
x_levels <- levels(x)[levels(x) %in% unique(x)]
# For plotting, the factor variable is transformed to a continuous variable:
x <- as.numeric(x)
denstemps <- list()
for(i in seq(along=allclasses)) {
xtemp <- x[y==allclasses[i]]
denstemp <- density(xtemp)
denstemp <- data.frame(x=denstemp$x, y=denstemp$y)
denstemp$y <- denstemp$y*classprob[i]
denstemps[[i]] <- denstemp
}
plotdata <- do.call("rbind", denstemps)
plotdata$class <- factor(rep(allclasses, times=sapply(denstemps, nrow)), levels=allclasses)
if (x_label=="")
xlabadd <- theme(axis.title.x=element_blank())
else
xlabadd <- xlab(x_label)
x_unique_sorted <- sort(unique(x))
p <- ggplot(plotdata, aes(x=.data$x, y=.data$y, color=.data$class, linetype=.data$class)) + theme_bw() + geom_line() +
scale_color_manual(values=colorsvec) + scale_linetype_manual(values = linetypesvec) +
# The labels of the categories of the covariate are added to the x-axis:
scale_x_continuous(breaks=x_unique_sorted, labels=x_levels) +
ylab("density") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
}
# Add labels to the plot if provided:
if (x_label=="")
p <- p + theme(axis.title.x=element_blank())
else
p <- p + xlab(x_label)
if (y_label!="")
p <- p + labs(colour=y_label, linetype=y_label)
if (plot_title!="")
p <- p + ggtitle(plot_title)
# The information on the colors and linetypes of the classes are returned too
# because these are required by "plotVarBoxplot" in cases in which both the
# densities and the boxplots are plotted:
plotres <- list(allclasses=allclasses, colorsvec=colorsvec, linetypesvec=linetypesvec)
reslist <- list(p=p, plotres=plotres)
return(reslist)
}
plotVarBoxplot <- function(x, y, x_label="", y_label="", plot_title="", plotres=NULL) {
# Create a boxplot for a numeric covariate:
if (inherits(x, "numeric")) {
plotdata <- data.frame(x=x, y=y)
# If no information on the colors and line types of the boxplots is provided
# (usually that returned by "plotVarDensity") boxplots with black lines are generated:
if (is.null(plotres))
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x)) + theme_bw() + geom_boxplot()
else {
# If information on the colors and line types of the boxplots is provided
# boxplots with the specified colors and line types are generated:
classes_dens <- plotres$allclasses
colorsvec_dens <- plotres$colorsvec
linetypesvec_dens <- plotres$linetypesvec
classtab <- table(y)
classes_present <- names(classtab[classtab >= 1])
colorsvec <- linetypesvec <- rep("", length(classes_present))
colorsvec[classes_present %in% classes_dens] <- colorsvec_dens
linetypesvec[classes_present %in% classes_dens] <- linetypesvec_dens
# Classes for which no colors or line types are provided are depicted
# in grey:
colorsvec[colorsvec==""] <- "grey"
linetypesvec[linetypesvec==""] <- "solid"
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x, color=.data$y, linetype=.data$y)) + theme_bw() + geom_boxplot() +
scale_color_manual(values=colorsvec) +
scale_linetype_manual(values=linetypesvec) + theme(legend.position = "none")
}
}
# Create a density plot for a factor covariate:
if (inherits(x, "ordered") || inherits(x, "factor")) {
if (inherits(x, "factor"))
warning("The plot is likely not meaningful because the variable is an unordered factor.")
x_levels <- levels(x)[levels(x) %in% unique(x)]
# For plotting, the factor variable is transformed to a continuous variable:
x <- as.numeric(x)
plotdata <- data.frame(x=x, y=y)
x_unique_sorted <- sort(unique(x))
if (is.null(plotres))
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x)) + theme_bw() + geom_boxplot() +
scale_y_continuous(breaks=x_unique_sorted, labels=x_levels)
else {
classes_dens <- plotres$allclasses
colorsvec_dens <- plotres$colorsvec
linetypesvec_dens <- plotres$linetypesvec
classtab <- table(y)
classes_present <- names(classtab[classtab >= 1])
colorsvec <- linetypesvec <- rep("", length(classes_present))
colorsvec[classes_present %in% classes_dens] <- colorsvec_dens
linetypesvec[classes_present %in% classes_dens] <- linetypesvec_dens
colorsvec[colorsvec==""] <- "grey"
linetypesvec[linetypesvec==""] <- "solid"
p <- ggplot(plotdata, aes(x=.data$y, y=.data$x, color=.data$y, linetype=.data$y)) + theme_bw() + geom_boxplot() +
# The labels of the categories of the covariate are added to the x-axis:
scale_y_continuous(breaks=x_unique_sorted, labels=x_levels) +
scale_color_manual(values=colorsvec) +
scale_linetype_manual(values=linetypesvec) + theme(legend.position = "none")
}
}
# Add labels to the plot if provided:
if (x_label=="")
p <- p + theme(axis.title.x=element_blank())
else
p <- p + ylab(x_label)
if (y_label=="")
p <- p + xlab("class")
else
p <- p + xlab(y_label)
if (plot_title!="")
p <- p + ggtitle(plot_title)
p
}
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