R/ggcorrplot.R
In caijun/ggcorrplot2: Visualize a Correlation Matrix using ggplot2
Defines functions
ggcorrplot
plot.method
Documented in
ggcorrplot
# internal
# @title Add the geom of correlation matrix to the existing plot
#
# @param p a ggplot object
# @param data a data.frame to be plotted
# @param method a character indicating the visualization method of correlation matrix to be used. Currently, it supports four methods, named "circle" (default), "square", "ellipse", "number".
#' @import ggplot2
#' @importFrom dplyr %>% mutate group_by group_modify case_when
#' @importFrom rlang .data
plot.method <- function(p, data, method = c("circle", "square", "ellipse", "number")) {
if (method == "ellipse") {
ellipse.xy <- function(rho, length = 100) {
theta <- seq(0, 2 * pi, length = length)
if (rho == 1) rho <- rho - 1e-4
d <- acos(-rho)
x <- cos(theta + d / 2) / 2
y <- cos(theta - d / 2) / 2
as.data.frame(cbind(x, y))
}
myfun <- function(df) {
res <- ellipse.xy(df$rho) %>%
mutate(rid = df$rid, cid = df$cid, rho = df$rho) %>%
# using .data prevents R CMD check from giving a NOTE about undefined global variable
mutate(x1 = 0.9 * .data$x + .data$cid, y1 = 0.9 * .data$y + .data$rid,
group = paste(.data$rid, .data$cid, sep = "-"))
}
ellipse.dat <- data %>%
group_by(.data$Var1, .data$Var2) %>%
group_modify(~myfun(.x))
p <- p +
geom_polygon(data = ellipse.dat,
mapping = aes(x = .data$x1, y = .data$y1, fill = .data$rho,
group = .data$group),
color = NA)
} else if (method == "circle") {
p <- p +
ggforce::geom_circle(data = data,
mapping = aes(x0 = .data$cid, y0 = .data$rid,
r = .data$abs.rho/2 - 0.02, fill = .data$rho),
color = NA)
} else if (method == "square") {
p <- p +
geom_rect(data = data, mapping = aes(xmin = .data$cid - 0.5*(.data$abs.rho - 0.04),
xmax = .data$cid + 0.5*(.data$abs.rho - 0.04),
ymin = .data$rid - 0.5*(.data$abs.rho - 0.04),
ymax = .data$rid + 0.5*(.data$abs.rho - 0.04),
fill = .data$rho))
} else if (method == "number") {
p <- p +
geom_text(data = data, mapping = aes(x = .data$cid, y = .data$rid, colour = .data$rho),
label = data$rho.label)
}
return(p)
}
#' @title Visualize a correlation matrix
#'
#' @param corr a correlation matrix to be visualized
#' @param method a character indicating the visualization method of correlation matrix to be used. Currently, it supports four methods, named \code{"circle"} (default), \code{"square"}, \code{"ellipse"}, \code{"number"}.
#' @param type a character indicating that the \code{"full"} (default), \code{"upper"} or \code{"lower"} triangular matrix is displayed.
#' @param col a vector of the colors to be used, which are distributed uniformly from -1 to 1. If NULL, col will be set to \code{RColorBrewer::brewer.pal(n = 11, name = "RdBu")}, the default colour scheme of \code{corrplot}.
#' @param p.mat a matrix of p-value
#' @param sig.lvl a numeric vector specifying significant level(s). If the p-value in \code{p.mat} is bigger than \code{sig.lvl} (0.05 by default), then the corresponding correlation coefficient is regarded as insignificant. If \code{insig} is \code{"label_sig"}, this may be an increasing vector of significance levels, for example \code{c(0.05, 0.01, 0.001)}, in which case \code{pch} will be used once for the highest p-value interval and multiple times (e.g. "*", "**", "***") for each lower p-value interval.
#' @param number.digits the number of decimal digits (2 by default) while the visualization method is \code{"number"}.
#' @param show.diag a logical indicating whether display the correlation coefficients on the principal diagonal.
#' @param insig a character specialized insignificant correlation coefficients, \code{"pch"} (default), \code{"blank"}, or \code{"label_sig"}.
#' @param pch a point character indicating the shape of insignificant correlation coefficients.
#' @param pch.cex a number controlling the shape size of insignificant correlation coefficients.
#' @export
ggcorrplot <- function(corr, method = c("circle", "square", "ellipse", "number"),
type = c("full", "lower", "upper"), p.mat = NULL,
col = NULL,
sig.lvl = 0.05, number.digits = 2,
show.diag = TRUE, insig = c("pch", "blank", "label_sig"),
pch = 4, pch.cex = 5) {
method <- match.arg(method)
type <- match.arg(type)
insig <- match.arg(insig)
if (is.null(colnames(corr))) {
stop("Correlation matrix needs column names!")
}
vars <- colnames(corr)
# number of variables
nvars <- length(vars)
corr <- reshape2::melt(corr, value.name = "rho") %>%
mutate(rid = as.integer(as.factor(.data$Var1)),
cid = as.integer(as.factor(.data$Var2))) %>%
mutate(part = case_when(
.$rid < .$cid ~ "upper",
.$rid == .$cid ~ "diag",
.$rid > .$cid ~ "lower"
)) %>%
mutate(abs.rho = abs(.data$rho)) %>%
mutate(rho.label = ifelse(.data$rho == 1, .data$rho, format(round(.data$rho, digits = number.digits),
nsmall = number.digits)))
if (!is.null(p.mat)) {
p.mat <- reshape2::melt(p.mat, value.name = "pval") %>%
mutate(rid = as.integer(as.factor(.data$Var1)),
cid = as.integer(as.factor(.data$Var2))) %>%
mutate(part = case_when(
.$rid < .$cid ~ "upper",
.$rid == .$cid ~ "diag",
.$rid > .$cid ~ "lower"
))
sig.codes <- sapply(seq_along(sig.lvl), function(i) {
# By default, mark significance with *
if (!is.character(pch)) {
pch <- "*"
}
paste(rep(pch, i), collapse = "")
})
corr <- corr %>%
mutate(pval = p.mat$pval) %>%
mutate(signif = as.numeric(.data$pval <= max(sig.lvl))) %>%
mutate(sig.codes = cut(.data$pval, breaks = c(sig.lvl, 0, 1), labels = c(rev(sig.codes), ""),
include.lowest = TRUE))
# insignificant p value matrix
p.mat.insig <- p.mat %>%
dplyr::filter(.data$pval > max(sig.lvl))
if (insig == "blank") {
corr <- corr %>%
mutate(rho = .data$rho * signif)
}
} else {
p.mat.insig <- NULL
}
if (type == "lower") {
corr <- corr %>%
dplyr::filter(.data$part != "upper")
if (!is.null(p.mat.insig)) {
p.mat.insig <- p.mat.insig %>%
dplyr::filter(.data$part != "upper")
}
} else if(type == "upper") {
corr <- corr %>%
dplyr::filter(.data$part != "lower")
if (!is.null(p.mat.insig)) {
p.mat.insig <- p.mat.insig %>%
dplyr::filter(.data$part != "lower")
}
}
if (!show.diag) {
corr <- corr %>%
dplyr::filter(.data$part != "diag")
}
if(is.null(col)) {
# default palette of corrplot
col <- RColorBrewer::brewer.pal(n = 11, name = "RdBu")
}
col2 <- grDevices::colorRampPalette(col)
p <- ggplot(data = corr) +
geom_rect(mapping = aes(xmin = .data$cid - 0.5, xmax = .data$cid + 0.5,
ymin = .data$rid - 0.5, ymax = .data$rid + 0.5),
color = "grey92", fill = NA) +
coord_fixed() +
theme_bw() +
theme(legend.margin = margin(0, unit = 'cm'),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
if (!show.diag & type == "full") {
p <- p +
geom_rect(mapping = aes(xmin = 1 - 0.5, xmax = 1 + 0.5,
ymin = 1 - 0.5, ymax = 1 + 0.5),
color = "grey92", fill = NA) +
geom_rect(mapping = aes(xmin = nvars - 0.5, xmax = nvars + 0.5,
ymin = nvars - 0.5, ymax = nvars + 0.5),
color = "grey92", fill = NA)
}
if (any(is.na(corr$rho))) {
corr.NA <- corr %>%
dplyr::filter(is.na(.data$rho))
corr <- corr %>%
dplyr::filter(!is.na(.data$rho))
} else {
corr.NA <- NULL
}
p <- plot.method(p, data = corr, method = method)
# add significant codes except number method
if (!is.null(p.mat) & insig == "label_sig" & method != "number") {
p <- p +
geom_text(data = corr, mapping = aes(x = .data$cid, y = .data$rid), label = corr$sig.codes,
size = pch.cex)
}
# indicate insigificant p value with point character
if (!is.null(p.mat.insig) & insig == "pch") {
p <- p + geom_point(data = p.mat.insig, mapping = aes(x = .data$cid, y = .data$rid),
shape = pch, size = pch.cex)
}
# indicate NA correlation coefficient
if (!is.null(corr.NA)) {
p <- p +
geom_text(data = corr.NA, mapping = aes(x = .data$cid, y = .data$rid), label = "NA",
size = pch.cex, color = "grey92")
}
# colorbar
if (type %in% c("full", "upper")) {
if (method %in% c("ellipse", "circle", "square")) {
p <- p + scale_fill_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 1.5,
barheight = 15))
} else {
p <- p + scale_colour_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 1.5,
barheight = 15))
}
} else if (type %in% c("lower")) {
if (method %in% c("ellipse", "circle", "square")) {
p <- p + scale_fill_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5))
} else {
p <- p + scale_colour_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5))
}
p <- p + theme(legend.position = "bottom")
}
# variable labels
axis.text.fontsize <- 11 # in pt
geom.text.fontsize <- axis.text.fontsize / ggplot2::.pt # in mm
if (type == "full") {
p <- p +
scale_x_continuous(breaks = 1:nvars, labels = vars, expand = c(0, 0)) +
scale_y_reverse(breaks = 1:nvars, labels = vars, expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
} else if (type == "lower") {
if (show.diag) {
y.vars <- data.frame(x = 0, y = 1:nvars)
diag.vars <- data.frame(x = 1:nvars, y = 0:(nvars - 1))
y.label <- diag.label <- vars
} else {
y.vars <- data.frame(x = 0, y = 2:nvars)
diag.vars <- data.frame(x = 1:(nvars - 1), y = 1:(nvars - 1))
y.label <- utils::tail(vars, -1)
diag.label <- utils::head(vars, -1)
}
p <- p +
geom_text(data = diag.vars, mapping = aes(.data$x, .data$y+0.4), label = diag.label,
colour = "grey30", size = geom.text.fontsize, angle = 90, hjust = 0) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_reverse(breaks = y.vars$y, labels = y.label,
expand = expansion(mult = c(0, 0.02*nchar(diag.label[1]))),
limits = c(nvars + 0.5, min(diag.vars$y) - 0.5)) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size = axis.text.fontsize))
} else if (type == "upper") {
if (show.diag) {
x.vars <- data.frame(x = 1:nvars, y = 0)
diag.vars <- data.frame(x = 0:(nvars - 1), y = 1:nvars)
x.label <- diag.label <- vars
} else {
x.vars <- data.frame(x = 2:nvars, y = 0)
diag.vars <- data.frame(x = 1:(nvars - 1), y = 1:(nvars - 1))
x.label <- utils::tail(vars, -1)
diag.label <- utils::head(vars, -1)
}
p <- p +
geom_text(data = diag.vars, mapping = aes(.data$x+0.4, .data$y), label = diag.label,
colour = "grey30", size = geom.text.fontsize, hjust = 1) +
scale_x_continuous(breaks = x.vars$x, labels = x.label,
expand = expansion(mult = c(0.02*nchar(diag.label[1]), 0)),
position = "top", limits = c(min(diag.vars$x) - 0.5, nvars + 0.5)) +
scale_y_reverse(expand = c(0, 0)) +
theme(axis.text.x = element_text(size = axis.text.fontsize, angle = 90, hjust = 0),
axis.text.y = element_blank())
}
return(p)
}
caijun/ggcorrplot2 documentation built on May 15, 2022, 1:28 a.m.
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Defines functions ggcorrplot plot.method
Documented in ggcorrplot
# internal
# @title Add the geom of correlation matrix to the existing plot
#
# @param p a ggplot object
# @param data a data.frame to be plotted
# @param method a character indicating the visualization method of correlation matrix to be used. Currently, it supports four methods, named "circle" (default), "square", "ellipse", "number".
#' @import ggplot2
#' @importFrom dplyr %>% mutate group_by group_modify case_when
#' @importFrom rlang .data
plot.method <- function(p, data, method = c("circle", "square", "ellipse", "number")) {
if (method == "ellipse") {
ellipse.xy <- function(rho, length = 100) {
theta <- seq(0, 2 * pi, length = length)
if (rho == 1) rho <- rho - 1e-4
d <- acos(-rho)
x <- cos(theta + d / 2) / 2
y <- cos(theta - d / 2) / 2
as.data.frame(cbind(x, y))
}
myfun <- function(df) {
res <- ellipse.xy(df$rho) %>%
mutate(rid = df$rid, cid = df$cid, rho = df$rho) %>%
# using .data prevents R CMD check from giving a NOTE about undefined global variable
mutate(x1 = 0.9 * .data$x + .data$cid, y1 = 0.9 * .data$y + .data$rid,
group = paste(.data$rid, .data$cid, sep = "-"))
}
ellipse.dat <- data %>%
group_by(.data$Var1, .data$Var2) %>%
group_modify(~myfun(.x))
p <- p +
geom_polygon(data = ellipse.dat,
mapping = aes(x = .data$x1, y = .data$y1, fill = .data$rho,
group = .data$group),
color = NA)
} else if (method == "circle") {
p <- p +
ggforce::geom_circle(data = data,
mapping = aes(x0 = .data$cid, y0 = .data$rid,
r = .data$abs.rho/2 - 0.02, fill = .data$rho),
color = NA)
} else if (method == "square") {
p <- p +
geom_rect(data = data, mapping = aes(xmin = .data$cid - 0.5*(.data$abs.rho - 0.04),
xmax = .data$cid + 0.5*(.data$abs.rho - 0.04),
ymin = .data$rid - 0.5*(.data$abs.rho - 0.04),
ymax = .data$rid + 0.5*(.data$abs.rho - 0.04),
fill = .data$rho))
} else if (method == "number") {
p <- p +
geom_text(data = data, mapping = aes(x = .data$cid, y = .data$rid, colour = .data$rho),
label = data$rho.label)
}
return(p)
}
#' @title Visualize a correlation matrix
#'
#' @param corr a correlation matrix to be visualized
#' @param method a character indicating the visualization method of correlation matrix to be used. Currently, it supports four methods, named \code{"circle"} (default), \code{"square"}, \code{"ellipse"}, \code{"number"}.
#' @param type a character indicating that the \code{"full"} (default), \code{"upper"} or \code{"lower"} triangular matrix is displayed.
#' @param col a vector of the colors to be used, which are distributed uniformly from -1 to 1. If NULL, col will be set to \code{RColorBrewer::brewer.pal(n = 11, name = "RdBu")}, the default colour scheme of \code{corrplot}.
#' @param p.mat a matrix of p-value
#' @param sig.lvl a numeric vector specifying significant level(s). If the p-value in \code{p.mat} is bigger than \code{sig.lvl} (0.05 by default), then the corresponding correlation coefficient is regarded as insignificant. If \code{insig} is \code{"label_sig"}, this may be an increasing vector of significance levels, for example \code{c(0.05, 0.01, 0.001)}, in which case \code{pch} will be used once for the highest p-value interval and multiple times (e.g. "*", "**", "***") for each lower p-value interval.
#' @param number.digits the number of decimal digits (2 by default) while the visualization method is \code{"number"}.
#' @param show.diag a logical indicating whether display the correlation coefficients on the principal diagonal.
#' @param insig a character specialized insignificant correlation coefficients, \code{"pch"} (default), \code{"blank"}, or \code{"label_sig"}.
#' @param pch a point character indicating the shape of insignificant correlation coefficients.
#' @param pch.cex a number controlling the shape size of insignificant correlation coefficients.
#' @export
ggcorrplot <- function(corr, method = c("circle", "square", "ellipse", "number"),
type = c("full", "lower", "upper"), p.mat = NULL,
col = NULL,
sig.lvl = 0.05, number.digits = 2,
show.diag = TRUE, insig = c("pch", "blank", "label_sig"),
pch = 4, pch.cex = 5) {
method <- match.arg(method)
type <- match.arg(type)
insig <- match.arg(insig)
if (is.null(colnames(corr))) {
stop("Correlation matrix needs column names!")
}
vars <- colnames(corr)
# number of variables
nvars <- length(vars)
corr <- reshape2::melt(corr, value.name = "rho") %>%
mutate(rid = as.integer(as.factor(.data$Var1)),
cid = as.integer(as.factor(.data$Var2))) %>%
mutate(part = case_when(
.$rid < .$cid ~ "upper",
.$rid == .$cid ~ "diag",
.$rid > .$cid ~ "lower"
)) %>%
mutate(abs.rho = abs(.data$rho)) %>%
mutate(rho.label = ifelse(.data$rho == 1, .data$rho, format(round(.data$rho, digits = number.digits),
nsmall = number.digits)))
if (!is.null(p.mat)) {
p.mat <- reshape2::melt(p.mat, value.name = "pval") %>%
mutate(rid = as.integer(as.factor(.data$Var1)),
cid = as.integer(as.factor(.data$Var2))) %>%
mutate(part = case_when(
.$rid < .$cid ~ "upper",
.$rid == .$cid ~ "diag",
.$rid > .$cid ~ "lower"
))
sig.codes <- sapply(seq_along(sig.lvl), function(i) {
# By default, mark significance with *
if (!is.character(pch)) {
pch <- "*"
}
paste(rep(pch, i), collapse = "")
})
corr <- corr %>%
mutate(pval = p.mat$pval) %>%
mutate(signif = as.numeric(.data$pval <= max(sig.lvl))) %>%
mutate(sig.codes = cut(.data$pval, breaks = c(sig.lvl, 0, 1), labels = c(rev(sig.codes), ""),
include.lowest = TRUE))
# insignificant p value matrix
p.mat.insig <- p.mat %>%
dplyr::filter(.data$pval > max(sig.lvl))
if (insig == "blank") {
corr <- corr %>%
mutate(rho = .data$rho * signif)
}
} else {
p.mat.insig <- NULL
}
if (type == "lower") {
corr <- corr %>%
dplyr::filter(.data$part != "upper")
if (!is.null(p.mat.insig)) {
p.mat.insig <- p.mat.insig %>%
dplyr::filter(.data$part != "upper")
}
} else if(type == "upper") {
corr <- corr %>%
dplyr::filter(.data$part != "lower")
if (!is.null(p.mat.insig)) {
p.mat.insig <- p.mat.insig %>%
dplyr::filter(.data$part != "lower")
}
}
if (!show.diag) {
corr <- corr %>%
dplyr::filter(.data$part != "diag")
}
if(is.null(col)) {
# default palette of corrplot
col <- RColorBrewer::brewer.pal(n = 11, name = "RdBu")
}
col2 <- grDevices::colorRampPalette(col)
p <- ggplot(data = corr) +
geom_rect(mapping = aes(xmin = .data$cid - 0.5, xmax = .data$cid + 0.5,
ymin = .data$rid - 0.5, ymax = .data$rid + 0.5),
color = "grey92", fill = NA) +
coord_fixed() +
theme_bw() +
theme(legend.margin = margin(0, unit = 'cm'),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
if (!show.diag & type == "full") {
p <- p +
geom_rect(mapping = aes(xmin = 1 - 0.5, xmax = 1 + 0.5,
ymin = 1 - 0.5, ymax = 1 + 0.5),
color = "grey92", fill = NA) +
geom_rect(mapping = aes(xmin = nvars - 0.5, xmax = nvars + 0.5,
ymin = nvars - 0.5, ymax = nvars + 0.5),
color = "grey92", fill = NA)
}
if (any(is.na(corr$rho))) {
corr.NA <- corr %>%
dplyr::filter(is.na(.data$rho))
corr <- corr %>%
dplyr::filter(!is.na(.data$rho))
} else {
corr.NA <- NULL
}
p <- plot.method(p, data = corr, method = method)
# add significant codes except number method
if (!is.null(p.mat) & insig == "label_sig" & method != "number") {
p <- p +
geom_text(data = corr, mapping = aes(x = .data$cid, y = .data$rid), label = corr$sig.codes,
size = pch.cex)
}
# indicate insigificant p value with point character
if (!is.null(p.mat.insig) & insig == "pch") {
p <- p + geom_point(data = p.mat.insig, mapping = aes(x = .data$cid, y = .data$rid),
shape = pch, size = pch.cex)
}
# indicate NA correlation coefficient
if (!is.null(corr.NA)) {
p <- p +
geom_text(data = corr.NA, mapping = aes(x = .data$cid, y = .data$rid), label = "NA",
size = pch.cex, color = "grey92")
}
# colorbar
if (type %in% c("full", "upper")) {
if (method %in% c("ellipse", "circle", "square")) {
p <- p + scale_fill_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 1.5,
barheight = 15))
} else {
p <- p + scale_colour_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 1.5,
barheight = 15))
}
} else if (type %in% c("lower")) {
if (method %in% c("ellipse", "circle", "square")) {
p <- p + scale_fill_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5))
} else {
p <- p + scale_colour_gradientn(colours = col2(200), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5))
}
p <- p + theme(legend.position = "bottom")
}
# variable labels
axis.text.fontsize <- 11 # in pt
geom.text.fontsize <- axis.text.fontsize / ggplot2::.pt # in mm
if (type == "full") {
p <- p +
scale_x_continuous(breaks = 1:nvars, labels = vars, expand = c(0, 0)) +
scale_y_reverse(breaks = 1:nvars, labels = vars, expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
} else if (type == "lower") {
if (show.diag) {
y.vars <- data.frame(x = 0, y = 1:nvars)
diag.vars <- data.frame(x = 1:nvars, y = 0:(nvars - 1))
y.label <- diag.label <- vars
} else {
y.vars <- data.frame(x = 0, y = 2:nvars)
diag.vars <- data.frame(x = 1:(nvars - 1), y = 1:(nvars - 1))
y.label <- utils::tail(vars, -1)
diag.label <- utils::head(vars, -1)
}
p <- p +
geom_text(data = diag.vars, mapping = aes(.data$x, .data$y+0.4), label = diag.label,
colour = "grey30", size = geom.text.fontsize, angle = 90, hjust = 0) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_reverse(breaks = y.vars$y, labels = y.label,
expand = expansion(mult = c(0, 0.02*nchar(diag.label[1]))),
limits = c(nvars + 0.5, min(diag.vars$y) - 0.5)) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size = axis.text.fontsize))
} else if (type == "upper") {
if (show.diag) {
x.vars <- data.frame(x = 1:nvars, y = 0)
diag.vars <- data.frame(x = 0:(nvars - 1), y = 1:nvars)
x.label <- diag.label <- vars
} else {
x.vars <- data.frame(x = 2:nvars, y = 0)
diag.vars <- data.frame(x = 1:(nvars - 1), y = 1:(nvars - 1))
x.label <- utils::tail(vars, -1)
diag.label <- utils::head(vars, -1)
}
p <- p +
geom_text(data = diag.vars, mapping = aes(.data$x+0.4, .data$y), label = diag.label,
colour = "grey30", size = geom.text.fontsize, hjust = 1) +
scale_x_continuous(breaks = x.vars$x, labels = x.label,
expand = expansion(mult = c(0.02*nchar(diag.label[1]), 0)),
position = "top", limits = c(min(diag.vars$x) - 0.5, nvars + 0.5)) +
scale_y_reverse(expand = c(0, 0)) +
theme(axis.text.x = element_text(size = axis.text.fontsize, angle = 90, hjust = 0),
axis.text.y = element_blank())
}
return(p)
}
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