#' Plot PCA projection only
#'
#' Plots projection from rotated.scores file (output of intersect_do_PCA_and_project_second_dataset)
#'
#' @param rotated.file2 File containing scores matrix
#' @param info.name Vector of sample names
#' @param info.type Vector of sample types in the same order
#' @param title Title of the plot
#' @param labels default=T
#' @param PCx,PCy PCs to display
#' @param ellipse Construct confidence region based on groups in info.type, default = T
#' @param conf default = 0.95
#' @param density plot x-y density plots
#' @param fliph,flipv flip plot hoirzontally or vertically
#'
# @importFrom ggplot2 ggplot aes aes_string element_rect element_text geom_point geom_text labs margin theme theme_bw
#' @export
#'
#'
plot_pca_projection_only =function (rotated.file2, info.name, info.type, title = "Projection",
labels = TRUE, PCx = "PC1", PCy = "PC2", ellipse = F, conf = 0.95, density = F,
fliph = F, flipv = F)
{
require(ggplot2)
require(vegan)
projected_data = read.delim(rotated.file2)
projected_data$type = info.type[match(projected_data[,1],
info.name)]
if (fliph==T){projected_data[,PCx] = projected_data[,PCx]*-1}
if (flipv==T){projected_data[,PCy] = projected_data[,PCy]*-1}
pcx.y <- ggplot(projected_data, aes_string(x = PCx, y = PCy)) +
geom_point(size = I(3), aes(color = factor(type))) +
theme(legend.position = "right", plot.title = element_text(size = 30),
legend.text = element_text(size = 22), legend.title = element_text(size = 20),
axis.title = element_text(size = 30), legend.background = element_rect(),
axis.text.x = element_text(margin = margin(b = -2)),
axis.text.y = element_text(margin = margin(l = -14))) +
guides(color = guide_legend(title = "Type")) + labs(title = title) +
# coord_equal(ratio=1) +
theme_bw(base_size=18) + if (labels == TRUE) {
geom_text(data = projected_data, mapping = aes(label = rownames(projected_data)),
check_overlap = TRUE, size = 3)
}
if(ellipse==TRUE){
plot(projected_data[,c(PCx, PCy)], main=title)
ord = ordiellipse(projected_data[,c(PCx, PCy)],projected_data$type, kind = "sd", conf = conf)
cov_ellipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
t(center + scale * t(Circle %*% chol(cov)))
}
df_ell <- data.frame(matrix(ncol = 0, nrow = 0))
for(g in (droplevels(projected_data$type))){
df_ell <- rbind(df_ell, cbind(as.data.frame(with(projected_data[projected_data$type==g,],
cov_ellipse(ord[[g]]$cov,ord[[g]]$center,ord[[g]]$scale)))
,type=g))
}
pcx.y2 = pcx.y + geom_path(data=df_ell, aes(x=df_ell[,PCx], y=df_ell[,PCy], colour = type), size=1, linetype=1)
print(pcx.y2)
if(density==TRUE){
# Marginal density plot of x (top panel) and y (right panel)
xplot <- ggdensity(projected_data, PCx, fill = "type")+ clean_theme()
yplot <- ggdensity(projected_data, PCy, fill = "type")+ rotate()+ clean_theme()
# Arranging the plot
print(ggarrange(xplot, NULL, pcx.y2, yplot,
ncol = 2, nrow = 2, align = "hv",
widths = c(2, 1), heights = c(1, 2),
common.legend = TRUE))
}
else{
print(pcx.y2)
}
}else{
pcx.y
}
if(density==TRUE){
# Marginal density plot of x (top panel) and y (right panel)
xplot <- ggdensity(projected_data, PCx, fill = "type")+ clean_theme()
yplot <- ggdensity(projected_data, PCy, fill = "type")+ rotate()+ clean_theme()
# Arranging the plot
print(ggarrange(xplot, NULL, pcx.y, yplot,
ncol = 2, nrow = 2, align = "hv",
widths = c(2, 1), heights = c(1, 2),
common.legend = TRUE))
}
else{
pcx.y
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.