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#devtools::use_package('ggplot2')
#library('ggplot2')
#' @title Variables factor map for \code{\link[rsvd]{rpca}} using \code{\link[ggplot2]{ggplot}}.
#
#' @description Creates a pretty plot which is showing the correlation of
#' the original variable with the principal component (PCs).
#'
#' @param rpcaObj Object returned by the \code{\link[rsvd]{rpca}} function.
#'
#' @param pcs Array_like. \cr
#' An array with two values indicating the two PCs which should be used for plotting.
#' By default the first two PCs are used, e.g., \eqn{c(1,2)}.
#'
#' @param loadings Bool (\eqn{TRUE}, \eqn{FALSE}), optional. \cr
#' If \eqn{TRUE}, the eigenvectors
#' are unit scaled by the square root of the eigenvalues \eqn{W = W * diag(sqrt(eigvals))}.
#'
#' @param var_labels Bool (\eqn{TRUE}, \eqn{FALSE}), optional. \cr
#' Plot variable names, if \eqn{TRUE}.
#'
#' @param var_labels.names Array_like, optional. \cr
#' User specific labels for the variables
#'
#' @param alpha Scalar, optional. \cr
#' Alpha transparency of the arrows.
#'
#' @param top.n Scalar, optional. \cr
#' Number of (most influencial) variables to label with small circles.
#'
#' @seealso \code{\link[rsvd]{rpca}}, \code{\link[ggplot2]{ggplot}}
#'
#' @author N. Benjamin Erichson, \email{erichson@berkeley.edu}
#'
#' @examples #
#'
#' @export
ggcorplot <- function(rpcaObj, pcs=c(1,2), loadings=TRUE, var_labels=FALSE, var_labels.names=NULL, alpha=1, top.n=NULL) {
if (!requireNamespace('ggplot2', quietly = TRUE)) {
stop("The package 'ggplot2' is needed for this function to work. Please install it.",
call. = FALSE)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Number of variables
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
p = nrow(rpcaObj$rotation)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Check selected pcs
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
stopifnot(length(pcs) == 2)
if(max(pcs) > ncol(rpcaObj$rotation)) stop("Selected PC is not valid.")
if(min(pcs) < 1) stop("Selected PC is not valid.")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Select PCs
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PC1 = paste("PC", pcs[1], sep="")
PC2 = paste("PC", pcs[2], sep="")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate circle
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
theta <- c(seq(-pi, pi, length = 360))
circle <- data.frame(x = cos(theta), y = sin(theta))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Create data frame
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if(loadings==FALSE) rotation = rpcaObj$rotation[,pcs]
if(loadings==TRUE) rotation = t(t(rpcaObj$rotation[,pcs]) * rpcaObj$eigvals[pcs]**0.5)
df <- data.frame(rotation=rotation, row.names = 1:p)
colnames(df) <- c( 'a', 'b')
if(is.null(rownames(rpcaObj$rotation))) {
df$"varName" <- as.character(1:p)
} else {
df$"varName" <- rownames(rpcaObj$rotation)
}
if(!is.null(var_labels.names)) df$"varName" <- var_labels.names
df$abs <- sqrt(df$a**2 + df$b**2)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Label PCs
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
variance = rpcaObj$sdev**2
explained_variance_ratio = round(variance / rpcaObj$var, 3) * 100
PC1 = paste("PC ", pcs[1], "(", explained_variance_ratio[pcs[1]] , "% explained var.)", sep="")
PC2 = paste("PC ", pcs[2], "(", explained_variance_ratio[pcs[2]] , "% explained var.)", sep="")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#Workaround for CRAN: Nulling
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
x <- NULL # Setting the variables to NULL first
y <- NULL # Setting the variables to NULL first
a <- NULL # Setting the variables to NULL first
b <- NULL # Setting the variables to NULL first
varName <- NULL # Setting the variables to NULL first
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Create ggplot2::ggplot
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
g <- ggplot2::ggplot( circle , ggplot2::aes( x , y) ) +
ggplot2::geom_path( size=0.5, colour="black" )
if(is.null(top.n)) top.n <- nrow(df)
if(top.n>nrow(df)) top.n <- nrow(df)
if(top.n < 50) {
g <- g + ggplot2::geom_point(data = df[order(df$abs, decreasing=TRUE)[1:top.n], ],
size = 4, mapping = ggplot2::aes(x = a, y = b, colour = varName ) ) +
ggplot2::theme(legend.position = "none")
}
g <- g + ggplot2::geom_segment(data = df,
ggplot2::aes(x = 0, y = 0, xend = a, yend = b ),
arrow = grid::arrow(length = grid::unit(0.5, 'picas')),
color = 'black' , size = 0.5, alpha = alpha)
g <- g + ggplot2::coord_fixed(ratio=1)
g <- g + ggplot2::ggtitle('Variables factor map (PCA)')
g <- g + ggplot2::xlab(PC1) + ggplot2::ylab(PC2)
g <- g + ggplot2::guides(colour=ggplot2::guide_legend(title=NULL))
g <- g + ggplot2::geom_vline(xintercept=0, linetype="dashed", color = "black")
g <- g + ggplot2::geom_hline(yintercept=0, linetype="dashed", color = "black")
# Label the variable axes
if(var_labels == TRUE) {
#df$a <- df$a *1.1
#df$b <- df$b * 1.1
g <- g + ggplot2::geom_text(data = df[order(df$abs, decreasing=TRUE)[1:top.n], ], ggplot2::aes(label = varName, x = a, y = b,
angle = 0, hjust = 0, vjust = 0),
color = 'black', size = 4)
}
g <- g + ggplot2::theme_bw() #+ ggplot2::xlim(c(-1.2,1.2)) + ggplot2::ylim(c(-1.2,1.2)) +
g <- g + ggplot2::theme(panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank())
return( g )
}
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