utils::globalVariables(c('i', 'j', 'value'))
#' Correlation Plot (Upper Triangle)
#'
#' Minimal ggplot2 correlation plot showing the upper triangle
#' in the top-right corner with no axis labels.
#' @param R A square numeric correlation matrix
#' @param title Optional plot title
#'
#' @return A ggplot object
#'
#' @examples
#' X <- matrix(data = rnorm(100), nrow = 10)
#' R <- cor(X)
#' corplot(R, title = 'Example Correlation Plot')
#'
#' @importFrom reshape2 melt
#' @import ggplot2
#' @export
corplot <- function(R, title = NULL){
	R <- tryCatch({
		if(is.matrix(R)){
			if(nrow(R) == ncol(R)){
				R[upper.tri(R)]
			}
		}else if(is.vector(R)){
			m <- length(R)
			n <- (1 + sqrt(1 + 8 * m)) / 2
			if(n == floor(n)){
				R
			}else{
				stop('Input length not compatible with upper triangle')
			}
		}else{
			Rm <- as.matrix(R)
			if(nrow(Rm) == ncol(Rm)){
				Rm[upper.tri(Rm)]
			}
		}
	}, error = function(e){
		stop(paste('Invalid input for corplot:', e$message))
	})

	# Now get a df from the upper triangle
	m <- length(R)
	n <- as.integer((1 + sqrt(1 + 8 * m)) / 2)
	M <- matrix(NA, n, n)
	M[upper.tri(M)] <- R
	df <- reshape2::melt(M, na.rm = TRUE)
	colnames(df) <- c('i', 'j', 'value')
	df$i <- factor(df$i, levels = 1:n)
	df$j <- factor(df$j, levels = 1:n)

	# And plot it
	p <- ggplot2::ggplot(
		data = df,
		ggplot2::aes(x = i, y = j, fill = value)
	) +
	ggplot2::geom_tile() +
	ggplot2::scale_fill_gradient2(
		low = 'red',
		mid = 'white',
		high = 'blue',
		midpoint = 0,
		limits = c(-1, 1),
		name = NULL
	) +
	ggplot2::scale_x_discrete(limits = levels(df$i)) +
	ggplot2::scale_y_discrete(limits = rev(levels(df$j))) +
	ggplot2::coord_fixed() +
	ggplot2::theme_void()

	if(!is.null(title)){
		p <- p +
		ggplot2::ggtitle(title) +
		ggplot2::theme(
			plot.title = ggplot2::element_text(hjust = 0.5)
		)
	}
	return(p)
}