#' @title PCA
#' @description This function does a principle component analysis on a data frame then creates
#' plots to visualize the data
#' @param d dataframe
#' @param group groups to create confidence interval for PCA plot
#' @keywords PCA
#' @export
#' @examples
#' PCA(d = iris, group = iris$Species)
PCA <- function(d, group) {
d2 <- d[,sapply(d, is.numeric)]
d2 <- na.omit(d2)
p <- prcomp(d2, scale = TRUE)
p1 <- plot(p, type = 'l')
p2 <- summary(p)
p3 <- biplot(p, scale = 0)
d3 <- cbind(d, p$x)
p4 <- ggplot2::ggplot(data = d3, ggplot2::aes(x = PC1, y = PC2, color = group, fill = group)) +
ggplot2::stat_ellipse(geom = "polygon", color = "black", alpha = 0.5) +
ggplot2::geom_point(color = "black")
stuff <- list(p1, p2, p3, p4, p, d3)
stuff
}
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