R/Functions.R

In ksanchez1215/Custom_Package_Vignette_Project: Principal Component Analysis Package

#' @title pca_tab
#' @description Performs principal components analysis on a data matrix and returns the result in tabular form
#' @param x a numeric or complex data frame that provides the data for the analysis
#' @param retx a logical value indicating whether rotated variables should be returned
#' @param center a logical value indicating if variables should be shifted to be zero centered
#' @param scale. a logical value indicating whether variables should be scaled to have unit variance before analysis
#' @param tol a value indicating the magnitude below which components should be omitted
#' @keywords PCA components
#' @export
#' @examples
#' pca(df, scale. = TRUE)

pca <- function(x, retx = TRUE, center = TRUE, scale. = FALSE, tol = NULL, ...)
  {
    chkDots(...)
    x <- as.matrix(x)
    x <- scale(x, center = center, scale = scale.)
    cen <- attr(x, "scaled:center")
    sc <- attr(x, "scaled:scale")
    if(any(sc == 0))
      stop("cannot rescale a constant/zero column to unit variance")
    s <- svd(x, nu = 0)
    s$d <- s$d / sqrt(max(1, nrow(x) - 1))
    if (!is.null(tol)) {
      rank <- sum(s$d > (s$d[1L]*tol))
      if (rank < ncol(x)) {
        s$v <- s$v[, 1L:rank, drop = FALSE]
        s$d <- s$d[1L:rank]
      }
    }
    dimnames(s$v) <-
      list(colnames(x), paste0("PC", seq_len(ncol(s$v))))
    r <- list(sdev = s$d, rotation = s$v,
              center = if(is.null(cen)) FALSE else cen,
              scale = if(is.null(sc)) FALSE else sc)
    if (retx) r$x <- x %*% s$v
    class(r) <- "prcomp"
    r
  }

pca_tab <- function(x) {
  summary(pca(x))}


#' @title network_data
#' @description dataset including pollinator species found at 50 transect locations
f <- "https://raw.githubusercontent.com/ksanchez1215/Custom_Package_Vignette_Project/master/2012_network_ord.csv"
network_data <- readr::read_csv(f, col_names = TRUE)
save(network_data, file = "network_data.RData")

#' @title pca_plot
#' @description Plots the results of a principal components analysis is 2 dimensions 
#' @param d a numeric or complex data frame that provides the data for the analysis
#' @param dataframe the entire dataframe that d came from, that can have variables as factors to group the results
#' @param groupby the variable from dataframe that you want to color your plot by
#' @param frame a logical value indicating whether your groups should have a geometric shape drawn around them
#' @param frame.type the shape of the frame, can be 'norm', 't', or 'euclid'. Default is 'norm' which creates an ellipse around the groups
#' @keywords PCA plot graph 
#' @export
#' @examples
#' pca_plot(d = network_data[,10:130], dataframe = network_data, groupby = 'History3', frame = TRUE, frametype = 'norm')

pca_plot<- function(d, dataframe, groupby, frame = TRUE, frametype = 'norm'){
  temp<- pca(d)
  plot<- ggplot2::autoplot(temp, data = dataframe, colour = groupby, frame = frame, frame.type = frametype)
  return(plot)
}


pca_plot(d = network_data[,10:130], dataframe = network_data, groupby = 'History3', frame = TRUE, frametype = 'norm')