PCA biplot

Runs a principal component analysis on the numeric columns of a data frame (or takes an existing stats::prcomp() object) and draws a biplot: the observations projected onto two components, with the variable loadings shown as arrows. Optionally colour the observations by a grouping variable.

Usage

pca_plot(
  x,
  cols = NULL,
  group = NULL,
  components = c(1, 2),
  scale = TRUE,
  loadings = TRUE,
  point_alpha = 0.7,
  palette = NULL,
  title = NULL
)

Arguments

x

A data frame, or a stats::prcomp() object.

cols

When x is a data frame, the numeric columns to analyse (default: all numeric).

group

Optional grouping variable mapped to colour. When x is a data frame, give a column of x, unquoted or as a string, or a vector with one value per row of x. Rows with missing values count, although the analysis drops them. When x is a stats::prcomp() object, give a vector with one value per observation. A name that matches no column, or a vector of another length, is an error.

components

Length-2 integer vector: which components to plot.

scale

Whether to scale the variables to unit variance before the PCA. This is advisable when the variables are on different scales.

loadings

Whether to draw variable-loading arrows.

point_alpha

Point transparency.

palette

Colours for the groups; defaults to depictr_palette().

title

Plot title.

Value

A ggplot2::ggplot object.

Details

Each arrow is a variable's loadings on the two components, taken from the rotation matrix and multiplied by a factor that all the arrows share. The factor makes the largest coordinate of any arrow three quarters of the largest absolute score. Both axes use that one factor, as stats::biplot() uses one ratio for both, and the aspect ratio is fixed at 1. An arrow's angle on the page is therefore its angle in loading space, whatever the size of the figure. The panel's height and width follow the ranges of its two axes, so a figure of another shape leaves blank space around the panel. Because the scores are drawn as they are and the arrows as loadings, the angle between two arrows does not estimate the correlation between their variables. Arrows and labels are drawn in a neutral dark grey (grey20), which is not in the default palette, so no arrow reads as part of a group.

Examples

pca_plot(crop_yield, cols = c("rainfall", "fertiliser", "soil_ph", "yield"),
         group = "treatment")