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 (a column name when x is a data frame, or a vector the length of the data) mapped to colour.

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.

Examples

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