Correlation Circle Plot for Principal Component Analysis

The active variables of a principal component analysis as arrows in the circle of correlations: the coordinate of a variable on an axis is its correlation with it. Hovering a variable shows its coordinates, and its projections on the two axes. ggfacto draws it for a principal component analysis with `profiles = FALSE` and nothing else asked for; by default, it draws the same arrows over the cloud of individuals.

Usage

ggpca_cor_circle(
  res.pca,
  axes = c(1, 2),
  proj = FALSE,
  interactive = FALSE,
  text_size = 3.5,
  lang = NULL,
  axes_names = NULL,
  axes_reverse = NULL,
  title,
  xlim,
  ylim
)

Arguments

res.pca

An analysis made with principal_component_analysis or FactoMineR::PCA.

axes

The axes to print, as a numeric vector of length 2.

proj

Set to `TRUE` to print projections of vectors over the two axes.

interactive

Set to `TRUE` to get the interactive graph at once, as ggi would make it. By default, a ggplot, to which elements can be added with `+` before passing it to ggi.

text_size

Size of the text.

lang

NULL (the session's language), "en" or "fr".

axes_names

Names of all the axes, as a character vector.

axes_reverse

`1` to invert left and right, `2` to invert up and down, `1:2` for both.

title

The title of the graph.

xlim, ylim

Horizontal and vertical limits, as numeric vectors of length 2.

Value

A ggplot, or an html widget with `interactive = TRUE`.

Examples

data(mtcars, package = "datasets")
mtcars <- mtcars[1:7] |> dplyr::rename(weight = wt)
res.pca <- principal_component_analysis(mtcars, 1:7)
ggpca_cor_circle(res.pca)

ggpca_cor_circle(res.pca) |> ggi()  # interactive