pca_plot: 'Clean' looking PCA plotting function using ggplot2

In JackAHutchings/jahrfun: JAH Miscellaneous R Functions

'Clean' looking PCA plotting function using ggplot2

Description

'Clean' looking PCA plotting function using ggplot2

Usage

pca_plot(
  data,
  cols,
  color = NA,
  shape = NA,
  label = NA,
  scale = T,
  var_scaling = 5,
  text_size = 8,
  legend_position = "top",
  font_family = "serif",
  axis_alpha = 0.5,
  geom_type = "text",
  point_size = 4,
  repel_variables = F,
  repel_samples = F,
  point_outline = F
)

Arguments

data	Input dataset.
cols	Selection of columns from input dataset to perform the PCA on. These must all be
color	Optional. Select a column for color aesthetic mapping.
shape	Optional. Select a column for shape aesthetic mapping.
label	Optional. Select a column for sample label aesthetic mapping.
scale	Boolean. If TRUE, then correlation PCA. if FALSE, then covariance PCA.
var_scaling	Multiplier for raw variable loadings. Helps scale them to similar values as sample loadings most of the time.
text_size	Font size of all printed labels in points.
legend_position	Legend position. Accepts the same input as legend.position in ggplot's theme function
font_family	Font family to use for all labels.
axis_alpha	Alpha of the plotting x and y axes.
geom_type	Either "text" or "label" to use either geom_text or geom_label, respectively.
point_size	Point size in mm of plotted points.
repel_variables	Boolean. Should plotted variables be repelled?
repel_samples	Boolean. Should plotted samples be repelled?
point_outline	Boolean. Should sample points have a black border?

JackAHutchings/jahrfun documentation built on June 8, 2025, 3:09 a.m.