R Biplot Example

admin  4/3/2022
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Biplot of individuals and variables. Note that, in the R code below, the argument data is required only when res.pca is an object of class prcomp or princomp.In others word, it can be omitted when the PCA is performed using FactoMineR or ade4. An implementation of the biplot using ggplot2. The package provides two functions: ggscreeplot and ggbiplot.ggbiplot aims to be a drop-in replacement for the built-in R function biplot.princomp with extended functionality for labeling groups, drawing a correlation circle, and adding Normal probability ellipsoids.

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Draw the graph of individuals/variables from the output of Principal Component Analysis (PCA).

The following functions, from factoextra package are use:

  • fviz_pca_ind(): Graph of individuals
  • fviz_pca_var(): Graph of variables
  • fviz_pca_biplot() (or fviz_pca()): Biplot of individuals and variables
R biplot examples

The package devtools is required for the installation as factoextra is hosted on github.

Load factoextra :

ArgumentDescription
Xan object of class PCA [FactoMineR]; prcomp and princomp [stats]; dudi and pca [ade4].
axesa numeric vector of length 2 specifying the dimensions to be plotted.
geoma text specifying the geometry to be used for the graph. Allowed values are the combination of c(“point”, “arrow”, “text”). Use “point” (to show only points); “text” to show only labels; c(“point”, “text”) or c(“arrow”, “text”) to show both types.
labela text specifying the elements to be labelled. Default value is “all”. Allowed values are “none” or the combination of c(“ind”, “ind.sup”, “quali”, “var”, “quanti.sup”). “ind” can be used to label only active individuals. “ind.sup” is for supplementary individuals. “quali” is for supplementary qualitative variables. “var” is for active variables. “quanti.sup” is for quantitative supplementary variables.
invisiblea text specifying the elements to be hidden on the plot. Default value is “none”. Allowed values are the combination of c(“ind”, “ind.sup”, “quali”, “var”, “quanti.sup”).
labelsizefont size for the labels.
pointsizethe size of points.
habillagean optional factor variable for coloring the observations by groups. Default value is “none”. If X is a PCA object from FactoMineR package, habillage can also specify the supplementary qualitative variable (by its index or name) to be used for coloring individuals by groups (see ?PCA in FactoMineR).
addEllipseslogical value. If TRUE, draws ellipses around the individuals when habillage != “none”.
ellipse.levelthe size of the concentration ellipse in normal probability.
col.ind,col.varcolors for individuals and variables, respectively. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the colors for individuals/variables are automatically controlled by their qualities of representation (“cos2”), contributions (“contrib”), coordinates (x^2 + y^2, “coord”), x values (“x”) or y values (“y”). To use automatic coloring (by cos2, contrib, ….), make sure that habillage =“none”.
col.ind.supcolor for supplementary individuals.
alpha.ind,alpha.varcontrols the transparency of individual and variable colors, respectively. The value can variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values include also : “cos2”, “contrib”, “coord”, “x” or “y”. In this case, the transparency for the individual/variable colors are automatically controlled by their qualities (“cos2”), contributions (“contrib”), coordinates (x^2 + y^2 , “coord”), x values(“x”) or y values(“y”). To use this, make sure that habillage =“none”.
select.ind,select.var

a selection of individuals/variables to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:

  • name: is a character vector containing individuals/variables to be drawn
  • cos2: if cos2 is in [0, 1], ex: 0.6, then individuals/variables with a cos2 > 0.6 are drawn. if cos2 > 1, ex: 5, then the top 5 individuals/variables with the highest cos2 are drawn.
  • contrib: if contrib > 1, ex: 5, then the top 5 individuals/variables with the highest cos2 are drawn
jittera parameter used to jitter the points in order to reduce overplotting. It’s a list containing the objects what, width and height (Ex.; jitter = list(what, width, height)). what: the element to be jittered. Possible values are “point” or “p”; “label” or “l”; “both” or “b”. width: degree of jitter in x direction (ex: 0.2). height: degree of jitter in y direction (ex: 0.2).
col.quanti.supa color for the quantitative supplementary variables.
col.circlea color for the correlation circle.
…Arguments to be passed to the function fviz_pca_biplot().

Principal component analysis

A principal component analysis (PCA) is performed using the built-in R function prcomp() and iris data:

fviz_pca_var(): Graph of variables

fviz_pca_biplot(): Biplot of individuals of variables

This analysis has been performed using R software (ver. 3.2.1) and factoextra (ver. 1.0.3)


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