Interpreting and visualising PCA models with factoextra

Dimensionality Reduction in R

Alexandros Tantos

Assistant Professor, Aristotle University of Thessaloniki

Plotting contributions of variables

fviz_pca_var(mtcars_pca, 
 col.var = "contrib",
 gradient.cols = c("#bb2e00", "#002bbb"),
 repel = TRUE)

fviz_pca_var(mtcars_pca,
 select.var = list(contrib = 4),
 repel = TRUE)

fviz_contrib(mtcars_pca,
    choice = "var",
    axes = 1,
    top = 5)

fviz_pca_ind(mtcars_pca,
    col.ind="cos2",
    gradient.cols = c("#bb2e00", "#002bbb"),
    repel = TRUE)

fviz_pca_ind(mtcars_pca, 
    select.ind = list(cos2 = 0.8),
    gradient.cols = c("#bb2e00", "#002bbb"),
    repel = TRUE)

fviz_cos2(mtcars_pca,
    choice = "ind", 
    axes = 1, 
    top = 10)

fviz_pca_biplot(mtcars_pca)

mtcars$cyl <- as.factor(mtcars$cyl)

fviz_pca_ind(mtcars_pca, 
    label="var",
    habillage=mtcars$cyl,
    addEllipses=TRUE)

Dimensionality Reduction in R