Methods ...: Practical Guide To Principal Component

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.

: Principal Component Analysis (PCA) for quantitative variables.

: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results. Practical Guide To Principal Component Methods ...

: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It

: Specifically those looking to move beyond "old-school" base R graphics to more modern, publication-ready visualizations. Practical Guide To Principal Component Methods in R : Factor Analysis of Mixed Data (FAMD) and

: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation. : Those who need to analyze large multivariate

The book categorizes methods based on the types of data you are analyzing: