Practical Guide To Principal Component Methods ... ⚡

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

The book categorizes methods based on the types of data you are analyzing: Practical Guide To Principal Component Methods ...

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation. : Factor Analysis of Mixed Data (FAMD) and

: It is structured with short, self-contained chapters and "R lab" sections that walk through real-world applications and tested code examples. Core Methods Covered Core Methods Covered : Specifically those looking to

: 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

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

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