Practical Guide To Principal Component Methods ... < VALIDATED >

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

: 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

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation. Practical Guide To Principal Component Methods ...

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

: 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 : Simple Correspondence Analysis (CA) for two variables

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

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables. : The book heavily utilizes the author's own

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