: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results.
: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.
: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.
: 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:
: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory.
: 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
: 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
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: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results.
: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.
: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.
: 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:
: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory.
: 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
: 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