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Principal Component Analysis (PCA) on Stock Returns in R

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Principal Component Analysis Principal Component Analysis is a statistical process that distills measurement variation into vectors with greater ability to predict outcomes utilizing a process of scaling, covariance, and eigendecomposition.
MS Azure Notebook The work for this is done in the following notebook, Principal Component Analysis (PCA) on Stock Returns in R, with detailed code, output, and charts. An outline of the notebook contents are below.
Overview of DemonstrationSupporting MaterialPluralsightExplained VisuallyWikipediaLoad Data: Format Data & SortPrep Data: Create ReturnsGenerate Principal ComponentsEigen Decomposition and Scree PlotCreate Principal ComponentsAnalysisFVX using PCA versus Logistic RegressionAlternative Libraries: Psych for the Social Sciences