Naive Principal Component Analysis in R

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. Identify number of components (aka factors) In this stage, principal components (formally called'factors' at this stage) are identified among the set of variables. Cumulative var: variance added consecutively up to the last component. Cumulative proportion: the actually explained variance added consecutively up to the last component.