Principal Component Analysis (PCA) is a foundational, powerful technique for working with multiple variables. Applicable to a wide variety of data types, PCA can make the analysis of even thousands of variables possible by decomposing the variance-covariance or correlation matrix. Through PCA, you will be able to
- reduce the dimensionality of your data — potentially from thousands of variables to as few as two!
- obtain uncorrelated principal components that can be better suited for use in other statistical and machine learning analyses,
- identify relationships and structure among your variables by summarizing the structure of their correlations.
In this workshop, you will learn to apply and interpret PCA and come away knowing best practices and potential pitfalls and equipped with the tools needed to conduct and use PCA. We will explore several data sets using R.