Principal Component Analysis (PCA) in R studio | Biplot in R |Tutorial

In this tutorial, you'll learn how to perform Principal Component Analysis (PCA) in R Studio and visualize the results using a PCA biplot. PCA is a powerful statistical technique used to reduce the dimensionality of large datasets while retaining most of the variation in the data. This makes it especially useful in fields like plant breeding, genetics, bioinformatics, and data science.
🔍 What you’ll learn in this video:

How to prepare your dataset for PCA in R

Step-by-step PCA implementation using prcomp()

How to interpret eigenvalues, loadings, and scores

Creating and customizing biplots using ggbiplot or factoextra

Understanding the biological or experimental meaning behind principal components

Whether you're a researcher, student, or data analyst, this video will guide you through performing PCA and interpreting results visually.
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