Draw PCA Biplot & Loading Plot in R (Example) | Apply & Visualize Principal Component Analysis

How to perform a PCA and how to draw loading plots and biplots using R programming. Check out my comprehensive online course on Principal Component Analysis (PCA) – From Theory to Application in R for more details: https://statisticsglobe.com/online-course-pca-theory-application-r R code of this video: install.packages("factoextra") # Install & load factoextra library(factoextra) data(iris) # Load iris data head(iris) # Print first six rows iris_pca ＜- prcomp(iris[, 1:4], # Perform PCA on numeric columns scale = TRUE) # Scale the data before PCA summary(iris_pca) # PCA summary statistics fviz_pca_var(iris_pca, # Create loading plot col.var = "contrib", # Color variables by contribution repel = TRUE) # Prevent overlapping text labels fviz_pca_biplot(iris_pca, # Create biplot label = "var", # Label only the variables habillage = iris$Species) # Color by groups Follow me on Social Media: Facebook – Statistics Globe Page: https://www.facebook.com/statisticsglobecom/ Facebook – R Programming Group for Discussions & Questions: https://www.facebook.com/groups/statisticsglobe Facebook – Python Programming Group for Discussions & Questions: https://www.facebook.com/groups/statisticsglobepython LinkedIn – Statistics Globe Page: https://www.linkedin.com/company/statisticsglobe/ LinkedIn – R Programming Group for Discussions & Questions: https://www.linkedin.com/groups/12555223/ LinkedIn – Python Programming Group for Discussions & Questions: https://www.linkedin.com/groups/12673534/ Twitter: https://twitter.com/JoachimSchork Instagram: https://www.instagram.com/statisticsglobecom/ TikTok: https://www.tiktok.com/@statisticsglobe