Clustering in R: Methods, Algorithms, and Examples

A practical tutorial on clustering in R covering three main algorithms: k-means, Partitioning Around Medoids (PAM), and hierarchical clustering. Includes working R code using the UCI wine dataset and World Bank country data. Covers how to choose the number of clusters using Hartigan's Rule, the Gap statistic, elbow method, and silhouette scores. Also discusses linkage methods for hierarchical clustering, visualization techniques including dendrograms and world maps, and package recommendations for scaling (fastcluster, ClusterR, tidyclust) and reproducibility (renv).