# Install required packages install.packages(c("cluster", "ape", "poppr", "ade4")) # Load the packages library(cluster) library(ape) library(poppr) library(ade4) # Import genetic data # Read the data file into R data <- read.csv("Genetic_data.csv", header = TRUE) # Add unique identifiers to rows data$unique_id <- seq_len(nrow(data)) # Use the unique identifiers as row names row.names(data) <- data$unique_id # Remove the additional column if not needed data <- data[, -ncol(data)] # Add unique identifiers to rows data$unique_id <- 1:nrow(data) # Assuming there are no existing unique identifiers # Use the unique identifiers as row names row.names(data) <- data$unique_id # Remove the additional column if not needed genetic_data <- data[, -ncol(data)] # Load the vegan package library(vegan) # Calculate Jaccard's similarity matrix genetic_sim <- vegdist(genetic_data, method = "jaccard") # Perform UPGMA clustering genetic_cluster <- hclust(genetic_sim, method = "average") # Plot the dendrogram plot(genetic_cluster)