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Unsupervised learning in R

Step 1: Data preparation
Step 2: Data description
Step 3: Load required libraries
Step 4: Compute Gap Statistics to determine total number of clusters
Step 5: Compute hierarchical clustering
Step 6" Visualize the cluster dendrogram using factoextra R package

ul.R

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+#1.Import data
+###Import your own dataset from your device
+data1 <- read.csv (file="C:/Users/Mogana Darshini/OneDrive/unsupervised learning/Manuscript/Analysis/data1.csv")
+
+#2.describe data
+dim(data1) ##will give the details of number of columns and rows
+str(data1) ##will describe the structure of the data
+
+#3.Load libraries
+library(factoextra)
+library(cluster)
+library(magrittr)
+
+#4.Compute Gap Statistics to determine total number of clusters
+fviz_nbclust(data1, kmeans, method = "gap_stat")
+
+#5. Compute hierarchical clustering
+res.hc <- data1 %>%
+  scale() %>%                    # Scale the data
+  dist(method = "euclidean") %>% # Compute dissimilarity matrix
+  hclust(method = "ward.D2")     # Compute hierachical clustering
+
+print (res.hc)
+
+#6.# Visualize using factoextra
+# Cut in five groups and color by groups
+fviz_dend(res.hc, k = 5, # Cut in five groups
+          cex = 0.5, # label size
+          k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07", "1D08A5"),
+          color_labels_by_k = TRUE, # color labels by groups
+          rect = TRUE # Add rectangle around groups
+)
+
+#6.k means clustering
+set.seed (20)
+kmeans_clustering <- kmeans(data1, 5)
+#inspect 'kmeans_clustering'
+str(kmeans_clustering)
+#cluster: a vector of integers (from 1:k) indicating the cluster to which each point is allocated.
+#centers: a matrix of cluster centers.
+#withinss: vector of within-cluster sum of squares, one component per cluster.
+#tot.withinss: total within-cluster sum of squares. That is, sum(withinss).
+#size: the number of points in each cluster.
+
+print(kmeans_clustering)
+kmeans_clustering$cluster #variables in each cluster
+kmeans_clustering$size #total 
+
+# Scatter plot of x
+fviz_cluster(kmeans_clustering, data = data1,
+             palette = c("#1D08A5", "#00AFBB", "#E7B800", "#08A508", "#A50822"), 
+             geom = "point",
+             ellipse.type = "convex", 
+             ggtheme = theme_bw()
+)