{"id":16746,"date":"2020-06-02T22:28:33","date_gmt":"2020-06-02T21:28:33","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=16746"},"modified":"2020-06-02T22:28:33","modified_gmt":"2020-06-02T21:28:33","slug":"k-means-clustering-visualization-in-r-step-by-step-guide","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/k-means-clustering-visualization-in-r-step-by-step-guide\/","title":{"rendered":"K-Means Clustering Visualization in R: Step By Step Guide"},"content":{"rendered":"<div id=\"rdoc\">\n<p>This article provides examples of codes for <strong>K-means clustering visualization in R<\/strong> using the <code>factoextra<\/code> and the <code>ggpubr<\/code> R packages. You can learn more about the k-means algorithm by reading the following blog post: <a href=\"https:\/\/www.datanovia.com\/en\/lessons\/k-means-clustering-in-r-algorith-and-practical-examples\/\">K-means clustering in R: Step by Step Practical Guide<\/a>.<\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#required-r-packages\">Required R packages<\/a><\/li>\n<li><a href=\"#data-preparation\">Data preparation<\/a><\/li>\n<li><a href=\"#k-means-clustering-calculation-example\">K-means clustering calculation example<\/a><\/li>\n<li><a href=\"#plot-k-means\">Plot k-means<\/a>\n<ul>\n<li><a href=\"#using-the-factoextra-r-package\">Using the factoextra R package<\/a><\/li>\n<li><a href=\"#using-the-ggpubr-r-package\">Using the ggpubr R package<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"required-r-packages\" class=\"section level2\">\n<h2>Required R packages<\/h2>\n<ul>\n<li><a href=\"https:\/\/rpkgs.datanovia.com\/ggpubr\/\">ggpubr<\/a>: creates publication ready plots.<\/li>\n<li><a href=\"https:\/\/rpkgs.datanovia.com\/factoextra\/index.html\">factoextra<\/a>: Extract and Visualize the Results of Multivariate Data Analyses.<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(ggpubr)\r\nlibrary(factoextra)<\/code><\/pre>\n<\/div>\n<div id=\"data-preparation\" class=\"section level2\">\n<h2>Data preparation<\/h2>\n<pre class=\"r\"><code>data(\"iris\")\r\ndf &lt;- iris\r\nhead(df, 3)<\/code><\/pre>\n<pre><code>##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species\r\n## 1          5.1         3.5          1.4         0.2  setosa\r\n## 2          4.9         3.0          1.4         0.2  setosa\r\n## 3          4.7         3.2          1.3         0.2  setosa<\/code><\/pre>\n<\/div>\n<div id=\"k-means-clustering-calculation-example\" class=\"section level2\">\n<h2>K-means clustering calculation example<\/h2>\n<ul>\n<li>Removing the 5th column (<code>Species<\/code>) and scale the data to make variables comparable<\/li>\n<li>Calculate k-means clustering using k = 3. As the final result of k-means clustering result is sensitive to the random starting assignments, we specify nstart = 25. This means that R will try 25 different random starting assignments and then select the best results.<\/li>\n<\/ul>\n<pre class=\"r\"><code># Compute k-means with k = 3\r\nset.seed(123)\r\nres.km &lt;- kmeans(scale(df[, -5]), 3, nstart = 25)\r\n# K-means clusters showing the group of each individuals\r\nres.km$cluster<\/code><\/pre>\n<pre><code>##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 2 2 2 3 2 2 2\r\n##  [61] 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 3 3 3 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 2 2 3 3 3 3 2\r\n## [121] 3 2 3 2 3 3 2 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 2 3 3 3 2 3 3 2<\/code><\/pre>\n<\/div>\n<div id=\"plot-k-means\" class=\"section level2\">\n<h2>Plot k-means<\/h2>\n<div id=\"using-the-factoextra-r-package\" class=\"section level3\">\n<h3>Using the factoextra R package<\/h3>\n<p>The function <code>fviz_cluster()<\/code> [factoextra package] can be used to easily visualize k-means clusters. It takes k-means results and the original data as arguments. In the resulting plot, observations are represented by points, using principal components if the number of variables is greater than 2. It\u2019s also possible to draw concentration ellipse around each cluster.<\/p>\n<pre class=\"r\"><code>fviz_cluster(res.km, data = df[, -5],\r\n             palette = c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\"), \r\n             geom = \"point\",\r\n             ellipse.type = \"convex\", \r\n             ggtheme = theme_bw()\r\n             )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/k-means-clustering-visualization-in-r-plot-k-means-in-r-1.png\" width=\"528\" \/><\/p>\n<\/div>\n<div id=\"using-the-ggpubr-r-package\" class=\"section level3\">\n<h3>Using the ggpubr R package<\/h3>\n<p>If you want to adapt the k-means clustering plot, you can follow the steps below:<\/p>\n<ol style=\"list-style-type: decimal;\">\n<li>Compute principal component analysis (PCA) to reduce the data into small dimensions for visualization<\/li>\n<li>Use the <code>ggscatter()<\/code> R function [in ggpubr] or ggplot2 function to visualize the clusters<\/li>\n<\/ol>\n<div id=\"compute-pca-and-extract-individual-coordinates\" class=\"section level4\">\n<h4>Compute PCA and extract individual coordinates<\/h4>\n<pre class=\"r\"><code># Dimension reduction using PCA\r\nres.pca &lt;- prcomp(df[, -5],  scale = TRUE)\r\n# Coordinates of individuals\r\nind.coord &lt;- as.data.frame(get_pca_ind(res.pca)$coord)\r\n# Add clusters obtained using the K-means algorithm\r\nind.coord$cluster &lt;- factor(res.km$cluster)\r\n# Add Species groups from the original data sett\r\nind.coord$Species &lt;- df$Species\r\n# Data inspection\r\nhead(ind.coord)<\/code><\/pre>\n<pre><code>##   Dim.1  Dim.2   Dim.3    Dim.4 cluster Species\r\n## 1 -2.26 -0.478  0.1273  0.02409       1  setosa\r\n## 2 -2.07  0.672  0.2338  0.10266       1  setosa\r\n## 3 -2.36  0.341 -0.0441  0.02828       1  setosa\r\n## 4 -2.29  0.595 -0.0910 -0.06574       1  setosa\r\n## 5 -2.38 -0.645 -0.0157 -0.03580       1  setosa\r\n## 6 -2.07 -1.484 -0.0269  0.00659       1  setosa<\/code><\/pre>\n<pre class=\"r\"><code># Percentage of variance explained by dimensions\r\neigenvalue &lt;- round(get_eigenvalue(res.pca), 1)\r\nvariance.percent &lt;- eigenvalue$variance.percent\r\nhead(eigenvalue)<\/code><\/pre>\n<pre><code>##       eigenvalue variance.percent cumulative.variance.percent\r\n## Dim.1        2.9             73.0                        73.0\r\n## Dim.2        0.9             22.9                        95.8\r\n## Dim.3        0.1              3.7                        99.5\r\n## Dim.4        0.0              0.5                       100.0<\/code><\/pre>\n<\/div>\n<div id=\"visualize-k-means-clusters\" class=\"section level4\">\n<h4>Visualize k-means clusters<\/h4>\n<ul>\n<li>Color individuals according to the cluster groups<\/li>\n<li>Change point shapes according to the <code>Species<\/code> groups (ground truth of grouping)<\/li>\n<li>Add concentration ellipses<\/li>\n<li>Add cluster centroid using the <code>stat_mean()<\/code> [ggpubr] R function<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggscatter(\r\n  ind.coord, x = \"Dim.1\", y = \"Dim.2\", \r\n  color = \"cluster\", palette = \"npg\", ellipse = TRUE, ellipse.type = \"convex\",\r\n  shape = \"Species\", size = 1.5,  legend = \"right\", ggtheme = theme_bw(),\r\n  xlab = paste0(\"Dim 1 (\", variance.percent[1], \"% )\" ),\r\n  ylab = paste0(\"Dim 2 (\", variance.percent[2], \"% )\" )\r\n) +\r\n  stat_mean(aes(color = cluster), size = 4)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/k-means-clustering-visualization-in-r-step-by-step-visualization-of-k-means-1.png\" width=\"528\" \/><\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"conclusion\" class=\"section level2\">\n<h2>Conclusion<\/h2>\n<p>This article provides examples of codes for <strong>K-means clustering visualization in R<\/strong> using the <code>factoextra<\/code> and the <code>ggpubr<\/code> R packages. Read more about the k-means clustering algorithm at: <a href=\"https:\/\/www.datanovia.com\/en\/lessons\/k-means-clustering-in-r-algorith-and-practical-examples\/\">K-means clustering in R: Step by Step Practical Guide<\/a>.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article provides examples of codes for K-means clustering visualization in R using the factoextra and the ggpubr R packages. You can learn more about the k-means algorithm by reading [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":16747,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[123],"tags":[369,343,368],"class_list":["post-16746","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cluster-analysis","tag-factoextra","tag-ggpubr","tag-k-means"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>K-Means Clustering Visualization in R: Step By Step Guide - Datanovia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.datanovia.com\/en\/blog\/k-means-clustering-visualization-in-r-step-by-step-guide\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"K-Means Clustering Visualization in R: Step By Step Guide - Datanovia\" \/>\n<meta property=\"og:description\" content=\"This article provides examples of codes for K-means clustering visualization in R using the factoextra and the ggpubr R packages. 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