{"id":15714,"date":"2020-04-11T19:18:17","date_gmt":"2020-04-11T18:18:17","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=15714"},"modified":"2020-04-13T14:10:02","modified_gmt":"2020-04-13T13:10:02","slug":"how-to-create-an-interactive-correlation-matrix-heatmap-in-r","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/blog\/how-to-create-an-interactive-correlation-matrix-heatmap-in-r\/","title":{"rendered":"How to Create an Interactive Correlation Matrix Heatmap in R"},"content":{"rendered":"<div id=\"rdoc\">\n<p>This articles describes how to create an <strong>interactive correlation matrix heatmap in R<\/strong>. You will learn two different approaches:<\/p>\n<ol style=\"list-style-type: decimal;\">\n<li>Using the <em>heatmaply<\/em> R package<\/li>\n<li>Using the combination of the <code>ggcorrplot<\/code> and the <code>plotly<\/code> R packages.<\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/images\/heatmaply-interactive-heatmap-in-r.gif\" alt=\"Interactive Heatmap in R using heatmaply\" \/><\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#prerequisites\">Prerequisites<\/a><\/li>\n<li><a href=\"#data-preparation\">Data preparation<\/a><\/li>\n<li><a href=\"#correlation-heatmaps-using-heatmaply\">Correlation heatmaps using heatmaply<\/a>\n<ul>\n<li><a href=\"#load-r-packages\">Load R packages<\/a><\/li>\n<li><a href=\"#basic-correlation-matrix-heatmap\">Basic correlation matrix heatmap<\/a><\/li>\n<li><a href=\"#change-the-point-size-according-to-the-correlation-test-p-values\">Change the point size according to the correlation test p-values<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#correlation-heatmaps-using-ggcorrplot\">Correlation heatmaps using ggcorrplot<\/a>\n<ul>\n<li><a href=\"#load-r-packages-1\">Load R packages<\/a><\/li>\n<li><a href=\"#static-heatmap-of-the-correlation-matrix\">Static heatmap of the correlation matrix<\/a><\/li>\n<li><a href=\"#make-the-correlation-heatmap-interactive\">Make the correlation heatmap interactive<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#references\">References<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"prerequisites\" class=\"section level2\">\n<h2>Prerequisites<\/h2>\n<p>Install required R packages:<\/p>\n<pre class=\"r\"><code>install.packages(\"plotly\")\r\ninstall.packages(\"heatmaply\")\r\ninstall.packages(\"ggcorrplot\")<\/code><\/pre>\n<\/div>\n<div id=\"data-preparation\" class=\"section level2\">\n<h2>Data preparation<\/h2>\n<pre class=\"r\"><code>df &lt;- mtcars<\/code><\/pre>\n<\/div>\n<div id=\"correlation-heatmaps-using-heatmaply\" class=\"section level2\">\n<h2>Correlation heatmaps using heatmaply<\/h2>\n<div id=\"load-r-packages\" class=\"section level3\">\n<h3>Load R packages<\/h3>\n<pre class=\"r\"><code>library(heatmaply)<\/code><\/pre>\n<\/div>\n<div id=\"basic-correlation-matrix-heatmap\" class=\"section level3\">\n<h3>Basic correlation matrix heatmap<\/h3>\n<p>Use the arguments <code>k_col<\/code> and <code>k_row<\/code> to specify the desired number of groups by which to color the dendrogram\u2019s branches in the columns and rows, respectively.<\/p>\n<pre class=\"r\"><code>heatmaply_cor(\r\n  cor(df),\r\n  xlab = \"Features\", \r\n  ylab = \"Features\",\r\n  k_col = 2, \r\n  k_row = 2\r\n)<\/code><\/pre>\n<p><iframe width=\"95%\" height=\"600\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-correlation-matrix-heatmap-in-r-correlation-heatmaps.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"change-the-point-size-according-to-the-correlation-test-p-values\" class=\"section level3\">\n<h3>Change the point size according to the correlation test p-values<\/h3>\n<pre class=\"r\"><code># Compute correlation coefficients\r\ncor.coef &lt;- cor(df)\r\n\r\n# Compute correlation p-values\r\ncor.test.p &lt;- function(x){\r\n    FUN &lt;- function(x, y) cor.test(x, y)[[\"p.value\"]]\r\n    z &lt;- outer(\r\n      colnames(x), \r\n      colnames(x), \r\n      Vectorize(function(i,j) FUN(x[,i], x[,j]))\r\n    )\r\n    dimnames(z) &lt;- list(colnames(x), colnames(x))\r\n    z\r\n}\r\np &lt;- cor.test.p(df)<\/code><\/pre>\n<pre class=\"r\"><code># Create the heatmap\r\nheatmaply_cor(\r\n  cor.coef,\r\n  node_type = \"scatter\",\r\n  point_size_mat = -log10(p), \r\n  point_size_name = \"-log10(p-value)\",\r\n  label_names = c(\"x\", \"y\", \"Correlation\")\r\n)<\/code><\/pre>\n<p><iframe width=\"95%\" height=\"600\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-correlation-matrix-heatmap-in-r-correlation-heatmaps-change-poinsize-by-p-values.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<\/div>\n<div id=\"correlation-heatmaps-using-ggcorrplot\" class=\"section level2\">\n<h2>Correlation heatmaps using ggcorrplot<\/h2>\n<div id=\"load-r-packages-1\" class=\"section level3\">\n<h3>Load R packages<\/h3>\n<pre class=\"r\"><code>library(ggcorrplot)<\/code><\/pre>\n<\/div>\n<div id=\"static-heatmap-of-the-correlation-matrix\" class=\"section level3\">\n<h3>Static heatmap of the correlation matrix<\/h3>\n<pre class=\"r\"><code># Compute a correlation matrix\r\ncorr &lt;- round(cor(df), 1)\r\n\r\n# Compute a matrix of correlation p-values\r\np.mat &lt;- cor_pmat(df)\r\n\r\n# Visualize the lower triangle of the correlation matrix\r\n# Barring the no significant coefficient\r\ncorr.plot &lt;- ggcorrplot(\r\n  corr, hc.order = TRUE, type = \"lower\", outline.col = \"white\",\r\n  p.mat = p.mat\r\n  )\r\ncorr.plot<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-correlation-matrix-heatmap-in-r-static-plot-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"make-the-correlation-heatmap-interactive\" class=\"section level3\">\n<h3>Make the correlation heatmap interactive<\/h3>\n<pre class=\"r\"><code>library(plotly)\r\nggplotly(corr.plot)<\/code><\/pre>\n<p><iframe width=\"95%\" height=\"600\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-correlation-matrix-heatmap-in-r-interactive-ggcorrplot.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<p>`<\/p>\n<\/div>\n<\/div>\n<div id=\"references\" class=\"section level2\">\n<h2>References<\/h2>\n<ul>\n<li><a href=\"https:\/\/rpkgs.datanovia.com\/ggcorrplot\/\">ggcorrplot: Visualization of a correlation matrix using ggplot2<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/talgalili\/heatmaply\">Heatmaply: Interactive Heat Maps for R Using plotly<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This articles describes how to create an interactive correlation matrix heatmap in R. You will learn two different approaches: Using the heatmaply R package Using the combination of the ggcorrplot [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":15715,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[133],"tags":[363,135],"class_list":["post-15714","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-correlation-analysis","tag-heatmap","tag-interactive-visualization"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Create an Interactive Correlation Matrix Heatmap in R - 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\/how-to-create-an-interactive-correlation-matrix-heatmap-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Create an Interactive Correlation Matrix Heatmap in R - Datanovia\" \/>\n<meta property=\"og:description\" content=\"This articles describes how to create an interactive correlation matrix heatmap in R. 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