{"id":7691,"date":"2018-10-18T22:57:48","date_gmt":"2018-10-18T20:57:48","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=7691"},"modified":"2018-10-20T17:56:43","modified_gmt":"2018-10-20T15:56:43","slug":"examples-of-dendrograms-visualization","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/","title":{"rendered":"Examples of Dendrograms Visualization"},"content":{"rendered":"<div id=\"rdoc\">\n<p>As described in previous chapters, a <strong>dendrogram<\/strong> is a tree-based representation of a data created using hierarchical clustering methods.<\/p>\n<p>In this article, we provide <strong>examples of dendrograms visualization<\/strong> using R software. Additionally, we show how to save and to zoom a large dendrogram.<\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#computing-hierarchical-clustering\">Computing hierarchical clustering<\/a><\/li>\n<li><a href=\"#r-packages-and-functions\">R packages and functions<\/a><\/li>\n<li><a href=\"#creating-dendrograms\">Creating dendrograms<\/a><\/li>\n<li><a href=\"#case-of-dendrogram-with-large-data-sets\">Case of dendrogram with large data sets<\/a>\n<ul>\n<li><a href=\"#zooming-in-the-dendrogram\">Zooming in the dendrogram<\/a><\/li>\n<li><a href=\"#plotting-a-sub-tree-of-dendrograms\">Plotting a sub-tree of dendrograms<\/a><\/li>\n<li><a href=\"#saving-dendrogram-into-a-large-pdf-page\">Saving dendrogram into a large PDF page<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#manipulating-dendrograms-using-dendextend\">Manipulating dendrograms using dendextend<\/a><\/li>\n<li><a href=\"#summary\">Summary<\/a><\/li>\n<\/ul>\n<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div class='dt-sc-ico-content type1'><div class='custom-icon' ><a href='https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-cluster-analysis-in-r\/' target='_blank'><span class='fa fa-book'><\/span><\/a><\/div><h4><a href='https:\/\/www.datanovia.com\/en\/product\/practical-guide-to-cluster-analysis-in-r\/' target='_blank'> Related Book <\/a><\/h4>Practical Guide to Cluster Analysis in R<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div id=\"computing-hierarchical-clustering\" class=\"section level2\">\n<h2>Computing hierarchical clustering<\/h2>\n<p>We start by computing hierarchical clustering using the USArrests data sets:<\/p>\n<pre class=\"r\"><code># Load data\r\ndata(USArrests)\r\n\r\n# Compute distances and hierarchical clustering\r\ndd &lt;- dist(scale(USArrests), method = \"euclidean\")\r\nhc &lt;- hclust(dd, method = \"ward.D2\")<\/code><\/pre>\n<\/div>\n<div id=\"r-packages-and-functions\" class=\"section level2\">\n<h2>R packages and functions<\/h2>\n<p>To visualize the dendrogram, we\u2019ll use the following R functions and packages:<\/p>\n<ul>\n<li><em>fviz_dend<\/em>()[in factoextra R package] to create easily a ggplot2-based beautiful dendrogram.<\/li>\n<li><em>dendextend<\/em> package to manipulate dendrograms<\/li>\n<\/ul>\n<p>Before continuing, install the required package as follow:<\/p>\n<pre class=\"r\"><code>install.packages(c(\"factoextra\", \"dendextend\"))<\/code><\/pre>\n<\/div>\n<div id=\"creating-dendrograms\" class=\"section level2\">\n<h2>Creating dendrograms<\/h2>\n<p>We\u2019ll use the function <em>fviz_dend<\/em>()[in <em>factoextra<\/em> R package] to create easily a beautiful dendrogram using either the R base plot or ggplot2. It provides also an option for drawing circular dendrograms and phylogenic-like trees.<\/p>\n<p>To create a basic dendrograms, type this:<\/p>\n<pre class=\"r\"><code>library(factoextra)\r\nfviz_dend(hc, cex = 0.5)<\/code><\/pre>\n<p>You can use the arguments main, sub, xlab, ylab to change plot titles as follow:<\/p>\n<pre class=\"r\"><code>fviz_dend(hc, cex = 0.5, \r\n          main = \"Dendrogram - ward.D2\",\r\n          xlab = \"Objects\", ylab = \"Distance\", sub = \"\")<\/code><\/pre>\n<p>To draw a horizontal dendrogram, type this:<\/p>\n<pre class=\"r\"><code>fviz_dend(hc, cex = 0.5, horiz = TRUE)<\/code><\/pre>\n<p>It\u2019s also possible to cut the tree at a given height for partitioning the data into multiple groups as described in the previous chapter. In this case, it\u2019s possible to color branches by groups and to add rectangle around each group.<\/p>\n<p>For example:<\/p>\n<\/p>\n<div class=\"error\">Here, there are contents hidden to non-premium members. You can buy the course containing this lesson or signup to read all of our premium contents and to be awarded a certificate of course completion. <a href='https:\/\/www.datanovia.com\/en\/pricing\/' target='_self'  class='dt-sc-button   medium  '  style=\"background-color:#FF6600;border-color:#FF6600;color:#ffffff;\">Claim Your Membership Now<\/a>.<\/div>\n<p>&nbsp;<\/p>\n<p>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/003-hierarchical-clustering-in-r\/figures\/005-visualizing-dendrograms-cutree-1.png\" width=\"518.4\" \/><\/p>\n<p>To change the plot theme, use the argument ggtheme, which allowed values include ggplot2 official themes [ <em>theme_gray<\/em>(), <em>theme_bw<\/em>(), <em>theme_minimal<\/em>(), <em>theme_classic<\/em>(), <em>theme_void<\/em>()] or any other user-defined ggplot2 themes.<\/p>\n<pre class=\"r\"><code>fviz_dend(hc, k = 4,                 # Cut in four groups\r\n          cex = 0.5,                 # label size\r\n          k_colors = c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n          color_labels_by_k = TRUE,  # color labels by groups\r\n          ggtheme = theme_gray()     # Change theme\r\n          )<\/code><\/pre>\n<p>Allowed values for k_color include brewer palettes from <em>RColorBrewer<\/em> Package (e.g. \u201cRdBu\u201d, \u201cBlues\u201d, \u201cDark2\u201d, \u201cSet2\u201d, \u2026; ) and scientific journal palettes from <em>ggsci<\/em> R package (e.g.: \u201cnpg\u201d, \u201caaas\u201d, \u201clancet\u201d, \u201cjco\u201d, \u201cucscgb\u201d, \u201cuchicago\u201d, \u201csimpsons\u201d and \u201crickandmorty\u201d).<\/p>\n<p>In the R code below, we\u2019ll change group colors using \u201cjco\u201d (journal of clinical oncology) color palette:<\/p>\n<\/div>\n<\/p>\n<div class=\"error\">Here, there are contents hidden to non-premium members. You can buy the course containing this lesson or signup to read all of our premium contents and to be awarded a certificate of course completion. <a href='https:\/\/www.datanovia.com\/en\/pricing\/' target='_self'  class='dt-sc-button   medium  '  style=\"background-color:#FF6600;border-color:#FF6600;color:#ffffff;\">Claim Your Membership Now<\/a>.<\/div>\n<p>&nbsp;<\/p>\n<p>\n<div id=\"manipulating-dendrograms-using-dendextend\" class=\"section level2\">\n<h2>Manipulating dendrograms using dendextend<\/h2>\n<p>The package <em>dendextend<\/em> provide functions for changing easily the appearance of a dendrogram and for comparing dendrograms.<\/p>\n<p>In this section we\u2019ll use the chaining operator (<em>%&gt;%<\/em>) to simplify our code. The chaining operator turns x %&gt;% f(y) into f(x, y) so you can use it to rewrite multiple operations such that they can be read from left-to-right, top-to-bottom. For instance, the results of the two R codes below are equivalent.<\/p>\n<ul>\n<li>Standard R code for creating a dendrogram:<\/li>\n<\/ul>\n<pre class=\"r\"><code>data &lt;- scale(USArrests)\r\ndist.res &lt;- dist(data)\r\nhc &lt;- hclust(dist.res, method = \"ward.D2\")\r\ndend &lt;- as.dendrogram(hc)\r\nplot(dend)<\/code><\/pre>\n<ul>\n<li>R code for creating a dendrogram using chaining operator:<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(dendextend)\r\ndend &lt;- USArrests[1:5,] %&gt;% # data\r\n        scale %&gt;% # Scale the data\r\n        dist %&gt;% # calculate a distance matrix, \r\n        hclust(method = \"ward.D2\") %&gt;% # Hierarchical clustering \r\n        as.dendrogram # Turn the object into a dendrogram.\r\nplot(dend)<\/code><\/pre>\n<ul>\n<li>Functions to customize dendrograms: The function <em>set<\/em>() [in dendextend package] can be used to change the parameters of a dendrogram. The format is:<\/li>\n<\/ul>\n<pre class=\"r\"><code>set(object, what, value)<\/code><\/pre>\n<div class=\"block\">\n<ol style=\"list-style-type: decimal;\">\n<li><strong>object<\/strong>: a dendrogram object<\/li>\n<li><strong>what<\/strong>: a character indicating what is the property of the tree that should be set\/updated<\/li>\n<li><strong>value<\/strong>: a vector with the value to set in the tree (the type of the value depends on the \u201cwhat\u201d).<\/li>\n<\/ol>\n<\/div>\n<p>Possible values for the argument <strong>what<\/strong> include:<\/p>\n<table style=\"width: 88%;\">\n<colgroup>\n<col width=\"45%\" \/>\n<col width=\"41%\" \/> <\/colgroup>\n<thead>\n<tr class=\"header\">\n<th>Value for the argument <strong>what<\/strong><\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td><strong>labels<\/strong><\/td>\n<td>set the labels<\/td>\n<\/tr>\n<tr class=\"even\">\n<td><strong>labels_colors<\/strong> and <strong>labels_cex<\/strong><\/td>\n<td>Set the color and the size of labels, respectively<\/td>\n<\/tr>\n<tr class=\"odd\">\n<td><strong>leaves_pch<\/strong>, <strong>leaves_cex<\/strong> and <strong>leaves_col<\/strong><\/td>\n<td>set the point type, size and color for leaves, respectively<\/td>\n<\/tr>\n<tr class=\"even\">\n<td><strong>nodes_pch<\/strong>, <strong>nodes_cex<\/strong> and <strong>nodes_col<\/strong><\/td>\n<td>set the point type, size and color for nodes, respectively<\/td>\n<\/tr>\n<tr class=\"odd\">\n<td><strong>hang_leaves<\/strong><\/td>\n<td>hang the leaves<\/td>\n<\/tr>\n<tr class=\"even\">\n<td><strong>branches_k_color<\/strong><\/td>\n<td>color the branches<\/td>\n<\/tr>\n<tr class=\"odd\">\n<td><strong>branches_col<\/strong>, <strong>branches_lwd <\/strong>, <strong>branches_lty<\/strong><\/td>\n<td>Set the color, the line width and the line type of branches, respectively<\/td>\n<\/tr>\n<tr class=\"even\">\n<td><strong>by_labels_branches_col<\/strong>, <strong>by_labels_branches_lwd<\/strong> and <strong>by_labels_branches_lty <\/strong><\/td>\n<td>Set the color, the line width and the line type of branches with specific labels, respectively<\/td>\n<\/tr>\n<tr class=\"odd\">\n<td><strong>clear_branches<\/strong> and <strong>clear_leaves<\/strong><\/td>\n<td>Clear branches and leaves, respectively<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>Examples:<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(dendextend)\r\n# 1. Create a customized dendrogram\r\nmycols &lt;- c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\")\r\ndend &lt;-  as.dendrogram(hc) %&gt;%\r\n   set(\"branches_lwd\", 1) %&gt;% # Branches line width\r\n   set(\"branches_k_color\", mycols, k = 4) %&gt;% # Color branches by groups\r\n   set(\"labels_colors\", mycols, k = 4) %&gt;%  # Color labels by groups\r\n   set(\"labels_cex\", 0.5) # Change label size\r\n\r\n# 2. Create plot \r\nfviz_dend(dend) <\/code><\/pre>\n<\/div>\n<div id=\"summary\" class=\"section level2\">\n<h2>Summary<\/h2>\n<p>We described functions and packages for visualizing and customizing dendrograms including:<\/p>\n<ul>\n<li><em>fviz_dend<\/em>() [in factoextra R package], which provides convenient solutions for plotting easily a beautiful dendrogram. It can be used to create rectangular and circular dendrograms, as well as, a phylogenic tree.<\/li>\n<li>and the <em>dendextend<\/em> package, which provides a flexible methods to customize dendrograms.<\/li>\n<\/ul>\n<p>Additionally, we described how to plot a subset of large dendrograms.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article provides  examples of beautiful dendrograms visualization using R software. Additionally, we show how to save and to zoom a large dendrogram.<\/p>\n","protected":false},"author":1,"featured_media":7974,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-7691","dt_lessons","type-dt_lessons","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Examples of Dendrograms Visualization - 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\/lessons\/examples-of-dendrograms-visualization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Examples of Dendrograms Visualization - Datanovia\" \/>\n<meta property=\"og:description\" content=\"This article provides examples of beautiful dendrograms visualization using R software. Additionally, we show how to save and to zoom a large dendrogram.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/\" \/>\n<meta property=\"og:site_name\" content=\"Datanovia\" \/>\n<meta property=\"article:modified_time\" content=\"2018-10-20T15:56:43+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/\",\"url\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/\",\"name\":\"Examples of Dendrograms Visualization - Datanovia\",\"isPartOf\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png\",\"datePublished\":\"2018-10-18T20:57:48+00:00\",\"dateModified\":\"2018-10-20T15:56:43+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage\",\"url\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png\",\"contentUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png\",\"width\":1024,\"height\":512},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.datanovia.com\/en\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Lessons\",\"item\":\"https:\/\/www.datanovia.com\/en\/lessons\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Examples of Dendrograms Visualization\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#website\",\"url\":\"https:\/\/www.datanovia.com\/en\/\",\"name\":\"Datanovia\",\"description\":\"Data Mining and Statistics for Decision Support\",\"publisher\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.datanovia.com\/en\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#organization\",\"name\":\"Datanovia\",\"url\":\"https:\/\/www.datanovia.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png\",\"contentUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png\",\"width\":98,\"height\":99,\"caption\":\"Datanovia\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Examples of Dendrograms Visualization - Datanovia","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/","og_locale":"en_US","og_type":"article","og_title":"Examples of Dendrograms Visualization - Datanovia","og_description":"This article provides examples of beautiful dendrograms visualization using R software. Additionally, we show how to save and to zoom a large dendrogram.","og_url":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/","og_site_name":"Datanovia","article_modified_time":"2018-10-20T15:56:43+00:00","og_image":[{"width":1024,"height":512,"url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/","url":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/","name":"Examples of Dendrograms Visualization - Datanovia","isPartOf":{"@id":"https:\/\/www.datanovia.com\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage"},"image":{"@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png","datePublished":"2018-10-18T20:57:48+00:00","dateModified":"2018-10-20T15:56:43+00:00","breadcrumb":{"@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#primaryimage","url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png","contentUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/10\/IMG_7013.png","width":1024,"height":512},{"@type":"BreadcrumbList","@id":"https:\/\/www.datanovia.com\/en\/lessons\/examples-of-dendrograms-visualization\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.datanovia.com\/en\/"},{"@type":"ListItem","position":2,"name":"Lessons","item":"https:\/\/www.datanovia.com\/en\/lessons\/"},{"@type":"ListItem","position":3,"name":"Examples of Dendrograms Visualization"}]},{"@type":"WebSite","@id":"https:\/\/www.datanovia.com\/en\/#website","url":"https:\/\/www.datanovia.com\/en\/","name":"Datanovia","description":"Data Mining and Statistics for Decision Support","publisher":{"@id":"https:\/\/www.datanovia.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.datanovia.com\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.datanovia.com\/en\/#organization","name":"Datanovia","url":"https:\/\/www.datanovia.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png","contentUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2018\/09\/datanovia-logo.png","width":98,"height":99,"caption":"Datanovia"},"image":{"@id":"https:\/\/www.datanovia.com\/en\/#\/schema\/logo\/image\/"}}]}},"multi-rating":{"mr_rating_results":[]},"_links":{"self":[{"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/dt_lessons\/7691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/dt_lessons"}],"about":[{"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/types\/dt_lessons"}],"author":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/comments?post=7691"}],"version-history":[{"count":1,"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/dt_lessons\/7691\/revisions"}],"predecessor-version":[{"id":7692,"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/dt_lessons\/7691\/revisions\/7692"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/media\/7974"}],"wp:attachment":[{"href":"https:\/\/www.datanovia.com\/en\/wp-json\/wp\/v2\/media?parent=7691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}