{"id":15819,"date":"2020-04-19T11:57:29","date_gmt":"2020-04-19T10:57:29","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=15819"},"modified":"2020-04-19T11:59:49","modified_gmt":"2020-04-19T10:59:49","slug":"comment-creer-une-superbe-heatmap-interactive-dans-r","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/fr\/blog\/comment-creer-une-superbe-heatmap-interactive-dans-r\/","title":{"rendered":"Comment Cr\u00e9er une Superbe Heatmap Interactive dans R"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Cet article d\u00e9crit comment cr\u00e9er et personnaliser une <strong>Heatmap interactive dans R<\/strong> en utilisant le package R <strong>heatmaply<\/strong>, qui est bas\u00e9 sur le syst\u00e8me <em>ggplot2<\/em> et <em>plotly.js<\/em>.<\/p>\n<p>Sommaire:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#pr\u00e9requis\">Pr\u00e9requis<\/a><\/li>\n<li><a href=\"#pr\u00e9paration-des-donn\u00e9es\">Pr\u00e9paration des donn\u00e9es<\/a><\/li>\n<li><a href=\"#heatmap-basique\">Heatmap basique<\/a><\/li>\n<li><a href=\"#diviser-les-dendrogrammes-de-lignes-et-de-colonnes-en-k-groupes\">Diviser les dendrogrammes de lignes et de colonnes en k groupes<\/a><\/li>\n<li><a href=\"#modifier-les-palettes-de-couleurs\">Modifier les palettes de couleurs<\/a><\/li>\n<li><a href=\"#personnaliser-les-dendrogrammes-en-utilisant-dendextend\">Personnaliser les dendrogrammes en utilisant dendextend<\/a><\/li>\n<li><a href=\"#ajouter-une-annotation-bas\u00e9e-sur-des-facteurs-suppl\u00e9mentaires\">Ajouter une annotation bas\u00e9e sur des facteurs suppl\u00e9mentaires<\/a><\/li>\n<li><a href=\"#ajouter-des-annotations-de-texte\">Ajouter des annotations de texte<\/a><\/li>\n<li><a href=\"#ajouter-un-texte-de-survol-personnalis\u00e9\">Ajouter un texte de survol personnalis\u00e9<\/a><\/li>\n<li><a href=\"#enregistrer-votre-heatmap-dans-un-fichier\">Enregistrer votre heatmap dans un fichier<\/a><\/li>\n<li><a href=\"#r\u00e9f\u00e9rences\">R\u00e9f\u00e9rences<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"pr\u00e9requis\" class=\"section level2\">\n<h2>Pr\u00e9requis<\/h2>\n<p>Installez le package R requis:<\/p>\n<pre class=\"r\"><code>install.packages(\"heatmaply\")<\/code><\/pre>\n<p>Charger le package:<\/p>\n<pre class=\"r\"><code>library(\"heatmaply\")<\/code><\/pre>\n<\/div>\n<div id=\"pr\u00e9paration-des-donn\u00e9es\" class=\"section level2\">\n<h2>Pr\u00e9paration des donn\u00e9es<\/h2>\n<p>Normaliser les donn\u00e9es afin de rendre les valeurs des variables comparables.<\/p>\n<pre class=\"r\"><code>df &lt;- normalize(mtcars)<\/code><\/pre>\n<div class=\"warning\">\n<p>Notez que d\u2019autres fonctions de transformation des donn\u00e9es sont <code>scale()<\/code> (pour la normalisation standard), <code>percentize()<\/code> [pour la transformation en percentile ; disponible dans le package R heatmaply]. Plus d\u2019informations ici : <a href=\"https:\/\/www.datanovia.com\/en\/fr\/blog\/comment-normaliser-et-standardiser-les-donnees-dans-r-pour-une-visualisation-en-heatmap-magnifique\/\">Comment Normaliser et Standardiser les Donn\u00e9es dans R pour une Visualisation en Heatmap Magnifique<\/a>.<\/p>\n<\/div>\n<\/div>\n<div id=\"heatmap-basique\" class=\"section level2\">\n<h2>Heatmap basique<\/h2>\n<pre class=\"r\"><code>heatmaply(df)<\/code><\/pre>\n<p><iframe width=\"95%\" height=\"600\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-heatmap-in-r-heatmaply-basic-heatmap.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<p>Heatmaply a \u00e9galement la possibilit\u00e9 de produire une Heatmap statique en utilisant la fonction R <code>ggheatmap<\/code>:<\/p>\n<pre class=\"r\"><code>ggheatmap(df)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-heatmap-in-r-ggheatmap-1.png\" width=\"576\" \/><\/p>\n<div class=\"block\">\n<p>Notez que, <code>heatmaply<\/code> utilise le <a href=\"https:\/\/www.datanovia.com\/en\/blog\/seriation-in-r-how-to-optimally-order-objects-in-a-data-matrice\/\">package seriation<\/a> pour trouver un ordre optimal des lignes et des colonnes.<\/p>\n<p>La fonction <code>heatmaply()<\/code> a une option appel\u00e9e <code>seriate<\/code>, dont les valeurs possibles sont:<\/p>\n<ul>\n<li><code>\u201cOLO\u201d<\/code> (Optimal leaf ordering ou Ordre optimal des feuilles en fran\u00e7ais) : C\u2019est la valeur par d\u00e9faut.<\/li>\n<li><code>\u201cmean\u201d<\/code>: Cette option donne le r\u00e9sultat que nous obtiendrions par d\u00e9faut des fonctions de Heatmap dans d\u2019autres packages tels que <code>gplots::heatmap.2()<\/code>.<\/li>\n<li><code>\u201cnone\u201d<\/code>: Cette option nous donne les dendrogrammes sans aucune rotation. Le r\u00e9sultat est similaire \u00e0 celui que nous obtiendrions par d\u00e9faut avec hclust.<\/li>\n<\/ul>\n<\/div>\n<p><strong>Reproduction de l\u2019ordre des dendrogrammes de gplots::heatmap.2()<\/strong>.<\/p>\n<ul>\n<li>Cr\u00e9er une Heatmap \u00e0 l\u2019aide du package R <code>gplots<\/code>:<\/li>\n<\/ul>\n<pre class=\"r\"><code>gplots::heatmap.2(\r\n  as.matrix(df),\r\n  trace = \"none\",\r\n  col = viridis(100),\r\n  key = FALSE\r\n)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-heatmap-in-r-heatmap-using-gplots-1.png\" width=\"576\" \/><\/p>\n<ul>\n<li>Cr\u00e9er une version similaire en utilisant le package R <code>heatmaply<\/code>:<\/li>\n<\/ul>\n<pre class=\"r\"><code>heatmaply(\r\n  as.matrix(df),\r\n  seriate = \"mean\", \r\n  row_dend_left = TRUE,\r\n  plot_method = \"plotly\"\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-heatmap-in-r-gplots-like-heatmap-using-heatmaply.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"diviser-les-dendrogrammes-de-lignes-et-de-colonnes-en-k-groupes\" class=\"section level2\">\n<h2>Diviser les dendrogrammes de lignes et de colonnes en k groupes<\/h2>\n<p>L\u2019algorithme k-means est utilis\u00e9.<\/p>\n<pre class=\"r\"><code>heatmaply(\r\n  df,\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-heatmap-in-r-heatmaply-split-dendrograms.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"modifier-les-palettes-de-couleurs\" class=\"section level2\">\n<h2>Modifier les palettes de couleurs<\/h2>\n<p>La palette de couleurs par d\u00e9faut est la suivante <code>viridis<\/code>. D\u2019autres excellentes palettes de couleurs sont disponibles dans les packages <a href=\"https:\/\/cran.r-project.org\/package=cetcolor\">cetcolor<\/a> et <a href=\"https:\/\/www.datanovia.com\/en\/blog\/the-a-z-of-rcolorbrewer-palette\/\">RColorBrewer<\/a>.<\/p>\n<p>Utilisez les couleurs viridis avec l\u2019option \u201cmagma\u201d:<\/p>\n<pre class=\"r\"><code>heatmaply(\r\n  df,\r\n  colors = viridis(n = 256,  option = \"magma\"),\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-heatmap-in-r-heatmaply-use-viridis-colors.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<p>Utilisez la palette <code>RColorBrewer<\/code> :<\/p>\n<pre class=\"r\"><code>library(RColorBrewer)\r\nheatmaply(\r\n  df,\r\n  colors = colorRampPalette(brewer.pal(3, \"RdBu\"))(256),\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-heatmap-in-r-heatmaply-use-rcolorbrewer-colors.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<p>Sp\u00e9cifiez des gradients de couleurs personnalis\u00e9es en utilisant la fonction <code>scale_fill_gradient2()<\/code> [package ggplot2].<\/p>\n<pre class=\"r\"><code>gradient_col &lt;- ggplot2::scale_fill_gradient2(\r\n   low = \"blue\", high = \"red\", \r\n    midpoint = 0.5, limits = c(0, 1)\r\n  )\r\nheatmaply(\r\n  df,\r\n  scale_fill_gradient_fun = gradient_col\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-heatmap-in-r-heatmaply-use-custom-colors.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"personnaliser-les-dendrogrammes-en-utilisant-dendextend\" class=\"section level2\">\n<h2>Personnaliser les dendrogrammes en utilisant dendextend<\/h2>\n<p>Un utilisateur peut fournir ses propres dendrogrammes pour les lignes\/colonnes de la Heatmap en utilisant les param\u00e8tres <code>Rowv<\/code> et <code>Colv<\/code>:<\/p>\n<pre class=\"r\"><code>library(dendextend)\r\n# Cr\u00e9er un dendrogramme pour les lignes\r\nmycols &lt;- c(\"#2E9FDF\", \"#00AFBB\", \"#E7B800\", \"#FC4E07\")\r\nrow_dend &lt;-  df %&gt;%\r\n  dist() %&gt;%\r\n  hclust() %&gt;%\r\n  as.dendrogram() %&gt;%\r\n  set(\"branches_lwd\", 1) %&gt;% \r\n  set(\"branches_k_color\", mycols[1:3], k = 3) \r\n\r\n# Cr\u00e9er un dendrogramme pour les colonnes\r\ncol_dend &lt;-  df %&gt;%\r\n  t() %&gt;%\r\n  dist() %&gt;%\r\n  hclust() %&gt;%\r\n  as.dendrogram() %&gt;%\r\n  set(\"branches_lwd\", 1) %&gt;% \r\n  set(\"branches_k_color\", mycols[1:2], k = 2)<\/code><\/pre>\n<pre class=\"r\"><code># Visualiser la heatmap\r\nheatmaply(\r\n  df,\r\n  Rowv = row_dend,\r\n  Colv = col_dend\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-heatmap-in-r-heatmaply-customized-dendrograms-using-dendextend.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"ajouter-une-annotation-bas\u00e9e-sur-des-facteurs-suppl\u00e9mentaires\" class=\"section level2\">\n<h2>Ajouter une annotation bas\u00e9e sur des facteurs suppl\u00e9mentaires<\/h2>\n<p>Le code R suivant ajoute une annotation sur les c\u00f4t\u00e9s des colonnes et des lignes:<\/p>\n<pre class=\"r\"><code>heatmaply(\r\n  df[, -c(8, 9)],\r\n  col_side_colors = c(rep(0, 5), rep(1, 4)),\r\n  row_side_colors = df[, 8:9]\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-heatmap-in-r-heatmaply-adding-annotation.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"ajouter-des-annotations-de-texte\" class=\"section level2\">\n<h2>Ajouter des annotations de texte<\/h2>\n<p>Par d\u00e9faut, la couleur du texte de chaque cellule est choisie pour assurer la lisibilit\u00e9, le texte noir \u00e9tant affich\u00e9 sur les cellules claires et le texte blanc sur les cellules fonc\u00e9es.<\/p>\n<pre class=\"r\"><code>heatmaply(df, cellnote = mtcars)<\/code><\/pre>\n<p><iframe width=\"95%\" height=\"600\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/interactive-heatmap-in-r-heatmaply-add-text-annotation.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"ajouter-un-texte-de-survol-personnalis\u00e9\" class=\"section level2\">\n<h2>Ajouter un texte de survol personnalis\u00e9<\/h2>\n<pre class=\"r\"><code>mat &lt;- df\r\nmat[] &lt;- paste(\"This cell is\", rownames(mat))\r\nmat[] &lt;- lapply(colnames(mat), function(colname) {\r\n    paste0(mat[, colname], \", \", colname)\r\n})<\/code><\/pre>\n<pre class=\"r\"><code>heatmaply(\r\n  df,\r\n  custom_hovertext = mat\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-heatmap-in-r-heatmaply-custom-hover-text-.html\" frameborder=\"2\"><br \/>\n<\/iframe><\/p>\n<\/div>\n<div id=\"enregistrer-votre-heatmap-dans-un-fichier\" class=\"section level2\">\n<h2>Enregistrer votre heatmap dans un fichier<\/h2>\n<p>Cr\u00e9er un fichier html interactif:<\/p>\n<pre class=\"r\"><code>dir.create(\"folder\")\r\nheatmaply(mtcars, file = \"folder\/heatmaply_plot.html\")\r\nbrowseURL(\"folder\/heatmaply_plot.html\")<\/code><\/pre>\n<p>Enregistrement d\u2019un fichier statique (png\/jpeg\/pdf). Avant d\u2019utiliser ce code pour la premi\u00e8re fois, il se peut que vous deviez d\u2019abord executer <code>webshot::install_phantomjs()<\/code> ou installer le programme <a href=\"https:\/\/github.com\/plotly\/orca\">orca de plotly<\/a>.<\/p>\n<pre class=\"r\"><code>dir.create(\"folder\")\r\nheatmaply(mtcars, file = \"folder\/heatmaply_plot.png\")\r\nbrowseURL(\"folder\/heatmaply_plot.png\")<\/code><\/pre>\n<p>Sauvegarder le fichier, sans le tracer dans la console:<\/p>\n<pre class=\"r\"><code>tmp &lt;- heatmaply(mtcars, file = \"folder\/heatmaply_plot.png\")\r\nrm(tmp)<\/code><\/pre>\n<\/div>\n<div id=\"r\u00e9f\u00e9rences\" class=\"section level2\">\n<h2>R\u00e9f\u00e9rences<\/h2>\n<ul>\n<li><a href=\"https:\/\/cran.r-project.org\/web\/packages\/heatmaply\/vignettes\/heatmaply.html\">Introduction \u00e0 heatmaply<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cet article d\u00e9crit comment cr\u00e9er et personnaliser une Heatmap interactive dans R en utilisant le package R heatmaply, qui est bas\u00e9 sur le syst\u00e8me ggplot2 et plotly.js. Sommaire: Pr\u00e9requis Pr\u00e9paration [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":15716,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[274,275],"tags":[364,283],"class_list":["post-15819","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-clustering","category-visualisation-de-donnees","tag-heatmap-fr","tag-visualisation-interactive"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Comment Cr\u00e9er une Superbe Heatmap Interactive dans 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\/fr\/blog\/comment-creer-une-superbe-heatmap-interactive-dans-r\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comment Cr\u00e9er une Superbe Heatmap Interactive dans R - Datanovia\" \/>\n<meta property=\"og:description\" content=\"Cet article d\u00e9crit comment cr\u00e9er et personnaliser une Heatmap interactive dans R en utilisant le package R heatmaply, qui est bas\u00e9 sur le syst\u00e8me ggplot2 et plotly.js. 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