{"id":10468,"date":"2019-11-17T22:12:57","date_gmt":"2019-11-17T20:12:57","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=10468"},"modified":"2019-11-17T22:12:57","modified_gmt":"2019-11-17T20:12:57","slug":"ggplot-violin-plot","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/","title":{"rendered":"GGPlot Violin Plot"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Un <strong>violin plot<\/strong> est utilis\u00e9 pour visualiser la distribution des donn\u00e9es et sa densit\u00e9 de probabilit\u00e9.<\/p>\n<p>Ce graphique est une combinaison d\u2019un boxplot et d\u2019un density plot retourn\u00e9 plac\u00e9 de chaque c\u00f4t\u00e9, pour afficher la distribution des donn\u00e9es.<\/p>\n<p>Habituellement, les violin plots comprennent un marqueur pour la m\u00e9diane des donn\u00e9es et une case indiquant l\u2019\u00e9cart interquartile, comme des boxplots standard.<\/p>\n<p>Une repr\u00e9sentation graphique en violin plot montre plus d\u2019informations qu\u2019une repr\u00e9sentation graphique en boxplot. Par exemple, dans un violin plot, vous pouvez voir si la distribution des donn\u00e9es est bimodale ou multimodale.<\/p>\n<p>Cet article d\u00e9crit comment cr\u00e9er et personnaliser des <strong>violin plots<\/strong> en utilisant le package <strong>ggplot2<\/strong> dans R.<\/p>\n<p>Contents:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#fonctions-r-cles\">Fonctions R cl\u00e9s<\/a><\/li>\n<li><a href=\"#preparation-des-donnees\">Pr\u00e9paration des donn\u00e9es<\/a><\/li>\n<li><a href=\"#chargement-des-packages-r-requis\">Chargement des packages R requis<\/a><\/li>\n<li><a href=\"#violin-plot-de-basique\">Violin plot de basique<\/a><\/li>\n<li><a href=\"#creez-un-violin-plot-avec-plusieurs-groupes\">Cr\u00e9ez un violin plot avec plusieurs groupes<\/a><\/li>\n<li><a href=\"#conclusion\">Conclusion<\/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\/fr\/produit\/ggplot2-lessentiel-pour-une-visualisation-magnifique-des-donnees-dans-r\/' target='_blank'><span class='fa fa-book'><\/span><\/a><\/div><h4><a href='https:\/\/www.datanovia.com\/en\/fr\/produit\/ggplot2-lessentiel-pour-une-visualisation-magnifique-des-donnees-dans-r\/' target='_blank'> Livre Apparent\u00e9 <\/a><\/h4>GGPLOT2 - L\u2019Essentiel pour une Visualisation Magnifique des Donn\u00e9es dans R<\/div>\n<div class='dt-sc-hr-invisible-medium  '><\/div>\n<div id=\"fonctions-r-cles\" class=\"section level2\">\n<h2>Fonctions R cl\u00e9s<\/h2>\n<p>Fonction cl\u00e9:<\/p>\n<ul>\n<li><code>geom_violin()<\/code>: Cr\u00e9e des violin plots. Arguments cl\u00e9s:\n<ul>\n<li><code>color<\/code>, <code>size<\/code>, <code>linetype<\/code>: Couleur, taille et type de ligne de bordure<\/li>\n<li><code>fill<\/code>: Couleur des zones de remplissage<\/li>\n<li><code>trim<\/code>: valeur logique. Si TRUE (par d\u00e9faut), coupe les queues des violins jusqu\u2019\u00e0 la limite de la plage de donn\u00e9es. Si FALSE, ne coupe pas les queues.<\/li>\n<\/ul>\n<\/li>\n<li><code>stat_summary()<\/code>: Ajoute des statistiques descriptives (moyenne, m\u00e9diane, \u2026.) sur les violin plots.<\/li>\n<\/ul>\n<\/div>\n<div id=\"preparation-des-donnees\" class=\"section level2\">\n<h2>Pr\u00e9paration des donn\u00e9es<\/h2>\n<ul>\n<li>Donn\u00e9es de d\u00e9monstration: <code>ToothGrowth<\/code>\n<ul>\n<li>Variable continue : <code>len<\/code> (longueur des dents). Utilis\u00e9 sur l\u2019axe des y<\/li>\n<li>Variable de regroupement : <code>dose<\/code> (doses de vitamine C : 0,5, 1 et 2 mg\/jour). Utilis\u00e9 sur l\u2019axe des x.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Tout d\u2019abord, convertir la variable <code>dose<\/code> d\u2019une variable num\u00e9rique en une variable discr\u00e8te de groupes:<\/p>\n<pre class=\"r\"><code>data(\"ToothGrowth\")\r\nToothGrowth$dose &lt;- as.factor(ToothGrowth$dose)\r\nhead(ToothGrowth, 4)<\/code><\/pre>\n<pre><code>##    len supp dose\r\n## 1  4.2   VC  0.5\r\n## 2 11.5   VC  0.5\r\n## 3  7.3   VC  0.5\r\n## 4  5.8   VC  0.5<\/code><\/pre>\n<\/div>\n<div id=\"chargement-des-packages-r-requis\" class=\"section level2\">\n<h2>Chargement des packages R requis<\/h2>\n<p>Chargez le package ggplot2 et mettez le th\u00e8me par d\u00e9faut \u00e0 <code>theme_classic()<\/code> avec la l\u00e9gende en haut du graphique:<\/p>\n<pre class=\"r\"><code>library(ggplot2)\r\ntheme_set(\r\n  theme_classic() +\r\n    theme(legend.position = \"top\")\r\n  )<\/code><\/pre>\n<\/div>\n<div id=\"violin-plot-de-basique\" class=\"section level2\">\n<h2>Violin plot de basique<\/h2>\n<p>Nous commen\u00e7ons par initier un graphique nomm\u00e9 <code>e<\/code>, puis nous allons ajouter des couches. Le code R suivant cr\u00e9e des violin plots combin\u00e9s avec des statistiques descriptives (moyenne +\/- SD) et des boxplots.<\/p>\n<p>Cr\u00e9ez des violin plots de base avec des statistiques descriptives:<\/p>\n<pre class=\"r\"><code># Initialiser un ggplot\r\ne &lt;- ggplot(ToothGrowth, aes(x = dose, y = len))\r\n\r\n# Ajoute les points moyens +\/- SD\r\n# Utiliser geom = \"pointrange\" ou geom = \"crossbar\"\r\ne + geom_violin(trim = FALSE) + \r\n  stat_summary(\r\n    fun.data = \"mean_sdl\",  fun.args = list(mult = 1), \r\n    geom = \"pointrange\", color = \"black\"\r\n    )\r\n    \r\n# Combiner avec les box plots pour ajouter la m\u00e9diane et les quartiles\r\n# Changer la couleur de remplissage par groupe, supprimer la l\u00e9gende\r\ne + geom_violin(aes(fill = dose), trim = FALSE) + \r\n  geom_boxplot(width = 0.2)+\r\n  scale_fill_manual(values = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"))+\r\n  theme(legend.position = \"none\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/006-ggplot-violin-plot-geom_violin-violin-plot-with-summary-statistics-1.png\" width=\"268.8\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/006-ggplot-violin-plot-geom_violin-violin-plot-with-summary-statistics-2.png\" width=\"268.8\" \/><\/p>\n<div class=\"notice\">\n<p>La fonction <code>mean_sdl<\/code> est utilis\u00e9e pour ajouter la moyenne et l\u2019\u00e9cart-type. Il calcule la moyenne plus ou moins une constante fois l\u2019\u00e9cart-type. Dans le code R ci-dessus, la constante est sp\u00e9cifi\u00e9e en utilisant l\u2019argument <code>mult<\/code> (mult = 1). Par d\u00e9faut mult = 2. La moyenne +\/- SD peut \u00eatre ajout\u00e9e sous forme de crossbar ou de pointrange.<\/p>\n<\/div>\n<\/div>\n<div id=\"creez-un-violin-plot-avec-plusieurs-groupes\" class=\"section level2\">\n<h2>Cr\u00e9ez un violin plot avec plusieurs groupes<\/h2>\n<p>Deux variables de regroupement diff\u00e9rentes sont utilis\u00e9es : <code>dose<\/code> sur l\u2019axe des x et <code>supp<\/code> comme couleur de lignes (variable de la l\u00e9gende).<\/p>\n<p>L\u2019espace entre les graphiques group\u00e9s est ajust\u00e9 \u00e0 l\u2019aide de la fonction <code>position_dodge()<\/code>.<\/p>\n<pre class=\"r\"><code>e + geom_violin(aes(color = supp), trim = FALSE, position = position_dodge(0.9) ) +\r\n  geom_boxplot(aes(color = supp), width = 0.15, position = position_dodge(0.9)) +\r\n  scale_color_manual(values = c(\"#00AFBB\", \"#E7B800\"))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/006-ggplot-violin-plot-geom_violin-violin-plot-with-multiple-groups-1.png\" width=\"336\" \/><\/p>\n<\/div>\n<div id=\"conclusion\" class=\"section level2\">\n<h2>Conclusion<\/h2>\n<p>Cet article d\u00e9crit comment cr\u00e9er un violin plot \u00e0 l\u2019aide du package ggplot2.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Un Violin Plot est utilis\u00e9 pour visualiser la distribution des donn\u00e9es et leur densit\u00e9 de probabilit\u00e9. Ce graphique est une combinaison d&rsquo;un Box Plot et d&rsquo;un Density Plot qui est tourn\u00e9 et plac\u00e9 de chaque c\u00f4t\u00e9, pour afficher la forme de la distribution des donn\u00e9es. Un Violin Plot montre plus d&rsquo;informations qu&rsquo;un Box Plot. Par exemple, dans un violin plot, vous pouvez voir si la distribution des donn\u00e9es est bimodale ou multimodale. Cet article d\u00e9crit comment cr\u00e9er et personnaliser des violin plots \u00e0 l&rsquo;aide du package R ggplot2.<\/p>\n","protected":false},"author":1,"featured_media":10469,"parent":0,"menu_order":6,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-10468","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>GGPlot Violin Plot: Meilleure R\u00e9f\u00e9rence - 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\/lessons\/ggplot-violin-plot\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GGPlot Violin Plot: Meilleure R\u00e9f\u00e9rence - Datanovia\" \/>\n<meta property=\"og:description\" content=\"Un Violin Plot est utilis\u00e9 pour visualiser la distribution des donn\u00e9es et leur densit\u00e9 de probabilit\u00e9. 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Cet article d\u00e9crit comment cr\u00e9er et personnaliser des violin plots \u00e0 l'aide du package R ggplot2.","og_url":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/","og_site_name":"Datanovia","og_image":[{"width":1024,"height":512,"url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2019\/05\/P1040339.JPG.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/","url":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/","name":"GGPlot Violin Plot: Meilleure R\u00e9f\u00e9rence - Datanovia","isPartOf":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/#primaryimage"},"image":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2019\/05\/P1040339.JPG.jpg","datePublished":"2019-11-17T20:12:57+00:00","breadcrumb":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/#primaryimage","url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2019\/05\/P1040339.JPG.jpg","contentUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2019\/05\/P1040339.JPG.jpg","width":1024,"height":512},{"@type":"BreadcrumbList","@id":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-violin-plot\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.datanovia.com\/en\/fr\/"},{"@type":"ListItem","position":2,"name":"Le\u00e7ons","item":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/"},{"@type":"ListItem","position":3,"name":"GGPlot Violin Plot"}]},{"@type":"WebSite","@id":"https:\/\/www.datanovia.com\/en\/fr\/#website","url":"https:\/\/www.datanovia.com\/en\/fr\/","name":"Datanovia","description":"Exploration de Donn\u00e9es et Statistiques pour l'Aide \u00e0 la D\u00e9cision","publisher":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.datanovia.com\/en\/fr\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.datanovia.com\/en\/fr\/#organization","name":"Datanovia","url":"https:\/\/www.datanovia.com\/en\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.datanovia.com\/en\/fr\/#\/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\/fr\/#\/schema\/logo\/image\/"}}]}},"multi-rating":{"mr_rating_results":[]},"_links":{"self":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/dt_lessons\/10468","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/dt_lessons"}],"about":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/types\/dt_lessons"}],"author":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/comments?post=10468"}],"version-history":[{"count":0,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/dt_lessons\/10468\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/media\/10469"}],"wp:attachment":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/media?parent=10468"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}