{"id":15473,"date":"2020-03-22T22:07:19","date_gmt":"2020-03-22T21:07:19","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=15473"},"modified":"2020-03-22T22:07:19","modified_gmt":"2020-03-22T21:07:19","slug":"visualisation-elegante-de-courbes-de-distribution-de-densite-dans-r-avec-ridgeline","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/fr\/blog\/visualisation-elegante-de-courbes-de-distribution-de-densite-dans-r-avec-ridgeline\/","title":{"rendered":"Visualisation El\u00e9gante de Courbes de Distribution de Densit\u00e9 dans R avec Ridgeline"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Cet article explique comment <strong>visualiser la distribution de donn\u00e9es dans R<\/strong> en utilisant des courbes de densit\u00e9 ridgeline. Le graphique de densit\u00e9 <code>ridgeline<\/code> [package ggridges] est une alternative \u00e0 la fonction standard <code>geom_density()<\/code> [package ggplot2] qui peut \u00eatre utile pour visualiser les changements dans les distributions, d\u2019une variable continue, dans le temps ou dans l\u2019espace. Les graphiques ridgeline sont des line plots qui se chevauchent partiellement et donnent l\u2019impression d\u2019une cha\u00eene de montagnes.<\/p>\n<p>Vous apprendrez \u00e0:<\/p>\n<ul>\n<li>Cr\u00e9er des graphiques ridgelines basiques<\/li>\n<li>Ajouter des couleurs de remplissage d\u00e9grad\u00e9es sous les courbes<\/li>\n<li>Ajouter des statistiques descriptives telles que des lignes de quantiles sur les graphiques de densit\u00e9<\/li>\n<li>Ajouter des points de donn\u00e9es originaux sur les courbes de densit\u00e9<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-density-logo-1.png\" width=\"672\" \/><\/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=\"#graphiques-de-densit\u00e9s-ridgelines-basiques\">Graphiques de densit\u00e9s ridgelines basiques<\/a><\/li>\n<li><a href=\"#courbes-de-densit\u00e9-avec-des-couleurs-de-remplissage-en-gradient-le-long-de-laxe-des-x\">Courbes de densit\u00e9 avec des couleurs de remplissage en gradient le long de l\u2019axe des x<\/a><\/li>\n<li><a href=\"#ajouter-des-lignes-de-statistiques-descriptives\">Ajouter des lignes de statistiques descriptives<\/a>\n<ul>\n<li><a href=\"#ajouter-des-lignes-de-quantile\">Ajouter des lignes de quantile<\/a><\/li>\n<li><a href=\"#colorer-la-zone-de-densit\u00e9-par-des-quantiles\">Colorer la zone de densit\u00e9 par des quantiles<\/a><\/li>\n<li><a href=\"#colorier-les-courbes-de-densit\u00e9-par-des-probabilit\u00e9s\">Colorier les courbes de densit\u00e9 par des probabilit\u00e9s<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#ajouter-les-points-de-donn\u00e9es-originaux-sur-les-courbes-de-densit\u00e9\">Ajouter les points de donn\u00e9es originaux sur les courbes de densit\u00e9<\/a><\/li>\n<li><a href=\"#cr\u00e9er-des-distributions-dhistogrammes-en-utilisant-ridgeline\">Cr\u00e9er des distributions d\u2019histogrammes en utilisant ridgeline<\/a><\/li>\n<li><a href=\"#changer-de-th\u00e8me\">Changer de th\u00e8me<\/a><\/li>\n<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"pr\u00e9requis\" class=\"section level2\">\n<h2>Pr\u00e9requis<\/h2>\n<p>Charger les packages R requis:<\/p>\n<pre class=\"r\"><code>library(ggplot2)\r\nlibrary(ggridges)\r\ntheme_set(theme_minimal())<\/code><\/pre>\n<p>Fonctions R cl\u00e9s:<\/p>\n<ul>\n<li><code>geom_density_ridges()<\/code>: estime d\u2019abord les densit\u00e9s de donn\u00e9es et dessine ensuite celles ci en utilisant des ridgelines. Il dispose les graphiques \u00e0 densit\u00e9 multiple de mani\u00e8re \u00e9tag\u00e9e.<\/li>\n<\/ul>\n<\/div>\n<div id=\"graphiques-de-densit\u00e9s-ridgelines-basiques\" class=\"section level2\">\n<h2>Graphiques de densit\u00e9s ridgelines basiques<\/h2>\n<p>Fonctions R cl\u00e9s:<\/p>\n<ul>\n<li><code>geom_density_ridges()<\/code>: Estimes des densit\u00e9s de donn\u00e9es, puis dessine celles-ci avec des ridglines. Il dispose les graphiques \u00e0 densit\u00e9 multiple de mani\u00e8re \u00e9tag\u00e9e.<\/li>\n<\/ul>\n<pre class=\"r\"><code># Polygones ouverts\r\nggplot(iris, aes(x = Sepal.Length, y = Species, group = Species)) + \r\n  geom_density_ridges(fill = \"#00AFBB\")\r\n\r\n# Polygones ferm\u00e9s\r\nggplot(iris, aes(x = Sepal.Length, y = Species, group = Species)) + \r\n  geom_density_ridges2(fill = \"#00AFBB\")\r\n\r\n# Couper les queues. \r\n# Pr\u00e9cisez `rel_min_height` : un pourcentage de seuil de coupure\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges(fill = \"#00AFBB\", rel_min_height = 0.01)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-basic-density-ridgeline-plots-1.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-basic-density-ridgeline-plots-2.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-basic-density-ridgeline-plots-3.png\" width=\"336\" \/><\/p>\n<div class=\"notice\">\n<p>Notez que l\u2019esth\u00e9tique de regroupement n\u2019a pas besoin d\u2019\u00eatre fournie si une variable cat\u00e9gorielle est plac\u00e9e sur l\u2019axe des y, mais elle doit \u00eatre fournie si la variable est num\u00e9rique.<\/p>\n<\/div>\n<p><strong>Contr\u00f4ler la mesure dans laquelle les diff\u00e9rentes densit\u00e9s se chevauchent<\/strong>. Vous pouvez contr\u00f4ler le chevauchement entre les diff\u00e9rentes courbes de densit\u00e9 en utilisant l\u2019option <code>scale<\/code>. La valeur par d\u00e9faut est de 1. Des valeurs plus petites cr\u00e9ent une s\u00e9paration entre les courbes, et des valeurs plus grandes cr\u00e9ent plus de chevauchement.<\/p>\n<pre class=\"r\"><code># scale = 0,6, courbes s\u00e9par\u00e9es\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges(scale = 0.6)\r\n\r\n# scale = 1, courbes se touchant exactement\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges(scale = 1)\r\n\r\n# scale = 5, chevauchement important\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges(scale = 5, alpha = 0.7)\r\n\r\n# Modifier les couleurs de remplissage de la zone de densit\u00e9 par groupes\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(aes(fill = Species)) +\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\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-control-density-ridglines-overlap-1.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-control-density-ridglines-overlap-2.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-control-density-ridglines-overlap-3.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-control-density-ridglines-overlap-4.png\" width=\"336\" \/><\/p>\n<\/div>\n<div id=\"courbes-de-densit\u00e9-avec-des-couleurs-de-remplissage-en-gradient-le-long-de-laxe-des-x\" class=\"section level2\">\n<h2>Courbes de densit\u00e9 avec des couleurs de remplissage en gradient le long de l\u2019axe des x<\/h2>\n<p>Cet effet peut \u00eatre obtenu avec la fonction <code>geom_density_ridges_gradient()<\/code>, qui fonctionne comme <code>geom_density_ridges<\/code>, sauf qu\u2019elle permet de varier les couleurs de remplissage.<\/p>\n<div class=\"notice\">\n<p>Notez que, pour des raisons techniques, <code>geom_density_ridges_gradient()<\/code> ne permet pas la transparence alpha dans le remplissage.<\/p>\n<\/div>\n<p>Visualiser les donn\u00e9es m\u00e9t\u00e9orologiques de Lincoln:<\/p>\n<ul>\n<li>Jeu de donn\u00e9es : <code>lincoln_weather<\/code> [dans ggridges]. M\u00e9t\u00e9o \u00e0 Lincoln, Nebraska en 2016.<\/li>\n<li>Cr\u00e9ez les courbes de densit\u00e9 de <code>Mean Temperature<\/code>( \u201ctemp\u00e9rature moyenne\u201d) par <code>Month<\/code> (\u201cmois\u201d) et changez la couleur de remplissage en fonction de la valeur de la temp\u00e9rature (sur l\u2019axe des x).<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(\r\n  lincoln_weather, \r\n  aes(x = `Mean Temperature [F]`, y = `Month`, fill = stat(x))\r\n  ) +\r\n  geom_density_ridges_gradient(scale = 3, size = 0.3, rel_min_height = 0.01) +\r\n  scale_fill_viridis_c(name = \"Temp. [F]\", option = \"C\") +\r\n  labs(title = 'Temperatures in Lincoln NE') <\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-density-curves-with-gradient-fill-colors-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"ajouter-des-lignes-de-statistiques-descriptives\" class=\"section level2\">\n<h2>Ajouter des lignes de statistiques descriptives<\/h2>\n<div id=\"ajouter-des-lignes-de-quantile\" class=\"section level3\">\n<h3>Ajouter des lignes de quantile<\/h3>\n<p>Fonction R cl\u00e9: <code>stat_density_ridges()<\/code>. Par d\u00e9faut, trois lignes sont trac\u00e9es, correspondant au premier, deuxi\u00e8me et troisi\u00e8me quartile.<\/p>\n<pre class=\"r\"><code># Ajouter les quantiles Q1, Q2 (m\u00e9diane) et Q3\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  stat_density_ridges(quantile_lines = TRUE)\r\n\r\n# Afficher uniquement la ligne m\u00e9diane (50%)\r\n# Utiliser quantiles = 2 (pour Q2) ou quantiles = 50\/100\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  stat_density_ridges(quantile_lines = TRUE, quantiles = 0.5)\r\n\r\n# Indiquez les queues de 2,5 % et de 97,5%\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.025, 0.975), alpha = 0.7)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-quantile-lines-1.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-quantile-lines-2.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-quantile-lines-3.png\" width=\"336\" \/><\/p>\n<\/div>\n<div id=\"colorer-la-zone-de-densit\u00e9-par-des-quantiles\" class=\"section level3\">\n<h3>Colorer la zone de densit\u00e9 par des quantiles<\/h3>\n<div class=\"block\">\n<p>Vous devez sp\u00e9cifier l\u2019option <code>calc_ecdf = TRUE<\/code> requise pour le calcul des quantiles. L\u2019ECDF repr\u00e9sente la fonction de densit\u00e9 cumulative empirique de distribution.<\/p>\n<\/div>\n<pre class=\"r\"><code># Colorer en fonction des quantiles\r\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) +\r\n  stat_density_ridges(\r\n    geom = \"density_ridges_gradient\", calc_ecdf = TRUE,\r\n    quantiles = 4, quantile_lines = TRUE\r\n  ) +\r\n  scale_fill_viridis_d(name = \"Quartiles\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-color-the-density-area-by-quantiles-1.png\" width=\"576\" \/><\/p>\n<pre class=\"r\"><code># Mettre en \u00e9vidence les queues des distributions\r\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) +\r\n  stat_density_ridges(\r\n    geom = \"density_ridges_gradient\",\r\n    calc_ecdf = TRUE,\r\n    quantiles = c(0.025, 0.975)\r\n  ) +\r\n  scale_fill_manual(\r\n    name = \"Probability\", values = c(\"#FF0000A0\", \"#A0A0A0A0\", \"#0000FFA0\"),\r\n    labels = c(\"(0, 0.025]\", \"(0.025, 0.975]\", \"(0.975, 1]\")\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-color-the-density-area-by-quantiles-2.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"colorier-les-courbes-de-densit\u00e9-par-des-probabilit\u00e9s\" class=\"section level3\">\n<h3>Colorier les courbes de densit\u00e9 par des probabilit\u00e9s<\/h3>\n<p>Lorsque <code>calc_ecdf = TRUE<\/code>, nous avons \u00e9galement acc\u00e8s \u00e0 un esth\u00e9tique <code>stat(ecdf)<\/code> calcul\u00e9, qui repr\u00e9sente la fonction de densit\u00e9 cumulative empirique pour la distribution. Cela nous permet de mapper les probabilit\u00e9s directement sur la couleur.<\/p>\n<pre class=\"r\"><code>ggplot(iris, aes(x = Sepal.Length, y = Species, fill = 0.5 - abs(0.5 - stat(ecdf)))) +\r\n  stat_density_ridges(geom = \"density_ridges_gradient\", calc_ecdf = TRUE) +\r\n  scale_fill_viridis_c(name = \"Tail probability\", direction = -1)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-color-the-density-curves-by-probabilities-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"ajouter-les-points-de-donn\u00e9es-originaux-sur-les-courbes-de-densit\u00e9\" class=\"section level2\">\n<h2>Ajouter les points de donn\u00e9es originaux sur les courbes de densit\u00e9<\/h2>\n<p>Cela peut \u00eatre fait en d\u00e9finissant <code>jittered_points = TRUE<\/code>, soit dans <code>stat_density_ridges<\/code> soit dans <code>geom_density_ridges<\/code>.<\/p>\n<p>La position des points de donn\u00e9es peut \u00eatre contr\u00f4l\u00e9e en utilisant les options suivantes:<\/p>\n<ul>\n<li><code>position = \"sina\"<\/code>: R\u00e9partit au hasard les points d\u2019un graphique ridgeline entre la ligne de base et la ligne de cr\u00eate. C\u2019est l\u2019option par d\u00e9faut.<\/li>\n<li><code>position = \"jitter\"<\/code>: Distribue al\u00e9atoirement les points sur un graphique ridgeline. Les points sont dispos\u00e9s de mani\u00e8re al\u00e9atoire de haut en bas et\/ou de gauche \u00e0 droite.<\/li>\n<li><code>position = \"raincloud\"<\/code>: Cr\u00e9e un nuage de points al\u00e9atoirement distribu\u00e9s sous un graphique ridgeline.<\/li>\n<\/ul>\n<pre class=\"r\"><code># Ajouter des points dispers\u00e9s (jitter)\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(jittered_points = TRUE)\r\n\r\n# Contr\u00f4ler la position des points\r\n# position = \"raincloud\"\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(\r\n    jittered_points = TRUE, position = \"raincloud\",\r\n    alpha = 0.7, scale = 0.9\r\n  )\r\n\r\n# position = \"points_jitter\"\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(\r\n    jittered_points = TRUE, position = \"points_jitter\",\r\n    alpha = 0.7, scale = 0.9\r\n  )\r\n\r\n# Ajouter des rugs marginaux\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(\r\n    jittered_points = TRUE,\r\n    position = position_points_jitter(width = 0.05, height = 0),\r\n    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-data-points-1.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-data-points-2.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-data-points-3.png\" width=\"336\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-add-data-points-4.png\" width=\"336\" \/><\/p>\n<p><strong>Styliser les points et ajouter des lignes de quantification<\/strong>.<\/p>\n<pre class=\"r\"><code># Personnalisation des points\r\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +\r\n  geom_density_ridges(\r\n    aes(point_color = Species, point_fill = Species, point_shape = Species),\r\n    alpha = .2, point_alpha = 1, jittered_points = TRUE\r\n  ) +\r\n  scale_point_color_hue(l = 40) +\r\n  scale_discrete_manual(aesthetics = \"point_shape\", values = c(21, 22, 23))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-styling-the-jitterd-points-1.png\" width=\"576\" \/><\/p>\n<pre class=\"r\"><code># Styliser les lignes de quantile verticales\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\r\n  geom_density_ridges(\r\n    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,\r\n    vline_size = 1, vline_color = \"red\",\r\n    point_size = 0.4, point_alpha = 1,\r\n    position = position_raincloud(adjust_vlines = TRUE)\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-styling-the-jitterd-points-2.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"cr\u00e9er-des-distributions-dhistogrammes-en-utilisant-ridgeline\" class=\"section level2\">\n<h2>Cr\u00e9er des distributions d\u2019histogrammes en utilisant ridgeline<\/h2>\n<p>Utilisez les options suivantes:<\/p>\n<ul>\n<li><code>stat = \"binline\"<\/code>: Cr\u00e9e des histogrammes<\/li>\n<li><code>draw_baseline = FALSE<\/code>: Supprime les lignes de queue de chaque c\u00f4t\u00e9 de l\u2019histogramme. Pour les histogrammes, le param\u00e8tre <code>rel_min_height<\/code> ne fonctionne pas tr\u00e8s bien.<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(iris, aes(x = Sepal.Length, y = Species, height = stat(density))) + \r\n  geom_density_ridges(\r\n    stat = \"binline\", bins = 20, scale = 0.95,\r\n    draw_baseline = FALSE\r\n    )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-create-histogram-distributions-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"changer-de-th\u00e8me\" class=\"section level2\">\n<h2>Changer de th\u00e8me<\/h2>\n<pre class=\"r\"><code># Utilisez theme_ridges()\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges() + \r\n  theme_ridges()\r\n# Supprimer les grilles\r\nggplot(iris, aes(x = Sepal.Length, y = Species)) + \r\n  geom_density_ridges() + \r\n  theme_ridges(grid = FALSE, center_axis_labels = TRUE)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-change-themes-1.png\" width=\"355.2\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/ggridges-visualize-distribution-in-r-change-themes-2.png\" width=\"355.2\" \/><\/p>\n<\/div>\n<div id=\"conclusion\" class=\"section level2\">\n<h2>Conclusion<\/h2>\n<p>Cet article explique comment <strong>visualiser la distribution de donn\u00e9es dans R<\/strong> en utilisant des courbes de densit\u00e9 ridgeline.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cet article explique comment visualiser la distribution de donn\u00e9es dans R en utilisant des courbes de densit\u00e9 ridgeline. Le graphique de densit\u00e9 ridgeline [package ggridges] est une alternative \u00e0 la [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":15470,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[275],"tags":[287],"class_list":["post-15473","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-visualisation-de-donnees","tag-extensions-de-ggplot2"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Visualisation El\u00e9gante de Courbes de Distribution de Densit\u00e9 dans R avec Ridgeline - 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\/visualisation-elegante-de-courbes-de-distribution-de-densite-dans-r-avec-ridgeline\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Visualisation El\u00e9gante de Courbes de Distribution de Densit\u00e9 dans R avec Ridgeline - Datanovia\" \/>\n<meta property=\"og:description\" content=\"Cet article explique comment visualiser la distribution de donn\u00e9es dans R en utilisant des courbes de densit\u00e9 ridgeline. 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