{"id":10487,"date":"2019-11-18T00:24:44","date_gmt":"2019-11-17T22:24:44","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&#038;p=10487"},"modified":"2019-11-18T00:24:44","modified_gmt":"2019-11-17T22:24:44","slug":"ggplot-ecdf","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/fr\/lessons\/ggplot-ecdf\/","title":{"rendered":"GGPLOT ECDF"},"content":{"rendered":"<div id=\"rdoc\">\n<p><strong>ECDF<\/strong> (pour <strong>Empirical cumulative distribution function<\/strong> ou <strong>Fonction de distribution cumulative empirique<\/strong> en fran\u00e7ais) offre une visualisation alternative de la distribution. Il indique, pour un nombre donn\u00e9, le pourcentage de personnes qui se situent en dessous de ce seuil.<\/p>\n<p>Cet article d\u00e9crit comment cr\u00e9er un ECDF dans R en utilisant la fonction <code>stat_ecdf()<\/code> dans le package <strong>ggplot2<\/strong>.<\/p>\n<p>Sommaire:<\/p>\n<div id=\"TOC\">\n<ul>\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=\"#creer-des-ecdf-plots\">Cr\u00e9er des ECDF plots<\/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=\"preparation-des-donnees\" class=\"section level2\">\n<h2>Pr\u00e9paration des donn\u00e9es<\/h2>\n<p>Cr\u00e9er des donn\u00e9es (<code>wdata<\/code>) contenant les poids par sexe (M pour homme ; F pour femme):<\/p>\n<pre class=\"r\"><code>set.seed(1234)\r\nwdata = data.frame(\r\n        sex = factor(rep(c(\"F\", \"M\"), each=200)),\r\n        weight = c(rnorm(200, 55), rnorm(200, 58))\r\n        )\r\n\r\n# head(wdata, 4)<\/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_minimal()<\/code> avec la l\u00e9gende en haut du graphique:<\/p>\n<pre class=\"r\"><code>library(ggplot2)\r\ntheme_set(\r\n  theme_minimal() +\r\n    theme(legend.position = \"top\")\r\n  )<\/code><\/pre>\n<\/div>\n<div id=\"creer-des-ecdf-plots\" class=\"section level2\">\n<h2>Cr\u00e9er des ECDF plots<\/h2>\n<pre class=\"r\"><code># Une autre option pour geom = \"point\"\r\nggplot(wdata, aes(x = weight)) +\r\n  stat_ecdf(aes(color = sex,linetype = sex), \r\n              geom = \"step\", size = 1.5) +\r\n  scale_color_manual(values = c(\"#00AFBB\", \"#E7B800\"))+\r\n  labs(y = \"f(weight)\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/015-ggplot-ecdf-stat_ecdf-empirical-cumulative-distribution-function-1.png\" width=\"480\" \/><\/p>\n<p>Dans les graphiques ci-dessus, vous pouvez voir que:<\/p>\n<ul>\n<li>environ 25 % des femmes mesurent moins de 50 pouces<\/li>\n<li>environ 50% des hommes mesurent moins de 58 pouces<\/li>\n<\/ul>\n<\/div>\n<div id=\"conclusion\" class=\"section level2\">\n<h2>Conclusion<\/h2>\n<p>Cet article montre comment cr\u00e9er un ECDF plot \u00e0 l\u2019aide du package ggplot2 R.<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>ECDF (ou fonction de distribution cumulative empirique) fournit une visualisation alternative de la distribution. Il indique, pour un nombre donn\u00e9, le pourcentage de cas qui se situent en dessous de ce seuil. Cet article d\u00e9crit comment cr\u00e9er un ECDF dans R en utilisant la fonction stat_ecdf() du package ggplot2.<\/p>\n","protected":false},"author":1,"featured_media":10488,"parent":0,"menu_order":24,"comment_status":"open","ping_status":"closed","template":"","class_list":["post-10487","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 ECDF : 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-ecdf\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GGPLOT ECDF : Meilleure R\u00e9f\u00e9rence - Datanovia\" \/>\n<meta property=\"og:description\" content=\"ECDF (ou fonction de distribution cumulative empirique) fournit une visualisation alternative de la distribution. 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