{"id":10793,"date":"2019-11-23T11:21:13","date_gmt":"2019-11-23T09:21:13","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=10793"},"modified":"2019-11-23T11:21:13","modified_gmt":"2019-11-23T09:21:13","slug":"exemples-de-ggplot","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/fr\/blog\/exemples-de-ggplot\/","title":{"rendered":"Exemples de GGPLOT"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Cet article fournit une galerie d\u2019<strong>exemples de ggplots<\/strong>, notamment : des diagrammes de dispersion ou scatter plots, diagrammes de densit\u00e9 et histogrammes, diagrammes de barres et de lignes, barres d\u2019erreur, box plots, violin plots et plus encore.<\/p>\n<p>Sommaire:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#prerequis\">Pr\u00e9requis<\/a><\/li>\n<li><a href=\"#diagramme-de-dispersion\">Diagramme de dispersion<\/a><\/li>\n<li><a href=\"#distribution\">Distribution<\/a>\n<ul>\n<li><a href=\"#diagramme-de-densite\">Diagramme de densit\u00e9<\/a><\/li>\n<li><a href=\"#histogramme\">Histogramme<\/a><\/li>\n<li><a href=\"#qq-plot\">QQ Plot<\/a><\/li>\n<li><a href=\"#distribution-cumulative-empirique-ecdf\">Distribution cumulative empirique (ECDF)<\/a><\/li>\n<li><a href=\"#graphique-de-densite-ridgeline\">Graphique de densit\u00e9 ridgeline<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#bar-plots-et-alternatives\">Bar plots et alternatives<\/a><\/li>\n<li><a href=\"#line-plot-ex\">Line plot<\/a><\/li>\n<li><a href=\"#barres-derreur\">Barres d\u2019erreur<\/a><\/li>\n<li><a href=\"#box-plots-et-alternatives\">Box plots et alternatives<\/a><\/li>\n<li><a href=\"#visualisation-de-donnees-de-series-temporelles\">Visualisation de donn\u00e9es de s\u00e9ries temporelles<\/a><\/li>\n<li><a href=\"#matrice-de-diagramme-de-dispersion\">matrice de diagramme de dispersion<\/a><\/li>\n<li><a href=\"#analyse-de-correlation\">Analyse de corr\u00e9lation<\/a><\/li>\n<li><a href=\"#analyse-de-cluster\">Analyse de cluster<\/a><\/li>\n<li><a href=\"#balloon-plot\">Balloon plot<\/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=\"prerequis\" class=\"section level2\">\n<h2>Pr\u00e9requis<\/h2>\n<p>Charger les packages requis et d\u00e9finir la fonction th\u00e8me <code>theme_bw()<\/code> comme th\u00e8me par d\u00e9faut:<\/p>\n<pre class=\"r\"><code>library(tidyverse)\r\nlibrary(ggpubr) \r\ntheme_set(\r\n  theme_bw() + \r\n  theme(legend.position = \"top\")\r\n  )<\/code><\/pre>\n<\/div>\n<div id=\"diagramme-de-dispersion\" class=\"section level2\">\n<h2>Diagramme de dispersion<\/h2>\n<ul>\n<li><strong>Diagramme de dispersion basique<\/strong> avec coefficient de corr\u00e9lation. La fonction <code>stat_cor()<\/code>[Package R ggpubr] est utilis\u00e9e pour ajouter le coefficient de corr\u00e9lation.<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(\"ggpubr\")\r\np &lt;- ggplot(mtcars, aes(mpg, wt)) +\r\n  geom_point() +\r\n  geom_smooth(method = lm) +\r\n  stat_cor(method = \"pearson\", label.x = 20)\r\np<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-scatter-plot-1.png\" width=\"384\" \/><\/p>\n<ul>\n<li><strong>Zoom contextuel<\/strong>. Fonction R cl\u00e9 <code>facet_zoom()<\/code> [ggforce]<\/li>\n<\/ul>\n<pre class=\"r\"><code>library(ggforce)\r\nggplot(iris, aes(Petal.Length, Petal.Width, colour = Species)) +\r\n    geom_point() +\r\n    facet_zoom(x = Species == \"versicolor\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-zoom-1.png\" width=\"480\" \/><\/p>\n<ul>\n<li><strong>Encerclez quelques points<\/strong>. La fonction <code>geom_encircle()<\/code>[package R ggalt] peut \u00eatre utilis\u00e9e pour entourer un certain groupe de points<\/li>\n<\/ul>\n<pre class=\"r\"><code># Encerclez le groupe setosa\r\nlibrary(\"ggalt\")\r\ncircle.df &lt;- iris %&gt;% filter(Species == \"setosa\")\r\nggplot(iris, aes(Petal.Length, Petal.Width)) +\r\n    geom_point(aes(colour = Species)) + \r\n  geom_encircle(data = circle.df, linetype = 2)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-encircle-points-1.png\" width=\"384\" \/><\/p>\n<ul>\n<li><strong>Cr\u00e9ez des points jitter pour \u00e9viter les chevauchements<\/strong>. Les points qui se chevauchent sont dispers\u00e9s al\u00e9atoirement autour de leur position d\u2019origine en fonction d\u2019un seuil contr\u00f4l\u00e9 par l\u2019argument <code>width<\/code> dans la fonction <code>geom_jitter()<\/code><\/li>\n<\/ul>\n<pre class=\"r\"><code># Diagramme de dispersion de base\r\nggplot(mpg, aes(cty, hwy)) +\r\n  geom_point(size = 0.5)\r\n\r\n# Points jitter (dispers\u00e9s)\r\nggplot(mpg, aes(cty, hwy)) +\r\n  geom_jitter(size = 0.5, width = 0.5)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-avoid-overlap-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-avoid-overlap-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li><strong>Cr\u00e9ez des graphiques de comptage pour \u00e9viter les chevauchements<\/strong>. L\u00e0 o\u00f9 il y a plus de chevauchement de points, la taille du cercle augmente.<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(mpg, aes(cty, hwy)) +\r\n  geom_count()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-count-chart-1.png\" width=\"480\" \/><\/p>\n<ul>\n<li><strong>Graphique \u00e0 bulles<\/strong>. Dans un graphique \u00e0 bulles (bubble chart), la taille des points est contr\u00f4l\u00e9e par une variable continue, ici <code>qsec<\/code>.<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(mtcars, aes(mpg, wt)) +\r\n  geom_point(aes(size = qsec), alpha = 0.5) +\r\n  scale_size(range = c(0.5, 12))  # R\u00e9glage de la plage de tailles des points<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-bubble-chart-1.png\" width=\"432\" \/><\/p>\n<ul>\n<li><strong>Graphiques de densit\u00e9 marginaux<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code>library(ggpubr)\r\n# Diagramme de dispersion group\u00e9 avec graphique de densit\u00e9 marginale\r\nggscatterhist(\r\n  iris, x = \"Sepal.Length\", y = \"Sepal.Width\",\r\n  color = \"Species\", size = 3, alpha = 0.6,\r\n  palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n  margin.params = list(fill = \"Species\", color = \"black\", size = 0.2)\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-marginal-density-plots-1.png\" width=\"480\" \/><\/p>\n<pre class=\"r\"><code># Utiliser le box plot comme graphiques marginaux\r\nggscatterhist(\r\n  iris, x = \"Sepal.Length\", y = \"Sepal.Width\",\r\n  color = \"Species\", size = 3, alpha = 0.6,\r\n  palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n  margin.plot = \"boxplot\",\r\n  ggtheme = theme_bw()\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-marginal-density-plots-2.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"distribution\" class=\"section level2\">\n<h2>Distribution<\/h2>\n<div id=\"diagramme-de-densite\" class=\"section level3\">\n<h3>Diagramme de densit\u00e9<\/h3>\n<ul>\n<li>Graphique de densit\u00e9 basique:<\/li>\n<\/ul>\n<pre class=\"r\"><code># Graphique de densit\u00e9 basique\r\nggplot(iris, aes(Sepal.Length)) +\r\n  geom_density()\r\n\r\n# Ajouter la ligne moyenne\r\nggplot(iris, aes(Sepal.Length)) +\r\n  geom_density(fill = \"lightgray\") +\r\n  geom_vline(aes(xintercept = mean(Sepal.Length)), linetype = 2)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-density-plot-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-density-plot-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li>Changer la couleur par groupe<\/li>\n<\/ul>\n<pre class=\"r\"><code># Modifier la couleur des lignes par groupe\r\nggplot(iris, aes(Sepal.Length, color = Species)) +\r\n  geom_density() +\r\n  scale_color_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-colored-density-plot-1.png\" width=\"384\" \/><\/p>\n<pre class=\"r\"><code># Ajouter une ligne moyenne par groupe\r\nmu &lt;- iris %&gt;%\r\n  group_by(Species) %&gt;%\r\n  summarise(grp.mean = mean(Sepal.Length))\r\n\r\nggplot(iris, aes(Sepal.Length, color = Species)) +\r\n  geom_density() +\r\n  geom_vline(aes(xintercept = grp.mean, color = Species),\r\n             data = mu, linetype = 2) +\r\n  scale_color_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-colored-density-plot-2.png\" width=\"384\" \/><\/p>\n<\/div>\n<div id=\"histogramme\" class=\"section level3\">\n<h3>Histogramme<\/h3>\n<ul>\n<li>Histogrammemes basiques<\/li>\n<\/ul>\n<pre class=\"r\"><code># Histogrammeme basique avec ligne moyenne\r\nggplot(iris, aes(Sepal.Length)) +\r\n  geom_histogram(bins = 20, fill = \"white\", color = \"black\")  +\r\n  geom_vline(aes(xintercept = mean(Sepal.Length)), linetype = 2)\r\n\r\n# Ajouter des courbes de densit\u00e9\r\nggplot(iris, aes(Sepal.Length, stat(density))) +\r\n  geom_histogram(bins = 20, fill = \"white\", color = \"black\")  +\r\n  geom_density() +\r\n  geom_vline(aes(xintercept = mean(Sepal.Length)), linetype = 2)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-histogram-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-histogram-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li>Changer la couleur par groupe<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(iris, aes(Sepal.Length)) +\r\n  geom_histogram(aes(fill = Species, color = Species), bins = 20, \r\n                 position = \"identity\", alpha = 0.5) +\r\n  scale_fill_viridis_d() +\r\n  scale_color_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-histogram-colored-by-goups-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"qq-plot\" class=\"section level3\">\n<h3>QQ Plot<\/h3>\n<pre class=\"r\"><code>library(ggpubr)\r\nggqqplot(iris, x = \"Sepal.Length\",\r\n   ggtheme = theme_bw())<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-qq-plot-1.png\" width=\"336\" \/><\/p>\n<\/div>\n<div id=\"distribution-cumulative-empirique-ecdf\" class=\"section level3\">\n<h3>Distribution cumulative empirique (ECDF)<\/h3>\n<pre class=\"r\"><code>ggplot(iris, aes(Sepal.Length)) +\r\n  stat_ecdf(aes(color = Species)) +\r\n  scale_color_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-ecdf-1.png\" width=\"432\" \/><\/p>\n<\/div>\n<div id=\"graphique-de-densite-ridgeline\" class=\"section level3\">\n<h3>Graphique de densit\u00e9 ridgeline<\/h3>\n<p>Le graphique de densit\u00e9 ridgeline est une alternative \u00e0 la fonction standard geom_density() qui peut \u00eatre utile pour visualiser les changements dans les distributions, d\u2019une variable continue, dans le temps ou 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<pre class=\"r\"><code>library(ggridges)\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\"))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-density-ridgeline-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"bar-plots-et-alternatives\" class=\"section level2\">\n<h2>Bar plots et alternatives<\/h2>\n<ul>\n<li><strong>Data<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code>df &lt;- mtcars %&gt;%\r\n  rownames_to_column() %&gt;%\r\n  as_data_frame() %&gt;%\r\n  mutate(cyl = as.factor(cyl)) %&gt;%\r\n  select(rowname, wt, mpg, cyl)\r\ndf<\/code><\/pre>\n<pre><code>## # A tibble: 32 x 4\r\n##   rowname              wt   mpg cyl  \r\n##   &lt;chr&gt;             &lt;dbl&gt; &lt;dbl&gt; &lt;fct&gt;\r\n## 1 Mazda RX4          2.62  21   6    \r\n## 2 Mazda RX4 Wag      2.88  21   6    \r\n## 3 Datsun 710         2.32  22.8 4    \r\n## 4 Hornet 4 Drive     3.22  21.4 6    \r\n## 5 Hornet Sportabout  3.44  18.7 8    \r\n## 6 Valiant            3.46  18.1 6    \r\n## # \u2026 with 26 more rows<\/code><\/pre>\n<ul>\n<li><strong>Bar plot basique<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Bar plot basique\r\nggplot(df, aes(x = rowname, y = mpg)) +\r\n  geom_col() +\r\n  rotate_x_text(angle = 45)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-basic-barplot-1.png\" width=\"528\" \/><\/p>\n<pre class=\"r\"><code># Ordonner les noms de lignes par valeurs mpg\r\nggplot(df, aes(x = reorder(rowname, mpg), y = mpg)) +\r\n  geom_col()  +\r\n  rotate_x_text(angle = 45)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-basic-barplot-2.png\" width=\"528\" \/><\/p>\n<ul>\n<li><strong>Bar plot horizontaux<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Bar plot horizontaux, \r\n# changer la couleur de remplissage par groupe et ajouter des \u00e9tiquettes de texte\r\nggplot(df, aes(x = reorder(rowname, mpg), y = mpg)) +\r\n  geom_col( aes(fill = cyl)) + \r\n  geom_text(aes(label = mpg), nudge_y = 2) + \r\n  coord_flip() +\r\n  scale_fill_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-horizontal-bar-plot-1.png\" width=\"480\" \/><\/p>\n<ul>\n<li><strong>Classer les barres par groupes et par valeurs mpg<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code>df2 &lt;- df %&gt;% \r\n  arrange(cyl, mpg) %&gt;%\r\n  mutate(rowname = factor(rowname, levels = rowname))\r\n\r\nggplot(df2, aes(x = rowname, y = mpg)) +\r\n  geom_col( aes(fill = cyl)) + \r\n  scale_fill_viridis_d() +\r\n  rotate_x_text(45)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-ordered-barplots-1.png\" width=\"576\" \/><\/p>\n<ul>\n<li><strong>Graphique lollipop (sucette)<\/strong> : Lollipop est une alternative aux bar plots lorsque vous disposez de grands jeux de donn\u00e9es.<\/li>\n<\/ul>\n<pre class=\"r\"><code>ggplot(df2, aes(x = rowname, y = mpg)) +\r\n  geom_segment(\r\n    aes(x = rowname, xend = rowname, y = 0, yend = mpg), \r\n    color = \"lightgray\"\r\n    ) + \r\n  geom_point(aes(color = cyl), size = 3) +\r\n  scale_color_viridis_d() +\r\n  theme_pubclean() +\r\n  rotate_x_text(45)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-lollipop-chart-1.png\" width=\"576\" \/><\/p>\n<ul>\n<li><strong>Bar plot avec plusieurs groupes<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Data\r\ndf3 &lt;- data.frame(supp=rep(c(\"VC\", \"OJ\"), each=3),\r\n                dose=rep(c(\"D0.5\", \"D1\", \"D2\"),2),\r\n                len=c(6.8, 15, 33, 4.2, 10, 29.5))\r\n\r\n\r\n# Bar plot empil\u00e9s de y = counts par x = cut,\r\n# color\u00e9 par la variable\r\nggplot(df3, aes(x = dose, y = len)) +\r\n  geom_col(aes(color = supp, fill = supp), position = position_stack()) +\r\n  scale_color_manual(values = c(\"#0073C2FF\", \"#EFC000FF\"))+\r\n  scale_fill_manual(values = c(\"#0073C2FF\", \"#EFC000FF\"))\r\n\r\n# Utiliser position = position_dodge() \r\nggplot(df3, aes(x = dose, y = len)) +\r\n  geom_col(aes(color = supp, fill = supp), position = position_dodge(0.8), width = 0.7) +\r\n  scale_color_manual(values = c(\"#0073C2FF\", \"#EFC000FF\"))+\r\n  scale_fill_manual(values = c(\"#0073C2FF\", \"#EFC000FF\"))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-bar-chart-with-multiple-groups-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-bar-chart-with-multiple-groups-2.png\" width=\"288\" \/><\/p>\n<\/div>\n<div id=\"line-plot-ex\" class=\"section level2\">\n<h2>Line plot<\/h2>\n<pre class=\"r\"><code># Data\r\ndf3 &lt;- data.frame(supp=rep(c(\"VC\", \"OJ\"), each=3),\r\n                dose=rep(c(\"D0.5\", \"D1\", \"D2\"),2),\r\n                len=c(6.8, 15, 33, 4.2, 10, 29.5))\r\n\r\n# Line plot\r\nggplot(df3, aes(x = dose, y = len, group = supp)) +\r\n  geom_line(aes(linetype = supp)) +\r\n  geom_point(aes(shape = supp))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-line-plot-1.png\" width=\"384\" \/><\/p>\n<\/div>\n<div id=\"barres-derreur\" class=\"section level2\">\n<h2>Barres d\u2019erreur<\/h2>\n<ul>\n<li><strong>Data<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Donn\u00e9es brutes\r\ndf &lt;- ToothGrowth %&gt;% mutate(dose = as.factor(dose))\r\nhead(df, 3)<\/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<\/code><\/pre>\n<pre class=\"r\"><code># Statistiques descriptives\r\ndf.summary &lt;- df %&gt;%\r\n  group_by(dose) %&gt;%\r\n  summarise(sd = sd(len, na.rm = TRUE), len = mean(len))\r\ndf.summary<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 3\r\n##   dose     sd   len\r\n##   &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt;\r\n## 1 0.5    4.50  10.6\r\n## 2 1      4.42  19.7\r\n## 3 2      3.77  26.1<\/code><\/pre>\n<ul>\n<li><strong>Line plots et bar plots basiques avec barres d\u2019erreur<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># (1) Line plot\r\nggplot(df.summary, aes(dose, len)) +\r\n  geom_line(aes(group = 1)) +\r\n  geom_errorbar( aes(ymin = len-sd, ymax = len+sd),width = 0.2) +\r\n  geom_point(size = 2)\r\n\r\n# (2) Bar plot\r\nggplot(df.summary, aes(dose, len)) +\r\n  geom_bar(stat = \"identity\", fill = \"lightgray\", color = \"black\") +\r\n  geom_errorbar(aes(ymin = len, ymax = len+sd), width = 0.2) <\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-error-bars-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-error-bars-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li><strong>Graphiques de lignes\/barres group\u00e9es<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Pr\u00e9paration des donn\u00e9es\r\ndf.summary2 &lt;- df %&gt;%\r\n  group_by(dose, supp) %&gt;%\r\n  summarise( sd = sd(len), len = mean(len))\r\ndf.summary2<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 4\r\n## # Groups:   dose [3]\r\n##   dose  supp     sd   len\r\n##   &lt;fct&gt; &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt;\r\n## 1 0.5   OJ     4.46 13.2 \r\n## 2 0.5   VC     2.75  7.98\r\n## 3 1     OJ     3.91 22.7 \r\n## 4 1     VC     2.52 16.8 \r\n## 5 2     OJ     2.66 26.1 \r\n## 6 2     VC     4.80 26.1<\/code><\/pre>\n<pre class=\"r\"><code># (1) Line plot + barres d'erreur\r\nggplot(df.summary2, aes(dose, len)) +\r\n  geom_line(aes(linetype = supp, group = supp))+\r\n  geom_point()+\r\n  geom_errorbar(\r\n    aes(ymin = len-sd, ymax = len+sd, group = supp),\r\n     width = 0.2\r\n    )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-error-bars-group-1.png\" width=\"384\" \/><\/p>\n<pre class=\"r\"><code># (2) Bar plots + barres d'erreur sup\u00e9rieures.\r\nggplot(df.summary2, aes(dose, len)) +\r\n  geom_bar(aes(fill = supp), stat = \"identity\",\r\n           position = position_dodge(0.8), width = 0.7)+\r\n  geom_errorbar(\r\n    aes(ymin = len, ymax = len+sd, group = supp),\r\n    width = 0.2, position = position_dodge(0.8)\r\n    )+\r\n  scale_fill_manual(values = c(\"grey80\", \"grey30\"))<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-error-bars-group-2.png\" width=\"384\" \/><\/p>\n<\/div>\n<div id=\"box-plots-et-alternatives\" class=\"section level2\">\n<h2>Box plots et alternatives<\/h2>\n<ul>\n<li><strong>Data<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code>ToothGrowth$dose &lt;- as.factor(ToothGrowth$dose)<\/code><\/pre>\n<ul>\n<li><strong>Box plots basique<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Basique\r\nggplot(ToothGrowth, aes(dose, len)) +\r\n  geom_boxplot()\r\n\r\n# Box plot + violon plot\r\nggplot(ToothGrowth, aes(dose, len)) +\r\n  geom_violin(trim = FALSE) +\r\n  geom_boxplot(width = 0.2)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-basic-box-plots-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-basic-box-plots-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li><strong>Ajoutez des points jitter et des dot plot<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Ajouter des points dispers\u00e9s (jitter)\r\nggplot(ToothGrowth, aes(dose, len)) +\r\n  geom_boxplot() +\r\n  geom_jitter(width = 0.2)\r\n\r\n# Dot plot + box plot\r\nggplot(ToothGrowth, aes(dose, len)) +\r\n  geom_boxplot() +\r\n  geom_dotplot(binaxis = \"y\", stackdir = \"center\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-box-plot-with-points-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-box-plot-with-points-2.png\" width=\"288\" \/><\/p>\n<ul>\n<li><strong>Plots group\u00e9s<\/strong><\/li>\n<\/ul>\n<pre class=\"r\"><code># Box plots\r\nggplot(ToothGrowth, aes(dose, len)) +\r\n  geom_boxplot(aes(color = supp)) +\r\n  scale_color_viridis_d()\r\n\r\n# Ajouter des points dispers\u00e9s (jitter)\r\nggplot(ToothGrowth, aes(dose, len, color = supp)) +\r\n  geom_boxplot() +\r\n  geom_jitter(position = position_jitterdodge(jitter.width = 0.2)) +\r\n  scale_color_viridis_d()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-grouped-box-plots-1.png\" width=\"288\" \/><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-grouped-box-plots-2.png\" width=\"288\" \/><\/p>\n<\/div>\n<div id=\"visualisation-de-donnees-de-series-temporelles\" class=\"section level2\">\n<h2>Visualisation de donn\u00e9es de s\u00e9ries temporelles<\/h2>\n<pre class=\"r\"><code># Pr\u00e9paration des donn\u00e9es\r\ndf &lt;- economics %&gt;%\r\n  select(date, psavert, uempmed) %&gt;%\r\n  gather(key = \"variable\", value = \"value\", -date)\r\nhead(df, 3)<\/code><\/pre>\n<pre><code>## # A tibble: 3 x 3\r\n##   date       variable value\r\n##   &lt;date&gt;     &lt;chr&gt;    &lt;dbl&gt;\r\n## 1 1967-07-01 psavert   12.6\r\n## 2 1967-08-01 psavert   12.6\r\n## 3 1967-09-01 psavert   11.9<\/code><\/pre>\n<pre class=\"r\"><code># Graphique \u00e0 lignes multiples\r\nggplot(df, aes(x = date, y = value)) + \r\n  geom_line(aes(color = variable), size = 1) +\r\n  scale_color_manual(values = c(\"#00AFBB\", \"#E7B800\")) +\r\n  theme_minimal()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-time-series-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"matrice-de-diagramme-de-dispersion\" class=\"section level2\">\n<h2>matrice de diagramme de dispersion<\/h2>\n<pre class=\"r\"><code>library(GGally)\r\nggpairs(iris[,-5])+ theme_bw()<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-scatter-plot-matrix-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"analyse-de-correlation\" class=\"section level2\">\n<h2>Analyse de corr\u00e9lation<\/h2>\n<pre class=\"r\"><code>library(\"ggcorrplot\")\r\n# Calculer une matrice de corr\u00e9lation\r\nmy_data &lt;- mtcars[, c(1,3,4,5,6,7)]\r\ncorr &lt;- round(cor(my_data), 1)\r\n# Visualiser\r\nggcorrplot(corr, p.mat = cor_pmat(my_data),\r\n           hc.order = TRUE, type = \"lower\",\r\n           color = c(\"#FC4E07\", \"white\", \"#00AFBB\"),\r\n           outline.col = \"white\", lab = TRUE)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-correlation-matrix-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"analyse-de-cluster\" class=\"section level2\">\n<h2>Analyse de cluster<\/h2>\n<pre class=\"r\"><code>library(factoextra)\r\nUSArrests %&gt;%\r\n  scale() %&gt;%                           # Mettre les donn\u00e9es \u00e0 l'\u00e9chelle\r\n  dist() %&gt;%                            # Calculer la matrice de distance\r\n  hclust(method = \"ward.D2\") %&gt;%        # Cluster hi\u00e9rarchique\r\n  fviz_dend(cex = 0.5, k = 4, palette = \"jco\") # Visualiser et couper \r\n                                              # en 4 Groupes<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-cluster-analysis-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"balloon-plot\" class=\"section level2\">\n<h2>Balloon plot<\/h2>\n<p>Le Balloon plot (ou graphique en ballon) est une alternative aux bar plots pour visualiser une grande quantit\u00e9 de donn\u00e9es cat\u00e9gorielles.<\/p>\n<pre class=\"r\"><code>library(ggpubr)\r\n# Pr\u00e9paration des donn\u00e9es\r\nhousetasks &lt;- read.delim(\r\n  system.file(\"demo-data\/housetasks.txt\", package = \"ggpubr\"),\r\n  row.names = 1\r\n  )\r\nhead(housetasks, 4)<\/code><\/pre>\n<pre><code>##            Wife Alternating Husband Jointly\r\n## Laundry     156          14       2       4\r\n## Main_meal   124          20       5       4\r\n## Dinner       77          11       7      13\r\n## Breakfeast   82          36      15       7<\/code><\/pre>\n<pre class=\"r\"><code># Visualisation\r\nggballoonplot(housetasks, fill = \"value\")+\r\n  scale_fill_viridis_c(option = \"C\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/ggplot2\/figures\/128-ggplot-examples-balloon-plot-1.png\" width=\"384\" \/><\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cet article fournit une galerie d\u2019exemples de ggplots, notamment : des diagrammes de dispersion ou scatter plots, diagrammes de densit\u00e9 et histogrammes, diagrammes de barres et de lignes, barres d\u2019erreur, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":10794,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rating_form_position":"","rating_results_position":"","mr_structured_data_type":"","footnotes":""},"categories":[279],"tags":[],"class_list":["post-10793","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ggplot2-fr"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Exemples de GGPLOT: Meilleure R\u00e9f\u00e9rence - Datanovia<\/title>\n<meta name=\"description\" content=\"Cet article fournit une galerie d&#039;exemples de ggplot, notamment : diagrammes de dispersion, diagrammes de densit\u00e9 et histogrammes, diagrammes en barres et en lignes, barres d&#039;erreur, box plot, violin plot et plus.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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