{"id":18285,"date":"2020-12-12T19:21:38","date_gmt":"2020-12-12T18:21:38","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?p=18285"},"modified":"2020-12-12T19:25:26","modified_gmt":"2020-12-12T18:25:26","slug":"magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot","status":"publish","type":"post","link":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/","title":{"rendered":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot"},"content":{"rendered":"<div id=\"rdoc\">\n<p>Un <strong>graphique radar<\/strong>, \u00e9galement appel\u00e9 <strong>graphique araign\u00e9e<\/strong>, est utilis\u00e9 pour visualiser les valeurs ou les scores attribu\u00e9s \u00e0 un individu sur plusieurs variables quantitatives, o\u00f9 chaque variable correspond \u00e0 un axe sp\u00e9cifique.<\/p>\n<p>Cet article d\u00e9crit comment cr\u00e9er un <strong>graphique radar dans R<\/strong> en utilisant deux packages R diff\u00e9rents : <code>fmsb<\/code> et <code>ggradar<\/code>.<\/p>\n<p>Notez que le graphique radar fmsb est un graphique R basic. Le package <code>ggradar<\/code> cr\u00e9e un graphique radar ggplot.<\/p>\n<p>Vous apprendrez:<\/p>\n<ul>\n<li>comment cr\u00e9er un beau <strong>graphique radar fmsb<\/strong><\/li>\n<li>comment cr\u00e9er un <strong>graphique radar avec ggplot<\/strong><\/li>\n<li><strong>les alternatives aux graphiques radar<\/strong><\/li>\n<\/ul>\n<p>Sommaire:<\/p>\n<div id=\"TOC\">\n<ul>\n<li><a href=\"#donn\u00e9es-de-d\u00e9monstration\">Donn\u00e9es de d\u00e9monstration<\/a><\/li>\n<li><a href=\"#graphique-radar-fmsb\">graphique radar fmsb<\/a>\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=\"#graphique-radar-de-base\">Graphique radar de base<\/a><\/li>\n<li><a href=\"#personnaliser-les-graphiques-radar\">Personnaliser les graphiques radar<\/a><\/li>\n<li><a href=\"#cr\u00e9er-des-graphiques-radar-pour-plusieurs-individus\">Cr\u00e9er des graphiques radar pour plusieurs individus<\/a><\/li>\n<li><a href=\"#comparer-chaque-profil-\u00e0-un-profil-moyen\">Comparer chaque profil \u00e0 un profil moyen<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#graphique-radar-ggplot-utilisant-le-package-r-ggradar\">graphique radar ggplot utilisant le package R ggradar<\/a>\n<ul>\n<li><a href=\"#pr\u00e9requis-1\">Pr\u00e9requis<\/a><\/li>\n<li><a href=\"#fonction-et-arguments-cl\u00e9s\">Fonction et arguments cl\u00e9s<\/a><\/li>\n<li><a href=\"#pr\u00e9paration-des-donn\u00e9es-1\">Pr\u00e9paration des donn\u00e9es<\/a><\/li>\n<li><a href=\"#graphique-radar-de-base-1\">Graphique radar de base<\/a><\/li>\n<li><a href=\"#personnaliser-les-graphiques-radar-1\">Personnaliser les graphiques radar<\/a><\/li>\n<li><a href=\"#graphique-radar-avec-plusieurs-individus-ou-groupes\">Graphique radar avec plusieurs individus ou groupes<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#alternatives-aux-graphiques-radar\">Alternatives aux graphiques radar<\/a>\n<ul>\n<li><a href=\"#cas-o\u00f9-toutes-les-variables-quantitatives-ont-la-m\u00eame-\u00e9chelle\">Cas o\u00f9 toutes les variables quantitatives ont la m\u00eame \u00e9chelle<\/a><\/li>\n<li><a href=\"#cas-o\u00f9-vous-avez-beaucoup-dindividus-\u00e0-repr\u00e9senter-ou-si-vos-variables-ont-des-\u00e9chelles-diff\u00e9rentes\">Cas o\u00f9 vous avez beaucoup d\u2019individus \u00e0 repr\u00e9senter ou si vos variables ont des \u00e9chelles diff\u00e9rentes<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\n<\/ul>\n<\/div>\n<div id=\"donn\u00e9es-de-d\u00e9monstration\" class=\"section level2\">\n<h2>Donn\u00e9es de d\u00e9monstration<\/h2>\n<p>Nous utiliserons une donn\u00e9e de d\u00e9monstration contenant les notes d\u2019examen de 3 \u00e9tudiants sur 9 sujets (Biologie, Physique, etc). Les notes vont de 0 \u00e0 20. Les colonnes sont des variables quantitatives et les lignes sont des individus.<\/p>\n<pre class=\"r\"><code># Donn\u00e9es de d\u00e9monstration\r\nexam_scores &lt;- data.frame(\r\n    row.names = c(\"Student.1\", \"Student.2\", \"Student.3\"),\r\n      Biology = c(7.9, 3.9, 9.4),\r\n      Physics = c(10, 20, 0),\r\n        Maths = c(3.7, 11.5, 2.5),\r\n        Sport = c(8.7, 20, 4),\r\n      English = c(7.9, 7.2, 12.4),\r\n    Geography = c(6.4, 10.5, 6.5),\r\n          Art = c(2.4, 0.2, 9.8),\r\n  Programming = c(0, 0, 20),\r\n        Music = c(20, 20, 20)\r\n)\r\nexam_scores<\/code><\/pre>\n<pre><code>##           Biology Physics Maths Sport English Geography Art Programming Music\r\n## Student.1     7.9      10   3.7   8.7     7.9       6.4 2.4           0    20\r\n## Student.2     3.9      20  11.5  20.0     7.2      10.5 0.2           0    20\r\n## Student.3     9.4       0   2.5   4.0    12.4       6.5 9.8          20    20<\/code><\/pre>\n<\/div>\n<div id=\"graphique-radar-fmsb\" class=\"section level2\">\n<h2>graphique radar fmsb<\/h2>\n<div id=\"pr\u00e9requis\" class=\"section level3\">\n<h3>Pr\u00e9requis<\/h3>\n<p>Installez le package R <code>fmsb<\/code>:<\/p>\n<pre class=\"r\"><code>install.packages(\"fmsb\")<\/code><\/pre>\n<p>Charger le package:<\/p>\n<pre class=\"r\"><code>library(fmsb)<\/code><\/pre>\n<\/div>\n<div id=\"pr\u00e9paration-des-donn\u00e9es\" class=\"section level3\">\n<h3>Pr\u00e9paration des donn\u00e9es<\/h3>\n<div class=\"block\">\n<p>Les donn\u00e9es doivent \u00eatre organis\u00e9es comme suit:<\/p>\n<ul>\n<li>La ligne 1 doit contenir les valeurs maximales de chaque variable<\/li>\n<li>La ligne 2 doit contenir les valeurs minimales de chaque variable<\/li>\n<li>Les donn\u00e9es relatives aux cas ou aux personnes doivent \u00eatre indiqu\u00e9es \u00e0 partir de la ligne 3<\/li>\n<li>Le nombre de colonnes ou de variables doit \u00eatre sup\u00e9rieur \u00e0 2.<\/li>\n<\/ul>\n<\/div>\n<pre class=\"r\"><code># D\u00e9finir les intervalles des variables : maximum et minimum\r\nmax_min &lt;- data.frame(\r\n  Biology = c(20, 0), Physics = c(20, 0), Maths = c(20, 0),\r\n  Sport = c(20, 0), English = c(20, 0), Geography = c(20, 0),\r\n  Art = c(20, 0), Programming = c(20, 0), Music = c(20, 0)\r\n)\r\nrownames(max_min) &lt;- c(\"Max\", \"Min\")\r\n\r\n# Rattacher les plages de variables aux donn\u00e9es\r\ndf &lt;- rbind(max_min, exam_scores)\r\ndf<\/code><\/pre>\n<pre><code>##           Biology Physics Maths Sport English Geography  Art Programming Music\r\n## Max          20.0      20  20.0  20.0    20.0      20.0 20.0          20    20\r\n## Min           0.0       0   0.0   0.0     0.0       0.0  0.0           0     0\r\n## Student.1     7.9      10   3.7   8.7     7.9       6.4  2.4           0    20\r\n## Student.2     3.9      20  11.5  20.0     7.2      10.5  0.2           0    20\r\n## Student.3     9.4       0   2.5   4.0    12.4       6.5  9.8          20    20<\/code><\/pre>\n<\/div>\n<div id=\"graphique-radar-de-base\" class=\"section level3\">\n<h3>Graphique radar de base<\/h3>\n<pre class=\"r\"><code># Repr\u00e9sentez les donn\u00e9es pour l'\u00e9l\u00e8ve 1\r\nlibrary(fmsb)\r\nstudent1_data &lt;- df[c(\"Max\", \"Min\", \"Student.1\"), ]\r\nradarchart(student1_data)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-basic-fmstb-radar-plot-1.png\" width=\"480\" \/><\/p>\n<div class=\"success\">\n<p>On peut constater que l\u2019\u00e9tudiant 1 a un score \u00e9lev\u00e9 en musique et en physique par rapport aux autres sujets.<\/p>\n<\/div>\n<\/div>\n<div id=\"personnaliser-les-graphiques-radar\" class=\"section level3\">\n<h3>Personnaliser les graphiques radar<\/h3>\n<p>Principaux arguments pour personnaliser les diff\u00e9rentes composantes du graphique radar <code>fmsb<\/code>:<\/p>\n<div class=\"block\">\n<ul>\n<li>Options variables\n<ul>\n<li><code>vlabels<\/code>: \u00e9tiquettes des variables<\/li>\n<li><code>vlcex<\/code>: contr\u00f4le la taille de la police des \u00e9tiquettes variables<\/li>\n<\/ul>\n<\/li>\n<li>Options de polygone:\n<ul>\n<li><code>pcol<\/code>: couleur de la ligne<\/li>\n<li><code>pfcol<\/code>: couleur de remplissage<\/li>\n<li><code>plwd<\/code>: largeur de ligne<\/li>\n<li><code>plty<\/code>: types de lignes. Peut \u00eatre un tableau num\u00e9rique 1:6 ou un tableau de caract\u00e8res c (\u201csolid\u201d, \u201cdashed\u201d, \u201cdotdash\u201d, \u201cdotdash\u201d, \u201clongdash\u201d, \u201ctwodash\u201d). Pour supprimer la ligne, utilisez <code>plty = 0<\/code> ou <code>plty = \u201cblank\u201d<\/code>.<\/li>\n<\/ul>\n<\/li>\n<li>Options de grille:\n<ul>\n<li><code>cglcol<\/code>: couleur de la ligne<\/li>\n<li><code>cglty<\/code>: type de ligne<\/li>\n<li><code>cglwd<\/code>: largeur de ligne<\/li>\n<\/ul>\n<\/li>\n<li>Options des axes:\n<ul>\n<li><code>axislabcol<\/code>: couleur de l\u2019\u00e9tiquette de l\u2019axe et num\u00e9ros. La valeur par d\u00e9faut est \u201cbleu\u201d.<\/li>\n<li><code>caxislabels<\/code>: Vecteur de caract\u00e8res \u00e0 utiliser comme \u00e9tiquettes sur l\u2019axe central.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<p><strong>Fonction d\u2019aide pour produire un beau graphique radar<\/strong>:<\/p>\n<pre class=\"r\"><code>create_beautiful_radarchart &lt;- function(data, color = \"#00AFBB\", \r\n                                        vlabels = colnames(data), vlcex = 0.7,\r\n                                        caxislabels = NULL, title = NULL, ...){\r\n  radarchart(\r\n    data, axistype = 1,\r\n    # Personnaliser le polygone\r\n    pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1,\r\n    # Personnaliser la grille\r\n    cglcol = \"grey\", cglty = 1, cglwd = 0.8,\r\n    # Personnaliser l'axe\r\n    axislabcol = \"grey\", \r\n    # \u00c9tiquettes des variables\r\n    vlcex = vlcex, vlabels = vlabels,\r\n    caxislabels = caxislabels, title = title, ...\r\n  )\r\n}<\/code><\/pre>\n<div class=\"warning\">\n<p>Dans le code ci-dessus, nous avons utilis\u00e9 la fonction <code>alpha()<\/code> [dans le package scales] pour changer la transparence de la couleur de remplissage du polygone.<\/p>\n<\/div>\n<pre class=\"r\"><code># R\u00e9duire la marge du graphique \u00e0 l'aide de par()\r\nop &lt;- par(mar = c(1, 2, 2, 1))\r\ncreate_beautiful_radarchart(student1_data, caxislabels = c(0, 5, 10, 15, 20))\r\npar(op)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"cr\u00e9er-des-graphiques-radar-pour-plusieurs-individus\" class=\"section level3\">\n<h3>Cr\u00e9er des graphiques radar pour plusieurs individus<\/h3>\n<p>Cr\u00e9er le graphique radar des trois \u00e9l\u00e8ves sur le m\u00eame graphique:<\/p>\n<pre class=\"r\"><code># R\u00e9duire la marge du graphique \u00e0 l'aide de par()\r\nop &lt;- par(mar = c(1, 2, 2, 2))\r\n# Cr\u00e9er les graphiques radar\r\ncreate_beautiful_radarchart(\r\n  data = df, caxislabels = c(0, 5, 10, 15, 20),\r\n  color = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\")\r\n)\r\n# Ajouter une l\u00e9gende horizontale\r\nlegend(\r\n  x = \"bottom\", legend = rownames(df[-c(1,2),]), horiz = TRUE,\r\n  bty = \"n\", pch = 20 , col = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n  text.col = \"black\", cex = 1, pt.cex = 1.5\r\n  )\r\npar(op)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-spider-chart-with-multiple-groups-1.png\" width=\"480\" \/><\/p>\n<p>Cr\u00e9er des diagrammes araign\u00e9es s\u00e9par\u00e9s pour chaque individu. Ceci est recommand\u00e9 lorsque vous avez plus de 3 s\u00e9ries.<\/p>\n<pre class=\"r\"><code># D\u00e9finir les couleurs et les titres\r\ncolors &lt;- c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\")\r\ntitles &lt;- c(\"Student.1\", \"Student.2\", \"Student.3\")\r\n\r\n# R\u00e9duire la marge du graphique \u00e0 l'aide de par()\r\n# Diviser l'\u00e9cran en 3 parties\r\nop &lt;- par(mar = c(1, 1, 1, 1))\r\npar(mfrow = c(1,3))\r\n\r\n# Cr\u00e9er le graphique radar\r\nfor(i in 1:3){\r\n  create_beautiful_radarchart(\r\n    data = df[c(1, 2, i+2), ], caxislabels = c(0, 5, 10, 15, 20),\r\n    color = colors[i], title = titles[i]\r\n    )\r\n}\r\npar(op)<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-radar-plot-for-each-individual-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<div id=\"comparer-chaque-profil-\u00e0-un-profil-moyen\" class=\"section level3\">\n<h3>Comparer chaque profil \u00e0 un profil moyen<\/h3>\n<p>Les graphiques radar sont plus utiles si le profil de chaque individu est compar\u00e9 \u00e0 un profil moyen.<\/p>\n<ol style=\"list-style-type: decimal;\">\n<li>Cr\u00e9er des donn\u00e9es de demo contenant les notes d\u2019examen de 10 \u00e9tudiants:<\/li>\n<\/ol>\n<pre class=\"r\"><code>set.seed(123)\r\ndf &lt;- as.data.frame(\r\n  matrix(sample(2:20 , 90 , replace = TRUE),\r\n         ncol=9, byrow = TRUE)\r\n  )\r\ncolnames(df) &lt;- c(\r\n  \"Biology\", \"Physics\", \"Maths\", \"Sport\", \"English\", \r\n  \"Geography\", \"Art\", \"Programming\", \"Music\"\r\n  )\r\nrownames(df) &lt;- paste0(\"Student.\", 1:nrow(df))\r\nhead(df)<\/code><\/pre>\n<pre><code>##           Biology Physics Maths Sport English Geography Art Programming Music\r\n## Student.1      16      20    15     4      11        19  12           6    15\r\n## Student.2       6      20    10     4       9         8  11          10    20\r\n## Student.3       5      15    18    12       8        13  16          11    14\r\n## Student.4       8      10    10    11       8         7   3           6     9\r\n## Student.5      13      14    19     2       7        16  10          16    17\r\n## Student.6       7      12     9     8      17        18  19          18     3<\/code><\/pre>\n<ol style=\"list-style-type: decimal;\" start=\"2\">\n<li>Normaliser chaque variable pour qu\u2019elle soit comprise entre 0 et 1:<\/li>\n<\/ol>\n<pre class=\"r\"><code>library(scales)\r\ndf_scaled &lt;- round(apply(df, 2, scales::rescale), 2)\r\ndf_scaled &lt;- as.data.frame(df_scaled)\r\nhead(df_scaled)<\/code><\/pre>\n<pre><code>##           Biology Physics Maths Sport English Geography  Art Programming Music\r\n## Student.1    1.00    1.00  0.69  0.11    0.47      1.00 0.56        0.00  0.71\r\n## Student.2    0.09    1.00  0.31  0.11    0.35      0.27 0.50        0.33  1.00\r\n## Student.3    0.00    0.69  0.92  0.56    0.29      0.60 0.81        0.42  0.65\r\n## Student.4    0.27    0.38  0.31  0.50    0.29      0.20 0.00        0.00  0.35\r\n## Student.5    0.73    0.62  1.00  0.00    0.24      0.80 0.44        0.83  0.82\r\n## Student.6    0.18    0.50  0.23  0.33    0.82      0.93 1.00        1.00  0.00<\/code><\/pre>\n<ol style=\"list-style-type: decimal;\" start=\"3\">\n<li>Pr\u00e9parer les donn\u00e9es pour cr\u00e9er le graphique radar \u00e0 l\u2019aide du package <code>fmsb<\/code>:<\/li>\n<\/ol>\n<pre class=\"r\"><code># Descriptif des variables\r\n# Obtenir le minimum et le maximum de chaque colonne\r\ncol_max &lt;- apply(df_scaled, 2, max)\r\ncol_min &lt;- apply(df_scaled, 2, min)\r\n# Calculer le profil moyen \r\ncol_mean &lt;- apply(df_scaled, 2, mean)\r\n# Rassembler le descriptif des colonnes\r\ncol_summary &lt;- t(data.frame(Max = col_max, Min = col_min, Average = col_mean))\r\n\r\n\r\n# Rattacher le descriptif des variables aux donn\u00e9es\r\ndf_scaled2 &lt;- as.data.frame(rbind(col_summary, df_scaled))\r\nhead(df_scaled2)<\/code><\/pre>\n<pre><code>##           Biology Physics Maths Sport English Geography   Art Programming Music\r\n## Max         1.000   1.000 1.000 1.000   1.000     1.000 1.000        1.00 1.000\r\n## Min         0.000   0.000 0.000 0.000   0.000     0.000 0.000        0.00 0.000\r\n## Average     0.464   0.575 0.476 0.427   0.423     0.587 0.544        0.50 0.629\r\n## Student.1   1.000   1.000 0.690 0.110   0.470     1.000 0.560        0.00 0.710\r\n## Student.2   0.090   1.000 0.310 0.110   0.350     0.270 0.500        0.33 1.000\r\n## Student.3   0.000   0.690 0.920 0.560   0.290     0.600 0.810        0.42 0.650<\/code><\/pre>\n<ol style=\"list-style-type: decimal;\" start=\"4\">\n<li>Produire des graphiques radar montrant \u00e0 la fois le profil moyen et le profil individuel:<\/li>\n<\/ol>\n<pre class=\"r\"><code>opar &lt;- par() \r\n# D\u00e9finir les param\u00e8tres graphiques dans une grille 3x4, avec des marges appropri\u00e9es:\r\npar(mar = rep(0.8,4))\r\npar(mfrow = c(3,4))\r\n# Produire un graphique radar pour chaque \u00e9l\u00e8ve\r\nfor (i in 4:nrow(df_scaled2)) {\r\n  radarchart(\r\n    df_scaled2[c(1:3, i), ],\r\n    pfcol = c(\"#99999980\",NA),\r\n    pcol= c(NA,2), plty = 1, plwd = 2,\r\n    title = row.names(df_scaled2)[i]\r\n  )\r\n}\r\n# Restaurer les param\u00e8tres standard de par()\r\npar &lt;- par(opar) <\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-compare-every-profile-to-average-profile-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"graphique-radar-ggplot-utilisant-le-package-r-ggradar\" class=\"section level2\">\n<h2>graphique radar ggplot utilisant le package R ggradar<\/h2>\n<div id=\"pr\u00e9requis-1\" class=\"section level3\">\n<h3>Pr\u00e9requis<\/h3>\n<p>Installation:<\/p>\n<pre class=\"r\"><code>devtools::install_github(\"ricardo-bion\/ggradar\")<\/code><\/pre>\n<p>Chargement du package:<\/p>\n<pre class=\"r\"><code>library(\"ggradar\")<\/code><\/pre>\n<\/div>\n<div id=\"fonction-et-arguments-cl\u00e9s\" class=\"section level3\">\n<h3>Fonction et arguments cl\u00e9s<\/h3>\n<pre class=\"r\"><code>ggradar(\r\n  plot.data, values.radar = c(\"0%\", \"50%\", \"100%\"),\r\n  grid.min = 0, grid.mid = 0.5, grid.max = 1, \r\n  )<\/code><\/pre>\n<ul>\n<li><code>plot.data<\/code>: les donn\u00e9es contenant une ligne par individu ou groupe<\/li>\n<li><code>values.radar<\/code>: les valeurs \u00e0 indiquer aux lignes de grille minimales, moyennes et maximales<\/li>\n<li><code>grid.min<\/code>: valeur \u00e0 laquelle la ligne de grille minimale est repr\u00e9sent\u00e9e<\/li>\n<li><code>grid.mid<\/code>: valeur \u00e0 laquelle la ligne moyenne de la grille est repr\u00e9sent\u00e9e<\/li>\n<li><code>grid.max<\/code>: valeur \u00e0 laquelle la ligne de grille maximale est repr\u00e9sent\u00e9e<\/li>\n<\/ul>\n<\/div>\n<div id=\"pr\u00e9paration-des-donn\u00e9es-1\" class=\"section level3\">\n<h3>Pr\u00e9paration des donn\u00e9es<\/h3>\n<div class=\"warning\">\n<p>Toutes les variables des donn\u00e9es doivent \u00eatre \u00e0 la m\u00eame \u00e9chelle. Si ce n\u2019est pas le cas, vous devez normaliser les donn\u00e9es.<\/p>\n<p>Par exemple, vous pouvez normaliser les variables pour qu\u2019elles aient un minimum de 0 et un maximum de 1 en utilisant la fonction <code>rescale()<\/code> [scales package]. Nous d\u00e9crirons cette m\u00e9thode dans les prochaines sections.<\/p>\n<\/div>\n<pre class=\"r\"><code>library(tidyverse)\r\n# Mettre les noms des lignes dans un groupe nomm\u00e9 colonne\r\ndf &lt;- exam_scores %&gt;% rownames_to_column(\"group\")\r\ndf<\/code><\/pre>\n<pre><code>##       group Biology Physics Maths Sport English Geography Art Programming Music\r\n## 1 Student.1     7.9      10   3.7   8.7     7.9       6.4 2.4           0    20\r\n## 2 Student.2     3.9      20  11.5  20.0     7.2      10.5 0.2           0    20\r\n## 3 Student.3     9.4       0   2.5   4.0    12.4       6.5 9.8          20    20<\/code><\/pre>\n<\/div>\n<div id=\"graphique-radar-de-base-1\" class=\"section level3\">\n<h3>Graphique radar de base<\/h3>\n<pre class=\"r\"><code># Repr\u00e9sentation graphique de l'\u00e9l\u00e8ve 1\r\nggradar(\r\n  df[1, ], \r\n  values.radar = c(\"0\", \"10\", \"20\"),\r\n  grid.min = 0, grid.mid = 10, grid.max = 20\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-ggradar-ggplot-radar-chart-basic-1.png\" width=\"432\" \/><\/p>\n<\/div>\n<div id=\"personnaliser-les-graphiques-radar-1\" class=\"section level3\">\n<h3>Personnaliser les graphiques radar<\/h3>\n<p>Principaux arguments pour personnaliser les diff\u00e9rentes composantes du graphique radar ggplot. Pour plus d\u2019options, voir la documentation.<\/p>\n<pre class=\"r\"><code>ggradar(\r\n  df[1, ], \r\n  values.radar = c(\"0\", \"10\", \"20\"),\r\n  grid.min = 0, grid.mid = 10, grid.max = 20,\r\n  # Polygones\r\n  group.line.width = 1, \r\n  group.point.size = 3,\r\n  group.colours = \"#00AFBB\",\r\n  # Arri\u00e8re-plan et lignes de grille\r\n  background.circle.colour = \"white\",\r\n  gridline.mid.colour = \"grey\"\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-ggradar-ggplot-radar-chart-customized-1.png\" width=\"432\" \/><\/p>\n<\/div>\n<div id=\"graphique-radar-avec-plusieurs-individus-ou-groupes\" class=\"section level3\">\n<h3>Graphique radar avec plusieurs individus ou groupes<\/h3>\n<p>Cr\u00e9er le graphique radar des trois \u00e9l\u00e8ves sur le m\u00eame graphique:<\/p>\n<pre class=\"r\"><code>ggradar(\r\n  df, \r\n  values.radar = c(\"0\", \"10\", \"20\"),\r\n  grid.min = 0, grid.mid = 10, grid.max = 20,\r\n  # Polygones\r\n  group.line.width = 1, \r\n  group.point.size = 3,\r\n  group.colours = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n  # Arri\u00e8re-plan et lignes de grille\r\n  background.circle.colour = \"white\",\r\n  gridline.mid.colour = \"grey\",\r\n  legend.position = \"bottom\"\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-ggradar-ggplot-radar-chart-multiple-individuals-1.png\" width=\"528\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"alternatives-aux-graphiques-radar\" class=\"section level2\">\n<h2>Alternatives aux graphiques radar<\/h2>\n<p>Un graphique circulaire est difficile \u00e0 lire. Une alternative \u00e0 un graphique radar est une lolliplot (graphique sucette) ou un dotchart. Cette section d\u00e9crit comment cr\u00e9er des dotcharts. Le package R <code>ggpubr<\/code> sera utilis\u00e9 dans cette section pour cr\u00e9er un dotchart.<\/p>\n<p>Charger les packages requis:<\/p>\n<pre class=\"r\"><code>library(tidyverse)\r\nlibrary(ggpubr)<\/code><\/pre>\n<div id=\"cas-o\u00f9-toutes-les-variables-quantitatives-ont-la-m\u00eame-\u00e9chelle\" class=\"section level3\">\n<h3>Cas o\u00f9 toutes les variables quantitatives ont la m\u00eame \u00e9chelle<\/h3>\n<div id=\"affichage-dun-individu\" class=\"section level4\">\n<h4>Affichage d\u2019un individu<\/h4>\n<p>Pr\u00e9paration des donn\u00e9es:<\/p>\n<pre class=\"r\"><code>df2 &lt;- t(exam_scores) %&gt;%\r\n  as.data.frame() %&gt;%\r\n  rownames_to_column(\"Field\")\r\ndf2<\/code><\/pre>\n<pre><code>##         Field Student.1 Student.2 Student.3\r\n## 1     Biology       7.9       3.9       9.4\r\n## 2     Physics      10.0      20.0       0.0\r\n## 3       Maths       3.7      11.5       2.5\r\n## 4       Sport       8.7      20.0       4.0\r\n## 5     English       7.9       7.2      12.4\r\n## 6   Geography       6.4      10.5       6.5\r\n## 7         Art       2.4       0.2       9.8\r\n## 8 Programming       0.0       0.0      20.0\r\n## 9       Music      20.0      20.0      20.0<\/code><\/pre>\n<p>Repr\u00e9sentation graphique:<\/p>\n<pre class=\"r\"><code>ggdotchart(\r\n  df2, x = \"Field\", y = \"Student.1\",\r\n  add = \"segments\", sorting = \"descending\",\r\n  ylab = \"Exam Score\", title = \"Student 1\"\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-loliplot-one-individual-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"affichage-de-deux-individus\" class=\"section level4\">\n<h4>Affichage de deux individus<\/h4>\n<p>Pr\u00e9paration des donn\u00e9es:<\/p>\n<pre class=\"r\"><code>df3 &lt;- df2 %&gt;%\r\n  select(Field, Student.1, Student.2) %&gt;%\r\n  pivot_longer(\r\n    cols = c(Student.1, Student.2),\r\n    names_to = \"student\",\r\n    values_to = \"value\"\r\n  )\r\nhead(df3)<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 3\r\n##   Field   student   value\r\n##   &lt;chr&gt;   &lt;chr&gt;     &lt;dbl&gt;\r\n## 1 Biology Student.1   7.9\r\n## 2 Biology Student.2   3.9\r\n## 3 Physics Student.1  10  \r\n## 4 Physics Student.2  20  \r\n## 5 Maths   Student.1   3.7\r\n## 6 Maths   Student.2  11.5<\/code><\/pre>\n<p>Repr\u00e9sentation graphique:<\/p>\n<pre class=\"r\"><code>ggdotchart(\r\n  df3, x = \"Field\", y = \"value\", \r\n  group = \"student\", color = \"student\", palette = \"jco\",\r\n  add = \"segment\", position = position_dodge(0.3),\r\n  sorting = \"descending\"\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-loliplot-two-individual-1.png\" width=\"480\" \/><\/p>\n<\/div>\n<div id=\"affichage-de-plusieurs-individus\" class=\"section level4\">\n<h4>Affichage de plusieurs individus<\/h4>\n<p>Pr\u00e9paration des donn\u00e9es:<\/p>\n<pre class=\"r\"><code>df4 &lt;- df2 %&gt;%\r\n  select(Field, Student.1, Student.2, Student.3) %&gt;%\r\n  pivot_longer(\r\n    cols = c(Student.1, Student.2, Student.3),\r\n    names_to = \"student\",\r\n    values_to = \"value\"\r\n  )\r\nhead(df4)<\/code><\/pre>\n<pre><code>## # A tibble: 6 x 3\r\n##   Field   student   value\r\n##   &lt;chr&gt;   &lt;chr&gt;     &lt;dbl&gt;\r\n## 1 Biology Student.1   7.9\r\n## 2 Biology Student.2   3.9\r\n## 3 Biology Student.3   9.4\r\n## 4 Physics Student.1  10  \r\n## 5 Physics Student.2  20  \r\n## 6 Physics Student.3   0<\/code><\/pre>\n<p>Repr\u00e9sentation graphique:<\/p>\n<pre class=\"r\"><code>ggdotchart(\r\n  df4, x = \"Field\", y = \"value\", \r\n  group = \"student\", color = \"student\", palette = \"jco\",\r\n  add = \"segment\", position = position_dodge(0.3),\r\n  sorting = \"descending\", facet.by = \"student\",\r\n  rotate = TRUE, legend = \"none\"\r\n  )<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-loliplot-multiple-individual-1.png\" width=\"576\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"cas-o\u00f9-vous-avez-beaucoup-dindividus-\u00e0-repr\u00e9senter-ou-si-vos-variables-ont-des-\u00e9chelles-diff\u00e9rentes\" class=\"section level3\">\n<h3>Cas o\u00f9 vous avez beaucoup d\u2019individus \u00e0 repr\u00e9senter ou si vos variables ont des \u00e9chelles diff\u00e9rentes<\/h3>\n<p>Une solution consiste \u00e0 cr\u00e9er un graphique de coordonn\u00e9es parall\u00e8les.<\/p>\n<pre class=\"r\"><code>library(GGally)\r\nggparcoord(\r\n  iris,\r\n  columns = 1:4, groupColumn = 5, order = \"anyClass\",\r\n  showPoints = TRUE, \r\n  title = \"Parallel Coordinate Plot for the Iris Data\",\r\n  alphaLines = 0.3\r\n  ) + \r\n  theme_bw() +\r\n  theme(legend.position = \"top\")<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/dn-tutorials\/r-tutorial\/figures\/radar-chart-in-r-parallele-coordinates-1.png\" width=\"480\" \/><\/p>\n<div class=\"warning\">\n<p>Notez que, par d\u00e9faut, la fonction <code>ggparcoord()<\/code> normalise chaque variable en soustrayant la moyenne et en divisant par l\u2019\u00e9cart-type.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"conclusion\" class=\"section level2\">\n<h2>Conclusion<\/h2>\n<p>Cet article d\u00e9crit comment cr\u00e9er un graphique radar dans R pour un ou plusieurs individus en utilisant le package fmsb et le package ggradar (une extension de ggplot2).<\/p>\n<\/div>\n<\/div>\n<p><!--end rdoc--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Un graphique radar, \u00e9galement appel\u00e9 graphique araign\u00e9e, est utilis\u00e9 pour visualiser les valeurs ou les scores attribu\u00e9s \u00e0 un individu sur plusieurs variables quantitatives, o\u00f9 chaque variable correspond \u00e0 un [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":18281,"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":[374,377],"class_list":["post-18285","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-visualisation-de-donnees","tag-francais","tag-graphique-circulair"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - 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\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - Datanovia\" \/>\n<meta property=\"og:description\" content=\"Un graphique radar, \u00e9galement appel\u00e9 graphique araign\u00e9e, est utilis\u00e9 pour visualiser les valeurs ou les scores attribu\u00e9s \u00e0 un individu sur plusieurs variables quantitatives, o\u00f9 chaque variable correspond \u00e0 un [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\" \/>\n<meta property=\"og:site_name\" content=\"Datanovia\" \/>\n<meta property=\"article:published_time\" content=\"2020-12-12T18:21:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-12-12T18:25:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"960\" \/>\n\t<meta property=\"og:image:height\" content=\"960\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Alboukadel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Alboukadel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\"},\"author\":{\"name\":\"Alboukadel\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/7767cf2bd5c91a1610c6eb53a0ff069e\"},\"headline\":\"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot\",\"datePublished\":\"2020-12-12T18:21:38+00:00\",\"dateModified\":\"2020-12-12T18:25:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\"},\"wordCount\":983,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\",\"keywords\":[\"Fran\u00e7ais\",\"Graphique Circulair\"],\"articleSection\":[\"Visualisation de Donn\u00e9es\"],\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\",\"url\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\",\"name\":\"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - Datanovia\",\"isPartOf\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\",\"datePublished\":\"2020-12-12T18:21:38+00:00\",\"dateModified\":\"2020-12-12T18:25:26+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage\",\"url\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\",\"contentUrl\":\"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png\",\"width\":960,\"height\":960},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.datanovia.com\/en\/fr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot\"}]},{\"@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\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/7767cf2bd5c91a1610c6eb53a0ff069e\",\"name\":\"Alboukadel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/ed3108646c5c7c3d188324ab972f96ad7d9975b41b94014d7f68257791be395a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/ed3108646c5c7c3d188324ab972f96ad7d9975b41b94014d7f68257791be395a?s=96&d=mm&r=g\",\"caption\":\"Alboukadel\"},\"url\":\"https:\/\/www.datanovia.com\/en\/fr\/blog\/author\/kassambara\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - Datanovia","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/","og_locale":"fr_FR","og_type":"article","og_title":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - Datanovia","og_description":"Un graphique radar, \u00e9galement appel\u00e9 graphique araign\u00e9e, est utilis\u00e9 pour visualiser les valeurs ou les scores attribu\u00e9s \u00e0 un individu sur plusieurs variables quantitatives, o\u00f9 chaque variable correspond \u00e0 un [&hellip;]","og_url":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/","og_site_name":"Datanovia","article_published_time":"2020-12-12T18:21:38+00:00","article_modified_time":"2020-12-12T18:25:26+00:00","og_image":[{"width":960,"height":960,"url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png","type":"image\/png"}],"author":"Alboukadel","twitter_card":"summary_large_image","twitter_misc":{"\u00c9crit par":"Alboukadel","Dur\u00e9e de lecture estim\u00e9e":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#article","isPartOf":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/"},"author":{"name":"Alboukadel","@id":"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/7767cf2bd5c91a1610c6eb53a0ff069e"},"headline":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot","datePublished":"2020-12-12T18:21:38+00:00","dateModified":"2020-12-12T18:25:26+00:00","mainEntityOfPage":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/"},"wordCount":983,"commentCount":0,"publisher":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/#organization"},"image":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png","keywords":["Fran\u00e7ais","Graphique Circulair"],"articleSection":["Visualisation de Donn\u00e9es"],"inLanguage":"fr-FR","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/","url":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/","name":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot - Datanovia","isPartOf":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage"},"image":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png","datePublished":"2020-12-12T18:21:38+00:00","dateModified":"2020-12-12T18:25:26+00:00","breadcrumb":{"@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#primaryimage","url":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png","contentUrl":"https:\/\/www.datanovia.com\/en\/wp-content\/uploads\/2020\/12\/radar-chart-in-r-customized-fmstb-radar-chart-1.png","width":960,"height":960},{"@type":"BreadcrumbList","@id":"https:\/\/www.datanovia.com\/en\/fr\/blog\/magnifique-graphique-radar-dans-r-avec-les-packages-fmsb-et-ggplot\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.datanovia.com\/en\/fr\/"},{"@type":"ListItem","position":2,"name":"Magnifique Graphique Radar dans R avec les Packages FMSB et GGPlot"}]},{"@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\/"}},{"@type":"Person","@id":"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/7767cf2bd5c91a1610c6eb53a0ff069e","name":"Alboukadel","image":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.datanovia.com\/en\/fr\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/ed3108646c5c7c3d188324ab972f96ad7d9975b41b94014d7f68257791be395a?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/ed3108646c5c7c3d188324ab972f96ad7d9975b41b94014d7f68257791be395a?s=96&d=mm&r=g","caption":"Alboukadel"},"url":"https:\/\/www.datanovia.com\/en\/fr\/blog\/author\/kassambara\/"}]}},"multi-rating":{"mr_rating_results":[]},"_links":{"self":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/posts\/18285","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/types\/post"}],"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=18285"}],"version-history":[{"count":1,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/posts\/18285\/revisions"}],"predecessor-version":[{"id":18288,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/posts\/18285\/revisions\/18288"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/media\/18281"}],"wp:attachment":[{"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/media?parent=18285"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/categories?post=18285"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datanovia.com\/en\/fr\/wp-json\/wp\/v2\/tags?post=18285"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}