{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Manipulation avec dplyr" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## le package dplyr\n", "Dans ce cours, afin de manipuler les données, nous allons utiliser la librairie `dplyr` qui assure une manipulation plus intuitive des données. Toutefois, vous pouvez utiliser tout autre libraire ou même les fonctions de base de `R`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# install.packages(\"dplyr\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "D'abord, téléchargeons un petit _df_ afin d'illustrer la théorie. Dans ce _df_, nous avons les données d'un cycliste qui est sorti un jour d'été faire un petit tour dans l'île de Montréal. Chaque observation, représente le nombre de km parcourus d'un parcours (_lap_), le temps que ça a pris, la vitesse moyenne en `km/h` de chaque _lap_, la puissance moyenne en `watts` et finalement, les battements de cours par minutes.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
kmtempsvitesseMoyennepuissanceMoyennebpm
1.244:01 19.1 160 134
4.849:42 30.2 133 146
1.021:57 30.8 141 139
17.6136:1129.2 125 144
9.2719:1029.0 121 143
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " km & temps & vitesseMoyenne & puissanceMoyenne & bpm\\\\\n", "\\hline\n", "\t 1.24 & 4:01 & 19.1 & 160 & 134 \\\\\n", "\t 4.84 & 9:42 & 30.2 & 133 & 146 \\\\\n", "\t 1.02 & 1:57 & 30.8 & 141 & 139 \\\\\n", "\t 17.61 & 36:11 & 29.2 & 125 & 144 \\\\\n", "\t 9.27 & 19:10 & 29.0 & 121 & 143 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "km | temps | vitesseMoyenne | puissanceMoyenne | bpm | \n", "|---|---|---|---|---|\n", "| 1.24 | 4:01 | 19.1 | 160 | 134 | \n", "| 4.84 | 9:42 | 30.2 | 133 | 146 | \n", "| 1.02 | 1:57 | 30.8 | 141 | 139 | \n", "| 17.61 | 36:11 | 29.2 | 125 | 144 | \n", "| 9.27 | 19:10 | 29.0 | 121 | 143 | \n", "\n", "\n" ], "text/plain": [ " km temps vitesseMoyenne puissanceMoyenne bpm\n", "1 1.24 4:01 19.1 160 134\n", "2 4.84 9:42 30.2 133 146\n", "3 1.02 1:57 30.8 141 139\n", "4 17.61 36:11 29.2 125 144\n", "5 9.27 19:10 29.0 121 143" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df<-read.csv(\"https://raw.githubusercontent.com/nmeraihi/data/master/exemple_2.txt\")\n", "df" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Il possible d'ordonner les données avec la fonction de base de `R` appelé `order`. Par exemple, on voudrait ordonner notre _df_ en ordre croissant sur la variable `puissanceMoyenne`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
kmtempsvitesseMoyennepuissanceMoyennebpm
5 9.2719:1029.0 121 143
417.6136:1129.2 125 144
2 4.849:42 30.2 133 146
3 1.021:57 30.8 141 139
1 1.244:01 19.1 160 134
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & km & temps & vitesseMoyenne & puissanceMoyenne & bpm\\\\\n", "\\hline\n", "\t5 & 9.27 & 19:10 & 29.0 & 121 & 143 \\\\\n", "\t4 & 17.61 & 36:11 & 29.2 & 125 & 144 \\\\\n", "\t2 & 4.84 & 9:42 & 30.2 & 133 & 146 \\\\\n", "\t3 & 1.02 & 1:57 & 30.8 & 141 & 139 \\\\\n", "\t1 & 1.24 & 4:01 & 19.1 & 160 & 134 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | km | temps | vitesseMoyenne | puissanceMoyenne | bpm | \n", "|---|---|---|---|---|\n", "| 5 | 9.27 | 19:10 | 29.0 | 121 | 143 | \n", "| 4 | 17.61 | 36:11 | 29.2 | 125 | 144 | \n", "| 2 | 4.84 | 9:42 | 30.2 | 133 | 146 | \n", "| 3 | 1.02 | 1:57 | 30.8 | 141 | 139 | \n", "| 1 | 1.24 | 4:01 | 19.1 | 160 | 134 | \n", "\n", "\n" ], "text/plain": [ " km temps vitesseMoyenne puissanceMoyenne bpm\n", "5 9.27 19:10 29.0 121 143\n", "4 17.61 36:11 29.2 125 144\n", "2 4.84 9:42 30.2 133 146\n", "3 1.02 1:57 30.8 141 139\n", "1 1.24 4:01 19.1 160 134" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[order(df$puissanceMoyenne),]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Par défaut, l'ordre est est croissant, on peut le rendre décroissant en ajoutant l'argument `decreasing = T`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
kmtempsvitesseMoyennepuissanceMoyennebpm
3 1.021:57 30.8 141 139
2 4.849:42 30.2 133 146
417.6136:1129.2 125 144
5 9.2719:1029.0 121 143
1 1.244:01 19.1 160 134
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & km & temps & vitesseMoyenne & puissanceMoyenne & bpm\\\\\n", "\\hline\n", "\t3 & 1.02 & 1:57 & 30.8 & 141 & 139 \\\\\n", "\t2 & 4.84 & 9:42 & 30.2 & 133 & 146 \\\\\n", "\t4 & 17.61 & 36:11 & 29.2 & 125 & 144 \\\\\n", "\t5 & 9.27 & 19:10 & 29.0 & 121 & 143 \\\\\n", "\t1 & 1.24 & 4:01 & 19.1 & 160 & 134 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | km | temps | vitesseMoyenne | puissanceMoyenne | bpm | \n", "|---|---|---|---|---|\n", "| 3 | 1.02 | 1:57 | 30.8 | 141 | 139 | \n", "| 2 | 4.84 | 9:42 | 30.2 | 133 | 146 | \n", "| 4 | 17.61 | 36:11 | 29.2 | 125 | 144 | \n", "| 5 | 9.27 | 19:10 | 29.0 | 121 | 143 | \n", "| 1 | 1.24 | 4:01 | 19.1 | 160 | 134 | \n", "\n", "\n" ], "text/plain": [ " km temps vitesseMoyenne puissanceMoyenne bpm\n", "3 1.02 1:57 30.8 141 139\n", "2 4.84 9:42 30.2 133 146\n", "4 17.61 36:11 29.2 125 144\n", "5 9.27 19:10 29.0 121 143\n", "1 1.24 4:01 19.1 160 134" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[order(df$vitesseMoyenne, decreasing = T),]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Toutefois, lorsque nous avons une base de données comportant un nombre plus important de variables, la syntaxe peut devenir plus compliquée et lourde d’écriture. Regardons un autre exemple:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoldebut_polfin_polfreq_paiement⋯cout6cout7nbsinequipe
4 11-4-199610-4-199712 ⋯ NA NA 0 3
4 11-4-199710-4-199812 ⋯ NA NA 0 3
4 11-4-200217-7-200212 ⋯ NA NA 0 3
⋮⋮⋮⋮⋱⋮⋮⋮⋮
4 11-4-200310-4-200412 ⋯ NA NA 0 3
12 3-5-1995 2-5-1996 1 ⋯ NA NA 0 3
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " numeropol & debut\\_pol & fin\\_pol & freq\\_paiement & ⋯ & cout6 & cout7 & nbsin & equipe\\\\\n", "\\hline\n", "\t 4 & 11-4-1996 & 10-4-1997 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t 4 & 11-4-1997 & 10-4-1998 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t 4 & 11-4-2002 & 17-7-2002 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 4 & 11-4-2003 & 10-4-2004 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t 12 & 3-5-1995 & 2-5-1996 & 1 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | debut_pol | fin_pol | freq_paiement | ⋯ | cout6 | cout7 | nbsin | equipe | \n", "|---|---|---|---|---|---|\n", "| 4 | 11-4-1996 | 10-4-1997 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| 4 | 11-4-1997 | 10-4-1998 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| 4 | 11-4-2002 | 17-7-2002 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 4 | 11-4-2003 | 10-4-2004 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| 12 | 3-5-1995 | 2-5-1996 | 1 | ⋯ | NA | NA | 0 | 3 | \n", "\n", "\n" ], "text/plain": [ " numeropol debut_pol fin_pol freq_paiement ⋯ cout6 cout7 nbsin equipe\n", "1 4 11-4-1996 10-4-1997 12 ⋯ NA NA 0 3 \n", "2 4 11-4-1997 10-4-1998 12 ⋯ NA NA 0 3 \n", "3 4 11-4-2002 17-7-2002 12 ⋯ NA NA 0 3 \n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ \n", "5 4 11-4-2003 10-4-2004 12 ⋯ NA NA 0 3 \n", "6 12 3-5-1995 2-5-1996 1 ⋯ NA NA 0 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass <-read.csv(\"https://raw.githubusercontent.com/nmeraihi/data/master/assurance.csv\", header = T)\n", "head(df_ass)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Affichons notre base de données en ordre croissant sur le nombre de sinistres et le numéro de police;" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoldebut_polfin_polfreq_paiement⋯cout6cout7nbsinequipe
9882006 13-6-199612-6-199712 ⋯ NA NA 2 3
9021820 1-10-199630-9-1997 1 ⋯ NA NA 2 3
8611733 10-5-19989-5-1999 1 ⋯ NA NA 2 3
⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮
44 18-7-200210-4-200312 ⋯ NA NA 0 3
54 11-4-200310-4-200412 ⋯ NA NA 0 3
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " & numeropol & debut\\_pol & fin\\_pol & freq\\_paiement & ⋯ & cout6 & cout7 & nbsin & equipe\\\\\n", "\\hline\n", "\t988 & 2006 & 13-6-1996 & 12-6-1997 & 12 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t902 & 1820 & 1-10-1996 & 30-9-1997 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t861 & 1733 & 10-5-1998 & 9-5-1999 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t4 & 4 & 18-7-2002 & 10-4-2003 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t5 & 4 & 11-4-2003 & 10-4-2004 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | numeropol | debut_pol | fin_pol | freq_paiement | ⋯ | cout6 | cout7 | nbsin | equipe | \n", "|---|---|---|---|---|---|\n", "| 988 | 2006 | 13-6-1996 | 12-6-1997 | 12 | ⋯ | NA | NA | 2 | 3 | \n", "| 902 | 1820 | 1-10-1996 | 30-9-1997 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "| 861 | 1733 | 10-5-1998 | 9-5-1999 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 4 | 4 | 18-7-2002 | 10-4-2003 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| 5 | 4 | 11-4-2003 | 10-4-2004 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "\n", "\n" ], "text/plain": [ " numeropol debut_pol fin_pol freq_paiement ⋯ cout6 cout7 nbsin equipe\n", "988 2006 13-6-1996 12-6-1997 12 ⋯ NA NA 2 3 \n", "902 1820 1-10-1996 30-9-1997 1 ⋯ NA NA 2 3 \n", "861 1733 10-5-1998 9-5-1999 1 ⋯ NA NA 2 3 \n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ \n", "4 4 18-7-2002 10-4-2003 12 ⋯ NA NA 0 3 \n", "5 4 11-4-2003 10-4-2004 12 ⋯ NA NA 0 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass[order(df_ass$nbsin, df_ass$numeropol, decreasing = T),]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Dans le tableau affiché ci-haut, on voit bien que cette fonction ne nous permet pas d'appliquer un ordre croissant ou décroissant sur une variable précise" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## arrange" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Maintenant, utilisons le paquet (_package_) `dplyr` qui nous permet de plus facilement d'appliquer un ordre quelconque sur une variable précise indépendamment des autres variables;" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "library(dplyr, warn.conflicts = FALSE)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoldebut_polfin_polfreq_paiement⋯cout6cout7nbsinequipe
71 15-2-1996 14-2-1997 1 ⋯ NA NA 2 3
79 20-11-199721-6-1998 12 ⋯ NA NA 2 3
116 4-9-1998 11-6-1999 1 ⋯ NA NA 2 3
⋮⋮⋮⋮⋱⋮⋮⋮⋮
2036 14-3-200026-2-200112 ⋯ NA NA 0 3
2036 27-2-200113-3-200112 ⋯ NA NA 0 3
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " numeropol & debut\\_pol & fin\\_pol & freq\\_paiement & ⋯ & cout6 & cout7 & nbsin & equipe\\\\\n", "\\hline\n", "\t 71 & 15-2-1996 & 14-2-1997 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t 79 & 20-11-1997 & 21-6-1998 & 12 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t 116 & 4-9-1998 & 11-6-1999 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 2036 & 14-3-2000 & 26-2-2001 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\t 2036 & 27-2-2001 & 13-3-2001 & 12 & ⋯ & NA & NA & 0 & 3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | debut_pol | fin_pol | freq_paiement | ⋯ | cout6 | cout7 | nbsin | equipe | \n", "|---|---|---|---|---|---|\n", "| 71 | 15-2-1996 | 14-2-1997 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "| 79 | 20-11-1997 | 21-6-1998 | 12 | ⋯ | NA | NA | 2 | 3 | \n", "| 116 | 4-9-1998 | 11-6-1999 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 2036 | 14-3-2000 | 26-2-2001 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "| 2036 | 27-2-2001 | 13-3-2001 | 12 | ⋯ | NA | NA | 0 | 3 | \n", "\n", "\n" ], "text/plain": [ " numeropol debut_pol fin_pol freq_paiement ⋯ cout6 cout7 nbsin equipe\n", "1 71 15-2-1996 14-2-1997 1 ⋯ NA NA 2 3 \n", "2 79 20-11-1997 21-6-1998 12 ⋯ NA NA 2 3 \n", "3 116 4-9-1998 11-6-1999 1 ⋯ NA NA 2 3 \n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ \n", "1000 2036 14-3-2000 26-2-2001 12 ⋯ NA NA 0 3 \n", "1001 2036 27-2-2001 13-3-2001 12 ⋯ NA NA 0 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "arrange(df_ass, desc(nbsin), numeropol)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## select" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Ce paquet nous permet aussi de sélectionner des variables d'intérêt. Par exemple, dans notre `df_ass`, on désire seulement sélectionner les variables `numeropol`, `type_territoire` et `nbsin`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoltype_territoirenbsin
4 Semi-urbain0
4 Semi-urbain0
4 Semi-urbain0
⋮⋮⋮
2036 Semi-urbain0
2036 Semi-urbain1
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " numeropol & type\\_territoire & nbsin\\\\\n", "\\hline\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t 2036 & Semi-urbain & 0 \\\\\n", "\t 2036 & Semi-urbain & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | type_territoire | nbsin | \n", "|---|---|---|---|---|---|\n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| ⋮ | ⋮ | ⋮ | \n", "| 2036 | Semi-urbain | 0 | \n", "| 2036 | Semi-urbain | 1 | \n", "\n", "\n" ], "text/plain": [ " numeropol type_territoire nbsin\n", "1 4 Semi-urbain 0 \n", "2 4 Semi-urbain 0 \n", "3 4 Semi-urbain 0 \n", "⋮ ⋮ ⋮ ⋮ \n", "1000 2036 Semi-urbain 0 \n", "1001 2036 Semi-urbain 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select(df_ass, numeropol,type_territoire, nbsin)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## filter" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Afin de filtrer des données sur des observations d'intérêt. On peut utiliser la fonction de base de **R** `which`. Par exemple dans les données `Cars93` du package `MASS`, on voudrait extraire les véhicules ayant 8 cylindres. On voudrait également afficher que les deux variables `'Horsepower'` et `'Passengers'`" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "library(MASS, warn.conflicts = F)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
HorsepowerPassengers
102006
112955
181706
⋮⋮⋮
482785
522106
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Horsepower & Passengers\\\\\n", "\\hline\n", "\t10 & 200 & 6 \\\\\n", "\t11 & 295 & 5 \\\\\n", "\t18 & 170 & 6 \\\\\n", "\t⋮ & ⋮ & ⋮\\\\\n", "\t48 & 278 & 5 \\\\\n", "\t52 & 210 & 6 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Horsepower | Passengers | \n", "|---|---|---|---|---|---|\n", "| 10 | 200 | 6 | \n", "| 11 | 295 | 5 | \n", "| 18 | 170 | 6 | \n", "| ⋮ | ⋮ | ⋮ | \n", "| 48 | 278 | 5 | \n", "| 52 | 210 | 6 | \n", "\n", "\n" ], "text/plain": [ " Horsepower Passengers\n", "10 200 6 \n", "11 295 5 \n", "18 170 6 \n", "⋮ ⋮ ⋮ \n", "48 278 5 \n", "52 210 6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cars93[which(Cars93$Cylinders==8), c('Horsepower' , 'Passengers')]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Toutefois, la fonction `filter` de la librairie `dyplr` est plus flexible lorsqu'il s'agit d'appliquer des filtres plus complexes. Essayons le même exemple avec cette fonction;" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
HorsepowerPassengers
2006
2955
1706
⋮⋮
2785
2106
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " Horsepower & Passengers\\\\\n", "\\hline\n", "\t 200 & 6 \\\\\n", "\t 295 & 5 \\\\\n", "\t 170 & 6 \\\\\n", "\t ⋮ & ⋮\\\\\n", "\t 278 & 5 \\\\\n", "\t 210 & 6 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Horsepower | Passengers | \n", "|---|---|---|---|---|---|\n", "| 200 | 6 | \n", "| 295 | 5 | \n", "| 170 | 6 | \n", "| ⋮ | ⋮ | \n", "| 278 | 5 | \n", "| 210 | 6 | \n", "\n", "\n" ], "text/plain": [ " Horsepower Passengers\n", "1 200 6 \n", "2 295 5 \n", "3 170 6 \n", "⋮ ⋮ ⋮ \n", "6 278 5 \n", "7 210 6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filter(Cars93, Cylinders==8)[c('Horsepower' , 'Passengers')]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Si l'on cherche les médecins qui ont eu deux sinistres dans notre base de données `df_ass`;" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoldebut_polfin_polfreq_paiement⋯cout6cout7nbsinequipe
71 15-2-199614-2-1997 1 ⋯ NA NA 2 3
140 15-4-199514-4-199612 ⋯ NA NA 2 3
1820 1-10-199630-9-1997 1 ⋯ NA NA 2 3
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllllll}\n", " numeropol & debut\\_pol & fin\\_pol & freq\\_paiement & ⋯ & cout6 & cout7 & nbsin & equipe\\\\\n", "\\hline\n", "\t 71 & 15-2-1996 & 14-2-1997 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t 140 & 15-4-1995 & 14-4-1996 & 12 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\t 1820 & 1-10-1996 & 30-9-1997 & 1 & ⋯ & NA & NA & 2 & 3 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | debut_pol | fin_pol | freq_paiement | ⋯ | cout6 | cout7 | nbsin | equipe | \n", "|---|---|---|\n", "| 71 | 15-2-1996 | 14-2-1997 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "| 140 | 15-4-1995 | 14-4-1996 | 12 | ⋯ | NA | NA | 2 | 3 | \n", "| 1820 | 1-10-1996 | 30-9-1997 | 1 | ⋯ | NA | NA | 2 | 3 | \n", "\n", "\n" ], "text/plain": [ " numeropol debut_pol fin_pol freq_paiement ⋯ cout6 cout7 nbsin equipe\n", "1 71 15-2-1996 14-2-1997 1 ⋯ NA NA 2 3 \n", "2 140 15-4-1995 14-4-1996 12 ⋯ NA NA 2 3 \n", "3 1820 1-10-1996 30-9-1997 1 ⋯ NA NA 2 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filter(df_ass, nbsin==2, type_prof==\"Médecin\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## mutate" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Dans ce package, on trouve aussi la fonction `mutate` qui permet d'ajouter de nouvelles variables à notre df" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
kmtempsvitesseMoyennepuissanceMoyennebpmarrondi
1.244:01 19.1 160 134 19
4.849:42 30.2 133 146 30
1.021:57 30.8 141 139 31
17.6136:1129.2 125 144 29
9.2719:1029.0 121 143 29
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " km & temps & vitesseMoyenne & puissanceMoyenne & bpm & arrondi\\\\\n", "\\hline\n", "\t 1.24 & 4:01 & 19.1 & 160 & 134 & 19 \\\\\n", "\t 4.84 & 9:42 & 30.2 & 133 & 146 & 30 \\\\\n", "\t 1.02 & 1:57 & 30.8 & 141 & 139 & 31 \\\\\n", "\t 17.61 & 36:11 & 29.2 & 125 & 144 & 29 \\\\\n", "\t 9.27 & 19:10 & 29.0 & 121 & 143 & 29 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "km | temps | vitesseMoyenne | puissanceMoyenne | bpm | arrondi | \n", "|---|---|---|---|---|\n", "| 1.24 | 4:01 | 19.1 | 160 | 134 | 19 | \n", "| 4.84 | 9:42 | 30.2 | 133 | 146 | 30 | \n", "| 1.02 | 1:57 | 30.8 | 141 | 139 | 31 | \n", "| 17.61 | 36:11 | 29.2 | 125 | 144 | 29 | \n", "| 9.27 | 19:10 | 29.0 | 121 | 143 | 29 | \n", "\n", "\n" ], "text/plain": [ " km temps vitesseMoyenne puissanceMoyenne bpm arrondi\n", "1 1.24 4:01 19.1 160 134 19 \n", "2 4.84 9:42 30.2 133 146 30 \n", "3 1.02 1:57 30.8 141 139 31 \n", "4 17.61 36:11 29.2 125 144 29 \n", "5 9.27 19:10 29.0 121 143 29 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutate(df, arrondi=round(df$vitesseMoyenne,0))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Ajoutons maintenant trois nouvlles variables;" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
kmtempsvitesseMoyennepuissanceMoyennebpmarrondisegementStravaarrondi_2
1.24 4:01 19.1 160 134 19 segment_1 9.5
4.84 9:42 30.2 133 146 30 segment_215.0
1.02 1:57 30.8 141 139 31 segment_315.5
17.61 36:11 29.2 125 144 29 segment_414.5
9.27 19:10 29.0 121 143 29 segment_514.5
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " km & temps & vitesseMoyenne & puissanceMoyenne & bpm & arrondi & segementStrava & arrondi\\_2\\\\\n", "\\hline\n", "\t 1.24 & 4:01 & 19.1 & 160 & 134 & 19 & segment\\_1 & 9.5 \\\\\n", "\t 4.84 & 9:42 & 30.2 & 133 & 146 & 30 & segment\\_2 & 15.0 \\\\\n", "\t 1.02 & 1:57 & 30.8 & 141 & 139 & 31 & segment\\_3 & 15.5 \\\\\n", "\t 17.61 & 36:11 & 29.2 & 125 & 144 & 29 & segment\\_4 & 14.5 \\\\\n", "\t 9.27 & 19:10 & 29.0 & 121 & 143 & 29 & segment\\_5 & 14.5 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "km | temps | vitesseMoyenne | puissanceMoyenne | bpm | arrondi | segementStrava | arrondi_2 | \n", "|---|---|---|---|---|\n", "| 1.24 | 4:01 | 19.1 | 160 | 134 | 19 | segment_1 | 9.5 | \n", "| 4.84 | 9:42 | 30.2 | 133 | 146 | 30 | segment_2 | 15.0 | \n", "| 1.02 | 1:57 | 30.8 | 141 | 139 | 31 | segment_3 | 15.5 | \n", "| 17.61 | 36:11 | 29.2 | 125 | 144 | 29 | segment_4 | 14.5 | \n", "| 9.27 | 19:10 | 29.0 | 121 | 143 | 29 | segment_5 | 14.5 | \n", "\n", "\n" ], "text/plain": [ " km temps vitesseMoyenne puissanceMoyenne bpm arrondi segementStrava\n", "1 1.24 4:01 19.1 160 134 19 segment_1 \n", "2 4.84 9:42 30.2 133 146 30 segment_2 \n", "3 1.02 1:57 30.8 141 139 31 segment_3 \n", "4 17.61 36:11 29.2 125 144 29 segment_4 \n", "5 9.27 19:10 29.0 121 143 29 segment_5 \n", " arrondi_2\n", "1 9.5 \n", "2 15.0 \n", "3 15.5 \n", "4 14.5 \n", "5 14.5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mutate(df, arrondi=round(df$vitesseMoyenne,0), segementStrava=paste(\"segment\",1:5,sep = \"_\"), arrondi_2=arrondi/2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## summarize" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "La fonction `summarize` est très similaire à la fonction `mutate`. Toutefois, contrairement à `mutate`, la fonction `summarize` ne travaille pas sur une copie du df, mais elle crée un tout nouveau df avec les nouvelles variables. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
TotalKmParcour
33.98
\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " TotalKmParcour\\\\\n", "\\hline\n", "\t 33.98\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "TotalKmParcour | \n", "|---|\n", "| 33.98 | \n", "\n", "\n" ], "text/plain": [ " TotalKmParcour\n", "1 33.98 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summarise(df,TotalKmParcour=sum(km))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
TotalKmParcourvitesseMoyennepuissanceMoyenne
33.9827.66136
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " TotalKmParcour & vitesseMoyenne & puissanceMoyenne\\\\\n", "\\hline\n", "\t 33.98 & 27.66 & 136 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "TotalKmParcour | vitesseMoyenne | puissanceMoyenne | \n", "|---|\n", "| 33.98 | 27.66 | 136 | \n", "\n", "\n" ], "text/plain": [ " TotalKmParcour vitesseMoyenne puissanceMoyenne\n", "1 33.98 27.66 136 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summarise(df,TotalKmParcour=sum(km), vitesseMoyenne= mean(df$vitesseMoyenne), puissanceMoyenne=mean(df$puissanceMoyenne))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "On voit bien que l'écriture du code commence à être un peu plus compliquée lorsque nous avons plusieurs parenthèses dans notre fonction. Pour remédier à ce problème, nous verrons la notion de _piiping_;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## L'opérateur Pipe: %>%" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Avant d'aller plus loin, introduisons l'opérateur de _pipe_:%>%. **dplyr** importe cet opérateur d'une autre librairie (`magrittr`). Cet opérateur vous permet de diriger la sortie d'une fonction vers l'entrée d'une autre fonction. Au lieu d'imbriquer des fonctions (lecture de l'intérieur vers l'extérieur), l'idée de _piping_ est de lire les fonctions de gauche à droite." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![piping data](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1510846626/Pipe-Mathematical_gczmab.png)\n", "\n", "Crédit de l'image [Pipes in R Tutorial For Beginners](https://www.datacamp.com/community/tutorials/pipe-r-tutorial)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Lorsque nous avons écrit:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoltype_territoirenbsin
4 Semi-urbain0
4 Semi-urbain0
4 Semi-urbain0
⋮⋮⋮
2036 Semi-urbain0
2036 Semi-urbain1
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " numeropol & type\\_territoire & nbsin\\\\\n", "\\hline\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t 2036 & Semi-urbain & 0 \\\\\n", "\t 2036 & Semi-urbain & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | type_territoire | nbsin | \n", "|---|---|---|---|---|---|\n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| ⋮ | ⋮ | ⋮ | \n", "| 2036 | Semi-urbain | 0 | \n", "| 2036 | Semi-urbain | 1 | \n", "\n", "\n" ], "text/plain": [ " numeropol type_territoire nbsin\n", "1 4 Semi-urbain 0 \n", "2 4 Semi-urbain 0 \n", "3 4 Semi-urbain 0 \n", "⋮ ⋮ ⋮ ⋮ \n", "1000 2036 Semi-urbain 0 \n", "1001 2036 Semi-urbain 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select(df_ass, numeropol, type_territoire, nbsin)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Si on lit ce que nous avons écrit précédemment de l'intérieur vert l'extérieur, en utilisant le _piping_, nous aurons ceci:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoltype_territoirenbsin
4 Semi-urbain0
4 Semi-urbain0
4 Semi-urbain0
⋮⋮⋮
2036 Semi-urbain0
2036 Semi-urbain1
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " numeropol & type\\_territoire & nbsin\\\\\n", "\\hline\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t 2036 & Semi-urbain & 0 \\\\\n", "\t 2036 & Semi-urbain & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | type_territoire | nbsin | \n", "|---|---|---|---|---|---|\n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| ⋮ | ⋮ | ⋮ | \n", "| 2036 | Semi-urbain | 0 | \n", "| 2036 | Semi-urbain | 1 | \n", "\n", "\n" ], "text/plain": [ " numeropol type_territoire nbsin\n", "1 4 Semi-urbain 0 \n", "2 4 Semi-urbain 0 \n", "3 4 Semi-urbain 0 \n", "⋮ ⋮ ⋮ ⋮ \n", "1000 2036 Semi-urbain 0 \n", "1001 2036 Semi-urbain 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass %>%\n", " select (numeropol,type_territoire, nbsin)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "ou;" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoltype_territoirenbsin
4 Semi-urbain0
4 Semi-urbain0
4 Semi-urbain0
⋮⋮⋮
4 Semi-urbain0
12 Semi-urbain0
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " numeropol & type\\_territoire & nbsin\\\\\n", "\\hline\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t 4 & Semi-urbain & 0 \\\\\n", "\t 12 & Semi-urbain & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | type_territoire | nbsin | \n", "|---|---|---|---|---|---|\n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| 4 | Semi-urbain | 0 | \n", "| ⋮ | ⋮ | ⋮ | \n", "| 4 | Semi-urbain | 0 | \n", "| 12 | Semi-urbain | 0 | \n", "\n", "\n" ], "text/plain": [ " numeropol type_territoire nbsin\n", "1 4 Semi-urbain 0 \n", "2 4 Semi-urbain 0 \n", "3 4 Semi-urbain 0 \n", "⋮ ⋮ ⋮ ⋮ \n", "5 4 Semi-urbain 0 \n", "6 12 Semi-urbain 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass %>%\n", " select (numeropol,type_territoire, nbsin) %>%\n", " head" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## group_by" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Nous pouvons aussi grouper les données comme nous le faisions dans `SAS` avec les `PROC SQL`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "df_ass$coutTot<-rowSums(df_ass[,c(19:25)], na.rm = T, dims = 1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropoldebut_polfin_polfreq_paiement⋯cout7nbsinequipecoutTot
4 11-4-199610-4-199712 ⋯ NA 0 3 0
4 11-4-199710-4-199812 ⋯ NA 0 3 0
4 11-4-200217-7-200212 ⋯ NA 0 3 0
⋮⋮⋮⋮⋱⋮⋮⋮⋮
2036 27-2-200113-3-200112 ⋯ NA 0 3 0.0
2036 14-3-200113-3-200212 ⋯ NA 1 3 231051.8
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllllll}\n", " numeropol & debut\\_pol & fin\\_pol & freq\\_paiement & ⋯ & cout7 & nbsin & equipe & coutTot\\\\\n", "\\hline\n", "\t 4 & 11-4-1996 & 10-4-1997 & 12 & ⋯ & NA & 0 & 3 & 0 \\\\\n", "\t 4 & 11-4-1997 & 10-4-1998 & 12 & ⋯ & NA & 0 & 3 & 0 \\\\\n", "\t 4 & 11-4-2002 & 17-7-2002 & 12 & ⋯ & NA & 0 & 3 & 0 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 2036 & 27-2-2001 & 13-3-2001 & 12 & ⋯ & NA & 0 & 3 & 0.0 \\\\\n", "\t 2036 & 14-3-2001 & 13-3-2002 & 12 & ⋯ & NA & 1 & 3 & 231051.8 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | debut_pol | fin_pol | freq_paiement | ⋯ | cout7 | nbsin | equipe | coutTot | \n", "|---|---|---|---|---|---|\n", "| 4 | 11-4-1996 | 10-4-1997 | 12 | ⋯ | NA | 0 | 3 | 0 | \n", "| 4 | 11-4-1997 | 10-4-1998 | 12 | ⋯ | NA | 0 | 3 | 0 | \n", "| 4 | 11-4-2002 | 17-7-2002 | 12 | ⋯ | NA | 0 | 3 | 0 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 2036 | 27-2-2001 | 13-3-2001 | 12 | ⋯ | NA | 0 | 3 | 0.0 | \n", "| 2036 | 14-3-2001 | 13-3-2002 | 12 | ⋯ | NA | 1 | 3 | 231051.8 | \n", "\n", "\n" ], "text/plain": [ " numeropol debut_pol fin_pol freq_paiement ⋯ cout7 nbsin equipe coutTot \n", "1 4 11-4-1996 10-4-1997 12 ⋯ NA 0 3 0 \n", "2 4 11-4-1997 10-4-1998 12 ⋯ NA 0 3 0 \n", "3 4 11-4-2002 17-7-2002 12 ⋯ NA 0 3 0 \n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ \n", "1000 2036 27-2-2001 13-3-2001 12 ⋯ NA 0 3 0.0\n", "1001 2036 14-3-2001 13-3-2002 12 ⋯ NA 1 3 231051.8" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
TotalNbSinTotCout
156 1078791
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " TotalNbSin & TotCout\\\\\n", "\\hline\n", "\t 156 & 1078791\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "TotalNbSin | TotCout | \n", "|---|\n", "| 156 | 1078791 | \n", "\n", "\n" ], "text/plain": [ " TotalNbSin TotCout\n", "1 156 1078791" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summarise(df_ass,TotalNbSin=sum(nbsin), TotCout= sum((coutTot), na.rm = T))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Cherchons par exemple nombre de sinistres totaux ainsi que leurs coûts par territoire. En utilisant la syntaxe du _piping_, ça devient plus facile d'inclure plus de sous-groupes;" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
type_territoireTotalNbSinTotCout
Rural 51 547105.01
Semi-urbain80 471157.64
Urbain 25 60528.81
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " type\\_territoire & TotalNbSin & TotCout\\\\\n", "\\hline\n", "\t Rural & 51 & 547105.01 \\\\\n", "\t Semi-urbain & 80 & 471157.64 \\\\\n", "\t Urbain & 25 & 60528.81 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "type_territoire | TotalNbSin | TotCout | \n", "|---|---|---|\n", "| Rural | 51 | 547105.01 | \n", "| Semi-urbain | 80 | 471157.64 | \n", "| Urbain | 25 | 60528.81 | \n", "\n", "\n" ], "text/plain": [ " type_territoire TotalNbSin TotCout \n", "1 Rural 51 547105.01\n", "2 Semi-urbain 80 471157.64\n", "3 Urbain 25 60528.81" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ass %>% \n", " group_by(type_territoire) %>%\n", " summarise(TotalNbSin=sum(nbsin), \n", " TotCout= sum((coutTot), na.rm = T)\n", " )" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Jointure des bases des données" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Dans cette section, nous allons joindre deux ou plusieurs df. Mais d'abord importons deux df afin illustrer quelques exemples;" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nameprovincecompanylanguedate_naissanceageeage_permisnumeropol
Shane Robinson Nova Scotia May Ltd fr 1944-10-20 72 24 1
Courtney Nguyen Saskatchewan Foley, Moore and Mitchellen 1985-12-09 31 24 5
Lori Washington Yukon Territory Robinson-Reyes fr 1970-01-27 47 28 13
⋮⋮⋮⋮⋮⋮⋮⋮
Heidi Freeman Northwest Territories Singh, Esparza and Santosen 1951-06-07 65 18 84
Morgan Buchanan Northwest Territories Rollins Inc fr 1971-07-31 45 31 91
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " name & province & company & langue & date\\_naissance & agee & age\\_permis & numeropol\\\\\n", "\\hline\n", "\t Shane Robinson & Nova Scotia & May Ltd & fr & 1944-10-20 & 72 & 24 & 1 \\\\\n", "\t Courtney Nguyen & Saskatchewan & Foley, Moore and Mitchell & en & 1985-12-09 & 31 & 24 & 5 \\\\\n", "\t Lori Washington & Yukon Territory & Robinson-Reyes & fr & 1970-01-27 & 47 & 28 & 13 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t Heidi Freeman & Northwest Territories & Singh, Esparza and Santos & en & 1951-06-07 & 65 & 18 & 84 \\\\\n", "\t Morgan Buchanan & Northwest Territories & Rollins Inc & fr & 1971-07-31 & 45 & 31 & 91 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "name | province | company | langue | date_naissance | agee | age_permis | numeropol | \n", "|---|---|---|---|---|---|\n", "| Shane Robinson | Nova Scotia | May Ltd | fr | 1944-10-20 | 72 | 24 | 1 | \n", "| Courtney Nguyen | Saskatchewan | Foley, Moore and Mitchell | en | 1985-12-09 | 31 | 24 | 5 | \n", "| Lori Washington | Yukon Territory | Robinson-Reyes | fr | 1970-01-27 | 47 | 28 | 13 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| Heidi Freeman | Northwest Territories | Singh, Esparza and Santos | en | 1951-06-07 | 65 | 18 | 84 | \n", "| Morgan Buchanan | Northwest Territories | Rollins Inc | fr | 1971-07-31 | 45 | 31 | 91 | \n", "\n", "\n" ], "text/plain": [ " name province company langue\n", "1 Shane Robinson Nova Scotia May Ltd fr \n", "2 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell en \n", "3 Lori Washington Yukon Territory Robinson-Reyes fr \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "19 Heidi Freeman Northwest Territories Singh, Esparza and Santos en \n", "20 Morgan Buchanan Northwest Territories Rollins Inc fr \n", " date_naissance agee age_permis numeropol\n", "1 1944-10-20 72 24 1 \n", "2 1985-12-09 31 24 5 \n", "3 1970-01-27 47 28 13 \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "19 1951-06-07 65 18 84 \n", "20 1971-07-31 45 31 91 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_demo <-read.csv(\"https://raw.githubusercontent.com/nmeraihi/data/master/donnes_demo.csv\", header = T)\n", "df_demo" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropolmarque_voiturecouleur_voiturepresence_alarmelicense_plate
1 Autres Autre 0 DW 3168
5 RENAULTAutre 0 926 1RL
13 RENAULTAutre 1 SOV 828
⋮⋮⋮⋮⋮
84 HONDA Autre 0 CBV 102
91 BMW Autre 1 UOR-0725
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " numeropol & marque\\_voiture & couleur\\_voiture & presence\\_alarme & license\\_plate\\\\\n", "\\hline\n", "\t 1 & Autres & Autre & 0 & DW 3168\\\\\n", "\t 5 & RENAULT & Autre & 0 & 926 1RL\\\\\n", "\t 13 & RENAULT & Autre & 1 & SOV 828\\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 84 & HONDA & Autre & 0 & CBV 102 \\\\\n", "\t 91 & BMW & Autre & 1 & UOR-0725\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | marque_voiture | couleur_voiture | presence_alarme | license_plate | \n", "|---|---|---|---|---|---|\n", "| 1 | Autres | Autre | 0 | DW 3168 | \n", "| 5 | RENAULT | Autre | 0 | 926 1RL | \n", "| 13 | RENAULT | Autre | 1 | SOV 828 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 84 | HONDA | Autre | 0 | CBV 102 | \n", "| 91 | BMW | Autre | 1 | UOR-0725 | \n", "\n", "\n" ], "text/plain": [ " numeropol marque_voiture couleur_voiture presence_alarme license_plate\n", "1 1 Autres Autre 0 DW 3168 \n", "2 5 RENAULT Autre 0 926 1RL \n", "3 13 RENAULT Autre 1 SOV 828 \n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", "24 84 HONDA Autre 0 CBV 102 \n", "25 91 BMW Autre 1 UOR-0725 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_auto <-read.csv(\"https://raw.githubusercontent.com/nmeraihi/data/master/cars_info.csv\", header = T)\n", "df_auto" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Dans ces deux df, nous avons une colonne en commun `numeropol`" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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numeropolnameprovincecompany⋯marque_voiturecouleur_voiturepresence_alarmelicense_plate
1 Shane Robinson Nova Scotia May Ltd ⋯ Autres Autre 0 DW 3168
5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell⋯ RENAULT Autre 0 926 1RL
13 Lori Washington Yukon Territory Robinson-Reyes ⋯ RENAULT Autre 1 SOV 828
⋮⋮⋮⋮⋱⋮⋮⋮⋮
84 Heidi Freeman Northwest Territories Singh, Esparza and Santos⋯ HONDA Autre 0 CBV 102
91 Morgan Buchanan Northwest Territories Rollins Inc ⋯ BMW Autre 1 UOR-0725
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " numeropol & name & province & company & ⋯ & marque\\_voiture & couleur\\_voiture & presence\\_alarme & license\\_plate\\\\\n", "\\hline\n", "\t 1 & Shane Robinson & Nova Scotia & May Ltd & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t 5 & Courtney Nguyen & Saskatchewan & Foley, Moore and Mitchell & ⋯ & RENAULT & Autre & 0 & 926 1RL \\\\\n", "\t 13 & Lori Washington & Yukon Territory & Robinson-Reyes & ⋯ & RENAULT & Autre & 1 & SOV 828 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 84 & Heidi Freeman & Northwest Territories & Singh, Esparza and Santos & ⋯ & HONDA & Autre & 0 & CBV 102 \\\\\n", "\t 91 & Morgan Buchanan & Northwest Territories & Rollins Inc & ⋯ & BMW & Autre & 1 & UOR-0725 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | name | province | company | ⋯ | marque_voiture | couleur_voiture | presence_alarme | license_plate | \n", "|---|---|---|---|---|---|\n", "| 1 | Shane Robinson | Nova Scotia | May Ltd | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| 5 | Courtney Nguyen | Saskatchewan | Foley, Moore and Mitchell | ⋯ | RENAULT | Autre | 0 | 926 1RL | \n", "| 13 | Lori Washington | Yukon Territory | Robinson-Reyes | ⋯ | RENAULT | Autre | 1 | SOV 828 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 84 | Heidi Freeman | Northwest Territories | Singh, Esparza and Santos | ⋯ | HONDA | Autre | 0 | CBV 102 | \n", "| 91 | Morgan Buchanan | Northwest Territories | Rollins Inc | ⋯ | BMW | Autre | 1 | UOR-0725 | \n", "\n", "\n" ], "text/plain": [ " numeropol name province company ⋯\n", "1 1 Shane Robinson Nova Scotia May Ltd ⋯\n", "2 5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell ⋯\n", "3 13 Lori Washington Yukon Territory Robinson-Reyes ⋯\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", "24 84 Heidi Freeman Northwest Territories Singh, Esparza and Santos ⋯\n", "25 91 Morgan Buchanan Northwest Territories Rollins Inc ⋯\n", " marque_voiture couleur_voiture presence_alarme license_plate\n", "1 Autres Autre 0 DW 3168 \n", "2 RENAULT Autre 0 926 1RL \n", "3 RENAULT Autre 1 SOV 828 \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "24 HONDA Autre 0 CBV 102 \n", "25 BMW Autre 1 UOR-0725 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "merge(df_demo,df_auto, by.x = \"numeropol\", by.y = \"numeropol\") # x est le df_demo et y est le df df_auto" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Que serait-il arrivé si l’on n'avait pas spécifié les arguments `by.x = \"numeropol\", by.y = \"numeropol\"`?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropolnameprovincecompany⋯marque_voiturecouleur_voiturepresence_alarmelicense_plate
1 Shane Robinson Nova Scotia May Ltd ⋯ Autres Autre 0 DW 3168
5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell⋯ RENAULT Autre 0 926 1RL
13 Lori Washington Yukon Territory Robinson-Reyes ⋯ RENAULT Autre 1 SOV 828
⋮⋮⋮⋮⋱⋮⋮⋮⋮
84 Heidi Freeman Northwest Territories Singh, Esparza and Santos⋯ HONDA Autre 0 CBV 102
91 Morgan Buchanan Northwest Territories Rollins Inc ⋯ BMW Autre 1 UOR-0725
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " numeropol & name & province & company & ⋯ & marque\\_voiture & couleur\\_voiture & presence\\_alarme & license\\_plate\\\\\n", "\\hline\n", "\t 1 & Shane Robinson & Nova Scotia & May Ltd & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t 5 & Courtney Nguyen & Saskatchewan & Foley, Moore and Mitchell & ⋯ & RENAULT & Autre & 0 & 926 1RL \\\\\n", "\t 13 & Lori Washington & Yukon Territory & Robinson-Reyes & ⋯ & RENAULT & Autre & 1 & SOV 828 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 84 & Heidi Freeman & Northwest Territories & Singh, Esparza and Santos & ⋯ & HONDA & Autre & 0 & CBV 102 \\\\\n", "\t 91 & Morgan Buchanan & Northwest Territories & Rollins Inc & ⋯ & BMW & Autre & 1 & UOR-0725 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | name | province | company | ⋯ | marque_voiture | couleur_voiture | presence_alarme | license_plate | \n", "|---|---|---|---|---|---|\n", "| 1 | Shane Robinson | Nova Scotia | May Ltd | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| 5 | Courtney Nguyen | Saskatchewan | Foley, Moore and Mitchell | ⋯ | RENAULT | Autre | 0 | 926 1RL | \n", "| 13 | Lori Washington | Yukon Territory | Robinson-Reyes | ⋯ | RENAULT | Autre | 1 | SOV 828 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 84 | Heidi Freeman | Northwest Territories | Singh, Esparza and Santos | ⋯ | HONDA | Autre | 0 | CBV 102 | \n", "| 91 | Morgan Buchanan | Northwest Territories | Rollins Inc | ⋯ | BMW | Autre | 1 | UOR-0725 | \n", "\n", "\n" ], "text/plain": [ " numeropol name province company ⋯\n", "1 1 Shane Robinson Nova Scotia May Ltd ⋯\n", "2 5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell ⋯\n", "3 13 Lori Washington Yukon Territory Robinson-Reyes ⋯\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", "24 84 Heidi Freeman Northwest Territories Singh, Esparza and Santos ⋯\n", "25 91 Morgan Buchanan Northwest Territories Rollins Inc ⋯\n", " marque_voiture couleur_voiture presence_alarme license_plate\n", "1 Autres Autre 0 DW 3168 \n", "2 RENAULT Autre 0 926 1RL \n", "3 RENAULT Autre 1 SOV 828 \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "24 HONDA Autre 0 CBV 102 \n", "25 BMW Autre 1 UOR-0725 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "merge(df_demo,df_auto)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Cela a bien fonctionné, car R a automatiquement trouvé les noms de colonnes communs au deux df;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Maintenant, changeons les noms de colonnes et voyons ce qui arrive" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "names(df_auto)[names(df_auto)==\"numeropol\"] <- \"auto_numpol\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Bien évidemment, cela crée une jointure croisée comme on l'avait vu dans les cours de SAS" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nameprovincecompanylangue⋯marque_voiturecouleur_voiturepresence_alarmelicense_plate
Shane Robinson Nova Scotia May Ltd fr ⋯ Autres Autre 0 DW 3168
Courtney Nguyen Saskatchewan Foley, Moore and Mitchellen ⋯ Autres Autre 0 DW 3168
Lori Washington Yukon Territory Robinson-Reyes fr ⋯ Autres Autre 0 DW 3168
⋮⋮⋮⋮⋱⋮⋮⋮⋮
Jeffrey Garcia Nunavut Berger-Thompsonen ⋯ Autres Autre 0 DW 3168
Colleen ColemanSaskatchewan Simmons-Smith en ⋯ Autres Autre 0 DW 3168
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllll}\n", " name & province & company & langue & ⋯ & marque\\_voiture & couleur\\_voiture & presence\\_alarme & license\\_plate\\\\\n", "\\hline\n", "\t Shane Robinson & Nova Scotia & May Ltd & fr & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t Courtney Nguyen & Saskatchewan & Foley, Moore and Mitchell & en & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t Lori Washington & Yukon Territory & Robinson-Reyes & fr & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t Jeffrey Garcia & Nunavut & Berger-Thompson & en & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t Colleen Coleman & Saskatchewan & Simmons-Smith & en & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "name | province | company | langue | ⋯ | marque_voiture | couleur_voiture | presence_alarme | license_plate | \n", "|---|---|---|---|---|---|\n", "| Shane Robinson | Nova Scotia | May Ltd | fr | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| Courtney Nguyen | Saskatchewan | Foley, Moore and Mitchell | en | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| Lori Washington | Yukon Territory | Robinson-Reyes | fr | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| Jeffrey Garcia | Nunavut | Berger-Thompson | en | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| Colleen Coleman | Saskatchewan | Simmons-Smith | en | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "\n", "\n" ], "text/plain": [ " name province company langue ⋯\n", "1 Shane Robinson Nova Scotia May Ltd fr ⋯\n", "2 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell en ⋯\n", "3 Lori Washington Yukon Territory Robinson-Reyes fr ⋯\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", "5 Jeffrey Garcia Nunavut Berger-Thompson en ⋯\n", "6 Colleen Coleman Saskatchewan Simmons-Smith en ⋯\n", " marque_voiture couleur_voiture presence_alarme license_plate\n", "1 Autres Autre 0 DW 3168 \n", "2 Autres Autre 0 DW 3168 \n", "3 Autres Autre 0 DW 3168 \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "5 Autres Autre 0 DW 3168 \n", "6 Autres Autre 0 DW 3168 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(merge(df_demo,df_auto))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "On vient bien que dans la dernière colonne `license_plate`, nous obtenons la même observation ce qui est clairement une erreur;" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Corrigeons le problème;" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
numeropolnameprovincecompany⋯marque_voiturecouleur_voiturepresence_alarmelicense_plate
1 Shane Robinson Nova Scotia May Ltd ⋯ Autres Autre 0 DW 3168
5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell⋯ RENAULT Autre 0 926 1RL
13 Lori Washington Yukon Territory Robinson-Reyes ⋯ RENAULT Autre 1 SOV 828
⋮⋮⋮⋮⋱⋮⋮⋮⋮
22 Jeffrey Garcia Nunavut Berger-Thompson⋯ VOLKSWAGEN Autre 1 453 CFM
22 Jeffrey Garcia Nunavut Berger-Thompson⋯ VOLKSWAGEN Autre 0 FHH 537
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " numeropol & name & province & company & ⋯ & marque\\_voiture & couleur\\_voiture & presence\\_alarme & license\\_plate\\\\\n", "\\hline\n", "\t 1 & Shane Robinson & Nova Scotia & May Ltd & ⋯ & Autres & Autre & 0 & DW 3168 \\\\\n", "\t 5 & Courtney Nguyen & Saskatchewan & Foley, Moore and Mitchell & ⋯ & RENAULT & Autre & 0 & 926 1RL \\\\\n", "\t 13 & Lori Washington & Yukon Territory & Robinson-Reyes & ⋯ & RENAULT & Autre & 1 & SOV 828 \\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t 22 & Jeffrey Garcia & Nunavut & Berger-Thompson & ⋯ & VOLKSWAGEN & Autre & 1 & 453 CFM \\\\\n", "\t 22 & Jeffrey Garcia & Nunavut & Berger-Thompson & ⋯ & VOLKSWAGEN & Autre & 0 & FHH 537 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "numeropol | name | province | company | ⋯ | marque_voiture | couleur_voiture | presence_alarme | license_plate | \n", "|---|---|---|---|---|---|\n", "| 1 | Shane Robinson | Nova Scotia | May Ltd | ⋯ | Autres | Autre | 0 | DW 3168 | \n", "| 5 | Courtney Nguyen | Saskatchewan | Foley, Moore and Mitchell | ⋯ | RENAULT | Autre | 0 | 926 1RL | \n", "| 13 | Lori Washington | Yukon Territory | Robinson-Reyes | ⋯ | RENAULT | Autre | 1 | SOV 828 | \n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | \n", "| 22 | Jeffrey Garcia | Nunavut | Berger-Thompson | ⋯ | VOLKSWAGEN | Autre | 1 | 453 CFM | \n", "| 22 | Jeffrey Garcia | Nunavut | Berger-Thompson | ⋯ | VOLKSWAGEN | Autre | 0 | FHH 537 | \n", "\n", "\n" ], "text/plain": [ " numeropol name province company ⋯\n", "1 1 Shane Robinson Nova Scotia May Ltd ⋯\n", "2 5 Courtney Nguyen Saskatchewan Foley, Moore and Mitchell ⋯\n", "3 13 Lori Washington Yukon Territory Robinson-Reyes ⋯\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", "5 22 Jeffrey Garcia Nunavut Berger-Thompson ⋯\n", "6 22 Jeffrey Garcia Nunavut Berger-Thompson ⋯\n", " marque_voiture couleur_voiture presence_alarme license_plate\n", "1 Autres Autre 0 DW 3168 \n", "2 RENAULT Autre 0 926 1RL \n", "3 RENAULT Autre 1 SOV 828 \n", "⋮ ⋮ ⋮ ⋮ ⋮ \n", "5 VOLKSWAGEN Autre 1 453 CFM \n", "6 VOLKSWAGEN Autre 0 FHH 537 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(merge(df_demo,df_auto, by.x = \"numeropol\", by.y = \"auto_numpol\"))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ " ## left_join" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "la fonction `left_join` prend toute l'information de gauche et l'information existante de la partie droite qui est basée sur le critère en commun" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nombureaux
Gabriel5518
Adel 4538
NM 5518
⋮⋮
Amine 4538
Mohamed4540
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " nom & bureaux\\\\\n", "\\hline\n", "\t Gabriel & 5518 \\\\\n", "\t Adel & 4538 \\\\\n", "\t NM & 5518 \\\\\n", "\t ⋮ & ⋮\\\\\n", "\t Amine & 4538 \\\\\n", "\t Mohamed & 4540 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "nom | bureaux | \n", "|---|---|---|---|---|---|\n", "| Gabriel | 5518 | \n", "| Adel | 4538 | \n", "| NM | 5518 | \n", "| ⋮ | ⋮ | \n", "| Amine | 4538 | \n", "| Mohamed | 4540 | \n", "\n", "\n" ], "text/plain": [ " nom bureaux\n", "1 Gabriel 5518 \n", "2 Adel 4538 \n", "3 NM 5518 \n", "⋮ ⋮ ⋮ \n", "5 Amine 4538 \n", "6 Mohamed 4540 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x<-data.frame(nom=c(\"Gabriel\", \"Adel\", \"NM\", \"Mathieu\", \"Amine\", \"Mohamed\"), \n", " bureaux=c(\"5518\", \"4538\", \"5518\", \"5517\", \"4538\", \"4540\"))\n", "x" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nomdiplome
GabrielM.Sc
Adel Ph.D
JP Ph.D
MathieuPh.D
Amine Ph.D
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " nom & diplome\\\\\n", "\\hline\n", "\t Gabriel & M.Sc \\\\\n", "\t Adel & Ph.D \\\\\n", "\t JP & Ph.D \\\\\n", "\t Mathieu & Ph.D \\\\\n", "\t Amine & Ph.D \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "nom | diplome | \n", "|---|---|---|---|---|\n", "| Gabriel | M.Sc | \n", "| Adel | Ph.D | \n", "| JP | Ph.D | \n", "| Mathieu | Ph.D | \n", "| Amine | Ph.D | \n", "\n", "\n" ], "text/plain": [ " nom diplome\n", "1 Gabriel M.Sc \n", "2 Adel Ph.D \n", "3 JP Ph.D \n", "4 Mathieu Ph.D \n", "5 Amine Ph.D " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y<-data.frame(nom=c(\"Gabriel\", \"Adel\", \"JP\", \"Mathieu\", \"Amine\"), \n", " diplome=c(\"M.Sc\", \"Ph.D\", \"Ph.D\", \"Ph.D\", \"Ph.D\"))\n", "y" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Column `nom` joining factors with different levels, coercing to character vector”" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nombureauxdiplome
Gabriel5518 M.Sc
Adel 4538 Ph.D
NM 5518 NA
⋮⋮⋮
Amine 4538 Ph.D
Mohamed4540 NA
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " nom & bureaux & diplome\\\\\n", "\\hline\n", "\t Gabriel & 5518 & M.Sc \\\\\n", "\t Adel & 4538 & Ph.D \\\\\n", "\t NM & 5518 & NA \\\\\n", "\t ⋮ & ⋮ & ⋮\\\\\n", "\t Amine & 4538 & Ph.D \\\\\n", "\t Mohamed & 4540 & NA \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "nom | bureaux | diplome | \n", "|---|---|---|---|---|---|\n", "| Gabriel | 5518 | M.Sc | \n", "| Adel | 4538 | Ph.D | \n", "| NM | 5518 | NA | \n", "| ⋮ | ⋮ | ⋮ | \n", "| Amine | 4538 | Ph.D | \n", "| Mohamed | 4540 | NA | \n", "\n", "\n" ], "text/plain": [ " nom bureaux diplome\n", "1 Gabriel 5518 M.Sc \n", "2 Adel 4538 Ph.D \n", "3 NM 5518 NA \n", "⋮ ⋮ ⋮ ⋮ \n", "5 Amine 4538 Ph.D \n", "6 Mohamed 4540 NA " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "left_join(x,y,by = \"nom\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## inner_join" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Cette fonction permet de retourner **seulement** les éléments en commun des deux df" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Column `nom` joining factors with different levels, coercing to character vector”" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
nombureauxdiplome
Gabriel5518 M.Sc
Adel 4538 Ph.D
Mathieu5517 Ph.D
Amine 4538 Ph.D
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " nom & bureaux & diplome\\\\\n", "\\hline\n", "\t Gabriel & 5518 & M.Sc \\\\\n", "\t Adel & 4538 & Ph.D \\\\\n", "\t Mathieu & 5517 & Ph.D \\\\\n", "\t Amine & 4538 & Ph.D \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "nom | bureaux | diplome | \n", "|---|---|---|---|\n", "| Gabriel | 5518 | M.Sc | \n", "| Adel | 4538 | Ph.D | \n", "| Mathieu | 5517 | Ph.D | \n", "| Amine | 4538 | Ph.D | \n", "\n", "\n" ], "text/plain": [ " nom bureaux diplome\n", "1 Gabriel 5518 M.Sc \n", "2 Adel 4538 Ph.D \n", "3 Mathieu 5517 Ph.D \n", "4 Amine 4538 Ph.D " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inner_join(x,y,by = \"nom\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## semi_join" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Cette fonction retourne seulement les éléments du premier df qui se retrouve dans le deuxième df, sans nous retourner les éléments de ce dernier" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "semi_join(x,y,by = \"nom\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## anti_join" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Cette fonction le contraire de la précédente" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "anti_join(x,y,by = \"nom\")" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.1.2" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": { "height": "444px", "width": "252px" }, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "1132px", "left": "0px", "right": "20px", "top": "159px", "width": "211px" }, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }