{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Nous travaillons avec la version 6.0.3 du Notebook Jupyter en langage R version 3.4.1 (2017-06-30).\n", "\n", "# Sujet 6 : Autour du Paradoxe de Simpson\n", "## Importation des données\n", "Dans un premier temps nous prenons les données en ligne. Puis je ferais une copie comme dans l'exo pour être sûre que le fichier soit toujours accessible." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAge
Yes Alive21.0
Yes Alive19.3
No Dead 57.5
No Alive47.1
Yes Alive81.4
No Alive36.8
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " Smoker & Status & Age\\\\\n", "\\hline\n", "\t Yes & Alive & 21.0 \\\\\n", "\t Yes & Alive & 19.3 \\\\\n", "\t No & Dead & 57.5 \\\\\n", "\t No & Alive & 47.1 \\\\\n", "\t Yes & Alive & 81.4 \\\\\n", "\t No & Alive & 36.8 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Smoker | Status | Age | \n", "|---|---|---|---|---|---|\n", "| Yes | Alive | 21.0 | \n", "| Yes | Alive | 19.3 | \n", "| No | Dead | 57.5 | \n", "| No | Alive | 47.1 | \n", "| Yes | Alive | 81.4 | \n", "| No | Alive | 36.8 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age \n", "1 Yes Alive 21.0\n", "2 Yes Alive 19.3\n", "3 No Dead 57.5\n", "4 No Alive 47.1\n", "5 Yes Alive 81.4\n", "6 No Alive 36.8" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set the working directory (optional if file is not in the working directory)\n", "data <- read.csv(\"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv?inline=false\")\n", "\n", "# Display the first few rows of the data\n", "head(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "if (!file.exists(data_file))\n", " download.file(data_url, data_file, method=\"auto\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse rapide des données" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Smoker Status Age \n", " No :732 Alive:945 Min. :18.00 \n", " Yes:582 Dead :369 1st Qu.:31.30 \n", " Median :44.80 \n", " Mean :47.36 \n", " 3rd Qu.:60.60 \n", " Max. :89.90 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t1314 obs. of 3 variables:\n", " $ Smoker: Factor w/ 2 levels \"No\",\"Yes\": 2 2 1 1 2 1 1 2 2 2 ...\n", " $ Status: Factor w/ 2 levels \"Alive\",\"Dead\": 1 1 2 1 1 1 1 2 1 1 ...\n", " $ Age : num 21 19.3 57.5 47.1 81.4 36.8 23.8 57.5 24.8 49.5 ...\n" ] } ], "source": [ "summary(data)\n", "str(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Analyse de l'effectif et de la mortalité" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " \n", " Alive Dead\n", " No 502 230\n", " Yes 443 139" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Var1 Var2 Freq\n", "1 No Alive 502\n", "2 Yes Alive 443\n", "3 No Dead 230\n", "4 Yes Dead 139\n" ] } ], "source": [ "analyse <- table(data$Smoker,data$Status)\n", "analyse\n", "analyse_data <- as.data.frame(analyse)\n", "print(analyse_data)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Freqmortality
No Alive 502 0.6857923
Yes Alive 443 0.7611684
No Dead 230 0.3142077
Yes Dead 139 0.2388316
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " Var1 & Var2 & Freq & mortality\\\\\n", "\\hline\n", "\t No & Alive & 502 & 0.6857923\\\\\n", "\t Yes & Alive & 443 & 0.7611684\\\\\n", "\t No & Dead & 230 & 0.3142077\\\\\n", "\t Yes & Dead & 139 & 0.2388316\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Freq | mortality | \n", "|---|---|---|---|\n", "| No | Alive | 502 | 0.6857923 | \n", "| Yes | Alive | 443 | 0.7611684 | \n", "| No | Dead | 230 | 0.3142077 | \n", "| Yes | Dead | 139 | 0.2388316 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Freq mortality\n", "1 No Alive 502 0.6857923\n", "2 Yes Alive 443 0.7611684\n", "3 No Dead 230 0.3142077\n", "4 Yes Dead 139 0.2388316" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Freqmortality
3No Dead 230 0.3142077
4Yes Dead 139 0.2388316
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & Var1 & Var2 & Freq & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 230 & 0.3142077\\\\\n", "\t4 & Yes & Dead & 139 & 0.2388316\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Freq | mortality | \n", "|---|---|\n", "| 3 | No | Dead | 230 | 0.3142077 | \n", "| 4 | Yes | Dead | 139 | 0.2388316 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Freq mortality\n", "3 No Dead 230 0.3142077\n", "4 Yes Dead 139 0.2388316" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "analyse_data$mortality <- ifelse(analyse_data$Var1=='No', analyse_data$Freq/(analyse_data$Freq[1]+analyse_data$Freq[3]), analyse_data$Freq/(analyse_data$Freq[2]+analyse_data$Freq[4]))\n", "analyse_data\n", "analyse_data_2 <- analyse_data[analyse_data$Var2==\"Dead\",]\n", "analyse_data_2" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x=analyse_data_2$Var1, y=analyse_data_2$mortality, ylim=c(0,1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il semble y a voir peu de différence de mortalité entre les fumeuses et les non fumeuses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Analyse de l'effectif et de la mortalité par groupe d'âge" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ ", , = 18-34 ans\n", "\n", " \n", " Alive Dead\n", " No 213 6\n", " Yes 174 5\n", "\n", ", , = 34-54 ans\n", "\n", " \n", " Alive Dead\n", " No 180 19\n", " Yes 198 41\n", "\n", ", , = 54-64 ans\n", "\n", " \n", " Alive Dead\n", " No 80 39\n", " Yes 64 51\n", "\n", ", , = plus de 65 ans\n", "\n", " \n", " Alive Dead\n", " No 29 166\n", " Yes 7 42\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Var1 Var2 Var3 Freq\n", "1 No Alive 18-34 ans 213\n", "2 Yes Alive 18-34 ans 174\n", "3 No Dead 18-34 ans 6\n", "4 Yes Dead 18-34 ans 5\n", "5 No Alive 34-54 ans 180\n", "6 Yes Alive 34-54 ans 198\n", "7 No Dead 34-54 ans 19\n", "8 Yes Dead 34-54 ans 41\n", "9 No Alive 54-64 ans 80\n", "10 Yes Alive 54-64 ans 64\n", "11 No Dead 54-64 ans 39\n", "12 Yes Dead 54-64 ans 51\n", "13 No Alive plus de 65 ans 29\n", "14 Yes Alive plus de 65 ans 7\n", "15 No Dead plus de 65 ans 166\n", "16 Yes Dead plus de 65 ans 42\n" ] } ], "source": [ "data$AgeGroup <- ifelse(data$Age<34, '18-34 ans', ifelse(data$Age<54, '34-54 ans', ifelse(data$Age<64, '54-64 ans', 'plus de 65 ans'))) \n", "age <- table(data$Smoker,data$Status, data$AgeGroup)\n", "age\n", "age_data <- as.data.frame(age)\n", "print(age_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons voir que plus les personnes sont âgées, plus il y a de décès (logique)." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroup
No Alive 18-34 ans 213 398
Yes Alive 18-34 ans 174 398
No Dead 18-34 ans 6 398
Yes Dead 18-34 ans 5 398
No Alive 34-54 ans 180 438
Yes Alive 34-54 ans 198 438
No Dead 34-54 ans 19 438
Yes Dead 34-54 ans 41 438
No Alive 54-64 ans 80 234
Yes Alive 54-64 ans 64 234
No Dead 54-64 ans 39 234
Yes Dead 54-64 ans 51 234
No Alive plus de 65 ans 29 244
Yes Alive plus de 65 ans 7 244
No Dead plus de 65 ans166 244
Yes Dead plus de 65 ans 42 244
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " Var1 & Var2 & Var3 & Freq & PopGroup\\\\\n", "\\hline\n", "\t No & Alive & 18-34 ans & 213 & 398 \\\\\n", "\t Yes & Alive & 18-34 ans & 174 & 398 \\\\\n", "\t No & Dead & 18-34 ans & 6 & 398 \\\\\n", "\t Yes & Dead & 18-34 ans & 5 & 398 \\\\\n", "\t No & Alive & 34-54 ans & 180 & 438 \\\\\n", "\t Yes & Alive & 34-54 ans & 198 & 438 \\\\\n", "\t No & Dead & 34-54 ans & 19 & 438 \\\\\n", "\t Yes & Dead & 34-54 ans & 41 & 438 \\\\\n", "\t No & Alive & 54-64 ans & 80 & 234 \\\\\n", "\t Yes & Alive & 54-64 ans & 64 & 234 \\\\\n", "\t No & Dead & 54-64 ans & 39 & 234 \\\\\n", "\t Yes & Dead & 54-64 ans & 51 & 234 \\\\\n", "\t No & Alive & plus de 65 ans & 29 & 244 \\\\\n", "\t Yes & Alive & plus de 65 ans & 7 & 244 \\\\\n", "\t No & Dead & plus de 65 ans & 166 & 244 \\\\\n", "\t Yes & Dead & plus de 65 ans & 42 & 244 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Var3 | Freq | PopGroup | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| No | Alive | 18-34 ans | 213 | 398 | \n", "| Yes | Alive | 18-34 ans | 174 | 398 | \n", "| No | Dead | 18-34 ans | 6 | 398 | \n", "| Yes | Dead | 18-34 ans | 5 | 398 | \n", "| No | Alive | 34-54 ans | 180 | 438 | \n", "| Yes | Alive | 34-54 ans | 198 | 438 | \n", "| No | Dead | 34-54 ans | 19 | 438 | \n", "| Yes | Dead | 34-54 ans | 41 | 438 | \n", "| No | Alive | 54-64 ans | 80 | 234 | \n", "| Yes | Alive | 54-64 ans | 64 | 234 | \n", "| No | Dead | 54-64 ans | 39 | 234 | \n", "| Yes | Dead | 54-64 ans | 51 | 234 | \n", "| No | Alive | plus de 65 ans | 29 | 244 | \n", "| Yes | Alive | plus de 65 ans | 7 | 244 | \n", "| No | Dead | plus de 65 ans | 166 | 244 | \n", "| Yes | Dead | plus de 65 ans | 42 | 244 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup\n", "1 No Alive 18-34 ans 213 398 \n", "2 Yes Alive 18-34 ans 174 398 \n", "3 No Dead 18-34 ans 6 398 \n", "4 Yes Dead 18-34 ans 5 398 \n", "5 No Alive 34-54 ans 180 438 \n", "6 Yes Alive 34-54 ans 198 438 \n", "7 No Dead 34-54 ans 19 438 \n", "8 Yes Dead 34-54 ans 41 438 \n", "9 No Alive 54-64 ans 80 234 \n", "10 Yes Alive 54-64 ans 64 234 \n", "11 No Dead 54-64 ans 39 234 \n", "12 Yes Dead 54-64 ans 51 234 \n", "13 No Alive plus de 65 ans 29 244 \n", "14 Yes Alive plus de 65 ans 7 244 \n", "15 No Dead plus de 65 ans 166 244 \n", "16 Yes Dead plus de 65 ans 42 244 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "age_data$PopGroup <- ifelse(age_data$Var3=='18-34 ans', age_data$Freq[1]+age_data$Freq[2]+age_data$Freq[3]+age_data$Freq[4], \n", " ifelse(age_data$Var3=='34-54 ans', age_data$Freq[5]+age_data$Freq[6]+age_data$Freq[7]+age_data$Freq[8], \n", " ifelse(age_data$Var3=='54-64 ans', age_data$Freq[9]+age_data$Freq[10]+age_data$Freq[11]+age_data$Freq[12], \n", " age_data$Freq[13]+age_data$Freq[14]+age_data$Freq[15]+age_data$Freq[16])))\n", "age_data$PopGroupFum <- ifelse(age_data$Var3=='18-34 ans', age_data$Freq[1]+age_data$Freq[2]+age_data$Freq[3]+age_data$Freq[4], \n", " ifelse(age_data$Var3=='34-54 ans', age_data$Freq[5]+age_data$Freq[6]+age_data$Freq[7]+age_data$Freq[8], \n", " ifelse(age_data$Var3=='54-64 ans', age_data$Freq[9]+age_data$Freq[10]+age_data$Freq[11]+age_data$Freq[12], \n", " age_data$Freq[13]+age_data$Freq[14]+age_data$Freq[15]+age_data$Freq[16])))\n", "\n", "age_data" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
No Alive 18-34 ans 213 398 0.97260274
Yes Alive 18-34 ans 174 398 0.97206704
No Dead 18-34 ans 6 398 0.02739726
Yes Dead 18-34 ans 5 398 0.02793296
No Alive 34-54 ans 180 438 0.90452261
Yes Alive 34-54 ans 198 438 0.82845188
No Dead 34-54 ans 19 438 0.09547739
Yes Dead 34-54 ans 41 438 0.17154812
No Alive 54-64 ans 80 234 0.67226891
Yes Alive 54-64 ans 64 234 0.55652174
No Dead 54-64 ans 39 234 0.32773109
Yes Dead 54-64 ans 51 234 0.44347826
No Alive plus de 65 ans 29 244 0.14871795
Yes Alive plus de 65 ans 7 244 0.14285714
No Dead plus de 65 ans166 244 0.85128205
Yes Dead plus de 65 ans 42 244 0.85714286
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t No & Alive & 18-34 ans & 213 & 398 & 0.97260274 \\\\\n", "\t Yes & Alive & 18-34 ans & 174 & 398 & 0.97206704 \\\\\n", "\t No & Dead & 18-34 ans & 6 & 398 & 0.02739726 \\\\\n", "\t Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296 \\\\\n", "\t No & Alive & 34-54 ans & 180 & 438 & 0.90452261 \\\\\n", "\t Yes & Alive & 34-54 ans & 198 & 438 & 0.82845188 \\\\\n", "\t No & Dead & 34-54 ans & 19 & 438 & 0.09547739 \\\\\n", "\t Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812 \\\\\n", "\t No & Alive & 54-64 ans & 80 & 234 & 0.67226891 \\\\\n", "\t Yes & Alive & 54-64 ans & 64 & 234 & 0.55652174 \\\\\n", "\t No & Dead & 54-64 ans & 39 & 234 & 0.32773109 \\\\\n", "\t Yes & Dead & 54-64 ans & 51 & 234 & 0.44347826 \\\\\n", "\t No & Alive & plus de 65 ans & 29 & 244 & 0.14871795 \\\\\n", "\t Yes & Alive & plus de 65 ans & 7 & 244 & 0.14285714 \\\\\n", "\t No & Dead & plus de 65 ans & 166 & 244 & 0.85128205 \\\\\n", "\t Yes & Dead & plus de 65 ans & 42 & 244 & 0.85714286 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| No | Alive | 18-34 ans | 213 | 398 | 0.97260274 | \n", "| Yes | Alive | 18-34 ans | 174 | 398 | 0.97206704 | \n", "| No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "| No | Alive | 34-54 ans | 180 | 438 | 0.90452261 | \n", "| Yes | Alive | 34-54 ans | 198 | 438 | 0.82845188 | \n", "| No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "| No | Alive | 54-64 ans | 80 | 234 | 0.67226891 | \n", "| Yes | Alive | 54-64 ans | 64 | 234 | 0.55652174 | \n", "| No | Dead | 54-64 ans | 39 | 234 | 0.32773109 | \n", "| Yes | Dead | 54-64 ans | 51 | 234 | 0.44347826 | \n", "| No | Alive | plus de 65 ans | 29 | 244 | 0.14871795 | \n", "| Yes | Alive | plus de 65 ans | 7 | 244 | 0.14285714 | \n", "| No | Dead | plus de 65 ans | 166 | 244 | 0.85128205 | \n", "| Yes | Dead | plus de 65 ans | 42 | 244 | 0.85714286 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "1 No Alive 18-34 ans 213 398 0.97260274\n", "2 Yes Alive 18-34 ans 174 398 0.97206704\n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "4 Yes Dead 18-34 ans 5 398 0.02793296\n", "5 No Alive 34-54 ans 180 438 0.90452261\n", "6 Yes Alive 34-54 ans 198 438 0.82845188\n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "8 Yes Dead 34-54 ans 41 438 0.17154812\n", "9 No Alive 54-64 ans 80 234 0.67226891\n", "10 Yes Alive 54-64 ans 64 234 0.55652174\n", "11 No Dead 54-64 ans 39 234 0.32773109\n", "12 Yes Dead 54-64 ans 51 234 0.44347826\n", "13 No Alive plus de 65 ans 29 244 0.14871795\n", "14 Yes Alive plus de 65 ans 7 244 0.14285714\n", "15 No Dead plus de 65 ans 166 244 0.85128205\n", "16 Yes Dead plus de 65 ans 42 244 0.85714286" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
3No Dead 18-34 ans 6 398 0.02739726
4Yes Dead 18-34 ans 5 398 0.02793296
7No Dead 34-54 ans 19 438 0.09547739
8Yes Dead 34-54 ans 41 438 0.17154812
11No Dead 54-64 ans 39 234 0.32773109
12Yes Dead 54-64 ans 51 234 0.44347826
15No Dead plus de 65 ans166 244 0.85128205
16Yes Dead plus de 65 ans 42 244 0.85714286
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 18-34 ans & 6 & 398 & 0.02739726 \\\\\n", "\t4 & Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296 \\\\\n", "\t7 & No & Dead & 34-54 ans & 19 & 438 & 0.09547739 \\\\\n", "\t8 & Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812 \\\\\n", "\t11 & No & Dead & 54-64 ans & 39 & 234 & 0.32773109 \\\\\n", "\t12 & Yes & Dead & 54-64 ans & 51 & 234 & 0.44347826 \\\\\n", "\t15 & No & Dead & plus de 65 ans & 166 & 244 & 0.85128205 \\\\\n", "\t16 & Yes & Dead & plus de 65 ans & 42 & 244 & 0.85714286 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|---|---|---|---|\n", "| 3 | No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| 4 | Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "| 7 | No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| 8 | Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "| 11 | No | Dead | 54-64 ans | 39 | 234 | 0.32773109 | \n", "| 12 | Yes | Dead | 54-64 ans | 51 | 234 | 0.44347826 | \n", "| 15 | No | Dead | plus de 65 ans | 166 | 244 | 0.85128205 | \n", "| 16 | Yes | Dead | plus de 65 ans | 42 | 244 | 0.85714286 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "4 Yes Dead 18-34 ans 5 398 0.02793296\n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "8 Yes Dead 34-54 ans 41 438 0.17154812\n", "11 No Dead 54-64 ans 39 234 0.32773109\n", "12 Yes Dead 54-64 ans 51 234 0.44347826\n", "15 No Dead plus de 65 ans 166 244 0.85128205\n", "16 Yes Dead plus de 65 ans 42 244 0.85714286" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "age_data$mortality <- ifelse(age_data$Var1=='No' & age_data$Var3=='18-34 ans', age_data$Freq/(age_data$Freq[1]+age_data$Freq[3]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='18-34 ans', age_data$Freq/(age_data$Freq[2]+age_data$Freq[4]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='34-54 ans', age_data$Freq/(age_data$Freq[5]+age_data$Freq[7]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='34-54 ans', age_data$Freq/(age_data$Freq[6]+age_data$Freq[8]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='54-64 ans', age_data$Freq/(age_data$Freq[9]+age_data$Freq[11]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='54-64 ans', age_data$Freq/(age_data$Freq[10]+age_data$Freq[12]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='plus de 65 ans', age_data$Freq/(age_data$Freq[13]+age_data$Freq[15]),\n", " age_data$Freq/(age_data$Freq[14]+age_data$Freq[16])))))))) \n", "age_data\n", "age_data_2 <- age_data[age_data$Var2==\"Dead\",]\n", "age_data_2" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x=age_data_2$Var1, y=age_data_2$mortality, ylim=c(0,1), col=age_data_2$Var3)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR while rich displaying an object: Error: Aesthetics must be either length 1 or the same as the data (8): size\n", "\n", "Traceback:\n", "1. FUN(X[[i]], ...)\n", "2. tryCatch(withCallingHandlers({\n", " . if (!mime %in% names(repr::mime2repr)) \n", " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", " . rpr <- repr::mime2repr[[mime]](obj)\n", " . if (is.null(rpr)) \n", " . return(NULL)\n", " . prepare_content(is.raw(rpr), rpr)\n", " . }, error = error_handler), error = outer_handler)\n", "3. tryCatchList(expr, classes, parentenv, handlers)\n", "4. tryCatchOne(expr, names, parentenv, handlers[[1L]])\n", "5. doTryCatch(return(expr), name, parentenv, handler)\n", "6. withCallingHandlers({\n", " . if (!mime %in% names(repr::mime2repr)) \n", " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", " . rpr <- repr::mime2repr[[mime]](obj)\n", " . if (is.null(rpr)) \n", " . return(NULL)\n", " . prepare_content(is.raw(rpr), rpr)\n", " . }, error = error_handler)\n", "7. repr::mime2repr[[mime]](obj)\n", "8. repr_text.default(obj)\n", "9. paste(capture.output(print(obj)), collapse = \"\\n\")\n", "10. capture.output(print(obj))\n", "11. evalVis(expr)\n", "12. withVisible(eval(expr, pf))\n", "13. eval(expr, pf)\n", "14. eval(expr, pf)\n", "15. print(obj)\n", "16. print.ggplot(obj)\n", "17. ggplot_build(x)\n", "18. ggplot_build.ggplot(x)\n", "19. by_layer(function(l, d) l$compute_geom_2(d))\n", "20. f(l = layers[[i]], d = data[[i]])\n", "21. l$compute_geom_2(d)\n", "22. f(..., self = self)\n", "23. self$geom$use_defaults(data, self$aes_params)\n", "24. f(..., self = self)\n", "25. check_aesthetics(params[aes_params], nrow(data))\n", "26. stop(\"Aesthetics must be either length 1 or the same as the data (\", \n", " . n, \"): \", paste(names(which(!good)), collapse = \", \"), call. = FALSE)\n" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAA1BMVEX///+nxBvIAAAACXBI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\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMA3yB4AAXYzOhIAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ggplot2)\n", "ggplot(age_data_2,aes(x=Var1,y=Var3))+geom_point(alpha=.3,size=3+theme_bw())" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...209322209340209358209362209369209390209406209436209451209451
1NaNAlbania41.15330020.168300000000...334391334408334408334427334427334427334427334427334443334457
2NaNAlgeria28.0339001.659600000000...271441271448271463271469271469271477271477271490271494271496
3NaNAndorra42.5063001.521800000000...47866478754787547875478754787547875478754789047890
4NaNAngola-11.20270017.873900000000...105255105277105277105277105277105277105277105277105288105288
5NaNAntarctica-71.94990023.347000000000...11111111111111111111
6NaNAntigua and Barbuda17.060800-61.796400000000...9106910691069106910691069106910691069106
7NaNArgentina-38.416100-63.616700000000...10044125100441251004412510044125100441251004412510044957100449571004495710044957
8NaNArmenia40.06910045.038200000000...446819446819446819446819446819446819446819446819447308447308
9Australian Capital TerritoryAustralia-35.473500149.012400000000...232018232018232619232619232619232619232619232619232619232974
10New South WalesAustralia-33.868800151.209300000034...3900969390096939081293908129390812939081293908129390812939081293915992
11Northern TerritoryAustralia-12.463400130.845600000000...104931104931105021105021105021105021105021105021105021105111
12QueenslandAustralia-27.469800153.025100000000...1796633179663318002361800236180023618002361800236180023618002361800236
13South AustraliaAustralia-34.928500138.600700000000...880207880207881911881911881911881911881911881911881911883620
14TasmaniaAustralia-42.882100147.327200000000...286264286264286264286897286897286897286897286897286897287507
15VictoriaAustralia-37.813600144.963100000011...2874262287426228772602877260287726028772602877260287726028772602880559
16Western AustraliaAustralia-31.950500115.860500000000...1291077129107712934611293461129346112934611293461129346112934611293461
17NaNAustria47.51620014.550100000000...5911294591961659261485931247593666659409355943417594941859558605961143
18NaNAzerbaijan40.14310047.576900000000...828548828588828628828648828682828721828730828783828819828825
19NaNBahamas25.025885-78.035889000000...37491374913749137491374913749137491374913749137491
20NaNBahrain26.02750050.550000000000...707480707828708061708532708768709230709230709858710306710693
21NaNBangladesh23.68500090.356300000000...2037773203782920378292037829203782920378292037829203782920378712037871
22NaNBarbados13.193900-59.543200000000...106645106645106645106645106645106645106645106645106645106798
23NaNBelarus53.70980027.953400000000...994037994037994037994037994037994037994037994037994037994037
24NaNBelgium50.8333004.469936000000...4717655471765547277954727795472779547277954727795472779547277954739365
25NaNBelize17.189900-88.497600000000...70757707577075770757707577075770757707577075770757
26NaNBenin9.3077002.315800000000...27990279902799027990279902799027990279992799927999
27NaNBhutan27.51420090.433600000000...62615626206262062620626206262062620626206262762627
28NaNBolivia-16.290200-63.588700000000...1193009119325611934181193650119381511939081193970119406911941871194277
29NaNBosnia and Herzegovina43.91590017.679100000000...401575401636401636401636401636401636401636401636401729401729
..................................................................
259NaNTuvalu-7.109500177.649300000000...2805280528052805280528052805280528052805
260NaNUS40.000000-100.000000112255...103443455103533872103589757103648690103650837103646975103655539103690910103755771103802702
261NaNUganda1.37333332.290275000000...170504170504170504170504170504170504170504170504170544170544
262NaNUkraine48.37940031.165600000000...5693846570124957013335701474570160257017435701855570195957118185711929
263NaNUnited Arab Emirates23.42407653.847818000000...1051998105212210522471052382105251910526641052664105292610530681053213
264AnguillaUnited Kingdom18.220600-63.068600000000...3904390439043904390439043904390439043904
265BermudaUnited Kingdom32.307800-64.750500000000...18799188141881418814188141881418814188141882818828
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...7305730573057305730573057305730573057305
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...31472314723147231472314723147231472314723147231472
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0000000000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...1930193019301930193019301930193019301930
270GibraltarUnited Kingdom36.140800-5.353600000000...20423204232042320433204332043320433204332043320433
271GuernseyUnited Kingdom49.448196-2.589490000000...34867349293492934929349293492934929349293499134991
272Isle of ManUnited Kingdom54.236100-4.548100000000...38008380083800838008380083800838008380083800838008
273JerseyUnited Kingdom49.213800-2.135800000000...66391663916639166391663916639166391663916639166391
274MontserratUnited Kingdom16.742498-62.187366000000...1403140314031403140314031403140314031403
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...4444444444
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...2166216621662166216621662166216621662166
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...6551655165516551655165516551655765576561
278NaNUnited Kingdom55.378100-3.436000000000...24370150243701502439653024396530243965302439653024396530243965302439653024425309
279NaNUruguay-32.522800-55.765800000000...1034303103430310343031034303103430310343031034303103430310343031034303
280NaNUzbekistan41.37749164.585262000000...250932251071251071251071251071251071251071251071251247251247
281NaNVanuatu-15.376700166.959200000000...12014120141201412014120141201412014120141201412014
282NaNVenezuela6.423800-66.589700000000...551981551986551986552014552051552051552125552157552157552162
283NaNVietnam14.058324108.277199022222...11526917115269261152693711526950115269621152696611526966115269861152699411526994
284NaNWest Bank and Gaza31.95220035.233200000000...703228703228703228703228703228703228703228703228703228703228
285NaNWinter Olympics 202239.904200116.407400000000...535535535535535535535535535535
286NaNYemen15.55272748.516388000000...11945119451194511945119451194511945119451194511945
287NaNZambia-13.13389727.849332000000...343012343012343079343079343079343135343135343135343135343135
288NaNZimbabwe-19.01543829.154857000000...263921264127264127264127264127264127264127264127264276264276
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289 rows × 1147 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region \\\n", "0 NaN Afghanistan \n", "1 NaN Albania \n", "2 NaN Algeria \n", "3 NaN Andorra \n", "4 NaN Angola \n", "5 NaN Antarctica \n", "6 NaN Antigua and Barbuda \n", "7 NaN Argentina \n", "8 NaN Armenia \n", "9 Australian Capital Territory Australia \n", "10 New South Wales Australia \n", "11 Northern Territory Australia \n", "12 Queensland Australia \n", "13 South Australia Australia \n", "14 Tasmania Australia \n", "15 Victoria Australia \n", "16 Western Australia Australia \n", "17 NaN Austria \n", "18 NaN Azerbaijan \n", "19 NaN Bahamas \n", "20 NaN Bahrain \n", "21 NaN Bangladesh \n", "22 NaN Barbados \n", "23 NaN Belarus \n", "24 NaN Belgium \n", "25 NaN Belize \n", "26 NaN Benin \n", "27 NaN Bhutan \n", "28 NaN Bolivia \n", "29 NaN Bosnia and Herzegovina \n", ".. ... ... \n", "259 NaN Tuvalu \n", "260 NaN US \n", "261 NaN Uganda \n", "262 NaN Ukraine \n", "263 NaN United Arab Emirates \n", "264 Anguilla United Kingdom \n", "265 Bermuda United Kingdom \n", "266 British Virgin Islands United Kingdom \n", "267 Cayman Islands United Kingdom \n", "268 Channel Islands United Kingdom \n", "269 Falkland Islands (Malvinas) United Kingdom \n", "270 Gibraltar United Kingdom \n", "271 Guernsey United Kingdom \n", "272 Isle of Man United Kingdom \n", "273 Jersey United Kingdom \n", "274 Montserrat United Kingdom \n", "275 Pitcairn Islands United Kingdom \n", "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", "277 Turks and Caicos Islands United Kingdom \n", "278 NaN United Kingdom \n", "279 NaN Uruguay \n", "280 NaN Uzbekistan \n", "281 NaN Vanuatu \n", "282 NaN Venezuela \n", "283 NaN Vietnam \n", "284 NaN West Bank and Gaza \n", "285 NaN Winter Olympics 2022 \n", "286 NaN Yemen \n", "287 NaN Zambia \n", "288 NaN Zimbabwe \n", "\n", " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "0 33.939110 67.709953 0 0 0 0 0 \n", "1 41.153300 20.168300 0 0 0 0 0 \n", "2 28.033900 1.659600 0 0 0 0 0 \n", "3 42.506300 1.521800 0 0 0 0 0 \n", "4 -11.202700 17.873900 0 0 0 0 0 \n", "5 -71.949900 23.347000 0 0 0 0 0 \n", "6 17.060800 -61.796400 0 0 0 0 0 \n", "7 -38.416100 -63.616700 0 0 0 0 0 \n", "8 40.069100 45.038200 0 0 0 0 0 \n", "9 -35.473500 149.012400 0 0 0 0 0 \n", "10 -33.868800 151.209300 0 0 0 0 3 \n", "11 -12.463400 130.845600 0 0 0 0 0 \n", "12 -27.469800 153.025100 0 0 0 0 0 \n", "13 -34.928500 138.600700 0 0 0 0 0 \n", "14 -42.882100 147.327200 0 0 0 0 0 \n", "15 -37.813600 144.963100 0 0 0 0 1 \n", "16 -31.950500 115.860500 0 0 0 0 0 \n", "17 47.516200 14.550100 0 0 0 0 0 \n", "18 40.143100 47.576900 0 0 0 0 0 \n", "19 25.025885 -78.035889 0 0 0 0 0 \n", "20 26.027500 50.550000 0 0 0 0 0 \n", "21 23.685000 90.356300 0 0 0 0 0 \n", "22 13.193900 -59.543200 0 0 0 0 0 \n", "23 53.709800 27.953400 0 0 0 0 0 \n", "24 50.833300 4.469936 0 0 0 0 0 \n", "25 17.189900 -88.497600 0 0 0 0 0 \n", "26 9.307700 2.315800 0 0 0 0 0 \n", "27 27.514200 90.433600 0 0 0 0 0 \n", "28 -16.290200 -63.588700 0 0 0 0 0 \n", "29 43.915900 17.679100 0 0 0 0 0 \n", ".. ... ... ... ... ... ... ... \n", "259 -7.109500 177.649300 0 0 0 0 0 \n", "260 40.000000 -100.000000 1 1 2 2 5 \n", "261 1.373333 32.290275 0 0 0 0 0 \n", "262 48.379400 31.165600 0 0 0 0 0 \n", "263 23.424076 53.847818 0 0 0 0 0 \n", "264 18.220600 -63.068600 0 0 0 0 0 \n", "265 32.307800 -64.750500 0 0 0 0 0 \n", "266 18.420700 -64.640000 0 0 0 0 0 \n", "267 19.313300 -81.254600 0 0 0 0 0 \n", "268 49.372300 -2.364400 0 0 0 0 0 \n", "269 -51.796300 -59.523600 0 0 0 0 0 \n", "270 36.140800 -5.353600 0 0 0 0 0 \n", "271 49.448196 -2.589490 0 0 0 0 0 \n", "272 54.236100 -4.548100 0 0 0 0 0 \n", "273 49.213800 -2.135800 0 0 0 0 0 \n", "274 16.742498 -62.187366 0 0 0 0 0 \n", "275 -24.376800 -128.324200 0 0 0 0 0 \n", "276 -7.946700 -14.355900 0 0 0 0 0 \n", "277 21.694000 -71.797900 0 0 0 0 0 \n", "278 55.378100 -3.436000 0 0 0 0 0 \n", "279 -32.522800 -55.765800 0 0 0 0 0 \n", "280 41.377491 64.585262 0 0 0 0 0 \n", "281 -15.376700 166.959200 0 0 0 0 0 \n", "282 6.423800 -66.589700 0 0 0 0 0 \n", "283 14.058324 108.277199 0 2 2 2 2 \n", "284 31.952200 35.233200 0 0 0 0 0 \n", "285 39.904200 116.407400 0 0 0 0 0 \n", "286 15.552727 48.516388 0 0 0 0 0 \n", "287 -13.133897 27.849332 0 0 0 0 0 \n", "288 -19.015438 29.154857 0 0 0 0 0 \n", "\n", " 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", "0 0 ... 209322 209340 209358 209362 \n", "1 0 ... 334391 334408 334408 334427 \n", "2 0 ... 271441 271448 271463 271469 \n", "3 0 ... 47866 47875 47875 47875 \n", "4 0 ... 105255 105277 105277 105277 \n", "5 0 ... 11 11 11 11 \n", "6 0 ... 9106 9106 9106 9106 \n", "7 0 ... 10044125 10044125 10044125 10044125 \n", "8 0 ... 446819 446819 446819 446819 \n", "9 0 ... 232018 232018 232619 232619 \n", "10 4 ... 3900969 3900969 3908129 3908129 \n", "11 0 ... 104931 104931 105021 105021 \n", "12 0 ... 1796633 1796633 1800236 1800236 \n", "13 0 ... 880207 880207 881911 881911 \n", "14 0 ... 286264 286264 286264 286897 \n", "15 1 ... 2874262 2874262 2877260 2877260 \n", "16 0 ... 1291077 1291077 1293461 1293461 \n", "17 0 ... 5911294 5919616 5926148 5931247 \n", "18 0 ... 828548 828588 828628 828648 \n", "19 0 ... 37491 37491 37491 37491 \n", "20 0 ... 707480 707828 708061 708532 \n", "21 0 ... 2037773 2037829 2037829 2037829 \n", "22 0 ... 106645 106645 106645 106645 \n", "23 0 ... 994037 994037 994037 994037 \n", "24 0 ... 4717655 4717655 4727795 4727795 \n", "25 0 ... 70757 70757 70757 70757 \n", "26 0 ... 27990 27990 27990 27990 \n", "27 0 ... 62615 62620 62620 62620 \n", "28 0 ... 1193009 1193256 1193418 1193650 \n", "29 0 ... 401575 401636 401636 401636 \n", ".. ... ... ... ... ... ... \n", "259 0 ... 2805 2805 2805 2805 \n", "260 5 ... 103443455 103533872 103589757 103648690 \n", "261 0 ... 170504 170504 170504 170504 \n", "262 0 ... 5693846 5701249 5701333 5701474 \n", "263 0 ... 1051998 1052122 1052247 1052382 \n", "264 0 ... 3904 3904 3904 3904 \n", "265 0 ... 18799 18814 18814 18814 \n", "266 0 ... 7305 7305 7305 7305 \n", "267 0 ... 31472 31472 31472 31472 \n", "268 0 ... 0 0 0 0 \n", "269 0 ... 1930 1930 1930 1930 \n", "270 0 ... 20423 20423 20423 20433 \n", "271 0 ... 34867 34929 34929 34929 \n", "272 0 ... 38008 38008 38008 38008 \n", "273 0 ... 66391 66391 66391 66391 \n", "274 0 ... 1403 1403 1403 1403 \n", "275 0 ... 4 4 4 4 \n", "276 0 ... 2166 2166 2166 2166 \n", "277 0 ... 6551 6551 6551 6551 \n", "278 0 ... 24370150 24370150 24396530 24396530 \n", "279 0 ... 1034303 1034303 1034303 1034303 \n", "280 0 ... 250932 251071 251071 251071 \n", "281 0 ... 12014 12014 12014 12014 \n", "282 0 ... 551981 551986 551986 552014 \n", "283 2 ... 11526917 11526926 11526937 11526950 \n", "284 0 ... 703228 703228 703228 703228 \n", "285 0 ... 535 535 535 535 \n", "286 0 ... 11945 11945 11945 11945 \n", "287 0 ... 343012 343012 343079 343079 \n", "288 0 ... 263921 264127 264127 264127 \n", "\n", " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", "0 209369 209390 209406 209436 209451 209451 \n", "1 334427 334427 334427 334427 334443 334457 \n", "2 271469 271477 271477 271490 271494 271496 \n", "3 47875 47875 47875 47875 47890 47890 \n", "4 105277 105277 105277 105277 105288 105288 \n", "5 11 11 11 11 11 11 \n", "6 9106 9106 9106 9106 9106 9106 \n", "7 10044125 10044125 10044957 10044957 10044957 10044957 \n", "8 446819 446819 446819 446819 447308 447308 \n", "9 232619 232619 232619 232619 232619 232974 \n", "10 3908129 3908129 3908129 3908129 3908129 3915992 \n", "11 105021 105021 105021 105021 105021 105111 \n", "12 1800236 1800236 1800236 1800236 1800236 1800236 \n", "13 881911 881911 881911 881911 881911 883620 \n", "14 286897 286897 286897 286897 286897 287507 \n", "15 2877260 2877260 2877260 2877260 2877260 2880559 \n", "16 1293461 1293461 1293461 1293461 1293461 1293461 \n", "17 5936666 5940935 5943417 5949418 5955860 5961143 \n", "18 828682 828721 828730 828783 828819 828825 \n", "19 37491 37491 37491 37491 37491 37491 \n", "20 708768 709230 709230 709858 710306 710693 \n", "21 2037829 2037829 2037829 2037829 2037871 2037871 \n", "22 106645 106645 106645 106645 106645 106798 \n", "23 994037 994037 994037 994037 994037 994037 \n", "24 4727795 4727795 4727795 4727795 4727795 4739365 \n", "25 70757 70757 70757 70757 70757 70757 \n", "26 27990 27990 27990 27999 27999 27999 \n", "27 62620 62620 62620 62620 62627 62627 \n", "28 1193815 1193908 1193970 1194069 1194187 1194277 \n", "29 401636 401636 401636 401636 401729 401729 \n", ".. ... ... ... ... ... ... \n", "259 2805 2805 2805 2805 2805 2805 \n", "260 103650837 103646975 103655539 103690910 103755771 103802702 \n", "261 170504 170504 170504 170504 170544 170544 \n", "262 5701602 5701743 5701855 5701959 5711818 5711929 \n", "263 1052519 1052664 1052664 1052926 1053068 1053213 \n", "264 3904 3904 3904 3904 3904 3904 \n", "265 18814 18814 18814 18814 18828 18828 \n", "266 7305 7305 7305 7305 7305 7305 \n", "267 31472 31472 31472 31472 31472 31472 \n", "268 0 0 0 0 0 0 \n", "269 1930 1930 1930 1930 1930 1930 \n", "270 20433 20433 20433 20433 20433 20433 \n", "271 34929 34929 34929 34929 34991 34991 \n", "272 38008 38008 38008 38008 38008 38008 \n", "273 66391 66391 66391 66391 66391 66391 \n", "274 1403 1403 1403 1403 1403 1403 \n", "275 4 4 4 4 4 4 \n", "276 2166 2166 2166 2166 2166 2166 \n", "277 6551 6551 6551 6557 6557 6561 \n", "278 24396530 24396530 24396530 24396530 24396530 24425309 \n", "279 1034303 1034303 1034303 1034303 1034303 1034303 \n", "280 251071 251071 251071 251071 251247 251247 \n", "281 12014 12014 12014 12014 12014 12014 \n", "282 552051 552051 552125 552157 552157 552162 \n", "283 11526962 11526966 11526966 11526986 11526994 11526994 \n", "284 703228 703228 703228 703228 703228 703228 \n", "285 535 535 535 535 535 535 \n", "286 11945 11945 11945 11945 11945 11945 \n", "287 343079 343135 343135 343135 343135 343135 \n", "288 264127 264127 264127 264127 264276 264276 \n", "\n", "[289 rows x 1147 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans le format CSV les dates ont 2 formats : MM/JJ/AAAA du 01 au 12 du mois et (M)M/JJ/AA du 13 à la fin du mois. Mais ça ne semble pas poser de problème à Python car le format est homogène ici (M)M/(J)J/AA.\n", "\n", "On " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' Province/State Country/Region \\\\\\n0 NaN Afghanistan \\n1 NaN Albania \\n2 NaN Algeria \\n3 NaN Andorra \\n4 NaN Angola \\n5 NaN Antarctica \\n6 NaN Antigua and Barbuda \\n7 NaN Argentina \\n8 NaN Armenia \\n9 Australian Capital Territory Australia \\n10 New South Wales Australia \\n11 Northern Territory Australia \\n12 Queensland Australia \\n13 South Australia Australia \\n14 Tasmania Australia \\n15 Victoria Australia \\n16 Western Australia Australia \\n17 NaN Austria \\n18 NaN Azerbaijan \\n19 NaN Bahamas \\n20 NaN Bahrain \\n21 NaN Bangladesh \\n22 NaN Barbados \\n23 NaN Belarus \\n24 NaN Belgium \\n25 NaN Belize \\n26 NaN Benin \\n27 NaN Bhutan \\n28 NaN Bolivia \\n29 NaN Bosnia and Herzegovina \\n.. ... ... \\n259 NaN Tuvalu \\n260 NaN US \\n261 NaN Uganda \\n262 NaN Ukraine \\n263 NaN United Arab Emirates \\n264 Anguilla United Kingdom \\n265 Bermuda United Kingdom \\n266 British Virgin Islands United Kingdom \\n267 Cayman Islands United Kingdom \\n268 Channel Islands United Kingdom \\n269 Falkland Islands (Malvinas) United Kingdom \\n270 Gibraltar United Kingdom \\n271 Guernsey United Kingdom \\n272 Isle of Man United Kingdom \\n273 Jersey United Kingdom \\n274 Montserrat United Kingdom \\n275 Pitcairn Islands United Kingdom \\n276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \\n277 Turks and Caicos Islands United Kingdom \\n278 NaN United Kingdom \\n279 NaN Uruguay \\n280 NaN Uzbekistan \\n281 NaN Vanuatu \\n282 NaN Venezuela \\n283 NaN Vietnam \\n284 NaN West Bank and Gaza \\n285 NaN Winter Olympics 2022 \\n286 NaN Yemen \\n287 NaN Zambia \\n288 NaN Zimbabwe \\n\\n Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\\\\n0 33.939110 67.709953 0 0 0 0 0 \\n1 41.153300 20.168300 0 0 0 0 0 \\n2 28.033900 1.659600 0 0 0 0 0 \\n3 42.506300 1.521800 0 0 0 0 0 \\n4 -11.202700 17.873900 0 0 0 0 0 \\n5 -71.949900 23.347000 0 0 0 0 0 \\n6 17.060800 -61.796400 0 0 0 0 0 \\n7 -38.416100 -63.616700 0 0 0 0 0 \\n8 40.069100 45.038200 0 0 0 0 0 \\n9 -35.473500 149.012400 0 0 0 0 0 \\n10 -33.868800 151.209300 0 0 0 0 3 \\n11 -12.463400 130.845600 0 0 0 0 0 \\n12 -27.469800 153.025100 0 0 0 0 0 \\n13 -34.928500 138.600700 0 0 0 0 0 \\n14 -42.882100 147.327200 0 0 0 0 0 \\n15 -37.813600 144.963100 0 0 0 0 1 \\n16 -31.950500 115.860500 0 0 0 0 0 \\n17 47.516200 14.550100 0 0 0 0 0 \\n18 40.143100 47.576900 0 0 0 0 0 \\n19 25.025885 -78.035889 0 0 0 0 0 \\n20 26.027500 50.550000 0 0 0 0 0 \\n21 23.685000 90.356300 0 0 0 0 0 \\n22 13.193900 -59.543200 0 0 0 0 0 \\n23 53.709800 27.953400 0 0 0 0 0 \\n24 50.833300 4.469936 0 0 0 0 0 \\n25 17.189900 -88.497600 0 0 0 0 0 \\n26 9.307700 2.315800 0 0 0 0 0 \\n27 27.514200 90.433600 0 0 0 0 0 \\n28 -16.290200 -63.588700 0 0 0 0 0 \\n29 43.915900 17.679100 0 0 0 0 0 \\n.. ... ... ... ... ... ... ... \\n259 -7.109500 177.649300 0 0 0 0 0 \\n260 40.000000 -100.000000 1 1 2 2 5 \\n261 1.373333 32.290275 0 0 0 0 0 \\n262 48.379400 31.165600 0 0 0 0 0 \\n263 23.424076 53.847818 0 0 0 0 0 \\n264 18.220600 -63.068600 0 0 0 0 0 \\n265 32.307800 -64.750500 0 0 0 0 0 \\n266 18.420700 -64.640000 0 0 0 0 0 \\n267 19.313300 -81.254600 0 0 0 0 0 \\n268 49.372300 -2.364400 0 0 0 0 0 \\n269 -51.796300 -59.523600 0 0 0 0 0 \\n270 36.140800 -5.353600 0 0 0 0 0 \\n271 49.448196 -2.589490 0 0 0 0 0 \\n272 54.236100 -4.548100 0 0 0 0 0 \\n273 49.213800 -2.135800 0 0 0 0 0 \\n274 16.742498 -62.187366 0 0 0 0 0 \\n275 -24.376800 -128.324200 0 0 0 0 0 \\n276 -7.946700 -14.355900 0 0 0 0 0 \\n277 21.694000 -71.797900 0 0 0 0 0 \\n278 55.378100 -3.436000 0 0 0 0 0 \\n279 -32.522800 -55.765800 0 0 0 0 0 \\n280 41.377491 64.585262 0 0 0 0 0 \\n281 -15.376700 166.959200 0 0 0 0 0 \\n282 6.423800 -66.589700 0 0 0 0 0 \\n283 14.058324 108.277199 0 2 2 2 2 \\n284 31.952200 35.233200 0 0 0 0 0 \\n285 39.904200 116.407400 0 0 0 0 0 \\n286 15.552727 48.516388 0 0 0 0 0 \\n287 -13.133897 27.849332 0 0 0 0 0 \\n288 -19.015438 29.154857 0 0 0 0 0 \\n\\n 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\\\\n0 0 ... 209322 209340 209358 209362 \\n1 0 ... 334391 334408 334408 334427 \\n2 0 ... 271441 271448 271463 271469 \\n3 0 ... 47866 47875 47875 47875 \\n4 0 ... 105255 105277 105277 105277 \\n5 0 ... 11 11 11 11 \\n6 0 ... 9106 9106 9106 9106 \\n7 0 ... 10044125 10044125 10044125 10044125 \\n8 0 ... 446819 446819 446819 446819 \\n9 0 ... 232018 232018 232619 232619 \\n10 4 ... 3900969 3900969 3908129 3908129 \\n11 0 ... 104931 104931 105021 105021 \\n12 0 ... 1796633 1796633 1800236 1800236 \\n13 0 ... 880207 880207 881911 881911 \\n14 0 ... 286264 286264 286264 286897 \\n15 1 ... 2874262 2874262 2877260 2877260 \\n16 0 ... 1291077 1291077 1293461 1293461 \\n17 0 ... 5911294 5919616 5926148 5931247 \\n18 0 ... 828548 828588 828628 828648 \\n19 0 ... 37491 37491 37491 37491 \\n20 0 ... 707480 707828 708061 708532 \\n21 0 ... 2037773 2037829 2037829 2037829 \\n22 0 ... 106645 106645 106645 106645 \\n23 0 ... 994037 994037 994037 994037 \\n24 0 ... 4717655 4717655 4727795 4727795 \\n25 0 ... 70757 70757 70757 70757 \\n26 0 ... 27990 27990 27990 27990 \\n27 0 ... 62615 62620 62620 62620 \\n28 0 ... 1193009 1193256 1193418 1193650 \\n29 0 ... 401575 401636 401636 401636 \\n.. ... ... ... ... ... ... \\n259 0 ... 2805 2805 2805 2805 \\n260 5 ... 103443455 103533872 103589757 103648690 \\n261 0 ... 170504 170504 170504 170504 \\n262 0 ... 5693846 5701249 5701333 5701474 \\n263 0 ... 1051998 1052122 1052247 1052382 \\n264 0 ... 3904 3904 3904 3904 \\n265 0 ... 18799 18814 18814 18814 \\n266 0 ... 7305 7305 7305 7305 \\n267 0 ... 31472 31472 31472 31472 \\n268 0 ... 0 0 0 0 \\n269 0 ... 1930 1930 1930 1930 \\n270 0 ... 20423 20423 20423 20433 \\n271 0 ... 34867 34929 34929 34929 \\n272 0 ... 38008 38008 38008 38008 \\n273 0 ... 66391 66391 66391 66391 \\n274 0 ... 1403 1403 1403 1403 \\n275 0 ... 4 4 4 4 \\n276 0 ... 2166 2166 2166 2166 \\n277 0 ... 6551 6551 6551 6551 \\n278 0 ... 24370150 24370150 24396530 24396530 \\n279 0 ... 1034303 1034303 1034303 1034303 \\n280 0 ... 250932 251071 251071 251071 \\n281 0 ... 12014 12014 12014 12014 \\n282 0 ... 551981 551986 551986 552014 \\n283 2 ... 11526917 11526926 11526937 11526950 \\n284 0 ... 703228 703228 703228 703228 \\n285 0 ... 535 535 535 535 \\n286 0 ... 11945 11945 11945 11945 \\n287 0 ... 343012 343012 343079 343079 \\n288 0 ... 263921 264127 264127 264127 \\n\\n 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \\n0 209369 209390 209406 209436 209451 209451 \\n1 334427 334427 334427 334427 334443 334457 \\n2 271469 271477 271477 271490 271494 271496 \\n3 47875 47875 47875 47875 47890 47890 \\n4 105277 105277 105277 105277 105288 105288 \\n5 11 11 11 11 11 11 \\n6 9106 9106 9106 9106 9106 9106 \\n7 10044125 10044125 10044957 10044957 10044957 10044957 \\n8 446819 446819 446819 446819 447308 447308 \\n9 232619 232619 232619 232619 232619 232974 \\n10 3908129 3908129 3908129 3908129 3908129 3915992 \\n11 105021 105021 105021 105021 105021 105111 \\n12 1800236 1800236 1800236 1800236 1800236 1800236 \\n13 881911 881911 881911 881911 881911 883620 \\n14 286897 286897 286897 286897 286897 287507 \\n15 2877260 2877260 2877260 2877260 2877260 2880559 \\n16 1293461 1293461 1293461 1293461 1293461 1293461 \\n17 5936666 5940935 5943417 5949418 5955860 5961143 \\n18 828682 828721 828730 828783 828819 828825 \\n19 37491 37491 37491 37491 37491 37491 \\n20 708768 709230 709230 709858 710306 710693 \\n21 2037829 2037829 2037829 2037829 2037871 2037871 \\n22 106645 106645 106645 106645 106645 106798 \\n23 994037 994037 994037 994037 994037 994037 \\n24 4727795 4727795 4727795 4727795 4727795 4739365 \\n25 70757 70757 70757 70757 70757 70757 \\n26 27990 27990 27990 27999 27999 27999 \\n27 62620 62620 62620 62620 62627 62627 \\n28 1193815 1193908 1193970 1194069 1194187 1194277 \\n29 401636 401636 401636 401636 401729 401729 \\n.. ... ... ... ... ... ... \\n259 2805 2805 2805 2805 2805 2805 \\n260 103650837 103646975 103655539 103690910 103755771 103802702 \\n261 170504 170504 170504 170504 170544 170544 \\n262 5701602 5701743 5701855 5701959 5711818 5711929 \\n263 1052519 1052664 1052664 1052926 1053068 1053213 \\n264 3904 3904 3904 3904 3904 3904 \\n265 18814 18814 18814 18814 18828 18828 \\n266 7305 7305 7305 7305 7305 7305 \\n267 31472 31472 31472 31472 31472 31472 \\n268 0 0 0 0 0 0 \\n269 1930 1930 1930 1930 1930 1930 \\n270 20433 20433 20433 20433 20433 20433 \\n271 34929 34929 34929 34929 34991 34991 \\n272 38008 38008 38008 38008 38008 38008 \\n273 66391 66391 66391 66391 66391 66391 \\n274 1403 1403 1403 1403 1403 1403 \\n275 4 4 4 4 4 4 \\n276 2166 2166 2166 2166 2166 2166 \\n277 6551 6551 6551 6557 6557 6561 \\n278 24396530 24396530 24396530 24396530 24396530 24425309 \\n279 1034303 1034303 1034303 1034303 1034303 1034303 \\n280 251071 251071 251071 251071 251247 251247 \\n281 12014 12014 12014 12014 12014 12014 \\n282 552051 552051 552125 552157 552157 552162 \\n283 11526962 11526966 11526966 11526986 11526994 11526994 \\n284 703228 703228 703228 703228 703228 703228 \\n285 535 535 535 535 535 535 \\n286 11945 11945 11945 11945 11945 11945 \\n287 343079 343135 343135 343135 343135 343135 \\n288 264127 264127 264127 264127 264276 264276 \\n\\n[289 rows x 1147 columns]'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(raw_data)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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