From b119af98458b07332bf1c9587effed613f160e1f Mon Sep 17 00:00:00 2001 From: Xavier Nardou Date: Wed, 25 Mar 2020 13:25:43 +0100 Subject: [PATCH] Exercice 2 du module 3 sur l'occurence de la varicelle --- module3/exo2/exercice_fr.Rmd | 180 +++- module3/exo2/incidence-PAY-7.csv | 1530 ++++++++++++++++++++++++++++++ 2 files changed, 1695 insertions(+), 15 deletions(-) create mode 100644 module3/exo2/incidence-PAY-7.csv diff --git a/module3/exo2/exercice_fr.Rmd b/module3/exo2/exercice_fr.Rmd index 7eece5e..70a459b 100644 --- a/module3/exo2/exercice_fr.Rmd +++ b/module3/exo2/exercice_fr.Rmd @@ -1,8 +1,19 @@ --- -title: "Votre titre" -author: "Votre nom" -date: "La date du jour" -output: html_document +title: "Analyse de l'incidence de la varicelle" +author: "Xavier Nardou" +date: "25 Mars 2020" +output: + pdf_document: + toc: true + html_document: + toc: true + theme: journal +documentclass: article +classoption: a4paper +header-includes: +- \usepackage[french]{babel} +- \usepackage[upright]{fourier} +- \hypersetup{colorlinks=true,pagebackref=true} --- @@ -10,24 +21,163 @@ output: html_document knitr::opts_chunk$set(echo = TRUE) ``` -## Quelques explications +On commence par récupérer les données sur la varicelle de 1991 à aujourd'hui. -Ceci est un document R markdown que vous pouvez aisément exporter au format HTML, PDF, et MS Word. Pour plus de détails sur R Markdown consultez . +```{r} +data_url = "https://www.sentiweb.fr/datasets/incidence-PAY-7.csv" +``` + +On va paramétrer pour faire en sorte que la copie locale des données soit utilisée de préférence, et qu'elle ne soit pas téléchargée à chaque fois. +```{r} + +``` + +```{r} +data_file="incidence-PAY-7.csv" +if(!file.exists(data_file)){ + download.file(data_url, data_file, method ="auto") + +} +``` + +On peut maintenant charger le fichier à partir de la copie locale qu'on a faite. + +```{r} +data = read.csv(data_file, skip=1, sep=",") +``` + +Regardons la structure de nos données + +```{r} +head(data) +tail(data) +str(data) +``` +Ce sont les bonnes classes correspondantes, incidence et week sont des entiers. +Maintenant, y'a t-il des données manquantes ? + + +```{r} +na_records = apply(data, 1, function (x) any(is.na(x))) +data[na_records,] +``` +Il n'y a pas de données manquantes dans ce jeu de données. + +### Conversion des numéros de semaine + +Utilisation de la library parsedate +```{r} +library(parsedate) +``` + +On remplace les semaines par les dates qui correspondent aux lundis + +```{r} +convert_week = function(w) { + ws = paste(w) + iso = paste0(substring(ws, 1, 4), "-W", substring(ws, 5, 6)) + as.character(parse_iso_8601(iso)) +} +``` + +Et on applique à tous les points + +```{r} +data$date = as.Date(convert_week(data$week)) +``` + +Vérifions qu'elle est de classe `Date`: +```{r} +class(data$date) +``` + +Les points sont dans l'ordre chronologique inverse, il est donc utile de les trier: +```{r} +data = data[order(data$date),] +``` + +C'est l'occasion pour faire une vérification: nos dates doivent être séparées d'exactement sept jours: +```{r} +all(diff(data$date) == 7) +``` + +### Inspection -Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante: +Regardons enfin à quoi ressemblent nos données ! +```{r} +plot(data$date, data$inc, type="l", xlab="Date", ylab="Incidence hebdomadaire") +``` + +La distribution annuelle a l'air plus étalée que la grippe. On va faire unu zoom sur les dernières années. +```{r} +with(tail(data, 200), plot(date, inc, type="l", xlab="Date", ylab="Incidence hebdomadaire")) +``` +Effectivement le pic est moins abrupt, et on voit qu'il y a un creux vers le mois de Septembre, donc on va séparer les années comme ça. + +## L'incidence annuelle + +### Calcul + +Nous définissons la période de référence entre deux minima de l'incidence, du 1er Septembre de l'année $N$ au 1er Septembre de l'année $N+1$. Nous mettons l'année $N+1$ comme étiquette sur cette année décalée, car le pic de l'épidémie est toujours au début de l'année $N+1$. Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions. + +```{r} +pic_annuel = function(annee) { + debut = paste0(annee-1,"-09-01") + fin = paste0(annee,"-09-01") + semaines = data$date > debut & data$date <= fin + sum(data$inc[semaines]) + } +``` -```{r cars} -summary(cars) +On va retirer la première année (1991) et 2020 car les données vont être incomplètes pour ces années là. + +```{r} +annees = 1992:2019 +``` + +Nous créons un nouveau jeu de données pour l'incidence annuelle, en applicant la fonction `pic_annuel` à chaque année: +```{r} +inc_annuelle = data.frame(annee = annees, + incidence = sapply(annees, pic_annuel)) +head(inc_annuelle) +``` + + +### Inspection + +Voici les incidences annuelles en graphique: +```{r} +plot(inc_annuelle, type="p", xlab="Année", ylab="Incidence annuelle") ``` -Et on peut aussi aisément inclure des figures. Par exemple: +### Identification des épidémies les plus fortes -```{r pressure, echo=FALSE} -plot(pressure) +Une liste triée par ordre décroissant d'incidence annuelle permet de plus facilement repérer les valeurs les plus élevées: +```{r} +head(inc_annuelle[order(-inc_annuelle$incidence),]) ``` -Vous remarquerez le paramètre `echo = FALSE` qui indique que le code ne doit pas apparaître dans la version finale du document. Nous vous recommandons dans le cadre de ce MOOC de ne pas utiliser ce paramètre car l'objectif est que vos analyses de données soient parfaitement transparentes pour être reproductibles. +Pour voir les épidémies les plus faibles + +```{r} +head(inc_annuelle[order(inc_annuelle$incidence),]) +``` + +Et un petit histogramme +```{r} +hist(inc_annuelle$incidence, breaks=10, xlab="Incidence annuelle", ylab="Nb d'observations", main="") +``` + + + + + + + + + + + + -Comme les résultats ne sont pas stockés dans les fichiers Rmd, pour faciliter la relecture de vos analyses par d'autres personnes, vous aurez donc intérêt à générer un HTML ou un PDF et à le commiter. -Maintenant, à vous de jouer! Vous pouvez effacer toutes ces informations et les remplacer par votre document computationnel. diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv new file mode 100644 index 0000000..b7d2c81 --- /dev/null +++ b/module3/exo2/incidence-PAY-7.csv @@ -0,0 +1,1530 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202011],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-03-25T12:54:12+01:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202011,7,10209,7575,12843,16,12,20,FR,France +202010,7,9011,6691,11331,14,10,18,FR,France +202009,7,13631,10544,16718,21,16,26,FR,France +202008,7,10424,7708,13140,16,12,20,FR,France +202007,7,8959,6574,11344,14,10,18,FR,France +202006,7,9264,6925,11603,14,10,18,FR,France +202005,7,8505,6314,10696,13,10,16,FR,France +202004,7,7991,5831,10151,12,9,15,FR,France 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