diff --git a/module3/exo2/exercice_fr.Rmd b/module3/exo2/exercice_fr.Rmd index 7eece5e296bb586e88166aa8a263ca75b44c2b9e..694220236b3230463075258a9a4fc4c3f6cef881 100644 --- a/module3/exo2/exercice_fr.Rmd +++ b/module3/exo2/exercice_fr.Rmd @@ -1,33 +1,196 @@ --- -title: "Votre titre" -author: "Votre nom" -date: "La date du jour" -output: html_document +title: "Analyse de l'incidence de la varicelle" +author: "Dorian Goepp" +date: "2022-11-23" +output: + html_document: + toc: true + theme: journal + pdf_document: + toc: true +documentclass: article +classoption: a4paper +header-includes: +- \usepackage[french]{babel} +- \hypersetup{colorlinks=true,pagebackref=true} --- - ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` -## Quelques explications +## Préparation des données + +Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente. L'URL est: + +```{r} +data_url = "http://www.sentiweb.fr/datasets/incidence-PAY-7.csv" +``` + +Voici l'explication des colonnes donnée sur le [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json): + +| Nom de colonne | Libellé de colonne | +|--------------|----------------------------------------------------------| +| `week` | Semaine calendaire (ISO 8601) | +| `indicator` | Code de l'indicateur de surveillance | +| `inc` | Estimation de l'incidence de consultations en nombre de cas | +| `inc_low` | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation | +| `inc_up` | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation | +| `inc100` | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) | +| `inc100_low` | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) | +| `inc100_up` | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) | +| `geo_insee` | Code de la zone géographique concernée (Code INSEE) | +| `geo_name` | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) | + +La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skip=1`. + +### Téléchargement + +Si le fichier n'existe pas déjà sur l'ordinateur, on le télécharge depuis la source indiquée plus haut. Cette vérification nous évite des téléchargements répétés et inutiles, et nous permet de travailler sans connexion internet (si le fichier existe). + +```{r} +library(fs) + +file_name = "incidence_varicelle.csv" +if (!file_exists(file_name)) { + download.file(url=data_url, destfile=file_name, method="auto") +} +data = read.csv(file_name, skip=1) +``` + +Regardons ce que nous avons obtenu: + +```{r} +head(data) +tail(data) +``` + +Y a-t-il des points manquants dans nos données ? -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} +na_records = apply(data, 1, function (x) any(is.na(x))) +data[na_records,] +``` + +Les deux colonnes qui nous intéressent sont `week` et `inc`. Vérifions leurs classes: + +```{r} +class(data$week) +class(data$inc) +``` + +Ce sont des entiers, tout va bien ! + +### Conversion des numéros de semaine + +La gestion des dates est toujours un sujet délicat. Il y a un grand nombre de conventions différentes qu'il ne faut pas confondre. Notre jeux de données utilise un format que peu de logiciels savent traiter: les semaines en format [ISO-8601](https://en.wikipedia.org/wiki/ISO_8601). En `R`, il est géré par la bibliothèque [parsedate](https://cran.r-project.org/package=parsedate): + +```{r} +library(parsedate) +``` + +Pour faciliter le traitement suivant, nous remplaçons ces semaines par les dates qui correspondent aux lundis. Voici une petite fonction qui fait la conversion pour une seule valeur: + +```{r} +convert_week = function(w) { + ws = paste(w) + iso = paste0(substring(ws, 1, 4), "-W", substring(ws, 5, 6)) + as.character(parse_iso_8601(iso)) +} +``` -Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante: +Nous appliquons cette fonction à tous les points, créant une nouvelle colonne `date` dans notre jeu de données: -```{r cars} -summary(cars) +```{r} +data$date = as.Date(convert_week(data$week)) ``` -Et on peut aussi aisément inclure des figures. Par exemple: +Vérifions qu'elle est de classe `Date`: -```{r pressure, echo=FALSE} -plot(pressure) +```{r} +class(data$date) ``` -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. +Les points sont dans l'ordre chronologique inverse, il est donc utile de les trier: -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. +```{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) +``` -Maintenant, à vous de jouer! Vous pouvez effacer toutes ces informations et les remplacer par votre document computationnel. +### Inspection + +Regardons enfin à quoi ressemblent nos données ! + +```{r} +plot(data$date, data$inc, type="l", xlab="Date", ylab="Incidence hebdomadaire") +``` + +Un zoom sur les dernières années montre mieux la localisation des pics en hiver. Le creux des incidences se trouve en été. + +```{r} +with(tail(data, 200), plot(date, inc, type="l", xlab="Date", ylab="Incidence hebdomadaire")) +``` + +## L'incidence annuelle + +### Calcul + +Étant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année $N$ au 1er août 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. L'argument `na.rm=True` dans la sommation précise qu'il faut supprimer les points manquants. Ce choix est raisonnable car il n'y a qu'un seul point manquant, dont l'impact ne peut pas être très fort. + +```{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$inc[semaines], na.rm=TRUE) + } +``` + +Nous devons aussi faire attention aux premières et dernières années de notre jeux de données. Les données commencent en décembre 1990, ce qui ne permet pas de quantifier complètement le pic attribué à 1991. Nous l'enlevons donc de notre analyse. Par contre, pour une exécution en novembre 2022, les données se terminent après le 1er septembre 2018, ce qui nous permet d'inclure cette année. + +```{r} +annees = 1991:2022 +``` + +Nous créons un nouveau jeu de données pour l'incidence annuelle, en applicant la fonction `pic_annuel` à chaque année: + +```{r} +inc_annuelle = data.frame(annee = annees, + incidence = sapply(annees, pic_annuel)) +head(inc_annuelle) +``` + +### Inspection + +Voici les incidences annuelles en graphique: + +```{r} +plot(inc_annuelle, type="p", xlab="Année", ylab="Incidence annuelle") +``` + +### Identification des épidémies les plus fortes + +Une liste triée par ordre décroissant d'incidence annuelle permet de plus facilement repérer les valeurs les plus élevées: + +```{r} +incidence_annuelle_triee = inc_annuelle[order(-inc_annuelle$incidence),] +head(incidence_annuelle_triee) +``` + +De la même manière, nous pouvons trouver l'année avec l'incidence la plus faible: + +```{r} +tail(incidence_annuelle_triee) +``` + +Enfin, un histogramme semble indiquer que les épidémies sont en majorité regroupées autour d'incidences "moyennes" entre 600 000 et 650 000 personnes. + +```{r} +hist(inc_annuelle$incidence, breaks=10, xlab="Incidence annuelle", ylab="Nb d'observations", main="") +``` diff --git a/module3/exo2/exercice_fr.html b/module3/exo2/exercice_fr.html new file mode 100644 index 0000000000000000000000000000000000000000..aac159020acfdba550c51151a95d29879b6312b8 --- /dev/null +++ b/module3/exo2/exercice_fr.html @@ -0,0 +1,684 @@ + + + + + + + + + + + + + + + +Analyse de l’incidence de la varicelle + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + +
+

Préparation des données

+

Les données de l’incidence de la varicelle sont disponibles du site +Web du Réseau Sentinelles. Nous +les récupérons sous forme d’un fichier en format CSV dont chaque ligne +correspond à une semaine de la période demandée. Nous téléchargeons +toujours le jeu de données complet, qui commence en 1991 et se termine +avec une semaine récente. L’URL est:

+
data_url = "http://www.sentiweb.fr/datasets/incidence-PAY-7.csv"
+

Voici l’explication des colonnes donnée sur le sur le site +d’origine:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nom de colonneLibellé de colonne
weekSemaine calendaire (ISO 8601)
indicatorCode de l’indicateur de surveillance
incEstimation de l’incidence de consultations en nombre de cas
inc_lowEstimation de la borne inférieure de l’IC95% du nombre de cas de +consultation
inc_upEstimation de la borne supérieure de l’IC95% du nombre de cas de +consultation
inc100Estimation du taux d’incidence du nombre de cas de consultation (en +cas pour 100,000 habitants)
inc100_lowEstimation de la borne inférieure de l’IC95% du taux d’incidence du +nombre de cas de consultation (en cas pour 100,000 habitants)
inc100_upEstimation de la borne supérieure de l’IC95% du taux d’incidence du +nombre de cas de consultation (en cas pour 100,000 habitants)
geo_inseeCode de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/
geo_nameLibellé de la zone géographique (ce libellé peut être modifié sans +préavis)
+

La première ligne du fichier CSV est un commentaire, que nous +ignorons en précisant skip=1.

+
+

Téléchargement

+

Si le fichier n’existe pas déjà sur l’ordinateur, on le télécharge +depuis la source indiquée plus haut. Cette vérification nous évite des +téléchargements répétés et inutiles, et nous permet de travailler sans +connexion internet (si le fichier existe).

+
library(fs)
+
+file_name = "incidence_varicelle.csv"
+if (!file_exists(file_name)) {
+  download.file(url=data_url, destfile=file_name, method="auto")
+}
+data = read.csv(file_name, skip=1)
+

Regardons ce que nous avons obtenu:

+
head(data)
+
##     week indicator  inc inc_low inc_up inc100 inc100_low inc100_up geo_insee
+## 1 202245         7 3895    1762   6028      6          3         9        FR
+## 2 202244         7 4271    2231   6311      6          3         9        FR
+## 3 202243         7 5863    3302   8424      9          5        13        FR
+## 4 202242         7 3770    1950   5590      6          3         9        FR
+## 5 202241         7 4177    2219   6135      6          3         9        FR
+## 6 202240         7 4883    1472   8294      7          2        12        FR
+##   geo_name
+## 1   France
+## 2   France
+## 3   France
+## 4   France
+## 5   France
+## 6   France
+
tail(data)
+
##        week indicator   inc inc_low inc_up inc100 inc100_low inc100_up
+## 1662 199102         7 16277   11046  21508     29         20        38
+## 1663 199101         7 15565   10271  20859     27         18        36
+## 1664 199052         7 19375   13295  25455     34         23        45
+## 1665 199051         7 19080   13807  24353     34         25        43
+## 1666 199050         7 11079    6660  15498     20         12        28
+## 1667 199049         7  1143       0   2610      2          0         5
+##      geo_insee geo_name
+## 1662        FR   France
+## 1663        FR   France
+## 1664        FR   France
+## 1665        FR   France
+## 1666        FR   France
+## 1667        FR   France
+

Y a-t-il des points manquants dans nos données ?

+
na_records = apply(data, 1, function (x) any(is.na(x)))
+data[na_records,]
+
##  [1] week       indicator  inc        inc_low    inc_up     inc100    
+##  [7] inc100_low inc100_up  geo_insee  geo_name  
+## <0 lignes> (ou 'row.names' de longueur nulle)
+

Les deux colonnes qui nous intéressent sont week et +inc. Vérifions leurs classes:

+
class(data$week)
+
## [1] "integer"
+
class(data$inc)
+
## [1] "integer"
+

Ce sont des entiers, tout va bien !

+
+
+

Conversion des numéros de semaine

+

La gestion des dates est toujours un sujet délicat. Il y a un grand +nombre de conventions différentes qu’il ne faut pas confondre. Notre +jeux de données utilise un format que peu de logiciels savent traiter: +les semaines en format ISO-8601. En +R, il est géré par la bibliothèque parsedate:

+
library(parsedate)
+

Pour faciliter le traitement suivant, nous remplaçons ces semaines +par les dates qui correspondent aux lundis. Voici une petite fonction +qui fait la conversion pour une seule valeur:

+
convert_week = function(w) {
+    ws = paste(w)
+    iso = paste0(substring(ws, 1, 4), "-W", substring(ws, 5, 6))
+    as.character(parse_iso_8601(iso))
+}
+

Nous appliquons cette fonction à tous les points, créant une nouvelle +colonne date dans notre jeu de données:

+
data$date = as.Date(convert_week(data$week))
+

Vérifions qu’elle est de classe Date:

+
class(data$date)
+
## [1] "Date"
+

Les points sont dans l’ordre chronologique inverse, il est donc utile +de les trier:

+
data = data[order(data$date),]
+

C’est l’occasion pour faire une vérification: nos dates doivent être +séparées d’exactement sept jours:

+
all(diff(data$date) == 7)
+
## [1] TRUE
+
+
+

Inspection

+

Regardons enfin à quoi ressemblent nos données !

+
plot(data$date, data$inc, type="l", xlab="Date", ylab="Incidence hebdomadaire")
+

+

Un zoom sur les dernières années montre mieux la localisation des +pics en hiver. Le creux des incidences se trouve en été.

+
with(tail(data, 200), plot(date, inc, type="l", xlab="Date", ylab="Incidence hebdomadaire"))
+

+
+
+
+

L’incidence annuelle

+
+

Calcul

+

Étant donné que le pic de l’épidémie se situe en hiver, à cheval +entre deux années civiles, nous définissons la période de référence +entre deux minima de l’incidence, du 1er août de l’année \(N\) au 1er août 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. L’argument na.rm=True dans la +sommation précise qu’il faut supprimer les points manquants. Ce choix +est raisonnable car il n’y a qu’un seul point manquant, dont l’impact ne +peut pas être très fort.

+
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], na.rm=TRUE)
+      }
+

Nous devons aussi faire attention aux premières et dernières années +de notre jeux de données. Les données commencent en décembre 1990, ce +qui ne permet pas de quantifier complètement le pic attribué à 1991. +Nous l’enlevons donc de notre analyse. Par contre, pour une exécution en +novembre 2022, les données se terminent après le 1er septembre 2018, ce +qui nous permet d’inclure cette année.

+
annees = 1991:2022
+

Nous créons un nouveau jeu de données pour l’incidence annuelle, en +applicant la fonction pic_annuel à chaque année:

+
inc_annuelle = data.frame(annee = annees,
+                          incidence = sapply(annees, pic_annuel))
+head(inc_annuelle)
+
##   annee incidence
+## 1  1991    553895
+## 2  1992    834935
+## 3  1993    642921
+## 4  1994    662750
+## 5  1995    651333
+## 6  1996    564994
+
+
+

Inspection

+

Voici les incidences annuelles en graphique:

+
plot(inc_annuelle, type="p", xlab="Année", ylab="Incidence annuelle")
+

+
+
+

Identification des épidémies les plus fortes

+

Une liste triée par ordre décroissant d’incidence annuelle permet de +plus facilement repérer les valeurs les plus élevées:

+
incidence_annuelle_triee = inc_annuelle[order(-inc_annuelle$incidence),]
+head(incidence_annuelle_triee)
+
##    annee incidence
+## 19  2009    841233
+## 2   1992    834935
+## 20  2010    834077
+## 26  2016    779816
+## 14  2004    778914
+## 13  2003    760765
+

De la même manière, nous pouvons trouver l’année avec l’incidence la +plus faible:

+
tail(incidence_annuelle_triee)
+
##    annee incidence
+## 1   1991    553895
+## 27  2017    552906
+## 28  2018    539765
+## 12  2002    515343
+## 31  2021    377933
+## 30  2020    221183
+

Enfin, un histogramme semble indiquer que les épidémies sont en +majorité regroupées autour d’incidences “moyennes” entre 600 000 et 650 +000 personnes.

+
hist(inc_annuelle$incidence, breaks=10, xlab="Incidence annuelle", ylab="Nb d'observations", main="")
+

+
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/module3/exo2/incidence_varicelle.csv b/module3/exo2/incidence_varicelle.csv new file mode 100644 index 0000000000000000000000000000000000000000..3c1ad3073d3448c22135b9080f28757a4d32c31a --- /dev/null +++ b/module3/exo2/incidence_varicelle.csv @@ -0,0 +1,1669 @@ +# @source="rseau Sentinelles, INSERM, Sorbonne Universit, http://www.sentiweb.fr", @meta={"period":[199049,202245],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2022-11-23T13:58:04+01:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202245,7,3895,1762,6028,6,3,9,FR,France +202244,7,4271,2231,6311,6,3,9,FR,France +202243,7,5863,3302,8424,9,5,13,FR,France +202242,7,3770,1950,5590,6,3,9,FR,France +202241,7,4177,2219,6135,6,3,9,FR,France +202240,7,4883,1472,8294,7,2,12,FR,France +202239,7,2041,331,3751,3,0,6,FR,France +202238,7,1771,419,3123,3,1,5,FR,France 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