version corrigée pour que ça fonctionne encore

.gitignore

@@ -0,0 +1,15 @@
+# History files
+.Rhistory
+.Rapp.history
+
+# Example code in package build process
+*-Ex.R
+
+# RStudio files
+.Rproj.user/
+*.Rproj
+
+# produced vignettes
+vignettes/*.html
+vignettes/*.pdf
+.Rproj.user

LICENSE

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README.md

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+# pipelineViz
+Working with pipeline incidents data from http://ouvert.canada.ca/data/fr/dataset/da1be1b4-5e2b-4c9a-ae0e-e8551fd6b265

dd-20150216.csv

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+Nom de colonne PODS,Nom du champ,Description du champ
+OCC_NO,Numéro d'événement,"Le numéro d'identification de l'événement. Le « P » sert à désigner le mode pipeline, suivi de deux chiffres qui indiquent l'année de l'événement. La lettre qui vient après l'année sert à désigner la région de l'événement, suivie du numéro de séquence attribué par le système PODS."
+OCC_YEAR,Année,L'année civile de l'événement.
+OCC_DATE,Date,"La date de l'événement, soit AAAA-MM-JJ. Le format de la date dépend de l'application utilisée pour ouvrir le fichier csv."
+CLASS_ID,Catégorie,"La classification d'enquête du BST pour l'événement. Lorsqu'un événement est en cours d'évaluation, la valeur 10 lui est assignée temporairement pour indiquer qu'il est « en cours d'évaluation »."
+OCC_TYPE,Type d'événement,Détermine si l'événement était un accident ou un incident à signaler.
+ACC_INC_TYPE,Type d'accident/d'incident,Détermine le type de l'accident ou de l'incident à signaler.
+PROVINCE,Province,La province du lieu de l'événement.
+LOCATION,Lieu,Ville ou village le plus proche du lieu où s'est produit l'évènement.
+TERRAIN_TYPE,Type de terrain,Zonage autorisé de l'utilisation des terres du lieu où s'est produit l'évènement.
+FACILITY_NAME,Nom de l'installation,Nom de l'installation.
+FACILITY_TYPE,Type d'installation,"Type d'installation tel que : station de compression, usine de traitement du gaz naturel, station de pompage, etc."
+PIPELINE_CO,Société de pipeline,Nom de la société de pipeline.
+PIPELINE_TYPE_CO,Type de société de pipeline,Type de société de pipeline : gaz ou HPV/FPV (haute/faible pression de vapeur)
+PROD_RELEASED_IND,Indicateur de produit déversé,Pour indiquer si un produit a été déversé relativement ou consécutivement à l'évènement.
+PRODUCT_TYPE,Type de produit,Type de produit déversé relativement ou consécutivement à l'évènement.
+QUANTITY_LOST,Quantité perdue,Quantité de produit déversée lorsque le déversement peut être quantifié et que cette information a été communiquée au BST. On mesure cette quantité en mètres cubes.
+DETECTION_METHOD,Méthode de détection,Moyen par lequel l'évènement a été détecté.
+LOCATION_ON_PIPELINE,Emplacement sur le pipeline,L'emplacement sur le pipeline où l'évènement s'est produit.
+ENVIRO_DAMAGE_IND,Indicateur de dégâts environnementaux,Pour indiquer si des dégâts ont été causés à l'environnement.
+PROPERTY_DAMAGE_IND,Indicateur de dégâts matériels,Pour indiquer si des biens ont été endommagés.
+FIRE_IND,Indicateur d'incendie,Indique s'il y a eu un incendie durant l'événement ou à la suite de l'événement.
+EXPLOSION_IND,Indicateur d'explosion,Indique s'il y a eu une explosion durant l'événement ou à la suite de l'événement.
+NO_EVACUATED,Nombre évacuées,Le nombre total de personnes évacuées à la suite de l'événement.
+FATALITIES,Total de morts,Nombre total connu de morts.
+SERIOUS_INJURIES,Total de blessés graves,Nombre total connu de blessés graves.
+PRODUCTION_DT,Date de production,La date et l'heure à laquelle le fichier de données a été généré. Format de date AAAA-MM-JJThh:mm:ss.

pipelineViz.Rmd

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+---
+title: "PipelineViz"
+author: "François Pelletier"
+date: "26 août 2015"
+output:
+  html_document:
+    fig_caption: yes
+    fig_height: 8
+    fig_width: 10
+    keep_md: yes
+    number_sections: yes
+    toc: yes
+---
+
+# Analyse d'un jeu de données ouvertes
+
+Source: [Données sur les évenements de pipeline de Janvier 2004 au présent](http://ouvert.canada.ca/data/fr/dataset/da1be1b4-5e2b-4c9a-ae0e-e8551fd6b265)
+
+## Chargement du dictionnaire de données
+
+```{r}
+# install.packages(c("data.table", "dplyr","maps","ggmap"))
+options(unzip = 'internal')
+library("data.table")
+library("dplyr")
+library("maps")
+library("ggmap")
+library("reshape2")
+library("lubridate")
+library("rpart")
+library("rpart.plot")
+```
+
+## Lecture des données
+
+```{r}
+datadict <- read.csv(file = "dd-20150216.csv",header = TRUE, stringsAsFactors = TRUE, encoding = "latin1") %>%
+  as.data.table()
+donnees <- read.csv(file = "p2015-07-fr.csv",header = TRUE,stringsAsFactors = TRUE) %>% 
+  as.data.table()
+
+class_donnees <- sapply(donnees,class)
+character_columns <- names(donnees)[class_donnees == "character"]
+num_columns <- names(donnees)[class_donnees != "character"]
+
+donnees_conv <- cbind(donnees[, lapply(.SD, iconv, "utf-8", "latin1"), 
+                   .SDcols = character_columns],donnees[,.SD,.SDcols = num_columns])
+```
+
+## Geocoder les incidents
+
+```{r, eval=FALSE}
+LocationsUnique <- unique(donnees_conv[,.(LOCATION,PROVINCE)])
+catLocationsUnique <- unique(paste(LocationsUnique$LOCATION, ifelse(!is.na(LocationsUnique$PROVINCE),LocationsUnique$PROVINCE, ""), "CANADA", sep = " "))
+geocodedLocations <- cbind(LocationsUnique,geocode(catLocationsUnique))
+save(geocodedLocations, file = "geocodedLocations.Rdata")
+```
+
+## Joindre les données géocodées aux données existantes
+
+```{r}
+load("geocodedLocations.Rdata")
+donnees_conv2 <-as.data.table(merge(donnees_conv,geocodedLocations, by=c("LOCATION","PROVINCE")))
+donnees_conv3 <- donnees_conv2[!is.na(PROVINCE) & !is.na(LOCATION)]
+```
+
+## Préparation de la carte de fond
+
+```{r, eval=FALSE}
+range_lat <- range(donnees_conv3$lat)
+range_lon <- range(donnees_conv3$lon)
+
+openstreetmap1 <- get_openstreetmap(bbox = c(left = range_lon[1], 
+                                        bottom = range_lat[1], 
+                                        right = range_lon[2], 
+                                        top = range_lat[2]),
+    scale = 32500000,
+    format = c("png"),
+    crop = TRUE, messaging = FALSE, urlonly = FALSE, color = c("color", "bw"))
+
+save(openstreetmap1, file = "openstreetmap1.Rdata")
+```
+
+## Chargement de la carte de fond
+
+```{r}
+load("openstreetmap1.Rdata")
+```
+
+
+## Carte des incidents avec déversement
+
+```{r, dev='png'}
+donnees_incidents <- donnees_conv3[QUANTITY_LOST > 0]
+ggmap(openstreetmap1) +
+  geom_point(aes(x = lon, 
+                 y = lat, 
+                 colour = PRODUCT_TYPE, 
+                 size = QUANTITY_LOST), 
+             data = donnees_incidents) + 
+  scale_size(range = c(2, 8), name=datadict[datadict$Nom.de.colonne.PODS=="QUANTITY_LOST"][2]) + 
+  scale_colour_discrete(name=datadict[datadict$Nom.de.colonne.PODS=="PRODUCT_TYPE"][2]) + 
+  ggtitle(label = "Incidents avec déversement") + 
+  xlab("Longitude") + 
+  ylab("Latitude")
+```
+
+## initiales
+
+Cette fonction extrait les initiales d'un mot
+
+```{r}
+initials <- function(str)
+  {
+  regex <- '(?<=^|\\s|-)[[:alpha:]]' 
+  ini <- regmatches(str, gregexpr(regex, str, perl=TRUE))
+  toupper(sapply(ini, paste0, collapse=''))
+  }
+```
+
+## Distribution des accidents et incidents
+
+```{r}
+dist_incidents <- donnees_conv3 %>% 
+  select(PROVINCE,OCC_TYPE,PRODUCT_TYPE) %>%
+  group_by(PROVINCE=factor(initials(PROVINCE)),OCC_TYPE=factor(OCC_TYPE)) %>%
+  summarise(N = n())
+```
+
+```{r, dev='png'}
+ggplot(dist_incidents,aes(x=PROVINCE,fill=OCC_TYPE)) + 
+  geom_bar(aes(y=N),stat="identity") + 
+  ggtitle(paste0(datadict[datadict$Nom.de.colonne.PODS=="OCC_TYPE"][2],", par ",datadict[datadict$Nom.de.colonne.PODS=="PROVINCE"][2])) +
+  scale_fill_discrete(name=datadict[datadict$Nom.de.colonne.PODS=="OCC_TYPE"][2]) + 
+  xlab(datadict[datadict$Nom.de.colonne.PODS=="PROVINCE"][2]) + 
+  ylab("Nombre")
+```
+
+## Série chronologique
+
+```{r}
+data_serie <- donnees_conv3 %>% 
+  select(PIPELINE_CO,OCC_DATE,QUANTITY_LOST) %>% 
+  mutate(N=1, date_clean = parse_date_time(OCC_DATE, orders = "%Y-%m-%d")) %>%
+  arrange(PIPELINE_CO,date_clean) %>% 
+  group_by(PIPELINE_CO,date_clean) %>%
+  summarise(Nsum = sum(N)) %>%
+  mutate(Ncumsum = cumsum(Nsum))
+```
+
+### Nombre d'évènements cumulés
+
+```{r, dev='png'}
+ggplot(data_serie,aes(x=date_clean,y=Ncumsum,color=PIPELINE_CO)) + 
+  geom_line(stat="identity") +
+  xlab("Date") + 
+  ylab("Nombre d'indicents cumulés") +
+  ggtitle("Nombre d'incidents cumulés, par compagnie")
+```
+
+## Arbre de décision
+```{r}
+tree1 <- rpart(OCC_TYPE~FACILITY_TYPE+PROD_RELEASED_IND+PIPELINE_TYPE_CO+ENVIRO_DAMAGE_IND+PROPERTY_DAMAGE_IND+FIRE_IND+EXPLOSION_IND, data=donnees_conv3, method="class")
+summary(tree1)
+```
+
+### Affichage de l'arbre de décision
+
+```{r}
+rpart.plot::rpart.plot(tree1, main = paste0("Arbre de classification pour ",
+                           datadict[datadict$Nom.de.colonne.PODS=="OCC_TYPE"]$Nom.du.champ))
+```
+
+
+
+
+
+ 
+