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