Cette semaine, le #Tidytuesday traite des contraventions de stationnement données à Philadelphie. J’ai choisi de visualiser l’évolution des prix moyen des contraventions par type d’autorité.

CONTEXTE

Les données de cette semaine proviennent de Open Data Philly.

OBJECTIFS

1. Visualiser l’évolution du prix moyen des contraventions pour les différentes autorités au cours de l’année 2017.

IMPORTER

tickets <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-03/tickets.csv")

FALSE Parsed with column specification:  
FALSE cols(  
FALSE   violation_desc = col_character(),  
FALSE   issue_datetime = col_datetime(format = ""),  
FALSE   fine = col_double(),  
FALSE   issuing_agency = col_character(),  
FALSE   lat = col_double(),  
FALSE   lon = col_double(),  
FALSE   zip_code = col_double()  
FALSE 
)

EXPLORER

summary(tickets)

##  violation_desc     issue_datetime                     fine          
##  Length:1260891     Min.   :2017-01-01 00:00:00   Min.   :  15.00    
##  Class :character   1st Qu.:2017-04-05 00:37:30   1st Qu.:  26.00    
##  Mode  :character   Median :2017-07-05 09:11:00   Median :  36.00    
##                     Mean   :2017-07-03 06:07:59   Mean   :  45.41    
##                     3rd Qu.:2017-10-01 13:20:30   3rd Qu.:  51.00    
##                     Max.   :2017-12-31 15:42:00   Max.   :1001.00    
##                                                                      
##  issuing_agency          lat             lon            zip_code       
##  Length:1260891     Min.   :39.57   Min.   :-75.99   Min.   :19102     
##  Class :character   1st Qu.:39.95   1st Qu.:-75.18   1st Qu.:19106     
##  Mode  :character   Median :39.95   Median :-75.16   Median :19123     
##                     Mean   :39.97   Mean   :-75.16   Mean   :19124     
##                     3rd Qu.:39.97   3rd Qu.:-75.15   3rd Qu.:19143     
##                     Max.   :40.37   Max.   :-74.96   Max.   :19154     
##                                                      NA's   :173588

glimpse(tickets)

## Observations: 1,260,891  
## Variables: 7  
## $ violation_desc <chr> "BUS ONLY ZONE", "STOPPING PROHIBITED", "OVER TIM…  
## $ issue_datetime <dttm> 2017-12-06 12:29:00, 2017-10-16 18:03:00, 2017-1…  
## $ fine           <dbl> 51, 51, 26, 26, 76, 51, 36, 36, 76, 26, 26, 301, …  
## $ issuing_agency <chr> "PPA", "PPA", "PPA", "PPA", "PPA", "POLICE", "PPA…  
## $ lat            <dbl> 40.03550, 40.02571, 40.02579, 40.02590, 39.95617,…  
## $ lon            <dbl> -75.08111, -75.22249, -75.22256, -75.22271, -75.1…  
## $ zip_code       <dbl> 19149, 19127, 19127, 19127, 19102, NA, NA, 19106,…

PRÉPARER

issuing_agency<-tickets %>%
       select('issuing_agency') %>%
       inspect_cat()    

data<-tickets %>%
     mutate(date=as.Date(floor_date(issue_datetime,"month"))) %>%
     group_by(issuing_agency, date) %>%
     summarise(total=sum(fine), count=n()) %>%
     mutate(moyenne=total/count)    

data_housing<-data %>%
     filter(issuing_agency=="HOUSING")

VISUALISER

library(scales)  
#Graphique  
gg<- ggplot(data=data,aes(x = date, y=moyenne, group=issuing_agency))  
gg <- gg +  geom_line(color="#C0C0C0", size=1.3)  
gg <- gg +  geom_line(data=data_housing,aes(x = date, y=moyenne, group=issuing_agency), color="#89023E", size=1.3)  
#ajuster les axes  
gg <- gg +  scale_x_date(labels = date_format("%B"), date_breaks = "1 months")  
gg <- gg +  scale_y_continuous(breaks=seq(0,130,20), limits=c(0, 130), expand=c(0.01,0))  
#retirer la légende  
gg <- gg +  theme(legend.position = "none")  
#modifier le thème  
gg <- gg +  theme(  panel.border = element_rect(color="#333333", ,size=1, fill=NA),
                      panel.background = element_rect(fill ="#333333"),
                      plot.background = element_rect(fill="#333333"),
                      panel.grid.major.y= element_line(size=0.5, color = "#616161", linetype = "dotted"),
                      panel.grid.major.x= element_blank(),
                      panel.grid.minor = element_blank(),                      axis.line.x = element_blank(),
                      axis.line.y =element_blank(),
                      axis.ticks.x = element_blank(),                       axis.ticks.y = element_blank())  
#ajouter les titres  
gg<-gg + labs(title="Philadephie: n'oubliez pas votre permis de stationnement résidentiel!",
                subtitle = "<br>En 2017, les contraventions les plus couteuses étaient celles données par les <span style='color:#89023E'>**agents de stationnement**.</span>",
                x=" ",
                y="Prix moyen des contraventions ($)\n",
                 caption="\nSOURCE: Open Data Philly   |  DESIGN: Johanie Fournier, agr.")  
gg<-gg + theme(    plot.title    =  element_text(size=26, hjust=0,vjust=0.5, color="#FFFFFF", family="Tw Cen MT", face="bold"),
                   plot.subtitle = element_markdown(lineheight = 1.1,size=16, hjust=0,vjust=0.5, color="#FFFFFF", family="Tw Cen MT"),
                   plot.caption  = element_text(size=10, hjust=1,vjust=0.5, color="#FFFFFF", family="Tw Cen MT"),
                   axis.title.y  = element_text(size=12, hjust=0.9,vjust=0.5, color="#FFFFFF", family="Tw Cen MT", angle=90),
                   axis.title.x  = element_blank(),
                   axis.text.y   = element_text(size=12, hjust=0,vjust=0.5, color="#FFFFFF", family="Tw Cen MT"),
                   axis.text.x   = element_text(size=14, hjust=0.5,vjust=0.5, color="#FFFFFF", family="Tw Cen MT"))

Voici ce que ça donne:

Alors, tu veux en savoir plus sur ma démarche? Abonne-toi à mon infolettre pour savoir quand est-ce que le prochain épisode de podcast sera disponible. J’y expliquerai toute la réflexion et les concepts de data visualisation qui ont menés à la création de cette viz.