TyT2019W43 - Heat Map

horror_movies <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-22/horror_movies.csv")

summary(horror_movies)

horror_movies$date<-as.Date(horror_movies$release_date, format="%d-%b-%y")
horror_movies$release_year<-year(horror_movies$date)
horror_movies$devise  <- str_match(horror_movies$budget,"^[^0-9]+")[,1]
horror_movies$devise<-str_remove_all(horror_movies$devise,"[\\s]+")
horror_movies$budget_numeric  <- as.numeric(gsub(horror_movies$budget, pattern = "[^0-9]",replacement = ""))

#Convertir toutes les devises en $US
conv_devise=data.frame("conversion"=c(1.290, 1.110, 0.760, 0.014, 0.680, 0.016,1,1,0.00085), "devise"=c("£", "€", "CAD", "INR", "AUD", "RUR ","$", NA,"KRW"))

#Sélectionner les 15 pays les plus présents dans les données
country<-horror_movies %>% 
  select('release_country') %>% 
  group_by(release_country) %>% 
  summarise(count=n()) %>% 
  top_n(15) %>% 
  select('release_country')

movie<-horror_movies %>% 
  filter(release_country %in% country$release_country) %>% 
  left_join(conv_devise, by="devise") %>% 
  mutate(budget_numeric_conv=budget_numeric*conversion) %>% 
  group_by(release_country, release_year) %>% 
  summarise(sum=sum(budget_numeric_conv,na.rm=TRUE), count=n()) %>%
  mutate(sum=ifelse(sum==0, NA, sum)) %>% 
  mutate(prix_film=sum/count) %>% 
  drop_na(release_year)

#Calculer les moyennes
autres<-movie %>% 
  filter(!release_country=="USA") %>% 
  group_by() %>% 
  summarise(moy=mean(sum, na.rm=TRUE)) 
#Réponse: 24 244 745$US pour période de 5 ans

USA<-movie %>% 
  filter(release_country=="USA") %>% 
  summarise(moy=mean(sum, na.rm=TRUE))
#Réponse: 305 680 008$ en 5 ans!!

Line_US<-movie %>% 
  filter(release_country=="USA") %>% 
  mutate(sum=sum/1000000)

Line_other<-movie %>% 
  filter(!release_country=="USA") %>% 
  mutate(sum=sum/1000000)

#Graphique
gg<- ggplot(data=Line_other,aes(x = release_year, y=sum, group=release_country))
gg <- gg +  geom_line(size=2, color="#F5BB00")
gg <- gg +  geom_line(data=Line_US,aes(x = release_year, y=sum, group=release_country),size=2, color="#FF4C00")
#retirer la légende
gg <- gg +  theme(legend.position = "none")
#ajuster les axes
gg<-gg + scale_x_continuous(breaks=seq(2012,2017,1), limits=c(2012, 2017.2), expand = c(0,0))
gg<-gg + scale_y_continuous(breaks=seq(0,500,100), limits=c(0, 520), expand = c(0,0))
#modifier le thème
gg <- gg +  theme(panel.border = element_rect(color="black",size=1, fill=NA),
                    panel.background = element_rect(fill="black"),
                    plot.background = element_rect(fill="black"),
                    panel.grid.major.x= element_blank(),
                    panel.grid.major.y= element_line(size=0.5, linetype="dotted",color="#8B8B8B"),
                    panel.grid.minor = element_blank(),
                    axis.line.x = element_line(size=1, color="#8B8B8B"),
                    axis.line.y =element_line(size=1, color="#8B8B8B"),
                    axis.ticks.x = element_blank(), 
                    axis.ticks.y = element_blank())

#Créer le titre
couleur <- image_read('~/Documents/ENTREPRISE/Projets R/couleur/000000.png')
titre<- couleur %>%
  image_scale("x20") %>% 
  image_background("#000000", flatten = TRUE) %>%
  image_border("#000000", "720x190") %>% 
  image_annotate("United States loves horror movies!",
                 color = "#FF4C00", size = 102, location = "+10+5", font='Impact') %>% 
    image_annotate("United States has invested nearly 306 millions to make horror\nmovies between 2012 and 2017. The other countries have\ninvested on average 24 millions for the same period.",
                 color = "#8B8B8B", size = 58, location = "+10+150", font='Tw Cen MT')