Revenue Category and Original Media by Cow Plot

p1<-media_franchises %>%
    group_by(revenue_category) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>%
ggplot(.,aes(x=str_wrap(revenue_category,15),y=revenue_total,fill=revenue_avg))+geom_col()+
       xlab("Revenue Category")+ylab("Revenue Total by $")+
       ggtitle("Revenue Category vs Revenue Total",
               subtitle = "By Billions of Dollars")+
       theme(axis.text.x = element_text(size=10.5,angle = 90))+
       labs(fill="Revenue\nAverage\nIn Billions")+
       scale_y_continuous(expand = c(0,150))+
       geom_text(aes(label=revenue_total),vjust=-0.5)
  
p2<-media_franchises %>%
    group_by(original_media) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>%
ggplot(.,aes(x=str_wrap(original_media,15),y=revenue_total,fill=revenue_avg))+geom_col()+
       xlab("Original Media")+
       ylab("Original media by $")+
       ggtitle("Original Media vs Revenue Total",
               subtitle = "By Billions of Dollars")+
       labs(fill="Revenue\nAverage\nIn Billions")+
       scale_y_continuous(expand = c(0,50))+
       geom_text(aes(label=revenue_total),hjust=-0.15)+coord_flip()

ggdraw()+
    draw_plot(p2+theme(legend.justification = "top"),0,0,1,1)+
    draw_plot(p1+scale_color_viridis_d()+theme(legend.justification = "bottom"), 0.45, 0.08, 0.55, 0.55)+
    draw_plot_label(c("A", "B"), c(0, 0.55), c(1, 0.45), size = 5.5)

Revenue Category and Original Media by Animations

p1<-media_franchises %>%
    group_by(year_created,revenue_category) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>% 
ggplot(.,aes(x=str_wrap(revenue_category,15),y=revenue_total,
             fill=revenue_avg))+ geom_col()+
       xlab("Revenue Category")+ ylab("Revenue Total by $")+
       transition_time(year_created)+ease_aes("linear")+
       ggtitle("Revenue Category vs Revenue Total",
               subtitle = "By Billions of Dollars of Year : {round(frame_time)}")+
       labs(fill="Revenue\nAverage\nIn Billions")+
       geom_text(aes(label=revenue_total),vjust=-0.5)

animate(p1,nframes=52,fps=1)

p1<-media_franchises %>%
    group_by(year_created,original_media) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>% 
ggplot(.,aes(x=str_wrap(original_media,15),y=revenue_total,
             fill=revenue_avg))+ geom_col()+coord_flip()+
       xlab("Original Media")+ ylab("Revenue Total by $")+
       transition_time(year_created)+ease_aes("linear")+
       ggtitle("Original Media vs Revenue Total",
               subtitle = "By Billions of Dollars of Year : {round(frame_time)}")+
       labs(fill="Revenue\nAverage\nIn Billions")+
       geom_text(aes(label=revenue_total),hjust=-0.05)

animate(p1,nframes=52,fps=1)

Revenue Category and Original Media by rayshader

p1<-media_franchises %>%
    group_by(revenue_category) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>%
    ggplot(.,aes(x=str_wrap(revenue_category,10),y=revenue_total,fill=revenue_avg))+geom_col()+
       xlab("Revenue Category")+ylab("Revenue Total by $")+
       ggtitle("Revenue Category vs Revenue Total",
               subtitle = "By Billions of Dollars")+theme_minimal()+
       theme(axis.text = element_text(size=7,angle = 90),
             title = element_text(size=10))+
       labs(fill="Revenue\nAverage\nIn Billions")+
       scale_y_continuous(expand = c(0,150))

plot_gg(p1,width=3.5,multicore = TRUE, windowsize = c(1000, 1000), 
        zoom = 0.85, phi = 60, theta = 30, sunangle = 270, soliddepth = -60)
render_snapshot(clear=TRUE)

p1<-media_franchises %>%
    group_by(original_media) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>%
    ggplot(.,aes(x=original_media,y=revenue_total,fill=revenue_avg))+geom_col()+
       xlab("Original Media")+ylab("Revenue Total by $")+
       ggtitle("Original Media vs Revenue Total",
               subtitle = "By Billions of Dollars")+theme_minimal()+
       theme(axis.text = element_text(size=7,angle = 90))+
       labs(fill="Revenue\nAverage\nIn Billions")

plot_gg(p1,width=3.5,multicore = TRUE, windowsize = c(1000, 800), 
        zoom = 0.85, phi = 40, theta = 50, sunangle = 270, soliddepth = -30)
render_snapshot(clear=TRUE)

Revenue Category In Decades

media_franchises %>%
    mutate(years=cut(year_created,
                     breaks=c(1920,1929,1939,1949,
                              1959,1969,1979,1989,
                              1999,2009,2017),
                     labels=c("1920s","1930s","1940s","1950s",
                              "1960s","1970s","1980s","1990s",
                              "2000s","2010s"))) %>%
    group_by(years,revenue_category) %>%
ggplot(.) + 
    geom_mosaic(aes(x=product(years),fill=revenue_category))+
    xlab("Years by Decade")+ylab("Revenue Category")+
    theme(axis.text.x = element_text(angle=90))+
    labs(fill="Revenue\nCategory")+
    ggtitle("Mosaic Graph for Decades of Year vs Revenue of Category ")

Revenue Category In Decades but with Total Revenue

media_franchises %>%
    mutate(years=cut(year_created,
                     breaks=c(1920,1929,1939,1949,
                              1959,1969,1979,1989,
                              1999,2009,2017),
                     labels=c("1920s","1930s","1940s","1950s",
                              "1960s","1970s","1980s","1990s",
                              "2000s","2010s"))) %>%
    group_by(years,revenue_category) %>%
    summarise(revenue_total=sum(revenue),revenue_avg=mean(revenue)) %>%
ggplot(.,aes(x=str_wrap(revenue_category,10),y=revenue_total,fill=revenue_avg))+
    xlab("Revenue Category")+ylab("Revenue Total")+geom_col()+
    transition_states(years)+ease_aes("linear")+
    labs(fill="Revenue\nAverage")+
    geom_text(aes(label=revenue_total),vjust=-1)+
    ggtitle("Revenue Total in Billion $ for Revenue Category",
            subtitle = "Year : {closest_state}")

THANK YOU