Code to Pixar Text on Medium

pixar_text.r

# Install from CRAN
install.packages("pixarfilms")

# Install directly from GitHub
remotes::install_github("erictleung/pixarfilms")

library(pixarfilms)
library(tidyverse)



pixar_all<-
  pixar_films %>%
  inner_join(genres) %>%
  inner_join(pixar_people) %>%
  inner_join(box_office) %>%
  inner_join(public_response)


##Avaliação do público por avaliadores
public_response %>%
  select(-cinema_score) %>%
  pivot_longer(-film, names_to = "critics", values_to = "grade") %>%
  mutate(critics =  factor(critics, labels= c("Rotten tomatoes", "Metacritic", "Critics choice" ), levels =  c("rotten_tomatoes", "metacritic", "critics_choice" ))) %>%
  mutate(film= reorder(film, grade, FUN = sum)) %>%
  ggplot(aes(y=film, x= grade)) +
  geom_col(fill = "#FFEF03", color= "#FD0B0A") +
  facet_wrap(critics~.) +
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank(),
    axis.text.y = element_text(color = "#FD0B0A"),
    axis.text.x = element_text(color = "#203DBD"),
    strip.background = element_rect(fill = "white"),
    strip.text = element_text(color = "#203DBD"),
    axis.title = element_blank()
  ) 


library(ggrepel)

#timeline
pixar_all %>%
  distinct(film, release_date, budget, box_office_worldwide) %>%
  rename(`box office worldwide` =  box_office_worldwide ) %>%
  pivot_longer(-c(1,2),names_to = "money", values_to = "value" ) %>%
  mutate(value = value/10^6) %>%
  ggplot(aes(x= release_date, y=value,fill= money )) +
  geom_col(color= "#203DBD") +
  #geom_text(aes(label = film),  color = "white",  position = "stack", vjust =0)+ 
  scale_fill_manual(values= c("#FFEF03","#FD0B0A"))+
  scale_x_date(date_breaks = "1 year", date_labels = "%Y",guide = guide_axis(n.dodge = 2)) +
  
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank(),
    legend.position = "bottom"
  ) +
  labs( x= "Release date",
        y="Box-office worldwide (US$ mi)")+
  annotate("text", x= as.Date("1995-11-22"), y= 450, label= "Toy Story", color = "#FD0B0A", fontface = "bold")+
  annotate("text", x= as.Date("2018-06-15"), y= 1480, label= "Incredibles 2", color = "#FD0B0A", fontface = "bold")+
  annotate("text", x= as.Date("2010-06-18"), y= 1310, label= "Toy Story 3", color = "#FD0B0A", fontface = "bold")+
  annotate("text", x= as.Date("2019-06-21"), y= 1310, label= "Toy Story 4", color = "#FD0B0A",hjust = 0.2, fontface = "bold" )+
  annotate("text", x= as.Date("2020-12-25"), y= 300, label= "Soul", color = "#FD0B0A",hjust = 0.2 , fontface = "bold")


#scattering budget x Box-office
pixar_all %>%
  distinct(film,  budget, box_office_worldwide) %>%
  ggplot(aes(x=budget/10^6, y=box_office_worldwide/10^6)) +
  geom_point(size =6, fill = "#FFEF03", color = "#203DBD", pch= 21  )+
  geom_text_repel(aes(label = str_wrap(film,15)), color= "#FD0B0A", fontface= "bold",  set.seed = 1972, box.padding = unit(1, "lines")) +
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank()
    
  ) +
  labs( x= "Budget (US$ mi)",
        y="Box-office worldwide (US$ mi)")

#scattering budget X public response

pixar_all %>%
  distinct(film,  budget, rotten_tomatoes,metacritic, critics_choice) %>%
  pivot_longer(-c(film, budget), names_to = "critics", values_to = "grade") %>%
  group_by(film) %>%
  summarise(
    budget = max(budget),
    grade = mean(grade, na.rm = TRUE)  
  ) %>%
  ungroup() %>%
  ggplot(aes(x=budget/10^6, y =grade)) +
  geom_point(size =6, fill = "#FFEF03", color = "#203DBD", pch= 21  )+
  geom_text_repel(aes(label = str_wrap(film,15)), fontface= "bold", color= "#FD0B0A",box.padding = unit(1, "lines")) +
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank()   
    
  ) +
  labs( x= "Budget  (US$ mi)",
        y="Grade") 

#scattering grade X box-office
pixar_all %>%
  distinct(film,  box_office_worldwide, rotten_tomatoes,metacritic, critics_choice) %>%
  pivot_longer(-c(film, box_office_worldwide), names_to = "critics", values_to = "grade") %>%
  group_by(film) %>%
  summarise(
    box_office_worldwide = max(box_office_worldwide),
    grade = mean(grade, na.rm = TRUE)  
  ) %>%
  ungroup() %>%
  ggplot(aes(y=box_office_worldwide/10^6, x =grade)) +
  geom_point(size =6, fill = "#FFEF03", color = "#203DBD", pch= 21  )+
  geom_text_repel(aes(label = str_wrap(film,15)),  fontface= "bold", color= "#FD0B0A",box.padding = unit(1, "lines")) +
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank()   
    
  ) +
  labs( y= "Box-office worldwide (US$ mi)",
        x="Grade") 

people_20<-
  pixar_all %>%
  mutate(name= case_when(
    name == "Stanton" ~ "Andrew Stanton",
    name=="Docter" ~ "Pete Docter",
    name=="Lasseter" ~ "John Lasseter",
    name=="Unkrich" ~ "Lee Unkrich",
    name=="Scanlon" ~ "Dan Scanlon",
    TRUE ~ name)) %>%
  group_by(name) %>%
  summarise(
    quant= n()
  ) %>%
  ungroup()%>%
  slice_max(order_by = quant, n=20) %>%
  select(name)
  


pixar_all %>%
  mutate(name= case_when(
    name == "Stanton" ~ "Andrew Stanton",
    name=="Docter" ~ "Pete Docter",
    name=="Lasseter" ~ "John Lasseter",
    name=="Unkrich" ~ "Lee Unkrich",
    name=="Scanlon" ~ "Dan Scanlon",
    TRUE ~ name)) %>%
  inner_join(people_20) %>%
  group_by(name, role_type) %>%
  summarise(
    quant= n()
  ) %>%
  ungroup()%>%
  #slice_max(order_by = quant, n=35) %>%
  mutate(name=  fct_reorder(name, quant, sum) ) %>%
  #arrange(desc(name)) %>%
  ggplot(aes(y=name, x= quant,fill= role_type)) +
  geom_col(color= "#EA623B") +
  scale_fill_manual(values= c("#628528", "#FD0B0A", "#203DBD", "#FDA40C", "#FFEF03"))+
  theme_light() +
  theme(
    panel.background = element_rect(fill = "black"),
    panel.grid = element_blank(),
    axis.text.y = element_text(color = "#FD0B0A"),
    axis.text.x = element_text(color = "#203DBD"),
    axis.title = element_blank(),
 
    legend.position = "bottom"
  ) +
  labs( fill = "Role type")