ggplot2.md

GGPlot2

1. Read the 'MovieRating.csv' into the dataframe called 'movies'

movies <- read.csv("MovieRating.csv")

#See the structure of 'movies' using str()
str(movies)
#'Genre' is categorical variable/column but 'year' is treated as non-categorical variable 

#We want to treat it as categorical variable , so this is done by using factor() function
movies$Year <- factor(movies$Year)
#Now 'Year' is a factor with 5 levels(2007,2008,2009,2010,2011)

2. Scatter Plot(with smoothing) to compare AudienceRating v/s CriticRating

q <- ggplot(data = movies , aes(x=CriticRatings,y=AudienceRatings,
                                color = Genre,size = BudgetMillion))
#Here we are mapping the color to genre , so it is put inside the aesthetics function called aes()

h <- q + geom_point(size=1.5,alpha=0.5) + geom_smooth(fill=NA,
                                     size=1.25)

#Adding themes
h + ggtitle("Audience  Rating v/s Critic Rating") +
  theme(axis.title.x = element_text(size=20,color = "Red"),
        axis.title.y = element_text(size=20,color = "DarkBlue"),
        axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 10),
        plot.title = element_text(size = 30,color = "DarkGreen",family="Courier"),
        plot.background = element_rect(fill = "LightCyan", color = "Black"),
        legend.position = c(0,1),
        legend.justification = c(0,1)
  ) +
  coord_cartesian(ylim=c(10,100))

Result

Conclusion

The purple line shows Romance Genre and it is clear that even when the Romantic movie is rated very low by the Critics (25) , even though it is liked by the Audience. For Action Genre , Audience and Critic shows the similar behaviour.You must have a good plot to impress the audience 😉 (Blue and Pink line)And for high Critic-Rating for Horror and Thriller , Audience choose Thriller over Horror. Horror Movie's business is risky 😥

3. AudienceRating v/s Film's Budget

j <- ggplot(data=movies , aes(x=BudgetMillion,y=AudienceRatings,
                              color=Genre,size=BudgetMillion)) + 
  ggtitle("Audience-Rating v/s Budget of the Film") +
  theme(
    axis.text.x = element_text(size=10,color="Black"),
    axis.text.y = element_text(size=10,color="Black"),
    axis.title.x = element_text(size=15,color="DarkBlue"),
    axis.title.y = element_text(size=15,color="DarkBlue"),
    plot.title = element_text(size=20,color="DarkGreen"),
    plot.background = element_rect(fill="LightCyan",color="Maroon")
  ) +
  xlab("Budget Millions ($)")

j + geom_point()

Result

Conclusion

Audience's Love for the Movie is independent of the Movie's Budget. You need a good script not good set 😜 Bingo!

4. Box-Plot - AudienceRating v/s Genre

l <- ggplot(data = movies , aes(x=Genre,y=AudienceRatings,colour=Genre))

m <- l + geom_jitter() + geom_boxplot(size=1.2,alpha=0.70)

m + ggtitle("AudienceRating v/s Genre") + theme(
          axis.title.x = element_text(size=20,color = "Blue"),
          axis.title.y = element_text(size=20,color = "DarkGreen"),
          axis.text.x = element_text(size = 12 , color ="Black"),
          axis.text.y = element_text(size = 10 , color = "Black"),
          plot.background = element_rect(fill="LightCyan",color="Black"),
          plot.title = element_text(size = 25 , color="DarkBlue")
) + ylab("Audience Rating") 

Result

Conclusion

Median of Thriller, Drama and Romance is high.The Thriller's box is narrow , this shows the deviation in Thriller's liking is least. AudienceRating is likely to fall between 60 to 75. And Audience loves Drama , as the Maximum value for drama is close to 80 (highest of all)

Let's see what box-plot is ...

5. Histogram - Movie Budget Distribution (Statistical Transformation)

l <- ggplot(data=movies,aes(x=BudgetMillion,fill=Genre))

l + geom_histogram(binwidth = 10,color="Black") +
   ggtitle("Movies Budget distribution") +
  theme(
    axis.title.x = element_text(size=15,color="DarkBlue"),
    axis.title.y = element_text(size=15,color="DarkBlue"),
    axis.text.x = element_text(size=10),
    axis.text.y = element_text(size=10),
    plot.title = element_text(size=20,color="DarkGreen"),
    plot.background = element_rect(fill="LightCyan",color="Maroon"),
    legend.title = element_text(size=20),
    legend.text = element_text(size=15),
    legend.position = c(1,1),
    legend.justification = c(1,1)
)

Result

Conclusion

It's not easy to visualize for a specific genre here! Let's try to have this graph of Movie Budget Distribution for each Genre. This can be done using facets.

6. Movie Budget Distribution for each Genre(Using Facets)

l <- ggplot(data=movies,aes(x=BudgetMillion,fill=Genre))

l + geom_histogram(binwidth = 10,color="Black") +
   ggtitle("Movies Budget distribution") +
  theme(
    axis.title.x = element_text(size=15,color="DarkBlue"),
    axis.title.y = element_text(size=15,color="DarkBlue"),
    axis.text.x = element_text(size=10),
    axis.text.y = element_text(size=10),
    plot.title = element_text(size=20,color="DarkGreen"),
    plot.background = element_rect(fill="LightCyan",color="Maroon")
) +
facet_grid(Genre~.)
  

Result

Conclusion

Action Genre plot has the longest tail implying that the high budget movies are made in Action. And Comedy Genre has high number of movies below buget 50 million dollars.

7. Audience-Rating and Critic-Rating distribution Comparsion

• Audience-Rating Distribution

  i <- ggplot(data=movies , aes(x=AudienceRatings)) + 
  ggtitle("Audience-Rating Distribution") +
  theme(
    axis.text.x = element_text(size=10,color="Black"),
    axis.text.y = element_text(size=10,color="Black"),
    axis.title.x = element_text(size=15,color="DarkBlue"),
    axis.title.y = element_text(size=15,color="DarkBlue"),
    plot.title = element_text(size=20,color="Red"),
    plot.background = element_rect(fill="LightCyan",color="Maroon")
    ) +
    xlab("Audience Rating")
  
    i + geom_histogram(binwidth=10,fill="White",color="Blue")

  Result

  

• Critic-Rating Distribution

  i <- ggplot(data=movies , aes(x=CriticRatings)) + 
  ggtitle("Critic-Rating Distribution") +
  theme(
   axis.text.x = element_text(size=10,color="Black"),
   axis.text.y = element_text(size=10,color="Black"),
   axis.title.x = element_text(size=15,color="DarkBlue"),
   axis.title.y = element_text(size=15,color="DarkBlue"),
   plot.title = element_text(size=20,color="Red"),
   plot.background = element_rect(fill="LightCyan",color="Maroon")
  ) +
  xlab("Critic Rating")
  
  i + geom_histogram(binwidth=10,fill="White",color="Blue")
  

  Result

  

  Conclusion

  Critic-Rating is uniformly distributed but Audience-Rating is not. Audience-Rating is biased.

8. Audience v/s CriticRating (for Genre and year)

  k <- ggplot(data=movies,aes(x=CriticRatings,y=AudienceRatings,color=Genre))

  k + geom_point(aes(size=BudgetMillion),alpha=0.75) + ggtitle("Audience v/s Critic") +
  geom_smooth(fill=NA)+
  facet_grid(Genre~Year) +
  coord_cartesian(ylim=c(0,100)) +
  theme(
   axis.title.x = element_text(size=15,color="DarkBlue"),
   axis.title.y = element_text(size=15,color="DarkBlue"),
   plot.title = element_text(size=20,color="DarkGreen"),
   plot.background = element_rect(fill="LightCyan",color="Maroon")
  )
  

Result