Movie Ratings Analysis

Data Title: Movie Ratings Data Set

Source: Collected through indenpendent survey

Database Components:

This analysis involves a MySQL source database called ‘movieratings’. This database has 3 tables -

1. movies

2. users

3. ratings

Note: MySQL database software need to be installed in the local computer in order to successfully execute the R markdown code.

Below are the steps involved in preparing the MySQL DB environment necesaary for the R markdown code to run successfully -

Run the DDL script (MovieRatings_DDL Script.sql) in order to create a database called ‘movieratings’ along with the necessary tables as mentioned above - GitHub location:DDL Script
Run the Data Load script (Data Load DML Script.sql) to load necessary data into the database - GitHub Location: Load Script

Note: All the necessary CSV data files are also available in the below mention location - GitHub Location: Source Data Files

Create a ODBC System DSN called ‘MySQL_MovieRatings’ in the local machine with ‘movieratings’ as the default database with appropriate root credentials or with an appropriate user id having READ access to ‘movieratings’ database.

R Libraries:

Load necessary libraries -

library(RODBC)
library(magrittr)
library(kableExtra)
library(data.table)
library(plyr)
library(ggplot2)
library(reshape2)

## 
## Attaching package: 'reshape2'

## The following objects are masked from 'package:data.table':
## 
##     dcast, melt

Establish DB Connection:

I have used an ODBC data source called ‘MySQL_MovieRatings’ in order to connect to the database.

con <- odbcConnect("MySQL_MovieRatings")

Data Sets:

A. Movies:

I have taken a list of six Hollywood movies released in 2018 for this analysis. Movies belonging to multiple genres have been captured as pipe (‘|’) separated values.

movies_df <- as.data.frame(sqlFetch(con,"movies"),stringsAsFactors = FALSE)

movies_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Movie_ID	Movie_Title	Movie_Genre	Movie_Year	Director_Name
1	Black Panther	Action\|Adventure\|Sci-Fi	2018	Ryan Coogler
2	Incredibles 2	Animation\|Action	2018	Brand Bird
3	Avengers: Infinity War	Action\|Adventure\|Fantasy	2018	Anthony Russo
4	Annihilation	Adventure\|Drama\|Horror	2018	Alex Garland
5	Ant-Man and the Wasp	Action\|Adventure\|Sci-Fi	2018	Peyton Reed
6	Mission Impossible: Fallout	Action\|Adventure\|Thriller	2018	Christopher McQuarrie

B. Users:

These users contributed in the movie rating survey process.

users_df <- as.data.frame(sqlFetch(con,"users"),stringsAsFactors = FALSE)

users_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

User_ID	First_Name	Last_Name	Gender	Age
1	Soumya	Ghosh	M	36
2	Oliver	Asmus	M	36
3	Ricky	Sharma	M	34
4	Idhant	Ghosh	M	6
5	Bidisha	Kar	F	35
6	Krishanu	Ghosh	M	34
7	Jim	Duffy	M	42
8	Arron	Higgins	M	41
9	Alex	Smith	M	43
10	Reese	McGarvey	F	7
11	Jacob	Oram	M	45
12	Pritam	Mukherjee	M	33

C. Ratings:

Each user rated all the 6 movies which has been loaded through SQL scripts into the database. A rank has been created in SQL based on the ratings provided by the users using SQL window function.

Query = "SELECT 
             User_ID
            ,Movie_ID
            ,Rating
            ,RANK() OVER (PARTITION BY User_ID ORDER BY Rating desc) AS Movie_Rank
         FROM
             ratings 
         ORDER BY 
             User_ID
            ,Movie_Rank;"

ratings_df <- sqlQuery(con,Query)

ratings_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

User_ID	Movie_ID	Rating	Movie_Rank
1	3	4.2	1
1	6	4.1	2
1	1	4.0	3
1	2	3.5	4
1	5	3.0	5
1	4	2.5	6
2	3	4.3	1
2	1	3.2	2
2	4	3.0	3
2	5	2.8	4
2	2	2.4	5
2	6	2.2	6
3	6	4.5	1
3	4	4.2	2
3	3	3.9	3
3	5	3.7	4
3	1	3.5	5
3	2	2.8	6
4	1	4.6	1
4	5	4.1	2
4	3	3.7	3
4	6	3.6	4
4	2	3.2	5
4	4	2.9	6
5	5	4.3	1
5	2	4.1	2
5	1	3.7	3
5	3	3.2	4
5	6	3.0	5
5	4	2.4	6
6	1	4.6	1
6	3	4.2	2
6	4	3.7	3
6	2	3.6	4
6	6	3.0	5
6	5	2.9	6
10	1	4.6	1
10	4	3.7	2
10	2	3.6	3
10	6	3.0	4
10	5	2.0	5
10	3	1.3	6

Prepare Analytics data:

Here I have joined all the 3 data sets and loaded into a data frame to prepare for further analysis along with dynamically created movie rank informtaion.

Query = "SELECT 
          m.Movie_ID
         ,m.Movie_Title
         ,m.Director_Name
         ,m.Movie_Year
         ,u.User_ID
         ,u.First_Name
         ,u.Last_Name
         ,u.Gender
         ,u.Age
         ,r.Rating
         ,RANK() OVER (PARTITION BY r.User_ID ORDER BY r.Rating desc) AS Movie_Rank
        FROM
          movieratings.ratings as r
        INNER JOIN
          movieratings.movies as m
        ON r.Movie_ID = m.Movie_ID
        INNER JOIN
        movieratings.users as u
        ON r.User_ID = u.User_ID;"

detail_ratings_df <- sqlQuery(con,Query)

detail_ratings_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Movie_ID	Movie_Title	Director_Name	Movie_Year	User_ID	First_Name	Last_Name	Gender	Age	Rating	Movie_Rank
3	Avengers: Infinity War	Anthony Russo	2018	1	Soumya	Ghosh	M	36	4.2	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	1	Soumya	Ghosh	M	36	4.1	2
1	Black Panther	Ryan Coogler	2018	1	Soumya	Ghosh	M	36	4.0	3
2	Incredibles 2	Brand Bird	2018	1	Soumya	Ghosh	M	36	3.5	4
5	Ant-Man and the Wasp	Peyton Reed	2018	1	Soumya	Ghosh	M	36	3.0	5
4	Annihilation	Alex Garland	2018	1	Soumya	Ghosh	M	36	2.5	6
3	Avengers: Infinity War	Anthony Russo	2018	2	Oliver	Asmus	M	36	4.3	1
1	Black Panther	Ryan Coogler	2018	2	Oliver	Asmus	M	36	3.2	2
4	Annihilation	Alex Garland	2018	2	Oliver	Asmus	M	36	3.0	3
5	Ant-Man and the Wasp	Peyton Reed	2018	2	Oliver	Asmus	M	36	2.8	4
2	Incredibles 2	Brand Bird	2018	2	Oliver	Asmus	M	36	2.4	5
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	2	Oliver	Asmus	M	36	2.2	6
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	3	Ricky	Sharma	M	34	4.5	1
4	Annihilation	Alex Garland	2018	3	Ricky	Sharma	M	34	4.2	2
3	Avengers: Infinity War	Anthony Russo	2018	3	Ricky	Sharma	M	34	3.9	3
5	Ant-Man and the Wasp	Peyton Reed	2018	3	Ricky	Sharma	M	34	3.7	4
1	Black Panther	Ryan Coogler	2018	3	Ricky	Sharma	M	34	3.5	5
2	Incredibles 2	Brand Bird	2018	3	Ricky	Sharma	M	34	2.8	6
1	Black Panther	Ryan Coogler	2018	4	Idhant	Ghosh	M	6	4.6	1
5	Ant-Man and the Wasp	Peyton Reed	2018	4	Idhant	Ghosh	M	6	4.1	2
3	Avengers: Infinity War	Anthony Russo	2018	4	Idhant	Ghosh	M	6	3.7	3
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	4	Idhant	Ghosh	M	6	3.6	4
2	Incredibles 2	Brand Bird	2018	4	Idhant	Ghosh	M	6	3.2	5
4	Annihilation	Alex Garland	2018	4	Idhant	Ghosh	M	6	2.9	6
5	Ant-Man and the Wasp	Peyton Reed	2018	5	Bidisha	Kar	F	35	4.3	1
2	Incredibles 2	Brand Bird	2018	5	Bidisha	Kar	F	35	4.1	2
1	Black Panther	Ryan Coogler	2018	5	Bidisha	Kar	F	35	3.7	3
3	Avengers: Infinity War	Anthony Russo	2018	5	Bidisha	Kar	F	35	3.2	4
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	5	Bidisha	Kar	F	35	3.0	5
4	Annihilation	Alex Garland	2018	5	Bidisha	Kar	F	35	2.4	6
1	Black Panther	Ryan Coogler	2018	6	Krishanu	Ghosh	M	34	4.6	1
3	Avengers: Infinity War	Anthony Russo	2018	6	Krishanu	Ghosh	M	34	4.2	2
4	Annihilation	Alex Garland	2018	6	Krishanu	Ghosh	M	34	3.7	3
2	Incredibles 2	Brand Bird	2018	6	Krishanu	Ghosh	M	34	3.6	4
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	6	Krishanu	Ghosh	M	34	3.0	5
5	Ant-Man and the Wasp	Peyton Reed	2018	6	Krishanu	Ghosh	M	34	2.9	6
1	Black Panther	Ryan Coogler	2018	10	Reese	McGarvey	F	7	4.6	1
4	Annihilation	Alex Garland	2018	10	Reese	McGarvey	F	7	3.7	2
2	Incredibles 2	Brand Bird	2018	10	Reese	McGarvey	F	7	3.6	3
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	10	Reese	McGarvey	F	7	3.0	4
5	Ant-Man and the Wasp	Peyton Reed	2018	10	Reese	McGarvey	F	7	2.0	5
3	Avengers: Infinity War	Anthony Russo	2018	10	Reese	McGarvey	F	7	1.3	6

Data Transformtaion:

I have parsed the pipe(‘|’) delimited genre column in the movies data frame to identify what all genres a particular movie belongs to as separate columns.

genres_df <- data.frame(sapply(tstrsplit(movies_df$Movie_Genre,'[|]',type.convert = TRUE),c),stringsAsFactors = FALSE)

genres_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

X1	X2	X3
Action	Adventure	Sci-Fi
Animation	Action	NA
Action	Adventure	Fantasy
Adventure	Drama	Horror
Action	Adventure	Sci-Fi
Action	Adventure	Thriller

I have Prepared a matrix with all the movie genres and assigned an indicator ‘1’ if the genre applicable for a given movie.

genre_list <- c("Action","Adventure","Animation","Drama","Fantasy","Horror","Sci-Fi","Thriller")
genre_matrix <- matrix(0,nrow(genres_df)+1,length(genre_list))
genre_matrix[1,] <- genre_list #set first row to genre list
colnames(genre_matrix) <- genre_list

#iterate through matrix
for (i in 1:nrow(genres_df)) {
 for (c in 1:ncol(genres_df)) {
 genmat_col = which(genre_matrix[1,] == genres_df[i,c])
 genre_matrix[i+1,genmat_col] <- 1
 }
}

genre_matrix %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Action	Adventure	Animation	Drama	Fantasy	Horror	Sci-Fi	Thriller
Action	Adventure	Animation	Drama	Fantasy	Horror	Sci-Fi	Thriller
1	1	0	0	0	0	1	0
1	0	1	0	0	0	0	0
1	1	0	0	1	0	0	0
0	1	0	1	0	1	0	0
1	1	0	0	0	0	1	0
1	1	0	0	0	0	0	1

Converted the matrix into a data frame with integer indcator values.

genre_matrix2 <- as.data.frame(genre_matrix[-1,], stringsAsFactors=FALSE) #remove first row, which was the genre list
for (c in 1:ncol(genre_matrix2)) {
  genre_matrix2[,c] <- as.integer(genre_matrix2[,c])
} #convert from characters to integers

genre_matrix2 %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Action	Adventure	Animation	Drama	Fantasy	Horror	Sci-Fi	Thriller
1	1	0	0	0	0	1	0
1	0	1	0	0	0	0	0
1	1	0	0	1	0	0	0
0	1	0	1	0	1	0	0
1	1	0	0	0	0	1	0
1	1	0	0	0	0	0	1

I have combined the genre indicators to the movies_df data frame.

movies_df <- cbind(movies_df,genre_matrix2)
movies_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Movie_ID	Movie_Title	Movie_Genre	Movie_Year	Director_Name	Action	Adventure	Animation	Drama	Fantasy	Horror	Sci-Fi	Thriller
1	Black Panther	Action\|Adventure\|Sci-Fi	2018	Ryan Coogler	1	1	0	0	0	0	1	0
2	Incredibles 2	Animation\|Action	2018	Brand Bird	1	0	1	0	0	0	0	0
3	Avengers: Infinity War	Action\|Adventure\|Fantasy	2018	Anthony Russo	1	1	0	0	1	0	0	0
4	Annihilation	Adventure\|Drama\|Horror	2018	Alex Garland	0	1	0	1	0	1	0	0
5	Ant-Man and the Wasp	Action\|Adventure\|Sci-Fi	2018	Peyton Reed	1	1	0	0	0	0	1	0
6	Mission Impossible: Fallout	Action\|Adventure\|Thriller	2018	Christopher McQuarrie	1	1	0	0	0	0	0	1

Here I have joined the Data Frames ‘detail_ratings_df’ and ‘movies_df’ based on ‘Movie_ID’ to add genres related indicators to analytical data sets.

detail_ratings_df <- merge(x = detail_ratings_df, y = subset(movies_df,select = c("Movie_ID",genre_list)), by = "Movie_ID")
detail_ratings_df %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Movie_ID	Movie_Title	Director_Name	Movie_Year	User_ID	First_Name	Last_Name	Gender	Age	Rating	Movie_Rank	Action	Adventure	Animation	Drama	Fantasy	Horror	Sci-Fi	Thriller
1	Black Panther	Ryan Coogler	2018	6	Krishanu	Ghosh	M	34	4.6	1	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	10	Reese	McGarvey	F	7	4.6	1	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	2	Oliver	Asmus	M	36	3.2	2	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	4	Idhant	Ghosh	M	6	4.6	1	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	3	Ricky	Sharma	M	34	3.5	5	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	1	Soumya	Ghosh	M	36	4.0	3	1	1	0	0	0	0	1	0
1	Black Panther	Ryan Coogler	2018	5	Bidisha	Kar	F	35	3.7	3	1	1	0	0	0	0	1	0
2	Incredibles 2	Brand Bird	2018	4	Idhant	Ghosh	M	6	3.2	5	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	3	Ricky	Sharma	M	34	2.8	6	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	2	Oliver	Asmus	M	36	2.4	5	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	1	Soumya	Ghosh	M	36	3.5	4	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	5	Bidisha	Kar	F	35	4.1	2	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	10	Reese	McGarvey	F	7	3.6	3	1	0	1	0	0	0	0	0
2	Incredibles 2	Brand Bird	2018	6	Krishanu	Ghosh	M	34	3.6	4	1	0	1	0	0	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	1	Soumya	Ghosh	M	36	4.2	1	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	3	Ricky	Sharma	M	34	3.9	3	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	10	Reese	McGarvey	F	7	1.3	6	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	4	Idhant	Ghosh	M	6	3.7	3	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	6	Krishanu	Ghosh	M	34	4.2	2	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	2	Oliver	Asmus	M	36	4.3	1	1	1	0	0	1	0	0	0
3	Avengers: Infinity War	Anthony Russo	2018	5	Bidisha	Kar	F	35	3.2	4	1	1	0	0	1	0	0	0
4	Annihilation	Alex Garland	2018	1	Soumya	Ghosh	M	36	2.5	6	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	6	Krishanu	Ghosh	M	34	3.7	3	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	5	Bidisha	Kar	F	35	2.4	6	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	2	Oliver	Asmus	M	36	3.0	3	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	3	Ricky	Sharma	M	34	4.2	2	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	4	Idhant	Ghosh	M	6	2.9	6	0	1	0	1	0	1	0	0
4	Annihilation	Alex Garland	2018	10	Reese	McGarvey	F	7	3.7	2	0	1	0	1	0	1	0	0
5	Ant-Man and the Wasp	Peyton Reed	2018	4	Idhant	Ghosh	M	6	4.1	2	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	5	Bidisha	Kar	F	35	4.3	1	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	2	Oliver	Asmus	M	36	2.8	4	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	6	Krishanu	Ghosh	M	34	2.9	6	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	10	Reese	McGarvey	F	7	2.0	5	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	3	Ricky	Sharma	M	34	3.7	4	1	1	0	0	0	0	1	0
5	Ant-Man and the Wasp	Peyton Reed	2018	1	Soumya	Ghosh	M	36	3.0	5	1	1	0	0	0	0	1	0
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	4	Idhant	Ghosh	M	6	3.6	4	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	2	Oliver	Asmus	M	36	2.2	6	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	3	Ricky	Sharma	M	34	4.5	1	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	10	Reese	McGarvey	F	7	3.0	4	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	5	Bidisha	Kar	F	35	3.0	5	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	6	Krishanu	Ghosh	M	34	3.0	5	1	1	0	0	0	0	0	1
6	Mission Impossible: Fallout	Christopher McQuarrie	2018	1	Soumya	Ghosh	M	36	4.1	2	1	1	0	0	0	0	0	1

Data Analysis:

Structure of the analytical data set.

str(detail_ratings_df)

## 'data.frame':    42 obs. of  19 variables:
##  $ Movie_ID     : int  1 1 1 1 1 1 1 2 2 2 ...
##  $ Movie_Title  : Factor w/ 6 levels "Annihilation",..: 4 4 4 4 4 4 4 5 5 5 ...
##  $ Director_Name: Factor w/ 6 levels "Alex Garland",..: 6 6 6 6 6 6 6 3 3 3 ...
##  $ Movie_Year   : int  2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
##  $ User_ID      : int  6 10 2 4 3 1 5 4 3 2 ...
##  $ First_Name   : Factor w/ 7 levels "Bidisha","Idhant",..: 3 5 4 2 6 7 1 2 6 4 ...
##  $ Last_Name    : Factor w/ 5 levels "Asmus","Ghosh",..: 2 4 1 2 5 2 3 2 5 1 ...
##  $ Gender       : Factor w/ 2 levels "F","M": 2 1 2 2 2 2 1 2 2 2 ...
##  $ Age          : int  34 7 36 6 34 36 35 6 34 36 ...
##  $ Rating       : num  4.6 4.6 3.2 4.6 3.5 4 3.7 3.2 2.8 2.4 ...
##  $ Movie_Rank   : int  1 1 2 1 5 3 3 5 6 5 ...
##  $ Action       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Adventure    : int  1 1 1 1 1 1 1 0 0 0 ...
##  $ Animation    : int  0 0 0 0 0 0 0 1 1 1 ...
##  $ Drama        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Fantasy      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Horror       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Sci-Fi       : int  1 1 1 1 1 1 1 0 0 0 ...
##  $ Thriller     : int  0 0 0 0 0 0 0 0 0 0 ...

Movie Ratings Analysis:

Here I have created a side by side Boxplot for Ratings by Movie titles. This plot provides a comparative between the 6 selected titles.

p<-ggplot(detail_ratings_df, aes(x = Movie_Title, y = Rating, color = Movie_Title)) +
  geom_boxplot()
p + theme(legend.position="bottom") + labs(title = "Movie Rating Boxplot By Title", x = "Movie Title") + theme(
plot.title = element_text(color="black", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)

Movie Genre Analysis:

Here, I have calculate Mean Percentage of Movie Genre by Gender:

summary_df <- subset(detail_ratings_df,select = c(8,12:19))
summary_df <- aggregate(summary_df,by = list(summary_df$Gender),FUN=mean, na.action=na.omit)
summary_df <- summary_df[-2] 
summary_df <- cbind(summary_df,gender=c(1:2))
summary_df <-summary_df[,2:length(summary_df)]
summary_df.long <- melt(summary_df,id.vars="gender")

ggplot(summary_df.long,aes(x=variable,y=value,fill=factor(gender)))+
  geom_bar(stat="identity",position="dodge")+
  scale_fill_discrete(name="Gender",
                      breaks=c(1, 2),
                      labels=c("Female", "Male")) + labs(title = "Movie Genre Popularity By Gender", x = "Genre", y = "Mean Percentage")

User_ID	Movie_ID	Rating	Movie_Rank
1	3	4.2	1
1	6	4.1	2
1	1	4.0	3
1	2	3.5	4
1	5	3.0	5
1	4	2.5	6
2	3	4.3	1
2	1	3.2	2
2	4	3.0	3
2	5	2.8	4
2	2	2.4	5
2	6	2.2	6
3	6	4.5	1
3	4	4.2	2
3	3	3.9	3
3	5	3.7	4
3	1	3.5	5
3	2	2.8	6
4	1	4.6	1
4	5	4.1	2
4	3	3.7	3
4	6	3.6	4
4	2	3.2	5
4	4	2.9	6
5	5	4.3	1
5	2	4.1	2
5	1	3.7	3
5	3	3.2	4
5	6	3.0	5
5	4	2.4	6
6	1	4.6	1
6	3	4.2	2
6	4	3.7	3
6	2	3.6	4
6	6	3.0	5
6	5	2.9	6
10	1	4.6	1
10	4	3.7	2
10	2	3.6	3
10	6	3.0	4
10	5	2.0	5
10	3	1.3	6

User_ID	Movie_ID	Rating	Movie_Rank
1	3	4.2	1
1	6	4.1	2
1	1	4.0	3
1	2	3.5	4
1	5	3.0	5
1	4	2.5	6
2	3	4.3	1
2	1	3.2	2
2	4	3.0	3
2	5	2.8	4
2	2	2.4	5
2	6	2.2	6
3	6	4.5	1
3	4	4.2	2
3	3	3.9	3
3	5	3.7	4
3	1	3.5	5
3	2	2.8	6
4	1	4.6	1
4	5	4.1	2
4	3	3.7	3
4	6	3.6	4
4	2	3.2	5
4	4	2.9	6
5	5	4.3	1
5	2	4.1	2
5	1	3.7	3
5	3	3.2	4
5	6	3.0	5
5	4	2.4	6
6	1	4.6	1
6	3	4.2	2
6	4	3.7	3
6	2	3.6	4
6	6	3.0	5
6	5	2.9	6
10	1	4.6	1
10	4	3.7	2
10	2	3.6	3
10	6	3.0	4
10	5	2.0	5
10	3	1.3	6