Project Report in RStudio

Analyze R studio data (graphs) in a word doc. You can skip introduction part and directly working on the analyze part. Codes may or may not be modified later to make graphs more colorful and readable based on professor’s comments.
https://www(dot)kaggle(dot)com/datasets/ashishjangra27/imdb-top-250-movies
library(tidyverse)
library(dplyr)
library(ggplot2)
movies<-read_csv('movies.csv')
sum(is.na(movies$certificate))
sum(is.na(movies$duration))
#Movie "Das Boot" does not have certificate and duration.
#Count of ratings
p<- ggplot(movies, aes(imdb_rating)) + geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=-0.1) +
ggtitle('Frequency of Ratings in Top 250')
p
p1 <- ggplot(movies, aes(year))+geom_bar()+ggtitle('Top Movies per Year')
p1
#In the top 250 movies according to user reviews on IMDB, most films are produced after 1971.
#Not every year before 1971 made the top 250 films,
#but after 1971, every year had at least one film in the top 250.
#The 1996 film has eight of these 250 films, the most of any year.
#movies rating influenced by movies duration.
length<-select(movies, imdb_rating, duration)
length$imdb_rating <- as.character(length$imdb_rating)
p2 <- ggplot(length,aes(x=imdb_rating,y=duration))+geom_boxplot()+ggtitle('Distribution of Duration by Rating')
p2
#This boxplot represents the the relationship between movie's duration and imdb_rating, group by rating.
#Movies rating influenced by movies genre
genre<-select(movies, imdb_rating, genre)
genre$genre<-strsplit(genre$genre,split=',')
#I try to split genre because there are more than 1 genre for some movies, but the result become to a list.
p3 <- ggplot(genre,aes(imdb_rating,genre))+geom_point()
p3
#Movies rating influenced by movies year.
year<-select(movies, imdb_rating, year)
p4 <- ggplot(year,aes(year,imdb_rating))+geom_point()+geom_smooth()+ggtitle('Ratings by Year')
p4
#From this graph, we found that there are no significant correlation between rating and year.
#Movies rating influenced by director
director<-select(movies, imdb_rating, director_id, director_name)
director_counts <- director%>%count(director_name)
#ggplot(director,aes(director_id,imdb_rating))+geom_point()
p5 <- ggplot(director_counts[director_counts$n > 1,],aes(x=director_name,y=n))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 90))+
ggtitle('Directors with Multiple Movies in the Top 250')+ylab('Count')
p5
Introduction
Description of Data and Methodology
Visualizations and Analysis
Summary