MovieDataAnalysis, prepared by Nazli Begum Cirpanli
The goal of MovieDataAnalysis package is to give the users basic insights about analysis and visualization of movie data.
First install the R package "devtools" if not installed
devtools::install_github('unimi-dse/891a3f81')Load the package:
require(MovieDataAnalysis)MovieDataAnalysis consists of functions which explore and visualize some critical aspects of the data. This package might be helpful for those who are interested in movies and want to discover relationships between key variables such as ratings, budget etc. This package comes with its dataset and the functions are particular to it.
aboveavr () Lists the movies which have above average budgets for a selected year
corre () Computes the correlation between Critics Ratings and Budget and tells how strong the computed correlation is
fltmovie () Lists the movies which have higher critics ratings than %90 of the data
ratingsmode () Computes the most recurring ratings for Critics and Audience Ratings over the five years
audratings() Displays Audience Ratings histogram charts by genre and year
bdg_movie() Displays budget distibution over the years 2007-2011
boxplot_ratings() Displays a boxplot chart of Critics Ratings vs. Audience Ratings by genre
critvsaud() Displays a line plot of Critics Ratings vs. Audience Ratings by genre and budget
density_bdg() Displays a density chart of budget by genre
Below are the basic examples which show you how to use the functions:
library(MovieDataAnalysis)
aboveavr (2009)
corre ()
fltmovie ("Action",100,2009)
ratingsmode ()
audratings()
bdg_movie()
boxplot_ratings()
critvsaud()
density_bdg()
Below are the arguments for functions aboveavr() and fltmovie():
aboveavr (n) : n, Year / must be in between 2007 - 2011
fltmovie ("gnr",n,yr):
gnr, Genre / must be "in quotations" / must be one of these: "Action","Adventure","Comedy","Drama","Horror","Romance"
n, Budget Amount / must be in between 0-300
yr, Year / must be in between 2007-2011
These functions are only useful for the particular dataset which is also included in the MovieDataAnalysis package.
This package provides a movie dataset containing 562 rows of 6 variables. The dataset was cleaned, organized and named accordingly before usage.
The dataset was downloaded from https://www.superdatascience.com/
Below are the codes to prepare the dataset. User does not have to run these codes, package has the final dataset ("movies") in itself.
Getdata <- function(){
#'import data.....
movies <- read.csv("C:\\Users\\TOSHIBA\\Desktop\\R study\\Movies\\Movie-Ratings.csv")
#'organize data.....
colnames(movies)<-c("Films","Genre","CriticsRatings","AudienceRatings","BudgetinMillions","Year")
movies$Year<-factor(movies$Year)
return(movies)
}
data("movies")
?movies