Movie Shuffle
Essential Questions: - What is the probabilty of a person who prefers
to watch action movies over other genres
- Can what an individual watches show their preference based on the type
of genre movies they’ve watched most?
We will be seeing if it is true whether the amount of movies do determine our preference in the genres that we might be most interested. while using the probability experimental design.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdf <- read.csv("movies.csv")
head(df)## Genres Titles
## 1 genre 1: Comedy Unfrosted
## 2 genre 1: Comedy Shrek
## 3 genre 1: Comedy smurfs:the lost village
## 4 genre 1: Comedy Happy Gilmore
## 5 genre 1: Comedy cat and dog
## 6 genre 1: Comedy think like a man too#The list of movies being rearranged from the genres in order
df %>% arrange(Genres)## Genres Titles
## 1 genre 10: Documentary What Jennifer Did
## 2 genre 10: Documentary Hack Your Health
## 3 genre 10: Documentary Lover, Stalker, Killer
## 4 genre 10: Documentary Einstien and the Bomb
## 5 genre 10: Documentary The greatest night in pop
## 6 genre 10: Documentary Hell Camp:Teen Nightmare
## 7 genre 10: Documentary Buried: The 1982 Alpine Meadows Avalanche
## 8 genre 10: Documentary Sly
## 9 genre 11: Sci-Fi Glass
## 10 genre 11: Sci-Fi Split
## 11 genre 11: Sci-Fi mortal engines
## 12 genre 11: Sci-Fi spaceman
## 13 genre 11: Sci-Fi code 8 part 2
## 14 genre 11: Sci-Fi Ready Player One
## 15 genre 11: Sci-Fi Love and Monsters
## 16 genre 11: Sci-Fi Lucy
## 17 genre 11: Sci-Fi Downsizing
## 18 genre 12: Thriller The postcards killings
## 19 genre 12: Thriller Deep Fear
## 20 genre 12: Thriller Leave the World Behind
## 21 genre 12: thriller On the line
## 22 genre 12: thriller your lucky day
## 23 genre 12: thriller alone
## 24 genre 12: thriller unthinkable
## 25 genre 12: thriller fear
## 26 genre 12: thriller May December
## 27 genre 12: thriller The impossible
## 28 genre 12: thriller Locked In
## 29 genre 1: Comedy Unfrosted
## 30 genre 1: Comedy Shrek
## 31 genre 1: Comedy smurfs:the lost village
## 32 genre 1: Comedy Happy Gilmore
## 33 genre 1: Comedy cat and dog
## 34 genre 1: Comedy think like a man too
## 35 genre 1: Comedy Horrible Bosses
## 36 genre 1: Comedy Lift
## 37 genre 1: Comedy Dumb Monney
## 38 genre 1: Comedy Queenpins
## 39 genre 1: Comedy Tyler perry's the single moms club
## 40 genre 1: Comedy Dumb and Dumber To
## 41 genre 1: Comedy Neighbors
## 42 genre 1: Comedy A Walk in the Woods
## 43 genre 2: Drama Miller's Girl
## 44 genre 2: Drama Megan Leavey
## 45 genre 2: Drama The Little Things
## 46 genre 2: Drama Mending the line
## 47 genre 2: Drama the miracle club
## 48 genre 2: Drama shirley
## 49 genre 2: Drama Mea culpa
## 50 genre 2: Drama Made in Italy
## 51 genre 2: Drama The vow
## 52 genre 2: Drama The hill
## 53 genre 2: Drama Fried Green Tomatoes
## 54 genre 2: Drama Blood and Bone
## 55 genre 2: Drama Society of the snow
## 56 genre 2: Drama After Everything
## 57 genre 2: Drama Trapped
## 58 genre 2: Drama The Courier
## 59 genre 2: Drama Elvis
## 60 genre 2: Drama Gran Turismo
## 61 genre 2: Drama See You on Venus
## 62 genre 3: Romantic Comedy Anyone But You
## 63 genre 3: Romantic Comedy Blended
## 64 genre 3: Romantic Comedy knocked up
## 65 genre 3: Romantic Comedy irish wish
## 66 genre 3: Romantic Comedy 27 dresses
## 67 genre 3: Romantic Comedy Players
## 68 genre 3: Romantic Comedy Crossroads
## 69 genre 3: Romantic Comedy Queen Bees
## 70 genre 3: Romantic Comedy Mother of the bride
## 71 genre 3: Romantic Comedy upgrade
## 72 genre 3: Romantic Comedy Holiday In the Vineyards
## 73 genre 3: Romantic Comedy Best.Christmas. Ever!
## 74 genre 3: Romantic Comedy No Hard Feelings
## 75 genre 3: Romantic Comedy 13 going on 30
## 76 genre 4: Action The Great Wall
## 77 genre 4: Action The equalizer
## 78 genre 4: Action Rebel Moon part 2
## 79 genre 4: Action Anna
## 80 genre 4: Action The Bricklayer
## 81 genre 4: Action Baby Driver
## 82 genre 4: Action Skyscraper
## 83 genre 4: Action The accountant
## 84 genre 4: Action The wages of fear
## 85 genre 4: Action damsel
## 86 genre 4: Action heart of the hunter
## 87 genre 4: Action noah
## 88 genre 4: Action The legend of Hercules
## 89 genre 4: Action The abyss
## 90 genre 4: Action American Assassin
## 91 genre 4: Action Seraphim Falls
## 92 genre 4: Action Fury
## 93 genre 4: Action Pacific Rim
## 94 genre 4: Action The equalizer 3
## 95 genre 4: Action Escape plan: the extractors
## 96 genre 4: Action Aquaman
## 97 genre 4: Action Those who wish me dead
## 98 genre 4: Action john wick
## 99 genre 4: Action Madame Web
## 100 genre 4: Action Meg 2: The Trench
## 101 genre 4: Action John Wick: Chapter 3
## 102 genre 4: Action John Wick: Chapter 2
## 103 genre 4: Action Reber Moon Part 1
## 104 genre 4: Action Sniper: Ultimate Kill
## 105 genre 4: Action Sniper: Assassin's End
## 106 genre 4: Action Suidcide Squad
## 107 genre 4: Action Black adam
## 108 genre 4: Action hunter killer
## 109 genre 4: Action The sliencing
## 110 genre 4: Action The Meg
## 111 genre 4: Action Lone Survivor
## 112 genre 4: Action The Killer
## 113 genre 4: Action Real Steel
## 114 genre 4: Action Harriet
## 115 genre 4: Action The mummy
## 116 genre 5: Crime The Judge
## 117 genre 6: Fantasy Mortal Kombat
## 118 genre 7: History King Richard
## 119 genre 8: Horror Rust creek
## 120 genre 8: Horror Thanksgiving
## 121 genre 8: Horror Insidious: The Red door
## 122 genre 8: Horror Legion
## 123 genre 9: Kids and Family turbo
## 124 genre 9: Kids and Family ruby gillman, teenage kraken
## 125 genre 9: Kids and Family Minions
## 126 genre 9: Kids and Family Trolls
## 127 genre 9: Kids and Family Orion and the dark
## 128 genre 9: Kids and Family Tom and Jerry
## 129 genre 9: Kids and Family The croods
## 130 genre 9: Kids and Family The flintstones
## 131 genre 9: Kids and Family teen titans go! to the movies
## 132 genre 9: Kids and Family Thelma the unicorn
## 133 genre 9: Kids and Family Shrek Forever After
## 134 genre 9: Kids and Family Madagascar: Escale 2 Africa
## 135 genre 9: Kids and Family Leo
## 136 genre 9: Kids and Family Family Switch
## 137 genre 9: Kids and Family Chicken Run: Dawn of the nugget
## 138 genre 9: Kids and Family The Christmas Chronicles
## 139 genre 9: Kids and Family The adventures of sharkboy and lavagirl
## 140 genre 9: Kids and Family DC league of super pets
## 141 genre 9: Kids and Family Spider-Man: Across the spider-verse
## 142 genre 9: Kids and Family The super Mario Bros.
## 143 genre 9: Kids and Family Woody woodpecker goes to camp
## 144 genre 9: Kids and Family Hotel Transylvania 2
## 145 genre 9: Kids and Family Space Jam
## 146 genre 9: Kids and Family Tysons run
## 147 genre 9: Kids and Family the boss baby#Count the amount of movies that are listed which are all collected from the site of Netflix
df %>% count(Genres)## Genres n
## 1 genre 10: Documentary 8
## 2 genre 11: Sci-Fi 9
## 3 genre 12: Thriller 3
## 4 genre 12: thriller 8
## 5 genre 1: Comedy 14
## 6 genre 2: Drama 19
## 7 genre 3: Romantic Comedy 14
## 8 genre 4: Action 40
## 9 genre 5: Crime 1
## 10 genre 6: Fantasy 1
## 11 genre 7: History 1
## 12 genre 8: Horror 4
## 13 genre 9: Kids and Family 19
## 14 genre 9: Kids and Family 6genre_counts <- df %>% count(Genres)genre_counts<-genre_counts %>% mutate( probility = n/148)
genre_counts## Genres n probility
## 1 genre 10: Documentary 8 0.054054054
## 2 genre 11: Sci-Fi 9 0.060810811
## 3 genre 12: Thriller 3 0.020270270
## 4 genre 12: thriller 8 0.054054054
## 5 genre 1: Comedy 14 0.094594595
## 6 genre 2: Drama 19 0.128378378
## 7 genre 3: Romantic Comedy 14 0.094594595
## 8 genre 4: Action 40 0.270270270
## 9 genre 5: Crime 1 0.006756757
## 10 genre 6: Fantasy 1 0.006756757
## 11 genre 7: History 1 0.006756757
## 12 genre 8: Horror 4 0.027027027
## 13 genre 9: Kids and Family 19 0.128378378
## 14 genre 9: Kids and Family 6 0.040540541#Tried a small sample so we can find the amount that is potentially watched and see if the greater amount of genres that are most watched still higher of a probability
sample_count <-sample_n(df, 10) %>% count(Genres)
sample_count## Genres n
## 1 genre 10: Documentary 1
## 2 genre 4: Action 5
## 3 genre 6: Fantasy 1
## 4 genre 9: Kids and Family 2
## 5 genre 9: Kids and Family 1sample_count<-sample_count %>% mutate(probility = n/10)
sample_count## Genres n probility
## 1 genre 10: Documentary 1 0.1
## 2 genre 4: Action 5 0.5
## 3 genre 6: Fantasy 1 0.1
## 4 genre 9: Kids and Family 2 0.2
## 5 genre 9: Kids and Family 1 0.1sample_count2 <-sample_n(df, 14) %>% count(Genres)
sample_count2## Genres n
## 1 genre 11: Sci-Fi 1
## 2 genre 12: thriller 2
## 3 genre 1: Comedy 3
## 4 genre 2: Drama 2
## 5 genre 4: Action 2
## 6 genre 8: Horror 2
## 7 genre 9: Kids and Family 2#This shows an increase of the probability
#This gives us the probability when we increased it by 4 more Movies
sample_count2<-sample_count2 %>% mutate(probility = n/14)
sample_count2## Genres n probility
## 1 genre 11: Sci-Fi 1 0.07142857
## 2 genre 12: thriller 2 0.14285714
## 3 genre 1: Comedy 3 0.21428571
## 4 genre 2: Drama 2 0.14285714
## 5 genre 4: Action 2 0.14285714
## 6 genre 8: Horror 2 0.14285714
## 7 genre 9: Kids and Family 2 0.14285714#This is our 3rd sample but with a much lowered number than our last test run to see what will our probability will change to and if it will increase the chances more or less
sample_count3 <-sample_n(df, 5) %>% count(Genres)
sample_count3## Genres n
## 1 genre 2: Drama 2
## 2 genre 4: Action 2
## 3 genre 9: Kids and Family 1#Here we are given our probability for our 3rd sample
sample_count3<-sample_count3 %>% mutate(probility = n/5)
sample_count3## Genres n probility
## 1 genre 2: Drama 2 0.4
## 2 genre 4: Action 2 0.4
## 3 genre 9: Kids and Family 1 0.2#From here is another experiment for a different amount of movies for a 4th sample
sample_count4 <-sample_n(df, 24) %>% count(Genres)
sample_count4## Genres n
## 1 genre 10: Documentary 2
## 2 genre 11: Sci-Fi 4
## 3 genre 1: Comedy 1
## 4 genre 2: Drama 4
## 5 genre 3: Romantic Comedy 1
## 6 genre 4: Action 8
## 7 genre 8: Horror 1
## 8 genre 9: Kids and Family 1
## 9 genre 9: Kids and Family 2sample_count4<-sample_count4 %>% mutate(probility = n/24)
sample_count4## Genres n probility
## 1 genre 10: Documentary 2 0.08333333
## 2 genre 11: Sci-Fi 4 0.16666667
## 3 genre 1: Comedy 1 0.04166667
## 4 genre 2: Drama 4 0.16666667
## 5 genre 3: Romantic Comedy 1 0.04166667
## 6 genre 4: Action 8 0.33333333
## 7 genre 8: Horror 1 0.04166667
## 8 genre 9: Kids and Family 1 0.04166667
## 9 genre 9: Kids and Family 2 0.08333333Analysis and synthesize We realized that each probability is different as the number of movies and genres change, so do the probabilities. Each of the samples were experiments to see how much do the probabilities change or if from the most watched movie from this individuals list explains its preference as many of the movies were mostly categorized as Action for the genre which explains how it mainly equaled to other movies or it became the most that was watched more constantly. The use of this type of simulation is used quite often such as flipping a coin, or throwing a dice, or even weather forecasting which is all based on the probability of what is likely to happen based on passed occurrences that has happened to predict it. This simulation of probabilities is one of many, but one that is very often known and used.