Code for just in case

[dasom1348]

---

title: "192_MP4" author: "Dasom An & Janell Lin" output: html_document: code_folding: hide

Gender Bias Against Directors in Film Field?

We sought to determine if there is an association between gender of directors and the quality of movie.

library(tidyverse)
library(ggplot2)
library(dplyr)
library(mdsr)
library(RMySQL)
db <- dbConnect_scidb(dbname = "imdb")

db %>%
  dbGetQuery("SELECT * FROM kind_type;")

# Extracting 100 best movies info from database

sql <- "
SELECT t.id, t.title, t.production_year, mii.info AS votes, mii2.info AS rating
FROM title t
JOIN movie_info_idx mii ON mii.movie_id = t.id
JOIN movie_info_idx mii2 ON mii2.movie_id = t.id
WHERE t.production_year >= 2008
AND t.kind_id = 1
AND mii.info_type_id = 100
AND mii2.info_type_id = 101
AND mii.info > 100000
ORDER BY mii2.info desc;
"
best_movies <- db %>%
  dbGetQuery(sql)
head(best_movies)

best_movies[!duplicated(best_movies[,c("title")]), ]

best_movies100 <- best_movies %>%
  head(100)

# Extracting directors info from database

sql_1 <- "SELECT n.name, ci.role_id, person_id,  movie_id, n.gender 
FROM cast_info ci
JOIN name n ON n.id = ci.person_id
WHERE role_id = '8';
"
people_info <- db %>%
  dbGetQuery(sql_1)
head(people_info)

all_info<- people_info %>%
  select(name, person_id, gender, movie_id)%>%
  group_by(name, person_id, gender, movie_id)%>%
  summarise(num=n()) 

# Join movie_id with best_movies100 for their movie_id

sql_2 <- "
SELECT movie_id, title, production_year, id 
FROM aka_title;
"
movie_id <- db %>%
  dbGetQuery(sql_2)

movie_id1<- movie_id %>%
  filter(production_year>=2008)
 group_by (movie_id, title) %>%
  summarise (num = n())

head(movie_id)

# best 100 movies with their movie_id
movie_id2 <- movie_id1 %>%
  inner_join(best_movies, by = c("title"= "title", "production_year" = "production_year")) 

movie_id100<-movie_id2 %>%
  head (100)

movie_id100[!duplicated(movie_id100[,c("title")]), ]

# Extracting 100 best movies gross info from database

sql_gross <- "
SELECT t.id, t.title, mi.info AS gross_info
FROM title t
JOIN movie_info mi ON mi.movie_id = t.id;
"

gross <- db %>%
dbGetQuery(sql_gross)
head(gross)

dbGetQuery(db, "SELECT * FROM info_type WHERE info = 'gross';")

dbGetQuery(db, "
SELECT * FROM info_type 
WHERE info LIKE '%gross%';
")

movie_gross<- dbGetQuery(db, "
SELECT t.id, t.title, mi.info AS gross_info, movie_id, production_year
FROM title t
JOIN movie_info mi ON mi.movie_id = t.id
WHERE mi.info_type_id = 107
")

movie_gross_1 <- movie_gross %>%
  group_by (movie_id)%>%
  filter(grepl("USA", gross_info))%>%
  summarise(max(gross_info))

movie_gross100<- movie_gross_1%>%
  inner_join(movie_id2, by= c("movie_id"="movie_id"))%>%
  head(100)

# Extracting gross info for best movies

sql_gross <- "
SELECT t.id, t.title, mi.info AS gross_info
FROM title t
JOIN movie_info mi ON mi.movie_id = t.id;
"

gross <- db %>%
dbGetQuery(sql_gross)
head(gross)

dbGetQuery(db, "SELECT * FROM info_type WHERE info = 'gross';")

dbGetQuery(db, "
SELECT * FROM info_type 
WHERE info LIKE '%gross%';
")

movie_gross<- dbGetQuery(db, "
SELECT t.id, t.title, mi.info AS gross_info, movie_id, production_year
FROM title t
JOIN movie_info mi ON mi.movie_id = t.id
WHERE mi.info_type_id = 107
")

movie_gross_1 <- movie_gross %>%
  group_by (movie_id)%>%
  filter(grepl("USA", gross_info))%>%
  summarise(max(gross_info))

movie_gross100<- movie_gross_1%>%
  inner_join(movie_id2, by= c("movie_id"="movie_id"))%>%
  head(100)

names(movie_gross100)[names(movie_gross100)=="max(gross_info)"]<-"gross_info" 

# Joining directors info with best movies

movie_people_info <- movie_gross100 %>%
  inner_join(all_info, by= c("movie_id"="movie_id"))

MP_info <- movie_people_info %>%
  select(title,movie_id, gross_info, production_year,gender,rating,votes, name)

# Visualizing findings
library(scales)

ggplot(data=MP_info, aes(x=gross_info, y=rating)) +
  geom_point(aes(color=gender)) +
  labs(title = "Quality of Best 100 Movies by Male and Female Directors", x = "Revenue($)", y = "Rating")

# We need to set the revenue units first to set x-axis to annotate 
  scale_y_discrete(labels = c("4.5", "5.0", "5.5", "6.0", "6.5", "7.0", "7.5", "8.0", "8.5"), limits = c(4.0, 9.0), breaks = seq(4.5,8.5,0.5)) +
    scale_x_discrete(labels = c("25M", "50M", "75M", "100M"), limits = c(0,105000000), breaks = seq(25000000,100000000, 25000000)) 

# Examples of annotating and so on from babynames hw
annotate("segment", x=1940, xend=1948, y=0.5, yend=0.5, color="black")+
annotate("segment", x=1940, xend=1940, y=0.4, yend=0.5, color="black") +

  labels =c("1940", "'60", "'80", "2000" ), limits =c(1930,2012) ,breaks =seq(1940,2000,20

[dasom1348]

These are what I've tried for drawing lines on graph, setting x and y scales, and changing gross_info to number by referencing hws and mp3! But there are some errors ha.. I pushed this. You can pull :)

Visualizing findings

library(scales)

ggplot(data=MP_info, aes(x=gross_info, y=rating)) + geom_point(aes(color=gender)) + labs(title = "Quality of Best 100 Movies by Male and Female Directors", x = "Revenue($)", y = "Rating")

---

CHECKING THROUGH HERE !!

We need to set the revenue units first to set x-axis to annotate

scale_y_discrete(labels = c("4.5", "5.0", "5.5", "6.0", "6.5", "7.0", "7.5", "8.0", "8.5"), limits = c(4.0, 9.0), breaks = seq(4.5,8.5,0.5)) + scale_x_discrete(labels = c("25M", "50M", "75M", "100M"), limits = c(0,105000000), breaks = seq(25000000,100000000, 25000000))

Examples of annotating and so on from babynames hw

annotate("segment", x=1940, xend=1948, y=0.5, yend=0.5, color="black")+ annotate("segment", x=1940, xend=1940, y=0.4, yend=0.5, color="black") +

labels =c("1940", "'60", "'80", "2000" ), limits =c(1930,2012) ,breaks =seq(1940,2000,20

Testing changing gross things to number

movie_test <- movie_gross100 %>% mutate(Revenue=as.numeric(gross_info))

Example from mp3

mutate (Difficulty= if_else(as.numeric(numeric_rating)<10, "Easy", ifelse(as.numeric(numeric_rating)<30, "Moderate", "Hard")))

[dasom1348]

I also started to write up something! And found some external resources. Plz checking rmd file by pulling it first :) then Feel free to add and edit my writings. I just roughed out.