Seinfeld Text Mining Analysis with a slight Sentiment approach.Rmd

# ---
# title: "Seinfeld Text Mining Analysis with a slight Sentiment approach"
# Name: 'Yeo Yee Wen'
# ID: '18076950'
# ---
# 
#Load Libraries


```{r,message=FALSE,warning=FALSE}

#setwd("......") - setting the working directory

# install.packages('tidyverse')
# install.packages('tidytext')
# install.packages('wordcloud')
# install.packages('stringr')
# install.packages('igraph')
# install.packages('ggraph')
# install.packages('widyr')
# install.packages('broom')
# install.packages('DT')
# install.packages('irlba')
# install.packages('topicmodels')
# install.packages('tm')
# install.packages('caret')
# install.packages('glmnet')
# install.packages('textdata')
# install.packages('hrbrthemes')
# install.packages('xgboost')
# install.packages('ggplot2')
# install.packages('dplyr')
# install.packages('gridExtra')
# install.packages('ggthemes')
# install.packages('RColorBrewer')
# install.packages('grid')
# install.packages('viridis')
# install.packages('lubridate')
# instrall.packages('readr')
# install.packages('plotly')
# install.packages('tidyyr')

library(tidyr) #create tidydata
library(tidyverse)
library(tidytext) # text manipulation
library(wordcloud) # word cloud
library(stringr) #string manipulation
library(igraph)
library(ggraph)
library(widyr)
library(broom)
library(DT)
library(irlba)
library(topicmodels) # for LDA topic modelling 
library(tm) # general text mining functions, making document term matrixes
library(caret)
library(glmnet)
library(textdata) #To borrow an amazing package from notebook
library(xgboost) #To borrow an amazing package from notebook
library(ggplot2) #Plotting graph
library(dplyr)
library(gridExtra)
library(ggthemes) 
library(RColorBrewer)#COLOUR
library(grid)
library(viridis)
library(lubridate)
library(readr)
library(plotly) #Plotting graph
library(hrbrthemes)
library(gganimate) 
```

#Read the data

```{r,message=FALSE,warning=FALSE}

rm(list=ls())
#setting fill colour constraint
fillColor = "#FFA07A"
fillColor2 = "#F1C40F"
#reading the data from csv file
episode = read_csv("episode_info.csv")
scripts = read_csv("scripts.csv")

#creating a function scripts for x1 and dialogue
scripts =  scripts %>% 
  rename(postID = X1) %>% 
  rename(text = Dialogue)


```

#Glimpse of Data{.tabset .tabset-fade .tabset-pills}

##Episode 

```{r,message=FALSE,warning=FALSE}
#to view the data type and dataset
glimpse(episode)
View(episode)

```

##Scripts 

```{r,message=FALSE,warning=FALSE}

#to view the data type and dataset
glimpse(scripts)
View(scripts)

```

#Who has Spoken more ?
      
```{r,message=FALSE,warning=FALSE}
#filer the top10 characters whom spoke most
Top10Characters = scripts %>%
  group_by(Character) %>%
  summarise(Count = n()) %>%
  arrange(desc(Count)) %>%
  ungroup() %>%
  mutate(Character = reorder(Character,Count)) %>%
  head(10)

 Top10Characters %>%
  
  ggplot(aes(x = Character,y = Count)) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = Character, y = 1, label = paste0("(",Count,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Character', 
       y = 'Count', 
       title = 'Character and Count: Which Character spoke more?') +
  coord_flip() + 
  theme_bw()

```

#Which Character spoke long Sentences?

```{r,message=FALSE,warning=FALSE}
#filer the top10 characters whom spoke the longest sentence
scripts$len = str_count(scripts$text)

scriptsTopTenCharacters = scripts %>%
  filter(Character %in% Top10Characters$Character)

scriptsTopTenCharacters %>%
  group_by(Character) %>%
  summarise(CountMedian = median(len,na.rm = TRUE)) %>%
  ungroup() %>%
  mutate(Character = reorder(Character,CountMedian)) %>%
  
  ggplot(aes(x = Character,y = CountMedian)) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = Character, y = 1, label = paste0("(",CountMedian,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Character', 
       y = 'Count', 
       title = 'Character and Count: Which Character Spoke Long Sentences?') +
  coord_flip() + 
  theme_bw()


```

#Tokenisation     

#To break the text into individual tokens which are simply #individual words. This process is called tokenisation. This #is accomplished through the **unnest_tokens** function.

```{r,message=FALSE,warning=FALSE}

#Tokenisation  
#To group a text according to a class object
scripts %>%
  unnest_tokens(word, text) %>%
  head(10)


```



```{r,message=FALSE,warning=FALSE}

#Remove the most Commonly occuring words in English language. 
scripts %>%
  unnest_tokens(word, text) %>%
  filter(!word %in% stop_words$word) %>% head(10)
#Removing the Stop words
```


#Top Ten  most Common Words

#Most common words include `yeah`,`jerry`,`hey`,`george`,`uh`       


```{r}
#Top Ten  most Common Words Overall Characters
createBarPlotCommonWords = function(train,title)
{
  train %>%
  unnest_tokens(word, text) %>%
  filter(!word %in% stop_words$word) %>%
  count(word,sort = TRUE) %>%
  ungroup() %>%
  mutate(word = factor(word, levels = rev(unique(word)))) %>%
  head(10) %>%
  
  ggplot(aes(x = word,y = n)) +
  geom_bar(stat='identity',colour="white", fill =fillColor) +
  geom_text(aes(x = word, y = 1, label = paste0("(",n,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Word', y = 'Word Count', 
       title = title) +
  coord_flip() + 
  theme_bw()

}

createBarPlotCommonWords(scripts,'Top 10 most Common Words Overall')

```


#BarPlot

```{r}
#Top Ten  most Common Words for Jerry
createBarPlotCommonWords(scripts %>%
                  filter(str_detect(Character,"JERRY")),
                  'Top 10 most Common Words Of Jerry')

```


###BarPlot

```{r}
#Top Ten  most Common Words for George
createBarPlotCommonWords(scripts %>%
                  filter(str_detect(Character,"GEORGE")),
                  'Top 10 most Common Words Of George')

```




###BarPlot

```{r}
#Top Ten  most Common Words for Elaine
createBarPlotCommonWords(scripts %>%
                  filter(str_detect(Character,"ELAINE")),
                  'Top 10 most Common Words Of Elaine')

```

#Most Common Bigrams                 

```{r,message=FALSE,warning=FALSE}

#Creating a Bigram to separate which counts all repeating two words
count_bigrams <- function(dataset) {
  dataset %>%
    unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
    separate(bigram, c("word1", "word2"), sep = " ") %>%
    filter(!word1 %in% stop_words$word,
           !word2 %in% stop_words$word) %>%
    count(word1, word2, sort = TRUE)
}


visualize_bigrams <- function(bigrams) {
  set.seed(2016)
  a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
  
  bigrams %>%
    graph_from_data_frame() %>%
    ggraph(layout = "fr") +
    geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a) +
    geom_node_point(color = "lightblue", size = 5) +
    geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
    theme_void()
  
}

visualize_bigrams_individual <- function(bigrams) {
  set.seed(2016)
  a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
  
  bigrams %>%
    graph_from_data_frame() %>%
    ggraph(layout = "fr") +
    geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a,end_cap = circle(.07, 'inches')) +
    geom_node_point(color = "lightblue", size = 5) +
    geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
    theme_void()
}

scripts %>%
  unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(!word1 %in% stop_words$word,
         !word2 %in% stop_words$word) %>%
  unite(bigramWord, word1, word2, sep = " ") %>%
  group_by(bigramWord) %>%
  tally() %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  mutate(bigramWord = reorder(bigramWord,n)) %>%
  head(10) %>%
  
  ggplot(aes(x = bigramWord,y = n)) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = bigramWord, y = 1, label = paste0("(",n,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Bigram', 
       y = 'Count', 
       title = 'Bigram and Count') +
  coord_flip() + 
  theme_bw()

```


#Most Common Trigrams                 


```{r,message=FALSE,warning=FALSE}

#Creating a Trigram to separate which counts all repeating two words
scripts %>%
  unnest_tokens(trigram, text, token = "ngrams", n = 3) %>%
  separate(trigram, c("word1", "word2","word3"), sep = " ") %>%
  filter(!word1 %in% stop_words$word,
         !word2 %in% stop_words$word,
         !word3 %in% stop_words$word) %>%
  unite(trigramWord, word1, word2, word3,sep = " ") %>%
  group_by(trigramWord) %>%
  tally() %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  mutate(trigramWord = reorder(trigramWord,n)) %>%
  head(10) %>%
  
  ggplot(aes(x = trigramWord,y = n)) +
  geom_bar(stat='identity',colour="white", fill = fillColor) +
  geom_text(aes(x = trigramWord, y = 1, label = paste0("(",n,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Trigram', 
       y = 'Count', 
       title = 'Trigram and Count') +
  coord_flip() + 
  theme_bw()

```

#Relationship among words
 

```{r,message=FALSE,warning=FALSE}
#Create a Relationship network among words
trainWords <- scripts %>%
  count_bigrams()

trainWords %>%
  filter(n > 10) %>%
  visualize_bigrams()

```


#Relationship with the word **jerry** and **george** and **elaine** and **kramer**

```{r,message=FALSE,warning=FALSE}
#Analyzing the relationship word with **jerry** and **george** and **elaine** and **kramer**

trainWords %>%
  filter(word1 == "jerry" | word2 == "jerry" | 
         word1 == "george" | word2 == "george" |
         word1 == "elaine" | word2 == "elaine" |
         word1 == "kramer" | word2 == "kramer") %>%
  filter(n > 5) %>%
  visualize_bigrams()

```


#Sentiment Analysis using **NRC Sentiment lexicon**

##Sentiment Analysis Words - Fear for Jerry

```{r,message=FALSE,warning=FALSE}
#Sentiment Analysis using **NRC Sentiment lexicon**
#The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and
#Checking how feared is he
getEmotionalWords = function(emotion,Character)
{
  nrcEmotions = get_sentiments("nrc") %>% 
    filter(sentiment == emotion) 
  
  emotionalWords = scripts %>%
    unnest_tokens(word, text) %>%
    filter(!word %in% stop_words$word) %>%
    filter(Character == Character) %>%
    inner_join(nrcEmotions) %>%
    group_by(word) %>%
    summarise(Count = n()) %>%
    arrange(desc(Count))
    
  
  return(emotionalWords)
}
FearWordsJerry = getEmotionalWords('fear','JERRY')

datatable(head(FearWordsJerry,10), style="bootstrap", class="table-condensed", options = list(dom = 'tp',scrollX = TRUE))

wordcloud(FearWordsJerry$word, FearWordsJerry$Count, max.words = 30,colors=brewer.pal(8, "Dark2"))

```

##Sentiment Analysis Words - Joy for Jerry

```{r,message=FALSE,warning=FALSE}
#Sentiment Analysis using **NRC Sentiment lexicon**
#The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and
#Checking how full of joy is he
JoyWordsJerry = getEmotionalWords('joy','JERRY')

datatable(head(JoyWordsJerry,10), style="bootstrap", class="table-condensed", options = list(dom = 'tp',scrollX = TRUE))

wordcloud(JoyWordsJerry$word, JoyWordsJerry$Count, max.words = 30,colors=brewer.pal(8, "Dark2"))

```

##Sentiment Analysis Words - Surprise for Jerry

```{r,message=FALSE,warning=FALSE}
#Sentiment Analysis using **NRC Sentiment lexicon**
#The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and
#Checking how suprised is he
SurpriseWordsJerry = getEmotionalWords('surprise','JERRY')

datatable(head(SurpriseWordsJerry,10), style="bootstrap", class="table-condensed", options = list(dom = 'tp',scrollX = TRUE))

wordcloud(SurpriseWordsJerry$word, SurpriseWordsJerry$Count, max.words = 30,colors=brewer.pal(8, "Dark2"))

```

## Character Level Analysis



```{r fig.height= 12, fig.width= 15}
# In terms of percentages, how often did each character speak?
top_50 <- bind_rows(scripts %>%
  group_by(Character) %>%
  summarise(n=n()) %>%
  
  ungroup() %>%
  filter(!grepl('setting',tolower(Character))) %>%
  arrange(desc(n)) %>%
  top_n(49),
scripts %>%
  group_by(Character) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  arrange(desc(n)) %>%
  tail(1590) %>%
  mutate(n=sum(n)) %>%
  mutate(Character='Others') %>%
  unique())


p <- top_50 %>%
  mutate(tot=sum(n)) %>%
  mutate(percentage=n/tot) %>%
  ggplot(aes(x=reorder(Character,percentage),y=percentage))+geom_bar(stat='identity')+
  theme_ipsum()+coord_flip()+
  labs(x='Characters',y='Percentage',title='Percentage of Dialogues by Character')+
  scale_y_continuous(labels = scales::percent)+
       geom_text(aes(label=stringr::str_c(as.character(round(percentage*100,2)),"%")), position=position_dodge(width=0.9),hjust=-0.5)

p 

```

* Jerry had the highest percentage of dialogues obviously. This is then followed by George, Elaine,Kramer and Newman. 
### Which character had the most diverse vocabulary?




```{r fig.height=10, fig.width= 15}

##Data 
episodes = read_csv('episode_info.csv')
scripts = read.csv('scripts.csv', stringsAsFactors = FALSE)
scripts$Dialogue= str_to_lower(scripts$Dialogue)


#This is also called *lexical diversity* .This calculates the number of unique words divided by the total number of words used.
scripts %>%
  select(SEID,Character,Dialogue) %>%
  mutate(SEID=stringr::str_sub(SEID,1,3)) %>%
  filter(Character %in% c("JERRY","ELAINE","GEORGE","KRAMER")) %>%
  unnest_tokens(word,Dialogue) %>%
  group_by(SEID,Character) %>%
  summarise(total_number_of_words =n()) %>%
  inner_join(scripts %>%
               select(SEID,Character,Dialogue) %>%
               mutate(SEID=stringr::str_sub(SEID,1,3)) %>%
               filter(Character %in% c("JERRY","ELAINE","GEORGE","KRAMER")) %>%
               unnest_tokens(word,Dialogue) %>%unique() %>% group_by(SEID,Character) %>% summarise(n_unique=n())) %>%
  mutate(percentage_diversity=n_unique/total_number_of_words) %>%
  ggplot(aes(x=Character,y=percentage_diversity))+geom_bar(stat = 'identity')+theme_ipsum()+facet_wrap(~SEID)+
  coord_flip()+scale_y_continuous(labels=scales::percent)+labs(y='Percentage Diversity',title='Lexical Diversity Amongst Characters across Seasons')



```

### Which characters have had the most interactions with each other?


```{r fig.height=10, fig.width= 15}
#On way to visualize this is to look at conversions in pairs. Here, I have tried to construct a bi-gram of characters with the help of `tidytext`'s `unnest_tokens()` functions. This might not be entirely accurate due to the lack of continuity in dialogues when the scenes end. The frequency of the character to character bi-grams is used as the strength in the network.
scripts %>%
  select(Character)%>%
  mutate(Character= gsub(" ", "", Character, fixed = TRUE)) %>%
  mutate(Character=paste(Character,collapse=' ')) %>%
  unique() %>%
  unnest_tokens(ngram, Character, token = "ngrams", n = 2) %>%
  tidyr::separate(ngram,into=c('ch1','ch2'),sep=' ') %>%
  group_by(ch1,ch2) %>%
  summarise(n=n()) %>%
  arrange(desc(n)) %>%
  mutate(strength = ifelse(n>1000,'Strong','Weak')) %>%
  filter(n>10) %>%
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_width=n,edge_colour=strength),alpha=0.2) +
  geom_node_point() +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1,check_overlap = TRUE,size=5,color='#109876',fontface='bold')+
  theme_ipsum()+
  theme(axis.text = element_blank(),axis.title = element_blank(),panel.grid = element_blank(),legend.position = 'none')
```


```{r,message=FALSE,warning=FALSE}

##Data 
episodes = read_csv('episode_info.csv')
scripts = read.csv('scripts.csv', stringsAsFactors = FALSE)


#fix strings
episodes$strfix = str_detect(episodes$Title, "(1)")
episodes$strfix2 = str_detect(episodes$Title, "(2)")
episodes$Title = ifelse(episodes$strfix ==TRUE | episodes$strfix2 ==TRUE, str_sub(episodes$Title, 1, -4), episodes$Title )
episodes$Title[episodes$Title == 'The Reverse Peephole (a.k.a. The Man Fur)'] = 'The Reverse Peephole'

episodes$Title = str_to_lower(episodes$Title)
scripts$Dialogue= str_to_lower(scripts$Dialogue)

##Merge data using dplyr
library(dplyr)

seinfeld = scripts %>% 
  left_join(episodes, by=c('EpisodeNo', 'SEID'))

##find name in scripts
seinfeld$New_Title = ifelse(str_sub(seinfeld$Title, 1, 3) =='the', 
                           str_sub(seinfeld$Title, 4, -1), seinfeld$Title)

seinfeld$count = str_count(seinfeld$Dialogue ,seinfeld$New_Title)


#count by episode

title_count = seinfeld %>% group_by(Title, New_Title, Season.x, Director, Writers) %>%
  summarise(count = sum(count)) 

title_count$Title = str_to_title(title_count$Title)

mean(title_count$count) #8.24

####Plot
top_episodes = subset(title_count, count >= 20)
top_episodes$Season.x = as.factor(top_episodes$Season.x)

top_episodes = top_episodes %>% mutate(Season = Season.x)


g = ggplot(top_episodes, aes(x=Title, y=count)) + geom_bar(stat = 'identity', aes(fill=Season)) + 
  ggtitle("The Most Times The Seinfeld Episode Title is Mentioned in The Episode") +
  theme_bw() + ylab("Number of Times The Title is Said in The Episode") +
  theme(axis.text.x = element_text(face = 'bold', angle=450), plot.title = element_text(hjust = 0.5))
g

print(ggplotly(g))#printing plotted graph

```


```{r setup, include=FALSE}



#scripts <- read.csv(file.path("input", "scripts.csv"), stringsAsFactors = F)
scripts <- read.csv("scripts.csv", stringsAsFactors = F)


main_characters <- c("ELAINE", "GEORGE", "JERRY", "KRAMER")

```

---

###Data cleaning

There are a few issues which need to be accounted for to make the data easier to process. Occasionally there is whitespace between an open-brackets and the direction text, easily fixable with a gsub regex

    KRAMER    Give me it..( tries to pull them off George's head)

The Character column mixes cases, usually main characters are capitalized however there are exceptions, particularly for "Elaine". easily fixed with a base-r function.

Usually stage directions are in round brackets, but some are in square. Another easy regex fix



```{r cleaning, include=FALSE}
#some initial cleaning:
#Elaine is sometines not in caps? fix
scripts$Character <- toupper(scripts$Character)

#occasioinally there is a space after an open-bracket. remove
scripts$Character <- gsub("\\(\\s+", "(", scripts$Character)
scripts$Dialogue <- gsub("\\(\\s+", "(", scripts$Dialogue)

scripts$Character <- gsub("\\[", "(", scripts$Character)
scripts$Dialogue <- gsub("\\[", "(", scripts$Dialogue)
scripts$Character <- gsub("\\]", ")", scripts$Character)
scripts$Dialogue <- gsub("\\]", ")", scripts$Dialogue)


w_stage_direction <- scripts %>%
  filter(grepl("\\(", Dialogue))

```


```{r crunch1, include=FALSE}
extract_directions <- function(row_i){
  text <- paste(w_stage_direction$Character[row_i], w_stage_direction$Dialogue[row_i])
  
  text_splits <- strsplit(text, split="\\(") %>% unlist
  direction_closes <- text_splits[grepl("\\)", text_splits)]
  
  #there are some errors in the script, where a stage direction bracket is
  #opend but not closed. skip these as it is hard to tell if/where it should
  #be closed or if the whole line is direction
  if(length(direction_closes) < 1) {
    return(data.frame(direction=NA,Character=NA, EpisodeNo=NA, SEID=NA, Season=NA) )
  }
  
  direction_data <- data.frame(Character=w_stage_direction$Character[row_i],
                               direction=gsub("\\).*$", "",  direction_closes),
                               EpisodeNo=w_stage_direction$EpisodeNo[row_i],
                               SEID=w_stage_direction$SEID[row_i],                               
                               Season=w_stage_direction$Season[row_i],
                               stringsAsFactors = F)
  return(direction_data)
}


stage_direction_data_list <- lapply(1:nrow(w_stage_direction), extract_directions)
stage_direction_DF <- do.call("rbind", stage_direction_data_list)


#extract the first word of the direction
stage_direction_DF$first_word <- gsub("\\s.*$", "", stage_direction_DF$direction) %>% tolower()


#exclude stopwords
stage_direction_DF <- stage_direction_DF %>%
  filter(!first_word %in% stopwords())




#if the first word is a character name, then this is actually a direction for
#another direction. correct and take the 2nd word
not_me <- stage_direction_DF %>% filter(first_word %in% (main_characters %>% tolower))
not_me$Character <- toupper(not_me$first_word)
not_me$first_word <- gsub("\\s.*$", "", sub("^[^\\s]*\\s", "", not_me$direction %>% tolower())) 

stage_direction_DF <- rbind(stage_direction_DF %>% 
                              filter(!first_word %in% (main_characters %>% tolower)),
                            not_me) 
 

#work out which words are used to direct which character
main_character_directions <- stage_direction_DF %>%
  filter(tolower(Character) %in% tolower(main_characters)) %>%
  group_by(Character, first_word) %>%
  summarise(ocs=n()) %>%
  spread(Character, ocs)
#sum up total for each word
main_character_directions$all_count <- rowSums(main_character_directions[,2:5], na.rm = T)


#now calculate more stats per character per word for plotting
main_character_directions_stats <- main_character_directions %>%
  gather(Character, character_count, -all_count, -first_word) %>%
  filter(!is.na(character_count)) %>%
  mutate(character_proportion=character_count/all_count)
```


###Amount of directions

First, a simple chart to see how much direction each character is given in the script

```{r most_dirsctions, echo=F, warning=F, message=F, fig.height = 5}
#see how much direction each character is given in the script

#who has been given most priority?
most_directions <- stage_direction_DF
most_directions$Character[!most_directions$Character %in% main_characters] <- "other"

most_directions <- most_directions %>% 
  group_by(Character) %>% summarise(direction_count=n())

p <- most_directions %>% 
  ggplot(aes(x=Character, y=direction_count, fill=Character)) +              geom_bar(stat="identity") +
  scale_fill_manual(values=brewer.pal(5, "Set2")) + ggtitle("Which character is given direction/priority in their respective given scripts?")
  
ggplotly(p, tooltip = c("direction_count"))

```

###How does this relate to quantity of lines?

```{r most_dirsctions2, echo=F, warning=F, message=F, fig.height = 5}
#Counting whom has the most number of lines
most_lines <- scripts
most_lines$Character[!most_lines$Character %in% main_characters] <- "other"
  
most_lines <- most_lines %>%
  group_by(Character) %>% summarise(num_lines=n())
#assigned variable function
p <- most_lines %>% 
  ggplot(aes(x=Character, y=num_lines, fill=Character)) +   geom_bar(stat="identity") +
  scale_fill_manual(values=brewer.pal(5, "Set2")) + ggtitle("How many number of lines are given to each character in their scripts?")
ggplotly(p, tooltip = c("num_lines"))   

```

And directions per-line:

```{r most_dirsctions3, echo=F, warning=F, message=F, fig.height = 5}
#Comparing overall between all main characters
directions_per_line <- left_join(most_lines, most_directions, by="Character")

directions_per_line$directions_per_line <- directions_per_line$direction_count / directions_per_line$num_lines
#assigned variable function
p <- directions_per_line %>% 
  ggplot(aes(x=Character, y=directions_per_line, fill=Character,
             num_lines=num_lines, direction_count=direction_count)) + 
    geom_bar(stat="identity") +
  scale_fill_manual(values=brewer.pal(5, "Set2")) + ggtitle("Comparing the directions of in a script, which lines are more prioritized by which character?")
ggplotly(p, tooltip = c("num_lines", "direction_count", "directions_per_line"))   

```


```{r top_words, echo=F, warning=F, message=F, fig.height = 4, fig.width = 7}
#Analyzing the information details of each character's top words used in their script
top_words <- main_character_directions_stats %>%
  group_by(Character) %>%
  top_n(7, wt=character_count) 

top_words_data <- main_character_directions_stats %>%
  filter(first_word %in% top_words$first_word)

top_words_data$Character_number <- as.factor(top_words_data$Character) %>% as.numeric

first_word_order <- top_words_data %>% 
  filter(Character==main_characters[3]) %>%
  arrange(character_proportion)
first_word_order <- first_word_order$first_word

top_words_data$first_word_f <- factor(top_words_data$first_word, levels = first_word_order)

p <- top_words_data %>%
  arrange(Character, character_proportion) %>%
  ggplot(aes(x=first_word_f, y=character_proportion, fill=Character, 
             text3=character_count, text2=first_word, text1=Character)) +
  scale_fill_manual(values=brewer.pal(4, "Set2")) + ggtitle("Information details of each characters top/first words that are used in their scripts") +
  geom_bar(stat="identity") + coord_flip()
  
ggplotly(p, tooltip = c("text1", "text2", "text3")) 
#p
```


```{r fig.width=12, fig.height=7, fig.align='center'}
#visually display the script number of lines of each charcter in the seasons
top<-data.frame(scripts %>% filter(!grepl("^\\[|^\\(", Character)) %>% group_by(Season, Character) %>% summarise(count=n()) %>% arrange(-count) %>% top_n(20))
top$Char<-ifelse(top$Character %in% c('JERRY','GEORGE','ELAINE','KRAMER','NEWMAN','PUDDY','PETERMAN','ESTELLE','SUSAN','HELEN','MORTY','FRANK'),top$Character,'OTHER')

top %>% 
  ggplot(aes(x=factor(Season),y=count,fill=Char)) + 
  geom_histogram(stat='identity',color='white',size=.2) + theme_fivethirtyeight() +
  scale_fill_manual(name='',values=colorRampPalette(brewer.pal(11,"Paired"))(13)) +
  guides(fill=guide_legend(ncol=8)) + 
  labs(title='Number of lines per character and season')
```

###Comparison word-cloud

```{r compcloud, echo=F, warning=F, message=F, fig.height = 7}
#manipulate data into a format comaprisoncloud can use
comp_clould_data <- stage_direction_DF %>% 
  filter(tolower(Character) %in% tolower(main_characters)) %>%
  group_by(Character) %>%
  summarise(char_directions=paste(first_word, collapse = " "))

all <- comp_clould_data$char_directions
corpus <- Corpus(VectorSource(all))
tdm <- TermDocumentMatrix(corpus)
tdm <- as.matrix(tdm)
colnames(tdm) <- comp_clould_data$Character

comparison.cloud(tdm, random.order=FALSE, 
                 colors=brewer.pal(4, "Set1"),
                 title.size=1.5,
                 rot.per=0)
```