Creole Linguistics Analysis(Final Report).Rmd

---
title: "Creole Linguistics Analysis"
author: "Tariq Williams" 
date: "April 2023"
output: 
  github_document: 
    toc: true
    toc_depth: 2
---

```{r setup, include = FALSE}
# This setup chunk sets global defaults and includes the tidyverse packages
# The option include = FALSE prevents warnings and messages from printing to your report.
# Change the next option to echo = FALSE to hide code chunks by default 
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
library(tm)
library(readxl)
```

#Read in data

This is all the code used to read in the datasets 

```{r}

english_words <- read.table("english_words_patois_edit.txt", header = FALSE, col.names = "words")

lyrics_final <- read_excel("lyrics_Final.xlsx")

```

#Data Cleaning 

````{r}

# remove words ending with "embed" from the Lyrics column
lyrics_final$Lyrics <- gsub("\\b\\w+embed\\b", "", lyrics_final$Lyrics, ignore.case = TRUE)




````


#Data Analysis 

This is all the code used to analyze the data 

#Word Count Analysis 

``` {r}

# read in the English Words 
english_words <- readLines("english_words_patois_edit.txt")

# convert all the words in the English dictionary to lowercase
english_words <- tolower(english_words)

# create variables to store the counts
english_word_count <- numeric(nrow(lyrics_final))
patois_word_count <- numeric(nrow(lyrics_final))

# loop through each row in the table
for (i in 1:nrow(lyrics_final)) {
  # extract the lyrics from the row
  lyrics <- lyrics_final$Lyrics[i]
  
  # convert all the words in the lyrics to lowercase
  lyrics <- tolower(lyrics)
  
  # split the lyrics into individual words
  words <- unlist(strsplit(lyrics, "\\W+"))
  
  # count the number of English and non-English words
  english_word_count[i] <- sum(words %in% english_words)
  patois_word_count[i] <- length(words) - english_word_count[i]
}

# create a data frame with the results
word_counts <- data.frame( lyrics_final$Artist, lyrics_final$Song, lyrics_final$Year, lyrics_final$Genre ,   english_word_count,patois_word_count)

colnames(word_counts) <- c( "Artist", "Song" , "Year" , "Genre" , "English Words", "Patois Words")

```

# Adding Variables and Filters

```{r}

# Calculate Patois and English Percentages 

word_counts$Patois_Percentage <- round(100 * word_counts$`Patois Words` / (word_counts$`English Words` + word_counts$`Patois Words`),2)

word_counts$English_Percentage <- round(100 * word_counts$`English Words`/ (word_counts$`English Words` + word_counts$`Patois Words`),2)

# Filters 

# Filter for songs with genre "Dancehall"
dancehall_songs <- word_counts[word_counts$Genre == "Dancehall",]

```

# Data Visualiation 

This is all the code that is used to visualize the data. 

```{r}




```