This repository holds the code for my project "Exploring Isaiah's Legacy in the Book of Mormon", which is illustrated in two blogs, one that talks about data collection, and the other about the EDA.

I like to organize my repository and coding into steps. In the folder Scripts there are six different Jupyter notebooks that perform different functions.

1-scrape_data.ipynb

• scrapes data from three different sources
• cleans and compiles data into csv files in Dirty Data

2-combine_data.ipynb

• takes previous csv files and turns it into a cross reference dataset with verse IDs and scriptural text
• exports into new csv found in Dirty Data

3-build_verse_data.ipynb

• main goal is to make the data more comprehensive
• analyze textual similarities
• calculate word counts in verses and verse counts in chapters
• detects biblical terminology in verses
• categorizes textual similarities and Isaiah chapters using Duhm's classifications
• exports into new datasets found in Clean Data

4-build_chapter_data

• I do not talk about these steps in the blog, but in simple terms it takes above data and aggerates it by chapter
• exports into new csv files

5-networks.ipynb

• takes chapter and verse cross reference data from Clean Data and generates chapter and verse networks
• uses chapter data to provide comprehensive hover node and edge information in chapter network
• uses verse data to provide comprehensive hover node and edge information in verse network
• IMPORTANT: read the blog before looking at the networks to know what certain illustrations mean

streamlit-app.py

• uses chapter and verse cross reference data and provides interactive element to explore textual similarities and comparisons
• the app can be viewed here