Google Data Analysis Capstone Project -- Which areas of the U.S. are most accommodating to vegans and vegetarians?

This readme is an abridged version of this project's full report. See report.pdf to view it in its entirety.

A common topic of debate in the vegan community is whether or not it is easier for certain communities to adopt healthy, plant-based diets than it is for others. Personally, I think the answer to this question is a no-brainer, but nonetheless was very interested in exploring it with data.
There are many factors to consider when it comes to transitioning to a new diet, but for this study I have judged how easy it is to be a vegetarian or vegan based on how many restaurants with vegetarian or vegan options are available, as this data is easy to access and eating out at restaurants that often will not accommodate your dietary restrictions is one of the most difficult parts of having a plant-based diet.

Hypothesis

There are more meat-free and plant-based dining options in areas with high levels of income, as well as in those with larger populations.

Preparation

The primary dataset I'll be using is a table containing the amount of vegetarian- and vegan-friendly restaurants per state. By vegetarian/vegan-friendly, I refer to restaurants that provide food that is meat- or animal-product-free on their menus. This data was scraped from the website HappyCow, an online directory of such restaurants, and was the most accurate I could find. Most of the sets containing the demographic data I'll be using are sourced from the U.S. Census Bureau; for consistency, all of this government data was collected in 2018. The additional data on political party preference was collected in 2018 by Gallup.

The tools I will be using are:

• Data Scraping: Python with Selenium
• Cleaning: PostgreSQL/PGAd4min4, Microsoft Excel
• Exploration/Analysis: Python with numpy, pandas, and scipy
• Visualization: Python with Seaborn, Tableau
• Final report: rmarkdown

Data Cleaning

The collected Happy Cow data was simple and straightforward, and thus did not require any major cleaning. I also originally used SQL to process a dataset of fast food restaurants, but tossed this set after realizing it doesn't contribute to answering the question at hand.

Analysis

From here I began my exploratory data analysis.

insert code here?

WIP :-)