alicewu1/healthcare-visuals

HHA507 / Data Science / Assignment 5 / AHI Microcourse Intro to Health Data Visualization

For HHA507 Assignment 5: AHI Microcourse Visualization

This repo aims to:

1. Recreate the ipynb demonstrated in the microcourse
2. Practice visualizations

Topics Covered:

Introduction to Python Data Tools

1. Introduction to Jupyter Notebook
2. Loading in packages: Pandas
3. Loading in packages: Numpy
4. Loading in packages: Seaborn, Matplotlib, and Plotly
5. Importing Data

Understanding the Data

1. Describing the dataset (Len, shape, variables)
2. Describing the variables
3. Getting counts of categorical values (value_counts)
4. Transforming features
5. Pandas to_datetime() function

Filtering for the Data We Want

1. Filtering rows by creating lists
2. Filtering rows by date
3. Keeping only columns/features we want
4. Outcome variables (hospitalizations, deaths, new cases)
5. Total COVID cases by MONTH - cumulative counts

Visualization

1. Pivot table - cases by month for five counties
2. Seaborn Barplot - COVID cases by month
3. Seaborn Barplot - COVID cases by month by county
4. Plotly - COVID cases by month
5. Total COVID cases by DAY - cumulative counts
6. Pivot tables - cases by day for five counties
7. Creating filters (startdate and enddate) for day time field
8. Filter gut check - looking at single county between April 26, 2020 and May 9, 2020
9. Seaborn Barplot - COVID cases by day
10. Seaborn Barplot - COVID cases by day by county
11. Plotly - COVID cases by day by county
12. Understanding we need to transform outcome variables together to replicate chart
13. Looking at new hospitalizations
14. Plotly chart that combines new hospitalizations, new deaths, and new COVID cases