Golden Retriever Lifetime Study Data Visualizations

This GitHub project provides a set of scripts to create visualizations for the Golden Retriever Lifetime Study data, which is available on the Morris Animal Foundation's Data Commons website. The project aims to help researchers and enthusiasts gain insights from this valuable dataset through visual representations.

Table of Contents

• Introduction
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Project Structure
• Contributing
• License

Introduction

The Morris Animal Foundation's Golden Retriever Lifetime Study is a comprehensive dataset that contains valuable information about the health, behavior, diet, and lifestyle of Golden Retrievers. This project provides a set of scripts to create visualizations for six major categories of data within the study:

1. Demographic Data
2. Medical Conditions Data
3. Primary Study Endpoints
4. Diet Data
5. Behavior Data
6. Lifestyle Data

The project is designed to be easy to use and customizable. You can run the provided scripts to generate visualizations for any specific category of interest.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.x installed on your system.
• Access to the Golden Retriever Lifetime Study data on the Morris Animal Foundation's Data Commons website.
  • You can register for data access here: https://datacommons.morrisanimalfoundation.org/
• Two variables:
  • 'dirpath': The path to the directory containing all the study data.
  • 'vizpath': The path for the directory where the visualization outputs will be saved as PNG files.

Installation

1. Clone this GitHub repository to your local machine:

   git clone https://github.com/morrisanimalfoundation/grls-visualizations.git
   

2. Install the required Python packages by running the following command in the project's root directory:

   pip install -r requirements.txt
   

Usage

To create visualizations for a specific category of data, follow these steps:

1. Set the 'dirpath' and 'vizpath' variables in the 'py_secrets.py' file to specify the directory paths for the data and visualization outputs.
2. Run the corresponding script for the desired data category:

• Demographic Data: 'demo-viz.py'
• Medical Conditions Data: 'med-viz.py'
• Primary Study Endpoints: 'endpoint-viz.py'
• Diet Data: 'diet-viz.py'
• Behavior Data: 'behavior-viz.py'
• Lifestyle Data: 'lifestyle-viz.py'

For example, to create visualizations for Demographic Data, run:

python3 demo-viz.py

3. The script will generate visualizations and save them in the specified 'vizpath' directory along with the data required for the graphics on the respective webpage.

Project Structure

The project structure is organized as follows:

grls-visualizations/
│
├── scripts/
│   ├── demo-viz.py           # Script for Demographic Data visualization
│   ├── medical-viz.py        # Script for Medical Conditions Data visualization
│   ├── endpoints-viz.py      # Script for Primary Study Endpoints visualization
│   ├── diet-viz.py           # Script for Diet Data visualization
│   ├── behavior-viz.py       # Script for Behavior Data visualization
│   └── lifestyle-viz.py      # Script for Lifestyle Data visualization
│
├── Dockerfile                 # Dockerfile for containerizing the application
├── run.sh                     # Shell script to run the application
├── settings.py                # Store your secret variables (dirpath, vizpath) here
├── .gitignore                 # Gitignore file to exclude sensitive data and outputs
├── requirements.txt           # List of Python packages required for this project
│
└── README.md                  # This README file

Contributing

Contributions to this project are welcome! Feel free to open issues, submit pull requests, or provide feedback to help improve the project.

License

This project is licensed under [---]