Assessment 1: Data Analysis (R Programming)

Health Data Science (7HMNT032W)

University of Westminster

Submission Date: 20 November 2024Student Name: Donald Philp

👋 Welcome to my repository for Assessment 1 of the Health Data Science module, part of the MSc Artificial Intelligence program at the University of Westminster! This project focused on learning the basics of R programming and applying data analysis techniques to solve healthcare challenges. Below, you'll find the details and file structure of the repository. Enjoy exploring! 🚀

---

📂 Contents

• File Structure
• Data
• Notebooks
• Reports
• Usage
• Assignment Overview
• Learning Outcomes
• Notes

---

🗂️ File Structure

This repository is organized as follows:

├── data
│   └── raw
│       ├── abalone.data                                   # Dataset for Question 4
│       ├── abalone.names                                  # Header names for Question 4
│       ├── index                                          # Index for Question 4
│       ├── Q6-File1.csv                                   # Dataset for Question 6
│       └── Q6-File2.csv                                   # Dataset for Question 6
├── .gitignore                                             # Ignore unnecessary files
├── notebooks
│   └── Assignment1.R                                      # R script for the assignment 💻
├── README.md                                              # This file 📖
├── reports
│   ├── Assessment 1 Health Data Science - Donald Philp.pdf   # Final report (PDF) 📄
│   ├── Assessment 1 Health Data Science - Donald Philp.docx  # Word version for submission 📝
│   └── HDS-Coursework-Assessment1.pdf                        # Assignment questions and guidelines 📑
├── indesign                                               # Files used for compiling the final report 📊
│   └── [InDesign project files]
└── Assessment 1 Health Data Science - Donald Philp.pdf    # Another copy of the final report in the root folder 🔄

---

📊 Data

The data/raw directory contains the datasets used for the assignment:

• abalone.data: Dataset for Question 4.
• abalone.names: Header names for Question 4.
• index: Index file for Question 4.
• Q6-File1.csv: Dataset for Question 6.
• Q6-File2.csv: Dataset for Question 6.

📓 Notebooks

The notebooks directory contains the R script used for this assignment:

• Assignment1.R: The R script containing all the code used to solve the assignment tasks. This script is well-documented and modularized to follow good programming practices. 🖋️

📑 Reports

The reports directory contains the documents related to this assessment:

• Assessment 1 Health Data Science - Donald Philp.pdf: The final compiled report submitted for assessment. 📁
• Assessment 1 Health Data Science - Donald Philp.docx: Word document version for submission requirements.
• HDS-Coursework-Assessment1.pdf: The assignment questions and guidelines provided by the module leader.

🚀 Usage

To run the R code:

1. Clone the repository to your local machine:

   git clone [repository URL]

2. Navigate to the notebooks directory:

   cd notebooks

3. Open Assignment1.R in RStudio or any R-compatible IDE. Make sure the working directory is set correctly:

   setwd("path/to/repository")

4. Install the required packages as specified at the top of the Assignment1.R script.

5. Run the script to reproduce the analyses and results! 🎉

📋 Assignment Overview

The assignment required us to:

• Investigate healthcare challenges using R programming. 💉
• Apply data science concepts to analyze provided datasets.
• Document the approach, methods, results, and key findings.
• Develop solutions in R, following good programming practices.

🎓 Learning Outcomes

Through this assignment, I was able to:

• Understand R Programming Basics: Learn how to write R scripts for data analysis tasks.
• Data Manipulation and Analysis: Utilize R functions to manipulate and analyze healthcare data.
• Problem-Solving Skills: Apply computational thinking to address healthcare challenges.
• Good Programming Practices: Write modular, clean, and well-documented code. ✨
• Communicate Results: Document the entire process effectively in a structured report.

📝 Notes

• The .gitignore file ensures that unnecessary files are not added to the repository.
• The indesign directory contains InDesign files used to compile the final report (optional for running the code).
• A duplicate of the final report PDF is provided in the root directory for easy access.
• All datasets are included in the data/raw directory; no external data sources are required.

📧 Contact

For any questions or further information, feel free to reach out:

Name: Donald PhilpEmail: [Your Email Address]

Disclaimer: This repository is for educational purposes to showcase the work completed for the Health Data Science assignment. Please do not plagiarize or directly copy any part of this work. 🤓✌️

---

Thank you for taking the time to explore my work! 🧠💡 I hope this helps you understand the data analysis involved in the healthcare space. 🚑✨