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Curiousity-driven Data Analysis and Coding Projects

Welcome to my repository for curiosity-driven data analysis and coding projects. This collection includes various projects I have worked on/am working on out of curiosity and a passion for data science and coding.

Table of Contents

Overview

This repository contains a variety of projects focused on data analysis and coding. Each project is self-contained with its own data, scripts, and README.md file.

Projects

  1. Project: Eugene Butte to Butte Race Results

    • Description: Web-scrapping and Analysis of the Eugene Butte to Butte Race Results from 2000 to 2024
    • Tools: R, RStudio, tidyverse, rvest, polite, janitor, RSelenium, netsta, binman, wdman, here
    • Link to Project 1
  2. Project: Making reproducible CV

    • Description: Using R-interface to make a professional CV
    • Tools: R, RStudio,
    • Link to Project 2
  3. Project: TidyTuesday Learning Challenge

    • Description: Exploring TidyTuesday Datasets
    • Tools: R, RStudio; other packages vary from week to week.
    • Link to Project 3

Usage

Each project has its own directory with detailed instructions on how to run the code and analyze the results. Please refer to the README file within each project directory for specific usage details.

Contributing

I welcome comments and suggestions for this repository. If you have feedback, please feel free to open an issue or leave a comment on the relevant project. For major changes or if you'd like to contribute code, please fork the repository and submit a pull request.

License

This repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the material as long as you provide appropriate credit to the original author.

Contact

If you have any questions or suggestions, feel free to reach out to me:


Thank you for visiting my repository; Happy coding!

About

This repository contains a collection of personal data analysis and coding projects undertaken out of curiosity and interest. Each project includes detailed documentation, code, and results to document the journey from data exploration to conclusion.

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