Health Data Science Journey: Your GitHub Checklist

by Alex Johnson 51 views

Hey everyone! 👋 Ready to dive into your Health Data Science adventure? This checklist is your trusty guide as you navigate the exciting world of health data. Think of it as your roadmap to mastering GitHub, RMarkdown, and building your very own repositories! Each item you tick off is a step closer to becoming a data whiz. Let's get started!

Week 2: Conquering Your First Repository

Your journey begins with setting up your digital workspace – your GitHub repository! This week is all about getting your hands dirty with version control. Don't worry, it's easier than you think. GitHub is an amazing platform for storing your code, collaborating with others, and tracking your progress. Think of it as a time machine for your code, letting you go back to previous versions if needed. That's the beauty of version control! Plus, it's a fantastic way to showcase your skills to potential employers. You will learn the importance of backing up your work. So, let’s get started with your tasks!

  1. Install GitHub Desktop and connect it to your GitHub account: This is your friendly interface to interact with GitHub. Download and install it, then link it to your account. It's like setting up your command center.
  2. Create a public repository and add a readme file: A repository is a project folder on GitHub. Make it public (so others can see your awesome work!) and add a README.md file. The README is like a welcome message to anyone visiting your repository. It explains what your project is about. It is very important.
  3. Publish the repository to GitHub: Once you create your repository locally, publish it to GitHub. This makes it accessible online.
  4. Add a new file in your local repository, commit the file addition, and push it to the remote repository on GitHub: Create a new file (e.g., a simple text file or a code file), and add it to your local repository. Then, commit it (save the changes with a descriptive message) and push it to GitHub (upload it). This is the core of how you'll be using GitHub.
  5. Modify the new file (or the Readme file), commit the change, and push it to the remote repository on GitHub: Make some changes to the file you just added (or the README.md file). Commit the changes and push them to GitHub. This demonstrates the version control aspect – tracking changes over time.
  6. Comment on this issue with a link to your repository: Share your repository link! It's a great way to celebrate your progress and get feedback from others.

Completing these steps will give you a solid foundation in using GitHub for your health data science projects. Remember to take it step by step, and don’t be afraid to experiment. Each project is an opportunity to learn and grow. Feel free to explore the platform to see what else it has to offer.

Week 4: Unleashing the Power of RMarkdown

Next up, you will learn the magic of RMarkdown! This tool is a game-changer for creating beautiful, reproducible reports and presentations. It's like combining your code, your analysis, and your narrative all in one place. RMarkdown is a very powerful tool that combines the power of R with the flexibility of markdown, enabling you to create dynamic documents, reports, presentations, and even websites. It’s perfect for creating reproducible reports that clearly explain your data analysis steps and results. You can embed R code directly into your document, and RMarkdown will execute the code and include the results (tables, plots, etc.) in your output. This ensures that your analyses are always up-to-date and easily reproducible by others.

  1. Create a new public repository on GitHub Desktop and add a readme file. Start fresh with a new repository specifically for your RMarkdown work.
  2. Publish the repository to GitHub: Make sure your repository is visible to the world!
  3. Open RStudio and create a new project in an existing directory (the directory of your repository): Link your RStudio project to your GitHub repository. This is where the magic happens.
  4. Download the file "R_Rstudio_Rmarkdown.Rmd" from the week 4 "Practical: R markdown" section on MyAberdeen and save it in your project: Get the example RMarkdown file to get started.
  5. Work through the Rmarkdown file, remembering to commit and push your changes to the remote repository on GitHub: The most important. Edit the file, run the code, and see the results. Every time you make changes, commit them to your repository and push it to GitHub. It’s a bit like taking snapshots of your progress.
  6. Optional: if you have questions you'd like to discuss with your group members, create issues on GitHub and let them know: Use GitHub's issue tracker to ask questions, discuss challenges, and collaborate with your group.
  7. Comment on this issue with a link to your new repository: Share your newly created RMarkdown repository. Let your colleagues know about what you have been doing.

By the end of this week, you'll be able to create stunning reports that tell a story with data. It’s a great skill to have. So, have fun and enjoy the process!

Week 5: Mastering Data in R

This week, you will dive into the core of health data science: reading and manipulating data in R. You will also learn about good coding practices. This will help you be organized and efficient as you work on more complex projects. Proper folder structure will also ensure your work is easy to understand, reproduce, and share with others. Keep in mind that code that's well-organized is easier to debug, modify, and collaborate on. So, let’s get started.

  1. Create a new public repository on GitHub Desktop and add a readme file. This will be the main repository for the R material from this course, so give it a good name :smiley:: Create a central repository for all your R materials.
  2. Publish the repository to GitHub: Make it public!
  3. Open RStudio and create a new project in an existing directory (the directory of your repository): Connect your project to your repository.
  4. Create an appropriate folder structure according to the good coding practices discussed in week 5: Organize your project with folders for data, scripts, and reports. This will help keep everything neat and tidy.
  5. Download the data files and Rmarkdown file from the week 5 "Practical: reading data in R" section on MyAberdeen and save them in the appropriate folders in your project: Get the necessary data and RMarkdown file for this week's practical.
  6. Work through the Rmarkdown file, remembering to commit and push your changes to the remote repository on GitHub: Execute the code, analyze the data, and make changes to the RMarkdown file. Commit and push your changes regularly.
  7. Optional: if you have questions you'd like to discuss with your collaborators, create issues on GitHub and let them know: Collaborate by creating and solving issues.
  8. Comment on this issue with a link to your new repository: Share your new repository, so everyone can see your amazing work.

By the end of the week, you will be equipped with skills for working with health data in R, preparing you for more advanced analyses. It's a great opportunity to explore the powerful capabilities of R and build your skills for data analysis.

Week 6-10: Your Project

Here comes the exciting part – your project! This is where you get to apply everything you've learned. It’s an opportunity to showcase your newly acquired skills, explore health data, and create something awesome. Remember, the journey is just as important as the destination. Have fun!

Comment on this issue with a link to your report repository: Share your report repository! This allows you to receive feedback and collaborate with your group.

By following this checklist, you'll not only complete the course requirements but also build a solid foundation in GitHub, RMarkdown, and data analysis. Enjoy the process and don't hesitate to ask questions. Good luck!

For further information, check out this GitHub Documentation.