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Sharing your work

An open source toolchain for creating and sharing data products.

Martin Frigaard

A primer on RStudio, Git, and GitHub.

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About

About

About the Book

This text covers a toolchain for creating data products with RStudio, Git, and Github. Through a single case study (the 2019 democratic presidential debates), we describe how to use Git (via the command line) to track changes to files, how to create beautifully formatted reproducible reports with Rmarkdown, and how to turn your data project into a website using Github pages.

You will walk away with a working data product (lots of code, data, and visualizations) a ton of resources, and a book for reproducing this process again!

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RSocial SciencesData Science

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Author

About the Author

Martin Frigaard

Contents

Table of Contents

Introduction: A toolchain for making data products

  1. What you will learn
  2. Why write a book for a toolchain?
  3. Who this book is for
  4. How we know this toolchain works
  5. Where do we find data?
  6. What tools will I need?
  7. After reading this book, you should feel better about…
  8. What this book covers
  9. What this book doesn’t cover
  10. How this book is structured
  11. What you’ll walk away with

Part 1: ‘Good enough’ data skills

  1. Why ‘good enough’?
  2. How to share your work
  3. ‘Good enough’ communication
  4. Get ‘good enough,’ then go for more if you need it

Part 2: “Have a workflow.”

  1. Principle 1: Use open-source software
  2. Principle 2: Write code
  3. Principle 3: Document everything in plain text
  4. Rmarkdown
  5. Additional reasons for using R & RStudio

Part 3: Setting up your data science process

  1. Example: FiveThirtyEight’s 2019 Presidential debate project
  2. The Command line: Unix and Windows
  3. Good enough command-line tools
  4. Command line recap

Part 4: Keep track of changes with version control

  1. Tracing our steps
  2. Git
  3. Setting up Git

Part 5: RStudio.Cloud

  1. What have we done so far?
  2. Recap on RStudio panes
  3. Where should we write code?
  4. Rmarkdown
  5. Documenting our projects
  6. Import
  7. Document changes with Git
  8. Wrangle
  9. Visualize
  10. Knitting RMarkdown files

Part 6: Putting your project on Github

  1. What we’ve done
  2. Sharing our work with Github pages
  3. Conclusion

Appendix

  1. Introduction: A toolchain for making data products
  2. Chapter 1
  3. Chapter 2
  4. Chapter 3
  5. Chapter 4
  6. Chapter 5
  7. Chapter 6

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