Tools of the Trade

Content for Wednesday, August 27, 2025

Today we’ll be talking about reproducible workflows, why they’re important (generally) and some considerations for working with geographic data. We’ll also get introduced to version control and it’s the practice of open, reproducible research. Finally, you’ll meet Quarto our go-to tool to try and keep all of the pieces of the data analysis pipeline together.

Readings

The following readings are intended to give you some sense of the discussion surrounding the role of spatial data in understanding the world. They are a mix of old favorites and relatively recent reviews. You don’t need to read all of them or memorize them, but they are worth a skim. I bet you’ll find something interesting.

Setting the Stage

Technical Details

  • Authoring in Quarto - an intro to Quarto for developing different kinds of documents. Lots of other resources linked here!!

Coding Help

  • Chapter 1 - 6 in Venables et al., An Introduction to R (Venables et al. 2009) - for a quick refresher on data types in R (it’s only 30 pages)

  • Chapters 1-2 in Douglas et al., An Introduction to R - provides another intro to R that’s been updated and is an open-source book.

  • This RStudio Education page has a lot of additional tutorials to help you get started with R.

Objectives

By the end of today you should be able to:

  • Describe the benefits of reproducible data analysis workflows

  • Explain the benefits of version control

  • Generate a Quarto document and render it into .html

  • Execute your first commit in GitHub classroom

Slides

The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.

View all slides in new window Download PDF of all slides

References

Venables, W. N., D. M. Smith, R. D. C. Team, and others. 2009. An introduction to r. Citeseer.
Wickham, H., and G. Grolemund. 2016. R for data science: Import, tidy, transform, visualize, and model data. " O’Reilly Media, Inc.".