Getting Started
If you are coming to computational educational research from a traditional research background, the technical setup can feel like the hardest part. This section is designed to lower that barrier and help you build a practical foundation before moving into method-heavy chapters (Estrellado et al. 2020).
Think of this section as the minimum viable toolkit for the rest of the book. The goal is not to teach everything about R or Positron, but to give you enough structure to reproduce examples, adapt workflows, and work confidently with your own data (Wickham and Grolemund 2017; Healy 2018).
The three chapters in this section establish that foundation. Specifically, they cover:
- Positron and RStudio introduces your working environment, project organization, and workflow habits that support reproducibility.
- R covers core file types, execution patterns, and practical help-seeking strategies for common coding roadblocks.
- Tidyverse builds the day-to-day data workflow vocabulary for reading, cleaning, transforming, and visualizing data in R.
By the end of this section, you should have a functioning analysis environment and a clear workflow for moving from data to interpretable output.
How the Full Book Connects
This book follows a four-part progression:
- Getting Started builds the technical and workflow baseline.
- Computational Methods applies text, network, and numeric approaches to core research tasks.
- LLM Methods introduces cloud/local LLM workflows and AI-assisted analysis with explicit attention to responsible use.
- Communication focuses on sharing computational research transparently and reproducibly.
Across all sections, we prioritize the same principles: methodological clarity, reproducibility, and responsible interpretation of results.
Additional Resources:
- R for Data Science: https://r4ds.had.co.nz/ and R for Data Science (2e): https://r4ds.hadley.nz/