This guide started as a tutorial that I developed for my fellow cognitive psychology graduate students at Penn State. R Markdown is a highly useful platform for combining data processing, analysis, and presentation, but there's a steep learning curve for developing proficiency beyond the basic skills. I wanted to make R Markdown approachable and efficient to use for graduate students like me.
I hope this guide will provide a solid jumping off point for other students who are curious about using R Markdown in graduate school. Here, you'll find the tips and tricks that I've compiled from countless hours of rifling through Stack Overflow and troubleshooting broken code. If you think this guide is missing anything or is unclear at any point, I encourage you to reach out and let me know.
I've included an overview of each of the chapters below.
The chapters are generally organized by complexity: they start with basic information on the parts of an R Markdown document, work through the important features, and end with information on troubleshooting.
While the guide assumes a basic level of comfort or familiarity with R, I also included supplementary materials on R itself at the very end, as well as practice documents for exploring R Markdown and tidyr.
What is R Markdown and why should you use it?
- YAML header
- Markdown
- Code chunks
R Markdown can transform plain text and code into several different document formats.
There are also multiple ways to Knit or render the output.
- Built-in templates
- Templates from R packages
- User-defined templates
The content of an R Markdown document includes the markdown text itself, as well as output from code chunks. Code chunks can output data, graphs, tables, and images. You can also reference variables from code chunks in markdown text.
There are many ways to customize the formatting of an R Markdown document. In the YAML header, you can specify different parameter options or reference external documents. You can also edit templates directly in certain cases. For local formatting, you can include inline code in the markdown sections.
Keeping documents and scripts organized is critical for reducing headaches (literal and figurative) when working on large R Markdown projects.
This chapter covers child R Markdown documents and importing R scripts to keep your content organized.
We've all been there. How can you avoid or handle warnings, errors, and other issues in R Markdown?
The supplementary materials are broken down into Beginner R, Intermediate R, and ggplot.
Beginner R covers basic R functionality, reading/loading in data, the tidyr philosophy, and using tidyr functions for transparent code.
Intermediate R focuses on free-response text, dynamic variables, and other functions.
The ggplot section goes over some of features that I use most often or think will make graphing more transparent for others.
The practice materials for R Markdown and R are also linked at the end.