Research Transparency, Reproducibility, and Basic Data Analysis in R!
Preface
I started writing this book in the summer of 2023 primarily for my students in PPE 4000 - Research in PPE: Research Transparency, Reproducibility and Basic Data Analysis in R. This is a small research course for advanced undergraduates in the Philosophy, Politics & Economics (PPE) Program at the University of Pennsylvania. I am currently a Postdoctoral Researcher in the PPE Program. I’m also a BITSS Catalyst, which means that I am dedicated to educating the next generation of social scientists on research transparency tools and practices.
Beyond my students, I believe that the book can be useful for just about anyone interested in learning basic data analysis in R, especially if you have never learned any coding before. You may be wondering what separates this course from other introductory R courses or books that are freely available in the public domain? While there are MANY free resources to learn R, and MANY free resources to understand issues of Research Transparency & Reproducibility (RT2), I try to bridge the two ideas in this book at an undergraduate level. I have not seen a similar book, and thus it may be useful.
The prime motivation for doing so is that combining them in this way makes understanding RT2 concepts more concrete by implementing them directly in R. Additionally, by learning some basic R skills, students can walk away not only with an understanding of RT2 concepts, but also with a marketable skill for industry and academia. I never liked separating RT2 concepts and practices from actual quantitative work, and for that reason I have attempted to bridge the two (somewhat) cohesively in this semester-long course. Eventually the bridge will be more evident in the book (at least that’s my hope).
I wrote this book using Quarto Book in R, and created the illustrations using Canva and DALL-E 2. I intend to keep updating this book and adding new content when I have time. If you have any feedback on this book, please email me at shaonl@sas.upenn.edu.
