About This Book

This book represents the applied companion to Reproducible Research Using R.

Over the course of the semester, students transformed structured assignments into fully reproducible analyses using real datasets, transparent workflows, and public-facing communication. What began as individual homework submissions evolved into polished research chapters assembled into this cohesive volume.

This book treats reproducibility not as an enhancement, but as a default professional practice.

Each chapter reflects a progression of skills: data import, cleaning, visualization, statistical testing, modeling, interpretation, and communication. More importantly, each chapter reflects a shift in mindset — from “running code” to designing complete analytical workflows.

This is not just a collection of assignments.
It is a curated portfolio of applied reproducible research.

What This Book Represents

This volume showcases:

Applied statistical reasoning
Transparent data cleaning and transformation
Reproducible modeling and interpretation
Clear, professional communication of results
Public-facing research using real-world datasets

Every analysis in this book can be rerun from start to finish. All figures, tables, and results are generated directly from executable code. Nothing has been manually edited or pasted from external software.

The goal is not perfection. The goal is transparency.

How This Book Is Organized

The chapters follow the structure of the course and mirror the conceptual arc of Reproducible Research Using R.

Each chapter applies a specific methodological framework to a real dataset.

Together, they form a complete analytical portfolio.

Reproducibility as a Professional Skill

Throughout this book, reproducibility is treated as a default practice rather than an optional enhancement.

Each chapter:

Loads data programmatically
Cleans variables transparently
Documents transformation decisions
Avoids hard-coded statistical results
Produces outputs directly from executable code

This workflow ensures that analyses are inspectable, repeatable, and defensible — qualities expected in graduate research, industry analytics, and public-facing data work.

From Assignment to Authorship

By assembling these projects into a Quarto book, students move beyond submission-based coursework and into publication-based thinking.

This book serves as:

A professional portfolio artifact
A demonstration of applied statistical competency
Evidence of reproducible workflow mastery
A foundation for conference submissions and civic engagement

Completing this book is not simply a course requirement.

It is a transition point — from student to analytical author.

License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

Share — copy and redistribute the material
Adapt — remix, transform, and build upon the material

Under the following terms:

Attribution — You must give appropriate credit.

Full license

How to Cite

If you use this textbook in your teaching, research, or projects, please cite it as:

Martinez, C. (2026). Teaching Reproducible Research Using R (Version 1.0.1). Zenodo. https://doi.org/10.5281/zenodo.19139302

Version History

v1.0.1 (2026)

Initial public release
Full textbook curriculum covering data analysis, visualization, and modeling in R
Used in Fall 2025 coursework at Brooklyn College (CUNY)

A Model of Publication in Practice

A previous cohort of students transformed their final projects into a collective publication: NYC Open Data Student Gallery - Brooklyn College

A Note to Readers

If you are a future employer, graduate program, research lab, or collaborator reviewing this book, you are not just reading assignments — you are reviewing a semester-long demonstration of analytical growth.

If you are a student currently enrolled in the course, this book represents what is possible when structured workflow and intentional practice are sustained over time.

Reproducibility is not a feature of advanced researchers.
It is a habit formed early.