index.qmd

# About {.unnumbered}

This book was inspired by my students—particularly graduate students at [Brooklyn College](https://www.brooklyn.edu/) who wanted to ask meaningful research questions but felt held back by tools that were opaque, brittle, or difficult to reproduce. Over time, it became clear that learning *R* was not just about writing code; it was about learning how to think clearly, document decisions, and produce work that others (and your future self) can understand and reuse.

**Reproducible Research in R** is an open educational resource (OER) designed to help students, researchers, and practitioners build confidence using R as a complete research tool—from data import and visualization to statistical analysis and polished reporting. The goal of this book is not just to teach *what* buttons to press or *which* functions to call, but to teach a **workflow** that is transparent, explainable, and most importantly, reproducible.

This book was created in conjunction with the Open Educational Resources initiative at Brooklyn College and is freely available for learning, teaching, and adaptation.

## What You’ll Learn {#sec-what-learn}

By working through this book, you will learn how to:

- Use R and RStudio as an integrated research environment\
- Import, clean, and explore data using modern R tools\
- Visualize data clearly and intentionally\
- Conduct common statistical analyses used in applied research\
- Interpret results in context—not just report numbers\
- Create fully reproducible reports using Quarto

Throughout the book, reproducibility is treated not as an “extra” or an advanced topic, but as a **default practice**.

## What You Should Know First {#sec-what-to-know}

You do *not* need prior experience with:

- R
- Programming
- Command-line tools

Basic familiarity with research methods and statistics is helpful, but the focus of this book is on **implementation and workflow**, not statistical theory.

## What This Book Does Not Cover {#sec-what-it-covers}

This book is not intended to be:

- A comprehensive statistics theory textbook\
- A software engineering or computer science text\
- An advanced machine learning or big-data resource

Instead, it focuses on the tools and practices most commonly needed to conduct and present reproducible research in applied settings.

# How to Use This Book {#sec-how-use}

This book is designed to be flexible. You can read it cover-to-cover, jump directly to specific chapters, or use it as a reference alongside your own projects.

## Chapter Anatomy {#sec-chapter-anatomy}

The breakdown of the book is as follows:

- Part I: Foundations
  - Getting Started with R
  - Working with Data Using the tidyverse
  - Data Visualization with ggplot2
- Part II: Making Comparisons
  - Comparing Two Groups: Data Wrangling, Visualization, and t-Tests
  - Comparing Multiple Means
  - Analyzing Categorical Data
- Part III: Relationships and Modeling
  - Correlation
  - Linear Regression
  - Logistic Regression
- Part IV: Reproducible Communication
  - Reproducible Reporting with Quarto

Most chapters follow a consistent structure:

- Conceptual explanation of *why* a tool or method is useful\
- Step-by-step code examples\
- Visualizations and outputs\
- Interpretation and best practices\
- A checklist to reinforce reproducible habits

This repetition is intentional. Consistency helps build intuition.

## Code, Data, and Reproducibility {#sec-code-etc}

All code in this book is meant to be **run**, **modified**, and occasionally **broken**. Learning happens when you experiment.

::: callout-tip
### As my father always says:

That's why they put erasers on pencils
:::

### The reproresearchR Package {#sec-reproresearchR-package}

The datasets used throughout this book are provided in the companion R package `reproresearchR` (@R-reproresearchR), allowing readers to load data directly into R without manually downloading files. This ensures that all examples run the same way for everyone. All figures, tables, and analyses in this book are generated directly from code—never copied and pasted from external software—so that every result is fully reproducible. Chapters @sec-cat-analysis, @sec-cor, and @sec-linear-reg all use data from the package

The `reproresearchR` package also includes two versions of each chapter’s R script:

- Full Script: the complete code used to generate all analyses and figures in the chapter.
- Helper Script: a partially completed script with key sections removed, allowing you to work along with the textbook by filling in the missing code.

Once R and the `reproresearchR` package are installed (with RStudio recommended), readers have everything they need to follow along and successfully complete the analyses in this textbook.

::: callout-tip
#### Getting a chapter script

Each chapter has both a *helper* and a *full* chapter script for users who want to code alongside the textbook. The helper script provides a starting point for working through the chapter, while the full script contains the completed code.

With the `reproresearchR` package installed and loaded, simply choose the chapter and the version you want. For example:

```         
chapter_script(3, "helper", dest = tmp, overwrite = TRUE)
```

This retrieves the helper script for Chapter 3. Check out the `reproresearchR` package documentation to learn more about the available chapter scripts.
:::

### R and RStudio {#R-and-RStudio-mentioning}

If it isn't already evident, this textbook is about the programming language R. If you want to work with R on your computer, it is suggested that you have both:

1.  [R](https://www.r-project.org/)
2.  [RStudio](https://posit.co/downloads/)

If you are not able to download both on your computer but still want to learn/use R, you can use the online version [here](https://posit.cloud/content/yours?sort=name_asc).

Instructions on how to download and install both R and RStudio on your computer can be found in @sec-downloading-r.

## The Accidental Author {#sec-accidental-author}

The learning experience built around this textbook extends beyond completing individual assignments. It is informed by *The Accidental Author*, a teaching framework designed around a simple idea: **student work should outlive the semester**.

Rather than treating assignments as isolated exercises that are submitted, graded, and set aside, the Accidental Author framework encourages students to revise, accumulate, and organize their work over time. Individual assignments become parts of a larger body of work, allowing students to leave a course with tangible evidence of what they learned and created.

In courses using this textbook, reproducible research provides the technical foundation for this process. R and Quarto allow analyses, explanations, visualizations, and results to be combined into documents that can be revised and eventually assembled into larger projects and portfolios.

The framework is designed to be broader than any particular technology, course, or discipline. R and Quarto are simply how it is implemented here.

- 📖 Learn more about the framework and how it can be implemented in the classroom: [***The Accidental Author: An Implementation Guide***](https://martinezc1-the-accidental-author.share.connect.posit.cloud/)

- 📖 A condensed overview is also available: [***The Accidental Author: At a Glance***](https://martinezc1-the-accidental-author--at-a-glance.share.connect.posit.cloud/)

## From Assignments to Portfolios {#sec-student-portfolios}

One way the Accidental Author framework is implemented alongside this textbook is through student portfolios.

Throughout the course, students complete reproducible assignments using R and Quarto. Rather than allowing those assignments to remain separate files, students revise and compile their work into individual Quarto books. By the end of the course, what began as a series of assignments has become a structured portfolio documenting their development and demonstrating the skills they built along the way.

These individual portfolios are brought together in the **Student Portfolio Book**, a collective resource that provides access to each student's work in one place.

The result demonstrates a central idea behind the Accidental Author framework: students do not necessarily need an entirely new capstone project to create something substantial. With the right structure, the work they are already doing throughout a course can accumulate into something larger.

- 📖 Check out the collection: [**Student Portfolio Book**](https://martinezc1-reproducible-research-using-r--student-work-in-pract.share.connect.posit.cloud/) [@Martinez_Student_Portfolio_2026].

## The NYC Open Data Student Gallery {#sec-nyc-gallery}

Alongside their individual portfolios, students at Brooklyn College used the workflow outlined in this book to conduct original research projects using datasets from [New York City’s Open Data portal](https://opendata.cityofnewyork.us/).

Each student developed a fully reproducible analysis in R, compiled their work into a structured report, and produced a final research project grounded in real public data. These projects were then assembled into a collective publication: the NYC Open Data Student Gallery.

The Gallery showcases student research on topics ranging from public health and environmental conditions to social infrastructure and urban policy—demonstrating that reproducibility is not just a technical skill, but a tool for meaningful civic inquiry.

- 📖 Explore the full collection of student projects: [**NYC Open Data Student Gallery**](https://martinezc1-nyc-open-data-student-gallery.share.connect.posit.cloud/) [@Martinez_NYC_Open_Data_2026].

The Gallery reflects what is possible when reproducibility, open data, and structured workflows are integrated into the classroom from day one.

## Acknowledgments {#sec-acknowledgments}

This book would not exist without the curiosity, questions, and persistence of students at Brooklyn College. Their willingness to wrestle with messy data and imperfect code shaped both the content and the tone of this text.

Additional thanks go to the Open Educational Resources team at Brooklyn College and to the broader R community, whose commitment to open tools and shared knowledge makes projects like this possible.

## Interested in Teaching with this Textbook? {#interested-in-teaching-textbook}

If you are an instructor planning to use *Reproducible Research Using R* in your course, you may also find the companion textbook, *Teaching Reproducible Research Using R* [@Martinez_Teaching_Reproducible_Research_2026], helpful.

It includes course design guidance, implementation strategies, and instructional resources for integrating reproducible research into undergraduate and graduate classrooms.

- 📖 Read the companion textbook [Teaching Reproducible Research Using R](https://martinezc1-teaching-reproducible-research-using-r.share.connect.posit.cloud/)

::: callout-note
### Part of a Larger Ecosystem

*Reproducible Research Using R* is one component of The Open Data Lab ecosystem, which brings together open educational resources, open-source software, and public-facing research to support reproducible workflows.

As you continue through this book, you'll be introduced to tools such as `nycOpenData` and other resources developed through the Lab.

🌐 Learn more about the NYC Open Data Lab: <https://www.nycopendatalab.org>
:::

## 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](https://creativecommons.org/licenses/by/4.0/)

## How to Cite

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

Martinez, C. (2026). *Reproducible Research Using R* (Version 1.0.0). Zenodo. https://doi.org/10.5281/zenodo.19136755

### Version History

#### v1.0.0 (2026)

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