Course Blueprint
Semester: Fall 2025
Instructor: Christian A. Martinez
Institution: Brooklyn College, CUNY
ORCID: https://orcid.org/0009-0005-6026-6454 | GitHub: github.com/martinezc1
Course Philosophy
This course is structured around a simple premise: reproducibility is not an advanced skill — it is a foundational habit.
Rather than treating R as a software tool to learn in isolation, this course approaches R as a complete research environment. Students learn how to move from raw data to interpretable results using transparent, inspectable, and repeatable workflows.
The emphasis is not merely on running statistical tests. The emphasis is on building analytical systems that others can understand, rerun, and verify.
By the end of the semester, students do not just complete assignments — they produce a cohesive analytical portfolio.
Learning Objectives
By the end of this course, students are able to:
- Write and execute R scripts for applied data analysis
- Clean and wrangle datasets using reproducible workflows
- Create publication-quality visualizations using
ggplot2
- Conduct and interpret common statistical analyses (t-tests, ANOVA, regression, logistic regression, Chi-Square)
- Reproduce analyses from published psychology papers with open datasets
- Communicate results in structured Quarto documents
- Apply open science practices (e.g., OSF, GitHub, transparent documentation)
- Assemble individual analyses into a polished Quarto book
Course Structure
The course follows a consistent weekly rhythm designed to reinforce applied learning.
Tuesdays — Concept + Guided Implementation
- Short conceptual lecture (20–25 minutes)
- Guided mini-lab exercises
- Emphasis on understanding the logic behind each method
Thursdays — Applied Workshop
- Live coding demonstration
- Hands-on work with real datasets
- Reproduction of analyses from published psychology papers
- Debugging as a collaborative process
- Screen-sharing and peer problem solving encouraged
This structure ensures that students immediately apply conceptual material in authentic research contexts.
Assessment Design
Evaluation in this course is cumulative and portfolio-driven.
Weekly Assignments (40%)
Each week introduces a new methodological framework. Assignments require students to:
- Clean and analyze real datasets
- Interpret statistical output
- Produce reproducible visualizations
- Write structured narrative explanations
Assignments progressively increase in complexity.
Midterm Project (30%)
The midterm integrates:
- Data import and merging
- Variable engineering
- t-tests and ANOVA
- Post-hoc comparisons
- Evidence-based recommendation
Students demonstrate their ability to conduct a complete inferential workflow independently.
Final Project (30%)
Students design and conduct an original research project using NYC Open Data or other civic datasets.
The final submission includes:
- Fully reproducible analysis
- Clear research framing
- Professional visualizations
- Public-facing interpretation
- In-class presentation
Conceptual Arc of the Semester
The course progresses through four major phases:
Foundations
- R basics
- Data structures
- Functions and packages
- Tidyverse workflows
Visualization & Exploration
ggplot2- Feature engineering
- Exploratory data analysis
- Data cleaning decisions
Statistical Inference
- t-tests
- ANOVA
- Correlation
- Linear regression
- Chi-Square tests
- Logistic regression
Reproducible Communication
- Quarto documents
- Structured reporting
- Transparent code practices
- Building a Quarto book
Each stage builds directly on the previous one.
Open Science Integration
Reproducibility is treated as a default expectation.
Students are required to:
- Load data programmatically
- Avoid manual spreadsheet edits
- Document all cleaning decisions
- Avoid hard-coding statistical results
- Use structured document formats
- Present analyses transparently
Open science practices such as preregistration, OSF usage, and GitHub version control are introduced and encouraged throughout the semester.
Institutional Policies & Resources
The course follows all Brooklyn College and CUNY policies regarding academic integrity, accessibility services, and student support resources.
Students are expected to adhere to the CUNY Academic Integrity Policy and to utilize campus support services when needed.
For official policy details, students should refer to the Brooklyn College website and Undergraduate Bulletin.
Closing Note
Reproducible research is not about perfection.
It is about clarity.
It is about transparency.
It is about building analytical habits that scale beyond a single course.
This blueprint reflects a semester-long progression from foundational skills to independent analytical authorship.