Syllabus, Autumn 2024


Instructor: Jessica Cooperstone, Ph.D.
Email address: cooperstone dot 1 at osu dot edu (preferred contact method)
Phone number: 614-292-2843 (non-preferred contact method)
TA: Daniel Quiroz Moreno, quirozmoreno dot 1 at osu dot edu
Office hours: TBD

Course meeting time and place

Tuesdays synchronously from 4:10-5:55 pm in:

Course description

This course aims to introduce students to the principles and practice of data visualization. Students will learn fundamental principles of data visualization and create figures that appropriately and ethically represent their data. Data visualizations will be created in the R programming environment, using tools including the grammar of graphics implemented in ggplot2. In the process of creating visualizations, students will also become familiar with data handling and wrangling in R.

Course learning outcomes
By the end of this course, students should successfully be able to:

  • Recall and describe the fundamental goals and principles of data visualization.
  • Distinguish between good and bad visualizations, and understand how to make those are ineffective more effective.
  • Learn to use R, R Markdown, and ggplot to make clear, descriptive, and aesthetic visualization.
  • Apply principles learned in class, both theoretical and technical, to create effective visualizations.

Course delivery

Mode of delivery: This course is a hybrid-delivered course where I provide ~ 50 min of lecture material, followed by ~1 hour of recitation activities to do in-class (with assistance from the instructor and the TA). You can attend class in person or virtually via Zoom. You are also welcome to come to class with questions about the weekly videos, or problems you are currently encountering in creating your visualizations.

Attendance and participation requirements: This is a hybrid course, but is taught synchronously, meaning it is expected that you attend class, either in person or virtually (via Zoom) during its meeting time. I will not take attendance. If circumstances require you to miss class, it will be expected you watch the recorded sessions, and catch up on material on your own. I have found that students who attend classes more quickly and completely master course content.

Class recordings: To help you master material, and to better accommodate students, classes will be recorded, and recordings uploaded directly after class to Carmen. You can find a link to a OneDrive folder with the recorded lectures on the Syllabus page on Carmen.

Course schedule

Date Module Topic
2024-08-20 1: Principles Principles of data visualization
2024-08-27 1: Principles Good and bad visualizations
2024-09-03 2: Coding fundamentals R Markdown for reproducible research
2024-09-10 2: Coding fundamentals Wrangling, the basics
2024-09-17 2: Coding fundamentals ggplot 101
2024-09-24 2: Coding fundamentals Themes, labels, facets (ggplot 102)
2024-10-01 3: Data exploration Data distributions
2024-10-08 3: Data exploration Correlations
2024-10-15 Open session, capstone prep Open session, capstone prep
2024-10-22 3: Data exploration Annotating statistics
2024-10-29 4: Putting it together Principal components analysis
2024-11-05 4: Putting it together Manhattan plots and making lots of plots at once
2024-11-12 4: Putting it together Interactive plots
2024-11-19 4: Putting it together ggplot extension packages and complexheatmap
2024-11-26 No class, Thanksgiving Relaxing and eating
2024-12-03 4: Putting it together Capstone assignment open session

Course resources

There are no required textbooks for this course, though you will find many of the recommend texts and resources belowvery useful.

Required software

  • R: We will use the R programming environment for this class (free). You can do so many things in R (including building this course website).
  • RStudio Desktop: This IDE (integrated development environment) allows a user-friendly interface with the R programming environment, which we will use in class as well. You must have R before you download RStudio (free).
  • Microsoft Office 365: All Ohio State students are now eligible for free Microsoft Office 365. Full instructions for downloading and installation can be found at

Prior R experience

You do not need to be an R expert for this class, but I will assume working-level knowledge of R programming. If you have no experience with R, but would still like to take this class, you can. I ask then you get yourself up to speed by taking this free online class (audit only) before the start of the 3rd week of class. The course will take 8-16 hours to complete so please leave yourself enough time to do so before week 3. Tips and tricks in R will be scattered throughout the course material.


How your grade is calculated

  • Module assignments: 40 points (10 points per assignment, 4 assignments)
  • Class reflections: 20 points (2 points per reflection, 10 reflections)
  • Capstone assignment: 40 points

See the Assignments tab for additional information.

Assignment descriptions

Module assignments

Description: After each module, there will be an assignment to provide practice for the techniques learned in class. Assignments will be posted at least one week prior to their due date, and due dates can be found on Carmen.

Grading: Each part of the assignment will have a certain number of points associated with it, provided along with the assignment.

Academic Integrity: Students may use class notes and class resource materials. Students are not permitted to collaborate on assignments. Students must complete work on their own.

Class reflections

After each week, you will write a 1 paragraph reflection on the material that was presented in class. This can include your thoughts on how you will use these lessons in your own research and data visualizations, ways in which you have investigated this topic (or expect to) on your own, or what else you’d like to learn in this area. The purpose of this assignment is not to be burdensome, but to keep you engaged in the course material, and providing feedback to me on what parts you’ve found useful, what you’ve struggled with, and what you’d like to see more of in the future.

There will be 10 class reflections (you can select which classes you want to reflect upon).

Due Date: Reflections are due 1 week after each class. For example, if class is on Tuesday September 1, the reflection for that class is due on Tuesday September 8 by 11:59pm.

Grading: Reflections will be graded as follows:

  • Full credit: Reflections thoughtfully engage with course content, demonstrate the student thought about material and how it would (or wouldn’t) be relevant to their work and development. This is the level of engagement I expect for this course.
  • Half credit: Reflections are superficial and demonstrate minimal engagement with the course content. A half credit grade indicates better engagement is required for the next reflection. I do not expect to assign these grades often.

Academic Integrity: Students may use class notes and class resource materials. Students are not permitted to collaborate on assignments. Students must complete work on their own.

Capstone assignment

Description: At the end of the semester, you will complete a capstone assignment where you create a series of visualizations based on your research data, data coming from your lab, or other data that is publicly available. I expect this assignment to be completed in R Markdown, annotated, and knitted into an easy-to-read .html file. I also expect your code to be fully commented such that I can understand what you are doing with each step, and why. You will be required to submit a capstone assignment “plan” by the beginning of November.

Grading: Guidance will be provided for the grading of the capstone assignment. Assignments completed outside R Markdown, or not knitted to a .html are not acceptable.

Academic Integrity: Students may use class notes and class resource materials. Students are not permitted to collaborate on assignments. Students must complete work on their own.

Due dates

Assignment Due Date
Reflections 1 week after each class
Module 1: Good and bad visualizations Monday, August 26, 2024
Module 2: Coding Fundamentals Tuesday, October 1, 2024
Module 3: Data Exploration Tuesday, October 29, 2024
Module 4: Putting it together Tuesday, December 3, 2024
Capstone plan Tuesday, November 5, 2024
Capstone Friday, December 6, 2024

Late assignments

I expect you will turn assignments in on time. Late assignments are not accepted. If there are extenuating circumstances that prevent you from turning in an assignment on time, please connect with me as soon as possible after such a situation arises for discussion about a possible deadline extension.

Please refer to the Assignments tab or Carmen for due dates.

Grading scale

Score Grade
93–100 A
90–92.9 A-
87–89.9 B+
83–86.9 B+
80–82.9 B-
77–79.9 C+
73–76.9 C
70–72.9 C-
67–69.9 D+
60–66.9 D
Below 60 E

Instructor feedback and response time

  • Grading and feedback: For assignments, you can generally expect feedback within 7 days.
  • Email: I will reply to emails within 48 hours on days when class is in session at the university.

Other course policies

Discussion and communication guidelines

I expect all communication will be respectful and thoughtful.

Academic Misconduct/Academic Integrity

Academic integrity is essential to maintaining an environment that fosters excellence in teaching, research, and other educational and scholarly activities. Thus, The Ohio State University and the Committee on Academic Misconduct (COAM) expect that all students have read and understand the University’s Code of Student Conduct, and that all students will complete all academic and scholarly assignments with fairness and honesty. Students must recognize that failure to follow the rules and guidelines established in the University’s Code of Student Conduct and this syllabus may constitute Academic Misconduct.

The Ohio State University’s Code of Student Conduct (Section 3335-23-04) defines academic misconduct as: Any activity that tends to compromise the academic integrity of the University, or subvert the educational process. Examples of academic misconduct include (but are not limited to) plagiarism, collusion (unauthorized collaboration), copying the work of another student, and possession of unauthorized materials during an examination. Ignorance of the University’s Code of Student Conduct is never considered an excuse for academic misconduct, so I recommend that you review the Code of Student Conduct and, specifically, the sections dealing with academic misconduct.

If I suspect that a student has committed academic misconduct in this course, I am obligated by University Rules to report my suspicions to the Committee on Academic Misconduct. If COAM determines that you have violated the University’s Code of Student Conduct (i.e., committed academic misconduct), the sanctions for the misconduct could include a failing grade in this course and suspension or dismissal from the University.

If you have any questions about the above policy or what constitutes academic misconduct in this course, please contact me.

Creating an environment free from harassment, discrimination, and sexual misconduct

The Ohio State University is committed to building and maintaining a community to reflect diversity and to improve opportunities for all. All Buckeyes have the right to be free from harassment, discrimination, and sexual misconduct. Ohio State does not discriminate on the basis of age, ancestry, color, disability, ethnicity, gender, gender identity or expression, genetic information, HIV/AIDS status, military status, national origin, pregnancy (childbirth, false pregnancy, termination of pregnancy, or recovery therefrom), race, religion, sex, sexual orientation, or protected veteran status, or any other bases under the law, in its activities, academic programs, admission, and employment. Members of the university community also have the right to be free from all forms of sexual misconduct: sexual harassment, sexual assault, relationship violence, stalking, and sexual exploitation.

To report harassment, discrimination, sexual misconduct, or retaliation and/or seek confidential and non-confidential resources and supportive measures, contact the Office of Institutional Equity by: 1. Online reporting form at, 2. Call 614-247-5838 or TTY 614-688-8605, 3. Or Email


The Ohio State University affirms the importance and value of diversity of people and ideas. We believe in creating equitable research opportunities for all students and to providing programs and curricula that allow our students to understand critical societal challenges from diverse perspectives and aspire to use research to promote sustainable solutions for all. We are committed to maintaining an inclusive community that recognizes and values the inherent worth and dignity of every person; fosters sensitivity, understanding, and mutual respect among all members; and encourages each individual to strive to reach their own potential. The Ohio State University does not discriminate on the basis of age, ancestry, color, disability, gender identity or expression, genetic information, HIV/AIDS status, military status, national origin, race, religion, sex, gender, sexual orientation, pregnancy, protected veteran status, or any other bases under the law, in its activities, academic programs, admission, and employment.

In addition, this course adheres to The Principles of Community adopted by the College of Food, Agricultural, and Environmental Sciences. These principles are located on the Carmen site for this course; and can also be found at For additional information on Diversity, Equity, and Inclusion in CFAES, contact the CFAES Office for Diversity, Equity, and Inclusion ( If you have been a victim of or a witness to a bias incident, you can report it online and anonymously (if you choose) at

Counseling and Consultation Services/Mental Health

As a student you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may lead to diminished academic performance or reduce a student’s ability to participate in daily activities. The Ohio State University offers services to assist you with addressing these and other concerns you may be experiencing. If you or someone you know are suffering from any of the aforementioned conditions, you can learn more about the broad range of confidential mental health services available on campus via the Office of Student Life Counseling and Consultation Services (CCS) by visiting or calling (614) 292- 5766. CCS is located on the 4th Floor of the Younkin Success Center and 10th Floor of Lincoln Tower. You can reach an on-call counselor when CCS is closed at (614) 292-5766 and 24 hour emergency help is also available through the 24/7 National Suicide Prevention Hotline at 1-(800)-273-TALK or at

David Wirt,, is the CFAES embedded mental health counselor in Columbus. He is available for new consultations and to establish routine care. To schedule with David, please call 614-292-5766. Students should mention their affiliation with CFAES when setting up a phone screening.

Dr. Schaad,, is the CFAES embedded mental health counselor in Wooster. She is available for new consultations and to establish routine care. To schedule with Dr. Schaad, please call 614-292-5766. Students should mention their affiliation with CFAES when setting up a phone screening.

Land Acknowledgement

We would like to acknowledge the land that The Ohio State University occupies is the ancestral and contemporary lands of the Shawnee, Potawatomi, Delaware, Miami, Peoria, Seneca, Wyandotte, Ojibwe and Cherokee peoples. The university resides on land ceded in the 1795 Treaty of Greeneville and the forced removal of tribes through the Indian Removal Act of 1830. We honor the resiliency of these tribal nations and recognize the historical contexts that have and continue to affect the Indigenous peoples of this land.

Accessibility accomodations

The university strives to make all learning experiences as accessible as possible. In light of the current pandemic, students seeking to request COVID-related accommodations may do so through the university’s request process, managed by Student Life Disability Services. If you anticipate or experience academic barriers based on your disability (including mental health, chronic, or temporary medical conditions), please let me know immediately so that we can privately discuss options. To establish reasonable accommodations, I may request that you register with Student Life Disability Services. After registration, make arrangements with me as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion.

SLDS contact information:; 614-292-3307;; 098 Baker Hall, 113 W. 12th Avenue.

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