Syllabus

STAT 198, Fall 2024: Principles of Data Visualization

📊 Description

This is an introductory course to the principles of data visualization and their applications with R. Topics include:

  • Role of graphics in a data analysis project;

  • Grammar of graphics and its implementation with ggplot2;

  • Use of analytical patterns/tasks to choose the right kind of graphic;

  • Understand common color models and their relationship to human vision;

  • Use of color with tools such as the color wheel, color schemes, color pickers, and color palettes;

  • Designing principles (e.g. data-to-ink ratio, good practices, visual perception);

  • Assessment of graphics (what makes a graphic good);

  • Animated and interactive graphics.

🎯 Learning Outcomes

At the end of the course students will be able to:

  • Explain the importance/need of visualizing data.

  • Describe the framework of the grammar of graphics for creating plots.

  • Use functions from ggplot2 and other packages for making graphics in R.

  • Specify colors through the use of a color model, and its hexadecimal syntax.

  • Demonstrate an adequate encoding of data with color.

  • Recognize color schemes when inspecting a graphic.

  • List five or more of the analytical patterns/tasks (proposed by Stephen Few) for choosing the right graphic.

  • Apply and/or suggest changes that increase the data-to-ink ratio of a graphic.

  • Assess the effectiveness of a graphic intended for reporting/communicating purposes.

  • Produce animated charts as well as graphics with interactive elements.

ℹ️ Prerequisites

Because our main computational tool is R (and RStudio), it is desirable to have taken a course in which you got an introduction to R (e.g. STAT 20, 33A, 33B, 133; PB HLTH 142). It is also nice to have some previous experience analyzing data. Being concurrently enrolled in these classes is also acceptable.

🎓 Methods of Instruction

We will be using a combination of materials such as slides, tutorials, sample scripts, reading assignments, live demos, and chalk-and-talk. In-class attendance is mandatory. The main computational tool will be the computing and programming environment R. The main workbench will be the IDE RStudio.

📚 Textbooks

The following books are not required, but will be referenced throughout the course for slides, assignments, and more. They can all be found online:

  • Data Visualization by K. Healy
  • Geocomputation with R by Lovelace, J. Nowosad, and J. Muenchow.
  • Now You See It: Simple Visualization Techniques for Quantitative Analysis by S. Few

📁 Assignments - Homeworks

Homework assignments will give the students a platform to demonstrate and reinforce concepts covered during lectures.

HWs will generally be assigned every week.

You will submit your homework to Gradescope: typically your source file (qmd file), and the produced output (html doc).

You must write your own answers (using your own words, and your coding style—see Academic Honesty policy below).

📖 Readings

There will also be weekly readings, mostly consisting of articles from The New York Times.

📆 Attendance

Attendance will be taken daily. In case of emergency, contact instructors for excused absences. You will still be expected to complete the class work from that date and email the instructors your work.

🏫 Learning Cooperatively

With the obvious exception of quizzes, we encourage you to discuss all of the course activities with your friends and classmates as you are working on them. You will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally.

☝️ Academic Honesty

Cooperation has a limit, however. You should not share your code or answers directly with other students. Doing so doesn’t help them; it just sets them up for trouble. Feel free to discuss approaches to the problems with others, but you must write your own solutions. Please complete your own work and keep it to yourself. If you suspect other people may be plagiarizing you, let us know ASAP. For more information please read the Honor Code Guide for Syllabi.

We expect you to do your own work and to uphold the standards of intellectual integrity. Collaborating on homework is fine and I encourage you to work together—but copying is not, nor is having somebody else submit assignments for you. Cheating will not be tolerated. Anyone found cheating will receive an NP grade and will be reported to the Center for the Student Conduct).

If you are having trouble with an assignment or studying for an exam, or if you are uncertain about permissible and impermissible conduct or collaboration, please come see me with your questions. Rather than copying someone else’s work, ask for help. You are not alone in this course! If you invest the time to learn the material and complete the projects, you won’t need to copy any answers.

🚸 Special Accommodations

Students needing accommodations for any physical, psychological, or learning disability, should speak with me during the first two weeks of the semester, either after or during class and see http://dsp.berkeley.edu to learn about Berkeley’s policy.

If you are a DSP student, please contact me as soon as possible so that we can work out acceptable accommodations.

For relevant DSP accommodations that provide occasional extensions on assignments, we may provide a two-day extension as long as you contact us before the assignment is due. More details about these considerations may be discussed with the DSP staff.

If you are an athlete or Cal band member, please check your calendar. Do not take the class if you are not available to attend 90% of classes.

🌻 Safe and Inclusive Environment

Whenever a faculty member, staff member, post-doc, or instructor is responsible for the supervision of a student, a personal relationship between them of a romantic or sexual nature, even if consensual, is against university policy. Any such relationship jeopardizes the integrity of the educational process.

Although faculty and staff can act as excellent resources for students, you should be aware that they are required to report any violations of this campus policy. If you wish to have a confidential discussion on matters related to this policy, you may contact the Confidential Care Advocates on campus for support related to counseling or sensitive issues. Appointments can be made by calling (510) 642-1988.

The classroom should be a safe and inclusive environment for everyone. The Office for the Prevention of Harassment and Discrimination (OPHD) is responsible for ensuring the University provides an environment for faculty, staff and students that is free from discrimination and harassment on the basis of categories including race, color, national origin, age, sex, gender, gender identity, and sexual orientation. Questions or concerns? Call (510) 643-7985, email ask_ophd@berkeley.edu, or go to http://survivorsupport.berkeley.edu.