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Instructors: Dr. Ben Bolker and Jonathan Dushoff | Course e-mail: macdataviz@gmail.com | Offices: HH-314 (Bolker), LSB 332 (Dushoff) | Office hours: TBA | Course web page : https://mac-theobio.github.io/DataViz/
Theoretical and practical grounding in data visualization in statistics and data science, including both the foundational work of Cleveland, Tufte, and Wilkinson and recent developments and controversies in the area of data visualization. Focus on reproducible production of effective, honest, aesthetically pleasing graphical outputs. For practical applications the course will focus on the ggplot2 package for R, but other more specialized platforms (e.g. ggobi, leaflet, D3.js) will also be discussed. Includes topics such as visualization of uncertainty; dynamic/animated graphics; high-dimensional and spatial data.
Prerequisite(s): Intermediate proficiency in R; knowledge of basic inferential statistics
Upon completion of this course, the student will be able to:
Work with R, RStudio (or other interfaces), GitHub, ggplot2 and other related R packages to generate elegant and reproducible graphical presentations of data.
Understand and apply principles of graphical design for quantitative information.
Effectively manipulate data in R to create reproducible pipelines for visualization.
Explain and justify graphical design choices on the basis of scientific, analytical, psychological, and aesthetic criteria.
The first half of the course will comprise weekly lectures and class discussion (1 2-hour and 1 1-hour in-person session per week; timing and location to be determined following an organizational meeting during the first week of the term). The second half of the course will shift over to mainly in-class work in groups on a term project. There will be some weekly reading assignments and 3-7 homework assignments, as well as a final project (possibly in pairs or groups).
Textbook: Claus Wilke, Fundamentals of Data Visualization; full text available online, hard copy can be purchased.
Computer resources: You will need access to a computer (Windows/MacOS/Linux) capable of running a recent release of the R programming language. Most computers from the last 5-10 years will be fine. It will be convenient, although not absolutely necessary, to have access to a laptop computer that can be brought to class for in-class work; please let the instructors know if this will be a problem.
We do not expect there to be a virtual component to the course. However, in the unlikely event that some components are online it is expected that you have reliable access to the following:
A computer that meets performance requirements found here.
An internet connection that is fast enough to stream video.
Computer accessories that enable class participation, such as a microphone, speakers and webcam when needed.
If you think that you will not be able to meet these requirements, please contact uts@mcmaster.ca as soon as you can. Please visit the Technology Resources for Students page for detailed requirements. If you use assistive technology or believe that our platforms might be a barrier to participating, please contact Student Accessibility Services, sas@mcmaster.ca, for support.
Part 1: Core topics (pre-midterm break, definitely covered)
| Topic | Schedule |
|---|---|
| Data manipulation basics | Week 1 |
| Graphical principles (Cleveland, Tufte, Wilkinson & Wickham) | Week 2 |
| Exploratory graphics/data analysis | Week 3 |
| Graphics for model diagnosis | Week 4 |
| Graphics for inference | Week 5 |
| Expository graphics; ethics and data viz | Week 6 |
Part 2: Additional topics (post break, order and details TBD)
| Topic | Schedule |
|---|---|
| Visualizing uncertainty | |
| Info viz vs. Data viz | |
| 3D/perspective plotting | |
| High-dimensional and compositional data | |
| Dynamic graphics | |
| Spatial data and mapping |
Grade Component |
Weight |
|---|---|
| Class participation (see below) | 15% |
| Weekly homework (3-7 assignments) | 35% |
| Project presentation (final week[s] of term) |
20% |
| Project writeup | 30% |
Homework rubric We mark homework assignments on a scale of 0-3. The rubric is:
0: no effort/not submitted
1: poor
2: OK (fulfils the terms of the assignment)
3: excellent
Historically most marks are in the 1.5 - 2.5 range. A mark less than 2 means that your work is deficient; you should work to address those deficiencies in subsequent assignments (if you don’t understand the cause of your low mark, please ask us). A mark of 2 means you shouldn’t worry about your grade in the course.
The mark/grade correspondence is approximately: 1.8-2 = B+, 2-2.2 = A-, 2.2-2.4 = A, 2.4-3 = A+.
Participation marks
To make participation marks more objective, part of this component will be based on out-of-class participation. At various times during the term, you will be asked to write a few sentences to a paragraph on a particular topic. The purpose of these assignments is to assess your general depth of engagement and understanding. If you don’t come to lecture you might not hear about these participatory emails. If you don’t complete these assignments you will receive a low participation mark. You can also enhance your participation mark by engaging in class.
You can gain your first participation mark by sending an e-mail to the instructors with the subject line “stat 744: <yourmacid> read the outline”. For example, Bolker’s subject line would be “stat 744: bolkerb read the outline”.
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It is your responsibility to understand what constitutes academic dishonesty.
Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at https://secretariat.mcmaster.ca/university-policies-procedures- guidelines/
The following illustrates only three forms of academic dishonesty:
plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
improper collaboration in group work.
copying or using unauthorized aids in tests and examinations.
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