Syllabus

Learning Outcomes

After successful completion of this course, you will be able to:

Understand the cognitive and psychological underpinnings of perceiving data visualization.
Identify good data visualizations and describe what makes them good.
Produce data visualizations according to types of questions, data, and more, with a particular emphasis on building a toolbox that you can carry into your own research.

Course Materials

There is no official textbook for this course (but if there was, it’d be Claus Wilke’s book which we’ll draw from heavily). However, many of you are coming in with different levels of knowledge and different types of questions, so I am providing some suggested readings below.

I have arranged for students in this course to receive free access to Data Camp, a library of R (other programming languages) tutorials. Sign up using your UC Davis email here: https://www.datacamp.com/groups/shared_links/9f274d67a5851e9f7c6245893b50a01c76ca3bdcc4aeb031a2265b6787d54357.

Beginner: If you have little knowledge of the R programming language (e.g., you are typically given example scripts that you do basic modifications to and struggle with debugging), parts of this course may be difficult for you. I suggest familiarizing yourself with the following (free) ebooks:

Hadley Wickham & Garret Grolemund: R for Data Science
Hadley Wickham: Advanced R

Advanced: If you have more knowledge of the R programming language, then I encourage you to use this course to think about how you program and to talk with me about typical challenges you have. For example, if you run dozens or hundreds of models and are typing all these out separately, there are solutions that are much less prone to human error.

Everyone: We will read a variety of books and articles throughout this course. One of the most frequent we will read excerpts from is below. Other readings will be updated throughout the course on the weekly descriptions below.

Claus Wilke: Fundamentals of Data Visualization

Kieran Healy, Data Visualization: A Practical Introduction

All course materials comply with copyright/fair use policies.

Technology Requirements

The lecture presentations, links to articles, assignments, and rubrics are located on this Canvas site for the course and on the Quarto site. To participate in learning activities and complete assignments, you will need:

Access to a working computer that has a current operating system with updates installed;
Reliable Internet access and a UCD email account;
A current Internet browser that is compatible with Canvas;
R and R Studio (see below)
Reliable data storage for your work, such as Box, Office 365, or a USB drive.

We will do all of our data cleaning work in this class using the R programming language. We will use RStudio to interface with R console for a more user-friendly experience.

Please install both R and RStudio before the first day of class. Here’s how:

Get the most recent version of R (free). Download the version of R compatible with your operating system (Mac, Linux, or Windows). If you are running Windows or MacOS, you should choose one of the precompiled binary distributions (i.e., ready-to-run applications; .exe for windows or .pkg for Mac) linked at the top of the R Project’s webpage.
Once R is installed, download and install R Studio (soon to be Pivot). R Studio is an “Integrated Development Environment”, or IDE. This means it is a front-end for R that makes it much easier to work with. R Studio is also free, and available for Windows, Mac, and Linux platforms.
Install the tidyverse library and several other add-on packages for R. These are sets of tolls or functions that will aid us in cleaning and wrangling data, and more. This is a non-exhaustive list that will get us started.

my_packages <- c(
  "plyr", "tidyverse", "furrr", "broom", "cowplot", "patchwork",
  "drat", "gapminder", "GGally", "ggforce", "ggridges", "gridExtra", 
  "MASS", "quantreg", "rlang", "scales", "here", "interplot", 
  "margins", "survey", "srvyr", "devtools", "future"
)

install.packages(my_packages, repos = "http://cran.rstudio.com")

Minimum Technical Skills Needed

Minimum technical skills are needed in this course. All work in this course must be completed and submitted online through Canvas and all assignments will be completed in R / Rmarkdown / Quarto. Therefore, you must have consistent and reliable access to a computer and the Internet.

The basic technical skills you have include the ability to:

Organize and save electronic files;
Use UCD email and attached files;
Check email and Canvas a few times / week;
Download and upload documents;
Locate information with a browser; and
Use Canvas.

However, you will spend about 50% of this course using R. Therefore, to get the most out of this class, I highly recommend having a better-than-beginner understanding or and experience with the R programming language. R is a skill, just like understanding the components of quality data and workflows, and for the purposes of this course, both are equally necessary and important. If you have any concerns about whether your R skills are strong enough for the course, please talk to the instructor or consider taking the course in a future year.

Course Assignments and Grading

General Assignment Information

All coursework (assignments) is secured in Canvas with a username and password.
All assignments are due on the day indicated on the course schedule.
Complete rubrics (final project presentations and paper only) will be provided in Canvas.

Problem Sets

One goal of this course is to teach you how to produce good data visualizations with a particular emphasis on being able to do so with your own data, rather than simply with toy data designed to work perfectly with different assignments. In the previous iteration of this class, we did not do this weekly, so this time, I want to experiment with a few different assignment formats throughout the quarter in order to see which is most effective. At the same time, I don’t want to overload you with assignments (or me with assignment prep and grading), so we won’t do assignments every week but rather every other week or so.

Problem sets (4% x 5 = 20%) will be due at the start of class the week after they are assigned. These will likely be graded for completion unless otherwise stated in the assignment.

This is good opportunity to:

Better understand challenges with your own data (relative to others)
Reflect on features of your current workflow you like or dislike
Critique your own work and note ideas to improve (I will probably do this a lot in class!).
Create a repository of ideas and code for future research.

Weekly Assignments

The goal of this course is not simply to teach you how to produce data visualizations. Rather, the goal is to teach you principles of good visualization, how to identify features of good visualizations, and how to produce visualizations with these features.

Weekly homework (20%) in this class will focus on principles of good visualizations. Each week, you will find two visualizations related to the topic of the week, one good example, and one bad example. Submit each of these via Canvas along with a one paragraph summary describing why each is a good or bad visualization by noon the day (Tuesday) before class. I’ll pull together some highlights for discussion the following day.

These will be graded for completion (you turned it in), relevance (it should be clear from the visualization or description how it connects to the weekly topic), and effort (a one sentence summary is not effort). You will not receive feedback on them unless there is an ongoing problem (e.g., lack of depth or effort).

This is good opportunity to:

Look to non-academic sources (newspapers, blogs, etc.) for good/bad visualizations.
Look at your literature of choice for visualizations you particularly like or dislike.
Critique your own work and note ideas to improve (I will probably do this a lot in class!).
Create a repository of ideas for future visualizations.

Final Exam

The final exam for this course is instead a final project, due at the day and time of the scheduled final exam. The last day of the course will (likely) be used for presentations on the final project in order to receive feedback from the class and instructor.

Additional information on the project will be provided as a separate document on Canvas. However, the basic structure of the project will be to take the skills that you have learned throughout the course and to create a new, original, and creative visualization of data from your own (or, if necessary, your lab’s) research. The goal will be that this will become a visualization in a publication that is, as I wrote above, worth at least 1000 words.

To ensure that these visualizations are as effective as possible, this will proceed in five parts:

Initial proposal of an idea submitted via Canvas.
15-30 minute meeting with me to refine the idea.
Updated proposal submitted via Canvas.
5-10 minute presentation to the class on the last day of the course (30% of your grade).
Results section write-up of the visualization coupled with a 3-5 page response summarizing the features of the visualization, how it was compiled, and any lingering questions or concerns you have with it (30% of your grade). Code will also be submitted (but data is not required).

Final Project (20%).

Extra Credit

Participate in a https://www.tidytuesday.com.
2 pt extra credit for each one you participate in (max 6 pt total).
Can post on Twitter or just create a document with the code and output
Submit on Canvas
- If posting, link the post in in your submission
- If not posting, attach the knitted file

Evaluation and Grading Scale

All grades will be posted on Canvas. You are strongly encouraged to check your scores in Canvas regularly. A final letter grade will be assigned based on percentages.

Assignment Weights	Percent
Problem Sets (5)	20%
Response Papers + Visualizations	20%
Final Project Proposal	10%
Class Presentation	20%
Final Project	30%
Total	100%

Grading Scale

Range	Letter Grade
92.5% - 100%	A
89.5% - 92.4%	A-
87.5% - 89.4%	B+
82.5% - 87.4%	B
79.5% - 82.4%	B-
77.5% - 79.4%	C+
72.5% - 77.4%	C
69.5% - 72.4%	C-
67.5% - 69.4%	D+
62.5% - 67.4%	D
59.5% - 62.4%	D-
0% - 59.4%	F

Course Policies and Procedures

Many of the below are also outlined in the UC Davis Code of Academic Conduct.

Attendance Policy

When you miss class, you miss important information, not all of which will be available in the zoom recordings. This course is only 10 class meetings, so each meeting comprises 10% of your in-class time. If you need to miss more than one class, I suggest considering whether taking this course in a future term. I will teach this course either annually or biennially, so there will be future opportunities to take this course in many cases (e.g., for example, if you are a second year student who will miss two meetings, taking the course in your fourth year may be more effective).

Late Work/Make-up Policy

Late work will be allowed per instructor discretion. Please try to proactively communicate these needs. Assignments due at midnight will have a 9 hour “grace period” with no penalty. Each day late is subject to a 20% drop in course grade (e.g., a 10-point response is worth 8 points on day 1 late, 6 points on day 2 late, etc.).

Academic Integrity

You are expected to practice the highest possible standards of academic integrity. Any deviation from this expectation will result in a minimum academic penalty of your failing the assignment, and will result in additional disciplinary measures. This includes improper citation of sources, using another student’s work, and any other form of academic misrepresentation.

Plagiarism

Using the words or ideas of another as if they were one’s own is a serious form of academic dishonesty. If another person’s complete sentence, syntax, key words, or the specific or unique ideas and information are used, one must give that person credit through proper citation.

Incomplete Grades

You may assigned an ‘I’ (Incomplete) grade if you are unable to complete some portion of the assigned course work because of an unanticipated illness, accident, work-related responsibility, family hardship, or verified learning disability. An Incomplete grade is not intended to give you additional time to complete course assignments or extra credit unless there is indication that the specified circumstances prevented you from completing course assignments on time.

Instructional Methods

The course will be taught using multiple instructional methods. I will typically briefly (45-50 minutes) lecture at the beginning of the class on conceptual topics related to data cleaning and management. We will then have a 75 minute workshop, which will be a mix of going through code and examples together and working in small groups (if preferred) on short exercises. The remainder of the class will be available to receive support on Problem Sets for that week and other general questions (optional). The proportion of these will vary by week and portions of the course will be shortened or dropped as needed.

Diversity and Inclusion

The university is committed to a campus environment that is inclusive, safe, and respectful for all persons. To that end, all course activities will be conducted in an atmosphere of friendly participation and interaction among colleagues, recognizing and appreciating the unique experiences, background, and point of view each student brings. You are expected at all times to apply the highest academic standards to this course and to treat others with dignity and respect.

Accessibility, Disability, and Triggers [credit to Dr. David Moscowitz]

I am committed to ensuring course accessibility for all students. If you have a documented disability and expect reasonable accommodation to complete course requirements, please notify me at least one week before accommodation is needed. Please also provide SDRC (https://sc.edu/about/offices_and_divisions/student_disability_resource_center/) documentation to me before requesting accommodation. Likewise, if you are aware of cognitive or emotional triggers that could disrupt your intellectual or mental health, please let me know so that I can be aware in terms of course content.

Absences for Personal or Religious Holidays

I am committed to allowing students to exercise their rights to religious freedom. Accommodations on assignment due dates and absences will be allowed for students observing religious holidays that fall on course days. Please email me to let me know ahead of time to allow for accommodations to be made.

Title IX and Gendered Pronouns [credit to Dr. David Moscowitz]

This course affirms equality and respect for all gendered identities and expressions. Please don’t hesitate to correct me regarding your preferred gender pronoun and/or name if different from what is indicated on the official class roster. Likewise, I am committed to nurturing an environment free from discrimination and harassment. Consistent with Title IX policy, please be aware that I as a responsible employee am obligated to report information that you provide to me about a situation involving sexual harassment or assault.

Values [credit to Dr. David Moscowitz]

Two core values, inquiry and civility, govern our class. Inquiry demands that we all cultivate an open forum for exchange and substantiation of ideas. Strive to be creative, to take risks, and to challenge our conventional wisdom when you see the opportunity. Civility supports our inquiry by demanding ultimate respect for the voice, rights, and safety of others. Threatening or disruptive conduct may result in course and/or university dismissal. Civility also presumes basic courtesy: please be well rested, on time, and prepared for class (class time also includes a break to use the restroom, etc.), which includes silencing all personal devices.

My perspective is that we never cease being students of this world, so I believe that attentive, reflective people always have something to learn from others. Good discussions can be energetic and passionate but are neither abusive nor offensive. Vibrant, vigorous inquiry derives from discussions that:

challenge, defend, and apply different ideas, theories, perspectives, and skills,
extend a body of knowledge into different arenas and applications, and
result in a synergy that compels us to seek resolution to these discussions.

Copyright/Fair Use

I will cite and/or reference any materials that I use in this course that I do not create. You, as students, are expected to not distribute any of these materials, resources, homework assignments, etc. (whether graded or ungraded) without permission from the instructor.