Assignment overview

You will get the most of out this class if you:

  1. Engage with the readings and lecture materials
  2. Regularly use R

Each type of assignment in this class helps with one of these strategies.

Weekly check-in

Each week you’ll submit a list of three most interesting and three most unclear things from the readings. (See the complete instructions and details here).

Exercises

Each class session has interactive lessons and fully annotated examples of code that teach and demonstrate how to do specific tasks in R. However, without practicing these principles and making graphics on your own, you won’t remember what you learn!

To practice working with {ggplot2} and making data-based graphics, you will complete a brief set of exercises for each class session. These exercises will have 1–3 short tasks that are directly related to the topic for the session. You need to show that you made a good faith effort to work each question. The problem sets will also be graded using a check system:

  • ✔+: (11.5 points (115%) in gradebook) Exercises are 100% completed. Every task was attempted and answered, and most answers are correct. Rendered document is clean and easy to follow. Work is exceptional. I will not assign these often.
  • ✔: (10 points (100%) in gradebook) Exercises are 70–99% complete and most answers are correct. This is the expected level of performance.
  • ✔−: (5 points (50%) in gradebook) Exercises are less than 70% complete and/or most answers are incorrect. This indicates that you need to improve next time. I will hopefully not assign these often.

Note that this is also essentially a pass/fail system. I’m not grading your coding ability, I’m not checking each line of code to make sure it produces some exact final figure, and I’m not looking for perfect. Also note that a ✓ does not require 100% completion—you will sometimes get stuck with weird errors that you can’t solve, or the demands of pandemic living might occasionally become overwhelming. I’m looking for good faith effort, that’s all. Try hard, do good work, and you’ll get a ✓.

You may (and should!) work together on the exercises, but you must turn in your own answers.

You will turn these exercises in using iCollege. You will include your weekly check-in in the first part of the document—the rest will be your exercise tasks.

Mini projects

To give you practice with the data and design principles you’ll learn in this class, you will complete two mini projects. I will provide you with real-world data and pose one or more questions—you will make a pretty picture to answer those questions.

The mini projects will be graded using a check system:

  • ✔+: (85 points (≈115%) in gradebook) Project is phenomenally well-designed and uses advanced R techniques. The project uncovers an important story that is not readily apparent from just looking at the raw data. I will not assign these often.
  • ✔: (75 points (100%) in gradebook) Project is fine, follows most design principles, answers a question from the data, and uses R correctly. This is the expected level of performance.
  • ✔−: (37.5 points (50%) in gradebook) Project is missing large components, is poorly designed, does not answer a relevant question, and/or uses R incorrectly. This indicates that you need to improve next time. I will hopefully not assign these often.

Because these mini projects give you practice for the final project, I will provide you with substantial feedback on your design and code.

#TidyTuesday

At some point before the end of the semester, you’ll need to (1) take a dataset, (2) do something neat with it, and (3) share it with the public somehow. (See the complete instructions and details here).

Final project

At the end of the course, you will demonstrate your data visualization skills by completing a final project.

Complete details for the final project (including past examples of excellent projects) are here.

There is no final exam. This project is your final exam.

The project will not be graded using a check system. Instead I will use a rubric to grade four elements of your project:

  1. Technical skills
  2. Visual design
  3. Truth and beauty
  4. Story

If you’ve engaged with the course content and completed the exercises and mini projects throughout the course, you should do just fine with the final project.