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

Hopefully these assignements help reinforce those strategies.

Weekly assignments

Each week during our ‘lab’ section (usually on Thursdays), you’ll have an assignment due. These assignements involve a mix of coding, analyis, and reflection. These exercises will have 1–5 short tasks that are directly related to the topic for the day along with 1-2 reflection questions. The material we cover in class along with the guided examples should be all you need to complete the assignments, but feel free to ask your classmates, the Intenets, or me if you get stuck. You may (and should!) work together on the exercises, but you must turn in your own answers.

I will grade these memos using a check system:

• ✔+: (17.25 points (115%) in gradebook) Exercises are 100% completed. Every task was attempted and answered, and most answers are correct. Knitted document is clean and easy to follow. Work is exceptional. I will not assign these often.
• ✔: (15 points (100%) in gradebook) Exercises are 70–99% complete and most answers are correct. This is the expected level of performance.
• ✔−: (7.5 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.

Notice that is essentially a pass/fail or completion-based system. I’m not grading your coding ability, I’m not counting the exact number of words you’re writing, and I’m not looking for encyclopedic citations of every single reading. I’m looking for a good faith effort and thoughtful engagement, that’s all. Do good work and you’ll get a ✓.

You will turn these exercises in using GitHub Classroom.

Mini projects

To give you practice with the data and design principles you’ll learn in this class, you will complete two mini projects designed to help you make progress towards the completion of your final project (described below). The first mini-project will involve creating a spatial database that will serve as the foundation for your final project using joins, summaries, mutates, and extracts. The second mini-project will ask you to develop several visualizations that help you clarify hypotheses/questions. These two components will help you prepare for the final project wherein you must develop a spatial analysis of the data you’ve collected.

The mini projects will be graded using a check system:

• ✔+: (57.5 points (≈115%) in gradebook) Project is phenomenally well-executed and uses advanced R techniques. Project addresses an interesting question and extends the approaches covers in class. Code is well-documented and efficient. I will not assign these often.
• ✔: (50 points (100%) in gradebook) Project is fine, follows most workflow principles, answers a question from the data, and uses R correctly. This is the expected level of performance.
• ✔−: (25 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.

Final project

At the end of the course, you will demonstrate your spatial analysis and visualization skills by completing a final project wherein you use statistical analyses to analyze the data you’ve gathered and visualized in the Mini-projects. You’ll need to assess assumptions (e.g., spatial autocorrelation), interpret results, and create visualizations that illustrate key results from the analysis.

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 (is the workflow robust? Scaleable?)
2. Quality of visualization (is a map necessary? how do we deal with uncertainty?)
3. Story (Why do we care about this? Why does space matter?)

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.