Syllabus

Instructor

Course details

Mondays and Wednesdays
August 21–December 13, 2023
1:30–2:45 PM
SMASH 116
Slack

Course Description

Spatial data are ubiquitous and form the basis for many of our inquiries into social, ecological, and evolutionary processes. As such, developing the skills necessary for incorporating spatial data into reproducible statistical workflows is critical. In this course, we will introduce the core components of manipulating spatial data within the R statistical environment including managing vector and raster data, projections, extraction of data values, interpolation, and plotting. Students will also learn to prototype and benchmark different workflows to aid in applying the appropriate tools to their research questions.

Course Objectives

Students completing this course should be able to:

Articulate the opportunities and challenges posed by geographic analysis.
Select the appropriate R packages and functions for manipulating different types of spatial data
Design statistical analyses that integrate geospatial and tabular data
Construct appropriate data visualizations for conveying geospatial data
Develop reproducible workflows for manipulating, visualizing, and analyzing spatial data.

Expectations

Be nice. Be honest. Try hard.

The beauty of working with open source software is the community of users working on problems just like yours (and nothing like yours). Like any community, this one functions best when its members are kind, genuine, and make good-faith efforts to solve their problems along the way (more on this below).

You can (and should) expect me to:

Create a space where you can ask questions without fear of embarrassment or retribution
Provide feedback on your work within 1 week of submission
Respond to email and slack messages within 48 hours
Make every attempt to answer your questions (when I can) or point you toward resources that may help

In turn, I expect you to:

Treat all of us with respect and compassion
Make an honest effort to work through the assignments
Demonstrate that you have tried to solve your coding errors before asking me
Communicate with me when the course isn’t working for you

Prerequisite Knowledge and Skills

You can succeed in this class.

Some familiarity with the R statistical environment is helpful, but not necessary. My goal is to foster an environment where we are all learning from each other and sharing the tips and tricks that help us along the way. Learning R can be difficult at first—it’s like learning a new language, just like Spanish, French, or Chinese. I find it helpful to remember the following:

It’s easy when you start out programming to get really frustrated and think, “Oh it’s me, I’m really stupid,” or, “I’m not made out to program.” But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later. Even experienced programmers find themselves bashing their heads against seemingly intractable errors. If you’re finding yourself taking way too long hitting your head against a wall and not understanding, take a break, talk to classmates, e-mail me, etc.
— Hadley Wickham

If you want to start learning a few of the basics, the Resources tab has some background information to get you started. Note that this is not an exhaustive list - the number of new R tutorials available on the internet seems to be growing exponentially.

Getting Help With R problems

I am happy to help you work through your R coding challenges, but there are a lot of you and only one of me. Moreover, I may not always know exactly how to fix your problem any better than you do. In order to make sure that I am not the primary obstacle to your ability to complete the class assignments, I’m asking that you use the following steps prior to emailing/Slacking me with your coding questions.

Tip

When you send me a question, please let me know what you searched, why the solutions you found don’t work for you, and what output you are expecting**

We’ll spend a bit of time on asking better questions and getting better answers so don’t worry if you aren’t quite sure how this all works.

Google it! Searching for help with R on Google can sometimes be tricky. Google is generally smart enough to figure out what you mean when you search for “r reproject polygons”, but if it does struggle, try searching for “rstats” instead (e.g. “rstats reproject polygons”). Also, since most of your R work will deal with the RSpatial packages, it’s often easier to just search for the package name and operation rather than the letter “r” (e.g. “sf reproject polygons”). I often paste the specific error message I get along with the spatial package I’m using to try and help Google find my solutions.
Ask your colleagues We have an r_spatial chatroom at Slack where anyone in this class can ask questions and anyone can answer. Ask questions about code or class materials. You’ll likely have similar questions as your peers, and you’ll likely be able to answer other peoples’ questions too. As a bonus, Slack allows you to format code to make it easy for all of us to copy and paste your code and distinguish it from the rest of your question.
Use the forums Two of the most important sources for help with R-coding are StackOverflow (a Q&A site with hundreds of thousands of answers to all sorts of programming questions) and RStudio Community (a forum specifically designed for people using RStudio and the tidyverse (i.e. you)). If you aren’t able to find an answer to your question from the thousands of existing questions, you can post your own. You’ll need to create a reproducible example so others can figure out what you’re trying to do and what error you’re receiving, but you’d be amazed how helpful the community can be.
Ask me! Sign up for a time to meet with me during student hours at https://calendly.com/mattwilliamson/. I’ll want to know what searches you’ve tried (so I don’t chase down answers that you’ve already seen) and what approaches you’ve tried and why they haven’t worked. Remember, I’m here to help (but not write your code for you).

Course Materials

R and RStudio

R is free, but it can sometimes be a pain to install and configure especially when dealing with spatial packages (we’ll talk more about why this is during class). To make life easier, I have set up an online RStudio server service, which lets you run a full instance of RStudio in your web browser. This means you won’t have to install anything on your computer and we should be able to avoid a number of the machine-specific issues that pop-up when 20 students have 20 different computers, operating systems (OS), etc. If you haven’t installed R on your local machine and would like some help getting that set up, there’ a useful set of instructions for installing R, RStudio, and all the tidyverse packages here.

Git and Github Classroom

All assignments will be managed using Github classroom. This will allow each you to have your own repositories for each assignment and make it easier for me to comment on and help with your code. To use this, you should sign up for the GitHub Student Developers Pack as soon as possible and send me your github username. Once I have that, I can add you to the course and make sure that you have access to all of the necessary data and example code.

Readings

The goal of this course is primarily to get you started with spatial workflows in R. That said, maps (and the spatial data that produce them) are extremely powerful and their use comes with risks and responsibilities. Although most of this course will focus on getting the code right, I’ll mix in a few readings each week to help tie the technical details of our code back to the broader contexts of spatial analysis or to illustrate new applications of the methods you are learning.

Course Schedule

This course is organized in 4 sections:

Getting Started: What is spatial analysis and how do we do it in R?
Spatial Data Operations in R: Prepping geospatial data for use in R
Statistical Workflows for Spatial Data: Putting spatial data to work!
Visualizing Spatial Data: Everyone loves a map…

The schedule page provides an overview of what to expect each week.

This syllabus reflects a plan for the semester. Deviations may become necessary as the course progresses.

Assignments and Grades

I teach this course because I believe that a) we can learn a lot about social and ecological processes by studying where they happen, b) integrating spatial analysis directly into statistical workflows makes those analyses more robust and reproducible, and c) overcoming coding challenges can provide a profound sense of accomplishment. That said, I recognize that there are many reasons that you are taking this course and that my objectives may differ from yours. In order to make sure that you get what you need out of this class, we’ll be using a mix of approaches for determining your grade in this course.

Self-assessment (12.5 pts x 2): During the first week of the course, I’m going to ask you to reflect on what you want out of this course (concepts, skills, practice, etc.). This assessment will help me do a better job of aligning the content of the course to your specific needs. Grading for the self-assessment is described on the assignments page.

Exercises (5pts x 10): There are ten homework assignments. These exercises are designed to reinforce the material we cover in lecture, give you practice designing and implementing your own workflows, and build habits that promote reproducibility in science. They also allow me get a sense for your engagement in the course. Exercises are due at 11:59PM on their due date (generally Thursdays). I will post the “key” within 3 days of the due date and will not accept submissions after the key is posted. If you turn the assignment in on-time with the required number of commits, you’ll recieve full credit.

Assignment Revisions (25pts x 3): We will have three “assignment revisions” due during the course. These provide an opportunity for me to check in and see how things are going. You’ll be able to update your responses to the homeworks based on the keys and reflect on what you’ve learned throughout the course of the assignments. You’ll also be able to provide additional feedback on how the course is going for you. Rather than assign arbitrary points to each assignment, I’m going to grade your assignment revisions using the following ‘levels’ (inspired by Sarah K. Johnson’s description of her graduate data analysis course at Tufts):

Please Resubmit: This indicates that either your code does not run as written (i.e., your Rmarkdown document will not compile on my computer), you did not use Git as instructed, and/or that your responses to the questions I posed indicate that you do not quite understand the material as well as I would like. You’ll need to schedule an appointment to talk with me and we’ll work out what you need to do to get credit for the assignment. Although there isn’t a hard deadline for this resubmission, the assignments build on each other so it’s in your best interest to complete the resubmission before you get to the next assessment. Failure to resubmit will result in no credit for the assessment.
Resubmit If You Like: This indicates that all of the code works as written and that you used Git, but that you may have missed some important concepts. Your are welcome to resubmit the assignment and address my comments to help polish the final product, but it is not required for you to get credit for the assignment.
Good To Go: All of your code works, you completed the necessary Git steps, and all of the pieces are there and polished. I may have some minor comments, but I don’t need you to address them for this assignment.

Final Project (50pts): The final project asks you to conduct an entire spatial analysis from layout to results. Grades on the final project are based on your objectives and your self-assessment of whether or not you achieved those objectives. Your first draft of the final project will be due December 5. I’ll make comments based on the same categories for the homework revision and you’ll have time to revise your submission prior to the final deadline of December 14.

You can find descriptions for all the assignments on the assignments page.

Grades

We’ll use a form of contract grading to determine your grades in the course. Contract grading allows us to have a conversation about what you want out of the course, what you expect to put into it, and what I think you need to be successful in deploying the skills we learn here. Based on your goals for course, we’ll sign a contract that instantiates your objectives into the grade you’ll receive for the course. Complete the assignments and meet your objectives and you’ll get the grade you chose.

The expectations for the grades are:

A You complete all of the self-assessments and at least 8 of the exercises. All of the assignment revisions achieve the “Good to Go” level. Your final project achieves the “Good to Go” level. My assessment of the various levels will be based on your objectives for the course and your ability to follow instructions.
B You complete all of the self-assessments and at least 8 of the exercises. At least one of the assignment revisions achieves the “Good to Go” level with the remainder achieving “Resubmit if you like”. Your final project achieves the “Resubmit if you like” level. My assessment of the various levels will be based on your objectives for the course and your ability to follow instructions.
C You complete all of the self-assessments and at least 6 of the exercises. All of your assignment revisions achieve the “Resubmit if you like” level. Your final project achieves the “Resubmit if you like” level. My assessment of the various levels will be based on your objectives for the course and your ability to follow instructions.
D You complete all of the self-assessments and at least 4 of the exercises. At least one of your assignment revisions achieves the “Resubmit if you like” level. Your final project achieves the “Resubmit if you like” level. My assessment of the various levels will be based on your objectives for the course and your ability to follow instructions.

Attendance and incomplete assignments

Attendance is an important part of this course. You are allowed to miss 2 classes without providing any justification (stuff happens). Beyond that, each additional absence will result in a 0.5 grading reduction (i.e., an A becomes and A-). Similarly, completing the assignments to a satisfactory level is vital to ensure you have a firm grip on the code and concepts. Hence, each assignment that fails to achieve a “Resubmit If You Like” will result in 0.5 grading reduction.

Late work

I would highly recommend staying caught up as much as possible, but if you need to turn something (other than the exercises and final project) in late, that’s fine—there’s no penalty.

Student Wellbeing

If you are struggling for any reason (COVID, relationship, family, or life’s stresses) and believe these may impact your performance in the course, I encourage you to contact the Dean of Students at (208) 426-1527 or emaildeanofstundents@boisestate.edu for support. If you notice a significant change in your mood, sleep, feelings of hopelessness or a lack of self worth, consider connecting immediately with Counseling Services (1529 Belmont Street, Norco Building) at (208) 426-1459 or email healthservices@boisestate.edu.

Learning during a pandemic

If you tell me you’re having trouble, I will not judge you or think less of you. I hope you’ll extend me the same grace.

You never owe me personal information about your health (mental or physical). You are always welcome to talk to me about things that you’re going through, though. If I can’t help you, I usually know somebody who can.

If you need extra help, or if you need more time with something, or if you feel like you’re behind or not understanding everything, do not suffer in silence! Talk to me! I will work with you. I promise.

This course was designed with you in mind

I developed this course to provide a welcoming environment and effective, equitable learning experience for all students. If you encounter barriers in this course, please bring them to my attention so that I may work to address them.

This class’s community is inclusive.

Students in this class represent a rich variety of backgrounds and perspectives. The Human-Environment Systems group is committed to providing an atmosphere for learning that respects diversity and creates inclusive environments in our courses. While working together to build this community, we ask all members to: * share their unique experiences, values, and beliefs, if comfortable doing so.

listen deeply to one another.
honor the uniqueness of their peers.
appreciate the opportunity we have to learn from each other in this community.
use this opportunity together to discuss ways in which we can create an inclusive environment in this course and across the campus community.
recognize opportunities to invite a community member to exhibit more inclusive, equitable speech or behavior—and then also invite them into further conversation. We also expect community members to respond with gratitude and to take a moment of reflection when they receive such an invitation, rather than react immediately from defensiveness.
keep confidential any discussions that the community has of a personal (or professional) nature, unless the speaker has given explicit permission to share what they have said.
respect the right of students to be addressed and referred to by the names and pronouns that correspond to their gender identities, including the use of non-binary pronouns.

We use each other’s preferred names and pronouns.

I will ask you to let me know your preferred or adopted name and gender pronoun(s), and I will make those changes to my own records and address you that way in all cases.

To change to a preferred name so that it displays on all BSU sites, including Canvas and our course roster, contact the Registrar’s Office at (208) 426-4249. Note that only a legal name change can alter your name on BSU official and legal documents (e.g., your transcript).

This course is accessible to students with disabilities.

I recognize that navigating your education and life can often be more difficult if you have disabilities. I want you to achieve at your highest capacity in this class. If you have a disability, I need to know if you encounter inequitable opportunities in my course related to:

accessing and understanding course materials engaging with course materials and other students in the course
demonstrating your skills and knowledge on assignments and exams.

If you have a documented disability, you may be eligible for accommodations in all of your courses. To learn more, make an appointment with the university’s Educational Access Center.

For students responsible for children

I recognize the unique challenges that can arise for students who are also parents or guardians of children. Any student needing to temporarily bring children or another dependent to class is welcome to do so to stay engaged with the class.

Academic Integrity

Academic integrity is the principle that asks students to engage with their academic work to the fullest and to behave honestly, transparently, and ethically in every assignment and every interaction with a peer, professor, or research participant. When a strong culture of academic integrity is fostered by students and faculty in an academic program, students learn more, build positive relationships and collaborations, and can feel more confident in the value of their degrees.

In order to cultivate fairness and credibility, everyone must participate in upholding academic integrity. Students in this class are responsible for asking for help or clarification when it’s needed, speaking up when they see unethical behavior taking place, and understanding and adhering to the Student Code of Conduct, including the section on academic misconduct. Boise State and I take academic misconduct very seriously. It’s important to know that when a student engages in academic misconduct, I will report the incident to the Office of the Dean of Students. I also have the right to assign sanctions, which could include requirements to revise or redo work, complete educational assignments to learn about academic integrity, and grade penalties ranging from lower credit on an assignment to failing this class¹. Students should learn more by reviewing the Student Code of Conduct.

Footnotes

So seriously, just don’t cheat or plagiarize!↩︎