Syllabus
Instructor
- Dr. Matt Williamson
- Environmental Research Building 4125
- mattwilliamson@boisestate.edu
- @MwilliamsonMatt
- Schedule an appointment
Course details
- T TH
- August 23 - December 10, 2021
- 10:30 - 11:45
- Albertson's Library Rm201C
- Slack
Contacting me
E-mail and Slack are the best ways to get in contact with me. I will try to respond to all course-related e-mails and Slack messages within 48 hours (please allow additional time if you send your message during the weekend).
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:
- Develop reproducible workflows for manipulating, visualizing, and analyzing spatial data.
- Understand the unique components of spatial data and how those components fit in R’s data structures.
- Articulate the different formats of spatial data and identify which R packages are suited to each type.
- Describe why space matters for many social and ecological questions
- Implement a variety of descriptive and statistical spatial analyses.
- Leverage functional programming to automate and expedite spatial data operations.
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 the problems along the way (more on this below).
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.
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 assignements, I’m asking that you use the following steps prior to emailing/Slacking me with your coding questions.
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.
Course schedule
This course is organized in 4 sections:
Data structures and using R as a Geographic Information System (GIS).
Introduction to spatial datatypes used in R.
Extracting, summarizing, and visualizing spatial data
Common statistical analyses of spatial data
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.
Student Well-being
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.
Assignments and grades
You can find descriptions for all the assignments on the assignments page.
| Assignment | Points | Percent |
|---|---|---|
| Exercises (14 × 15) | 210 | 51% |
| Mini project 1 | 50 | 12% |
| Mini project 2 | 50 | 12% |
| Final project | 100 | 24% |
| Total | 410 | — |
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 90–100% | D | 60–69% |
| B | 80–89% | F | < 60% |
| C | 70–79% |
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.
Late work
With the exception of the mini projects and the final project, there’s no such late work. I would highly recommend staying caught up as much as possible, but if you need to turn something in late, that’s fine—there’s no penalty.
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.
So seriously, just don’t cheat or plagiarize!↩︎