EEB313: Quantitative Methods in R for Biology
Syllabus
Land Acknowledgement
Although our students come from many locations around the world, we wish to recognize the land on which the University of Toronto was built. This land has historically been and still is the home of the Huron-Wendat, the Seneca, and the Mississaugas of the Credit River.
There is a First Nations House for Indigenous Student Services on campus. Please refer to their web page for more resources and information about honouring our land and their services for students.
Diversity and inclusion statement
As students, you all have something unique and special to offer to science. It is our intent that students from all backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be recognized as a resource, strength, and benefit.
Diversity can refer to multiple ways that we identify ourselves, including but not limited to race, national origin, language, cultural heritage, physical ability, neurodiversity, age, sexual orientation, gender identity, religion, and socio-economic class. Each of these varied, and often intersecting, identities, along with many others not mentioned here, shape the perspectives we bring to this class, to this department, and to the greater EEB community. We will work to promote diversity, equity, and inclusion not only because diversity fuels excellence and innovation, but because we want to pursue justice.
We expect that everybody in this class will respect each other, and demonstrate diligence in understanding how other people’s perspectives, behaviors, and worldviews may be different from their own. Racist, sexist, colonialist, homophobic, transphobic, and other abusive and discriminatory behavior and language will not be tolerated in this class and will result in disciplinary action, such as removal from class session or revocation of group working privileges. Please consult the University of Toronto Code of Student Conduct for details on unacceptable conduct and possible sanctions.
Please let us know if something said or done in this class, by either a member of the teaching team or other students, is particularly troubling or causes discomfort or offense. While our intention may not be to cause discomfort or offense, the impact of what happens throughout the course is not to be ignored and is something that we consider to be very important and deserving of attention. If and when this occurs, there are several ways to alleviate some of the discomfort or hurt you may experience:
- Discuss the situation privately with a member of the teaching team. We are always open to listening to students’ experiences, and want to work with students to find acceptable ways to process and address the issue.
- Notify us of the issue through another source such as a trusted faculty member or a peer. If for any reason you do not feel comfortable discussing the issue directly with us, we encourage you to seek out another, more comfortable avenue to address the issue.
- Contact the Anti-Racism and Cultural Diversity Office to report an incident and receive complaint resolution support, which may include consultations and referrals.
We acknowledge our imperfections while we also fully commit to the work, inside and outside of our classrooms, of building and sustaining a community that increasingly embraces these core values. Your suggestions and feedback are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups.
Wellness statement
We on the teaching team value your health and wellness. In order to succeed in this class, in university, and beyond, you must balance your work with rest, exercise, and attention to your mental and physical health. Working until exhaustion is NOT a badge of honor. If you are finding it difficult to balance your health and well-being with your work in this class, please do not hesitate to let us know. We are happy to help connect you with resources and services on campus and also to make accommodations to our course plan as needed. Our inboxes are always open, and we are also available for virtual chats by appointment. You have our support, and we believe in you.
Course Overview
This course covers statistical and data analysis, reproducible quantitative methods, and scientific computing in R to answer questions in ecology and evolutionary biology. Statistical and data analysis, modeling, and computing are essential skills for all biologists. This course is designed to meet a growing demand for reproducible, openly accessible, analytically thorough, and well-documented science. Students will learn to analyze and visualize data, develop mathematical models, and document their research using the R programming language. No programming experience is required.
Prerequisites: BIO220H1 and one of EEB225H1, STA288H1, or STA220H1
Time
Tuesdays 2:10 - 4:00 PM and Thursdays 12:10 - 2:00 PM EST.
Class Locations
All classes will be on the St. George campus. On Tuesdays, we will be in 371 Bloor, Room 36. On Thursdays, we will be in Sidney Smith Hall, Room 561.
| Office hours (in EST) | ||
|---|---|---|
| Mete | Tu 12-1pm | ESC3044 |
| Zoë | Wed 2-3pm | ESC3044 |
| Jessie | Tu 4-5pm | 371 Bloor, Room 36 |
| Gavia | Th 2-3pm | ESC3044 |
Contact protocol
If you have assignment-related questions, please email Gavia AND Jessie. If you have course-related or lecture-related questions, please email Mete AND Zoë. Include “EEB313” in the subject line. If you do not receive a reply within 48 hours (excluding weekends), please send us a reminder.
Absence policy
If you are feeling unwell, please do not come to class. Instead, take the time to recover fully. Please let us know if you are feeling sick - you will not be penalized for missing a lecture, and we will do our best to ensure that you are up-to-date with class materials when you return.
Accessibility needs
If you require accommodations for a disability, or have any accessibility concerns about the course or course materials, please notify your course instructors (Mete and Zoë), or contact Accessibility Services, as soon as possible regarding accommodations.
Course learning outcomes
- Develop proficiency in the programming language R.
- Use R to apply appropriate statistical tools to analyze and interpret data.
- Develop familiarity with mathematical models used in EEB.
- Develop familiarity with the command line and Git.
- Learn and use techniques and best practices for reproducible, high-quality science.
- Learn how to work as part of a research team to produce a scientific product.
Lecture schedule
| Week | Date | Topic | Instructor |
|---|---|---|---|
| 1 | Sep 3 | Intro to course | Everyone |
| 1 | Sep 5 | Base R: assignment, vectors, functions, strings, loops, etc. | Zoë |
| 2 | Sep 10 | Intro to command line and GitHub | Mete |
| 2 | Sep 12 | Data wrangling! | Zoë |
| 3 | Sep 17 | Data visualization in ggplot | Zoë |
| 4 | Sep 19 | Exploratory data analysis | Zoë |
| 4 | Sep 24 | Review using Farrell & Davies (2019) dataset | |
| 4 | Sep 26 | Project work | |
| 5 | Oct 01 | Introduction to statistical inference I | Mete |
| 5 | Oct 03 | Introduction to statistical inference II | Mete |
| 6 | Oct 08 | Linear models I | Mete |
| 6 | Oct 10 | Linear models II | Mete |
| 7 | Oct 15 | Model selection | Jessie |
| 7 | Oct 17 | Multivariate statistics | Gavia |
| 8 | Oct 22 | Mathematical models in ecology and evolution I | Mete |
| 8 | Oct 24 | Mathematical models in ecology and evolution II | Mete |
| 9 | Oct 29 | Reading break | - |
| 9 | Oct 31 | Reading break | - |
| 10 | Nov 05 | Project work | |
| 10 | Nov 07 | Project work | |
| 11 | Nov 12 | Project work | |
| 11 | Nov 14 | Project work | |
| 12 | Nov 19 | Project work | |
| 12 | Nov 21 | Project work | |
| 13 | Nov 26 | Project work | |
| 13 | Nov 28 | Project work | |
| 14 | Dec 03 | Group presentations | Everyone |
Assessment schedule
| Assignment | Type | Submitted on | Due date | Marks |
|---|---|---|---|---|
| Individual interest description | Individual | Quercus | Sep 12 | 1 |
| Basic R, command line, & Git | Individual | Quercus | Sep 19 | 8 |
| Data wrangling | Individual | Quercus | Sep 26 | 8 |
| Data visualization and exploration | Individual | Quercus | Oct 03 | 8 |
| Introduction to inference | Individual | Quercus | Oct 15 | 8 |
| Project proposal | Group | GitHub | Oct 17 | 3 |
| LMs, model selection, and multivariate | Individual | Quercus | Oct 24 | 8 |
| Challenge assignment | Individual | Quercus | Nov 17 | 20 |
| Mid-project update | Group | GitHub | Nov 21 | 6 |
| Presentation | Group | In-class | Dec 03 | 10 |
| Final report | Group | GitHub | Dec 10 | 20 |
There are 100 marks in total. Your final course mark will be the sum of your assignment scores, which will be translated to a letter grade according to the official grading scale of the Faculty of Arts and Science.
Assignments will be distributed and submitted in the R Markdown format via Quercus. Assignments will typically be handed out on Thursdays after class and are due at 8:00 PM on the following Thursday.
The Challenge Assignment is equivalent to a take home exam. The format will be the same as the other assignments, but this assignment is designed challenge you to go a little beyond what was taught in class. It will be distributed on 9:00 AM on Nov 11, and it will be due 11:59 PM on Nov 17. Students are welcome to work in a group on this assignment, but each student must submit their own original work. No extensions will be granted on this assignment except under the same extra-ordinary circumstances akin to those under which an exam might be deferred. We only expect you to do your best!
Per our stance on supporting student’s mental health, we are happy to accommodate a 72-hour extension for one of the assignments, no questions asked. Otherwise, except under extenuating circumstances, there will be a penalty of 5% per day (including weekends) for all late submissions. If you foresee needing an extension, please email Zoë AND Mete as soon as possible. This policy does not apply to the Challenge Assignment, Presentation, or Final Report.
All submissions to Quercus/GitHub must be submitted as PDFs (i.e., knitted).
Improving your writing skills
Effective communication is crucial in science. The University of Toronto provides services to help you improve your writing, from general advices on effective writing to writing centers and writing courses. The Faculty of Arts & Science also offers an English Language Learning (ELL) program, which provides free individualized instruction in English skills. Take advantage of these!
Academic integrity
You should be aware of the University of Toronto Code of Behaviour on Academic Matters. Also see How Not to Plagiarize. Notably, it is NOT appropriate to use large sections from internet sources, and inserting a few words here and there does not make it an original piece of writing. Be careful in using internet sources – most online material are not reviewed and there are many errors out there. Make sure you read material from many sources (published, peer-reviewed, trusted internet sources) and that you write an original text using this information. Always cite your sources. In case of doubt about plagiarism, talk to your instructors and TAs. Please make sure that what you submit for the final project does not overlap with what you submit for other classes, such as the 4th-year research project.
On the use of generative AI
We recognize that students use generative artificial intelligence tools such as ChatGPT. If you use such tools in this course, we ask that you let us know. Given the limitations of these tools, and the fact we will be available to support your learning without the use of AI, we would recommend that you be very cautious when using generative AI. On any submissions where, e.g., ChatGPT, is used please indicate in the answer to the question how the tool was used.
FAS student engagement programs
There are a few programs on campus aimed at increasing student engagement with their coursework and keeping them socially connected. Recognized Study Groups are voluntary, peer-led study groups of up to 8 students enrolled in the same course. Meet to Complete are online drop-in study sessions for A&S undergrads. These are worth checking out if you are interested in participating in a study group.