This course covers statistics and data analysis for ecology and reproducible quantitative methods in R. Statistical analysis, modelling, simulation, and data analysis are essential skills for applying ecology concepts to data. This course is designed to meet a growing demand for reproducible, openly accessible, analytically thorough, and well documented science. Students will learn to develop ecological population models, analyze data, and document their research using the R programming language. No prerequisite programming experience is required.
Prerequisites: BIO220H1 and one of EEB225H1, STA288H1, or STA220H1
Tue and Thu 2:10 - 4:00 pm. Office hours are Tue 4:00 - 5:00 pm.
| Day | Room |
|---|---|
| Tue | Ramsay Wright (RW 109) |
| Thu | Ramsay Wright (RW 109) |
Office hours are on Tuesdays from 4 to 5 PM in RW 109.
The lecture hall has access to individual computers for the students. To use the computer workstations, students can login with their UTORid and password. Programs and packages that you install, and files that you save, will be deleted from these computers daily. Please bring a USB key to save files onto or email them to yourself. Students can use any of the lecture halls when there are no classes scheduled. Lecture halls are usually open 9 am - 5 pm, see the online schedules for available times.
Quercus is the preferred communication channel. If you need to use email instead, please address all general course-related issues to james.santangelo@mail.utoronto.ca, and project specific communication to the respective TA of your group. Prefix the subject matter with “EEB313”. If you do not receive a reply within 48 hours (excluding week-ends), please send a reminder.
Prof. Benjamin Gilbert, benjamin.gilbert@utoronto.ca , 416-978-4065, ES3035
All course information is accessible on its own website and on Quercus, including the syllabus, assessments, and lecture slides. If you have any problem accessing the material, let us know via email right away so we can fix the problem.
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!
You should be aware of the University of Toronto Code of Behaviour on Academic Matters. Also see How Not to Plagiarize. Note that 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 – there is no review of most online material and there are many errors out there. Use only academic or government internet sources when absolutely necessary. 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 instructor. 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. We will not enforce this, but the department will.
| Week | Date | Topic | Instructor |
|---|---|---|---|
| 1 | Sep 10 | Intro to course, programming, RStudio, R Markdown | Everyone |
| 1 | Sep 12 | Assignment, vectors, functions | Ahmed |
| 2 | Sep 17 | Data frames, intro to dplyr | Ahmed |
| 2 | Sep 19 | Data wrangling in dplyr, ggplot, tidy data | Ahmed |
| 3 | Sep 24 | More dplyr and ggplot | Ahmed |
| 3 | Sep 26 | Exploratory data analysis | Zoe |
| 4 | Oct 01 | Linear models and statistical modelling | Zoe |
| 4 | Oct 03 | Mixed effects models | James |
| 5 | Oct 08 | Model selection | James |
| 5 | Oct 10 | Multivariate stats | Amber |
| 6 | Oct 15 | Spatial stats | Amber |
| 6 | Oct 17 | Simulating data | James |
| 7 | Oct 22 | Ecological modelling | Amber |
| 7 | Oct 24 | Evolutionary modelling | Zoe |
| 8 | Oct 29 | Reproducible science | Everyone |
| 8 | Oct 31 | Datasets, hypotheses, begin projects | Everyone |
| - | Nov 05 | Fall break | - |
| - | Nov 07 | Fall break | - |
| 9 | Nov 12 | Project work | Everyone |
| 9 | Nov 14 | Project work | Everyone |
| 10 | Nov 19 | Project work | Everyone |
| 10 | Nov 21 | Project work | Everyone |
| 11 | Nov 26 | Project work | Everyone |
| 11 | Nov 28 | Project work | Everyone |
| 12 | Dec 03 | Group presentations | Everyone |
| Assignment | Type | Due date | Marks |
|---|---|---|---|
| Getting set up | Individual | Sep 19 | 4 |
| Basic R and dplyr | Individual | Sep 26 | 8 |
| dplyr and tidy data | Individual | Oct 03 | 8 |
| Data exploration, linear models | Individual | Oct 10 | 8 |
| Model selection, multivar. stats | Individual | Oct 17 | 8 |
| Spatial stats, randomization | Individual | Oct 24 | 8 |
| Modelling | Individual | Oct 31 | 8 |
| Mid-project update | Project, Group | Nov 14 | 10 |
| Challenge assignment | Individual | Nov 21 | 16 |
| Final report, presentation | Project, Group | Dec 03 | 22 |
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 be handed out on Tuesdays and are due 11:59 pm on the Thursday seven weekdays later. There will be a penalty of 5% per day (including week-ends) for late submissions.
| Inadequate (0 marks) | Adequate (4 marks) | Excellent (8 marks) | |
|---|---|---|---|
| Contribution to group work | Student contributed little to project; self-assessed contributions are low in quality and/or quantity; self-assessment is not consistent with actual contribution. | Student contributed adequately to project; made some significant contributions | Student substantially contributed to project to ensure success; self-assessed contributions are crucial to project; self-assessment is consistent with actual contribution. |
| Content | Missing crucial information; methods and results are inconsistent, not logical, or not adequately explained; conclusions are confusing or unsupported by results; unnecessary information included as clutter | Most essential information included; methods and results are adequately described; conclusions supported by results; most included material is relevant to report | All essential information included; methods and results are succinct, clear, logical, and scientifically valid; conclusions are creative and meaningful; project is concise throughout |
| Style and reproducibility | Code and writing are poorly organized, poorly formatted, missing units, difficult to read, poorly documented, difficult to reproduce analyses | Code and writing are well-organized, well-formatted, consistent use of units and significant figures | Code and writing are precise and clear throughout, free of errors, well-organized, well-documented, easily reproducible analyses, publication-ready |
| Presentation | Presentation is poorly organized; much too long or much too short; presentation is unclear; presentation is missing information; presentation is not scientific and professional; presentation uses too much jargon; not all team members participate; does not adequately address audience questions | Presentation is adequately organized; timing is appropriate; most information is presented logically; presentation is scientific and professional; most jargon is avoided; all team members participate but equally; audience questions are sometimes addressed well | Presentation is clearly and logically organized; presentation flows and is easy to follow; presentation includes appropriate information without jargon; presentation is well-rehearsed and high-quality; all team members participate equally; audience questions are clearly addressed |
As the final project is a team effort, all members within a group will receive the same mark in the final three categories and an individual mark for their contribution to group work. A final project that is considered to lie between two of the defined levels will be marked accordingly, e.g. between “Adequate” and “Excellent” would be 5, 6, or 7 marks.
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