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.

Class locations

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.

Contact info

Quercus is the preferred communication channel. If you need to use email instead, please address all general course-related issues to, 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.

Course Instructors

Supervising professor

Prof. Benjamin Gilbert, , 416-978-4065, ES3035

Course Website and Quercus

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.

Course learning outcomes

  1. Develop proficiency in the programming language R.
  2. Use R to apply statistics to analyze and interpret data.
  3. Choose appropriate analysis techniques for a variety of data types and formats.
  4. Learn and use techniques and best practices for reproducible, high-quality science.
  5. Learn how to work as part of a research team to produce a scientific product.
  6. Learn what is required to generate a scientific item ready for publishing.

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. 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.

Lecture schedule

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

Assessment schedule

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.

Final project grading rubric

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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