Scientific Data Analytics with R
This website hosts a series of modules designed to build basic statistical computation skills with the software package, R (https://cran.r-project.org). R is an open-source statistical package that is widely used in academia, research and industry. It is available for free download from http://cran.ca.r-project.org for use on Windows, Mac OS, and Linux machines.
The modules are all designed around the scientific inquiry process—in particular the PPDAC (i.e., identify a Problem, develop a Plan, collect Data, perform Analysis, and draw a Conclusion) framework described in MacKay & Oldford (1994, 2000)—and use authentic examples from scientific research publications to apply statistical concepts and use R to run procedures typically taught in an introductory statistics or research methods course:
- Working with Data
- Visualizing and Summarizing Data
- Inference on the Centre of a Distribution
- Making Inferences on One Proportion
- Comparing Centres of Two Distributions
- Comparing Two Proportions
- Comparing More than Two Means
- Inference on Associations between Variables
Each module includes a set of learning resources including learning outcomes, an interactive instructional video and R exercises which allow for real-time feedback on R code. The modules are built on the assumption that the learner is already familiar with basic statistical concepts and methods, but no previous experience with R is necessary. These modules would fit well into a blended or fully online learning environment as a practical lab component, or they would work for a standalone course in statistical analyses using R, or for personal professional development.
Are you an instructor interested in using or adapting some of these modules for your course?
The materials are published as editable open educational resources and licensed under Creative Commons so that they can be modified, with attribution to the authors. They have been designed so that they can be incorporated into pre-existing courses in one of two main ways (although other options are possible);
- students can be directed to this website to work through some or all of the modules on their own (note: there is no ability to track student progress through the modules on this website), or,
- you, as an instructor can download the resources for local hosting.
If you download and host locally at your institution, you will be able to customize the modules for your course and you will be able to track student progress through the modules (e.g., the data generated by the embedded questions in the lecture videos support the SCORM standard) and build more comprehensive R exercises using PCRS-R.
Please visit Instructor Resources if you are interested in downloading any of the module resources to host locally in your Learning Management System (LMS) or on a server at your institution.
Accessibility Statement
Wherever possible, the content and resources in these modules have been designed to meet the WCAG 2.0 accessibility standards. All videos have been captioned and learning objects are keyboard accessible wherever possible.
Fair use
The content on these web pages (including lecture videos, PCRS-R exercises, PDFs, etc.) is released under a Creative Commons BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/), unless clearly noted as subject to an alternative license.
References:
MacKay, R.J. & Oldford, W. (1994). Stat 231 Course Notes Fall 1994. Waterloo: University of Waterloo.
MacKay, R.J. & Oldford, W. (2000). “Scientific Method, Statistical Method and the Speed of Light.” Statistical Science. 15(3).
Development Team
Project leads and instructors:
Bethany White
Associate Professor, Teaching Stream,
University of Toronto
Bethany White (PhD in Statistics-Biostatistics & MMATH in Statistics, both from the University of Waterloo, and BScH in Mathematics and Statistics from Acadia University) is an Associate Professor, Teaching Stream, in the Department of Statistical Sciences at the University of Toronto. Her research interests involve the impact of technology-enhanced and simulation activities on student learning and attitudes toward statistics. She has served on various committees of the Statistical Society of Canada, has been on the Editorial team for American Statistical Association publications, and organized and contributed to statistics and science education workshops and conferences in Canada and the US.
Jennifer Waugh
Lecturer,
University of Western Ontario
Jennifer Waugh (MSc in Biology, BEd with specialization in Biology and Mathematics, and BScH in Environmental Biology, all from Queen’s University, Kingston, ON) is a Lecturer in the Department of Biology, and the Department of Statistical & Actuarial Sciences at The University of Western Ontario. She is on the Executive Committee for the Ontario Consortium of Undergraduate Biology Educators (oCUBE), is journal manager for the open-access online publication, Ideas in Ecology and Evolution, and has been involved in the organization of science education conferences in Ontario and Canada.
PCRS-R Developers:
Andrew Petersen (University of Toronto Mississauga) led the development of PCRS – a platform which facilitates interactive programming exercises (https://mcs.utm.utoronto.ca/~pcrs/pcrs/). Jeremy Chinsen adapted this for R programming under the supervision of Andrew Petersen in the summer of 2017.
WordPress Developer and Graphic Design:
UI/UX and graphic design house
Designer/Developer: Tirzah Tward
Note: This project logo has been built upon the R logo which is © 2016 The R Foundation but is available under a CC-BY-SA 4.0 license
(https://creativecommons.org/licenses/by-nc/4.0/).
Acknowledgments:
This project was made possible by the generous support of Government of Ontario through eCampusOntario funding.
We would like to acknowledge and thank the University of Toronto Faculty of Arts and Science – Information & Instructional Technology (IIT), Yasha Farhan, Laurie Harrison (Director, Online Learning Strategies), Will Heikoop (Online Learning Coordinator, Centre for Teaching Support & Innovation) and Julian Weinrib (Special Projects Officer, Office of the Vice-Provost, Innovations in Undergraduate Education), all at the University of Toronto, for all their support on this project.
