PLNT6530|01 - Reproducible Data Sci for Ag

Winter 2026

Author

Dr. Adrian Correndo

DPAG-OAC-UOG

Welcome

Reproducible Data Science for Ag is offered at Department of Plant Agriculture (University of Guelph) by Dr. Adrian Correndo. It is designed for graduate students in crop and soil sciences to develop key skills in data science using R. This course emphasizes reproducibility in data analysis, ensuring that results can be consistently replicated. Students will learn essential data science concepts, and how to use functions, packages, and version control to effectively manage their data and collaborate with peers. Following tidy principles, the course promotes best coding practices for data wrangling, effective visualization, and clean deployment of statistical models common in agriculture. By the end of the course, students will be equipped to handle a variety of agricultural datasets and produce reliable, reproducible research outcomes.

Course Learning Outcomes

By the end of this course, students will be able to:

  1. Reproducibility: understand the principles and importance of reproducible data analysis.

  2. Data manipulation: develop proficiency in R, including key data structures, packages, and functions to read, clean, transform, and organize datasets.

  3. Data visualization: create informative and aesthetically pleasing visualizations of data.

  4. Modelling and iteration: apply various algorithms and statistical models common to plant agriculture, and implement techniques to handle multiple datasets simultaneously.

  5. Professional reporting: produce professional reports for sharing results.

  6. Version control: manage the basics of Git and Github for collaborative projects.

Course Technology Requirements

This course will use a variety of technologies and resources. To successfully participate in and complete this course, students will need access to the following

1. Communication tools:

CourseLink. This platform will be used as the main Course-Home Page. If you need any assistance with the software tools or the CourseLink website, contact CourseLink Support. Email: courselink@uoguelph.ca Tel: 519-824-4120 ext. 56939 Toll-Free (CAN/USA): 1-866- 275-1478. Support Hours (Eastern Time): Monday thru Friday: 8:30 am–8:30 pm; Saturday: 10:00 am–4:00 pm; Sunday: 12:00 pm–6:00 pm

Zoom. This course will use Zoom for lectures when in-person class is not possible. Check your system requirements to ensure you will be able to participate (https://opened.uoguelph.ca/student-resources/system-and-software-requirements/). A Zoom link for the class will be provided before the first day of class. Please, check Home-Page and announcements on CourseLink, and emails from the instructor (acorrend@uoguelph.ca).

2. Software & Tools:

  • R (latest stable version, available at CRAN).

  • RStudio/Posit IDE (desktop or cloud-based version for writing and running R code).

  • Course-Specific Libraries and Packages: Students will be required to install R packages. Detailed instructions will be provided in class.

  • Version Control and Collaboration Tools: Git (for version control) and a free GitHub account for collaborative project work and sharing code.

3. Computing Requirements:

A laptop or desktop computer capable of running R and RStudio (Windows, MacOS, or Linux). Minimum specifications include:

  • Processor: At least a dual-core processor.

  • RAM: 8 GB or more (16 GB recommended for handling larger datasets).

  • Storage: 10 GB of free space for software installation, course files, and datasets.

4. Internet Access:

Reliable high-speed internet for accessing online sessions, resources, downloading software, and using cloud-based platforms (e.g., Posit Cloud, GitHub).