R Package: moo4feed

Authors: Kehan (Sky) Sheng, Borbala Foris, Daniel Weary, Marina von Keyserlingk

Overview

moo4feed is an R package designed to extract novel individual-level traits from raw feeding and drinking data collected through precision livestock farming systems.

The package aims to support animal welfare research and data-driven monitoring by enabling reproducible, scalable analysis workflows.

Installation

To install the development version of moo4feed from GitHub, run:

install.packages("devtools")

# run below if you only want to install the package
devtools::install_github("skysheng7/moo4feed")

# run below if you want to read the vignettes, and install dependencies needed for development too
devtools::install_github("skysheng7/moo4feed", dependencies = TRUE, build_vignettes = TRUE)

Documentation

Full documentation and examples are available at the package website.

Contributors

• Principal Investigator: Marina von Keyserlingk
  • ORCID: 0000-0002-1427-3152
    
  • Affiliation: University of British Columbia
    
  • Email: nina@mail.ubc.ca
• Co-Investigator: Daniel Weary
  • ORCID: 0000-0002-0917-3982
    
  • Affiliation: University of British Columbia
    
  • Email: dan.weary@ubc.ca
• Contributor: Kehan Sheng
  • ORCID: 0000-0001-6442-5284
    
  • Affiliation: University of British Columbia
    
  • Email: skysheng7@gmail.com
• Contributor: Borbala Foris
  • ORCID: 0000-0002-0901-3057
    
  • Affiliation while working on this project: University of British Columbia
  • Current affiliation: University of Veterinary Medicine, Vienna
  • Email: forisbori@gmail.com
• Contributor: Kratika Rathi
  • Affiliation: University of British Columbia
    
  • Email: kratikar2011@gmail.com
• Contributor: Nicole Lopez
  • ORCID: 0009-0000-1828-0336
  • Affiliation: University of British Columbia
    
  • Email: nicoleangelpty@gmail.com
• Contributor: Colombe Tolokin
  • Affiliation: University of British Columbia
    
  • Email: colombe.tolokin@outlook.com

Acknowledgements

This R package was developed following the instructions and recommended workflows outlined in several key resources:

• R Packages by Hadley Wickham and Jenny Bryan
  
• Reproducible and Trustworthy Workflows for Data Science by Tiffany Timbers, Joel Ostblom, and Florencia D’Andrea
  
• Courses: DSCI 522 Data Science Workflows, DSCI 524 Collaborative Software Development by Dr. Tiffany Timbers

Project Information

• Funding: This project is funded by a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant (RGPIN-2021-02848; Ottawa, ON, Canada) awarded to MvK. KS also received funding from the Pei-Huang Tung and Tan-Wen Tung Graduate Fellowship (Vancouver, BC, Canada), Elizabeth R. Howland Fellowship (Vancouver, BC, Canada), Wilson Henderson Fellowship (Vancouver, BC, Canada), Hugo E Meilicke Memorial Fellowship (Vancouver, BC, Canada), and Mary and David Macaree Fellowship (Vancouver, BC, Canada).