After a brief review of the advantages of a code-based workflow for ecological survey data, we introduce participants to some useful tools available via the R programming language for moving data along the data life cycle. We suggest some accessible tools in R for each step of the life cycle, and conclude with a walk through of how the functionality available in R can increase the reliability, efficiency, and transparency of scientific data management.
June 24, 2020 (3:00-4:30 EST)
Introduction to R
Resources for Teaching R
- DataCamp's tidyverse course
- learnr package
- RStudio teaching resources
- Data Wrangling, Exploration and Analysis with R "STAT 545"
- Learn the tidyverse
- Geocomputation with R
R Resources
Style Guides
R Packages
- Packaging your reproducible analysis
- R packages
- Packaging data analytical work reproducibly using R (and friends)
Project management
- Stop working directory insanity!
- A minimal project tree in R
- Organizing the project directory
- Designing projects
- Project management with RStudio
- File structure for data management
- Organizing files for data analysis
- A meaningful file structure for R projects
- An introduction to Docker for R users
- R Docker tutorial
Project Directory Templates
General Coding Best Practices
- What's in a name? The concepts and language of replication and reproducibility
- Best practices for scientific computing
- Good enough practices in scientific computing
- Ten simple rules for documenting scientific software
- Art of README - see examples and checklist
- Introduction to
roxygen2vignette
Version Control
Other