Introduction and The Big Idea
School of Life Sciences, University of Hawaii
2025-01-14
The Modern Computationally-Literate Scientist
- Uses computational tools to test ideas
- Has Computing Skills to:
- Handle any kind of data
- Implement any kind of test
- Produce graphics for exploration and communication
- Test and validate code (how do we know its right?)
- Interact with other computing systems and the Cloud
- Can archive and disseminate data and workflow
- Produce reproducible results!
Data Science Workflow
From R for Data Science 2e by Hadley Wickam, Garrett Grolemund, and Mine Çetinkaya-Rundel
- Question Development
- Exploration and Testing
- Communication
Our Work is Interdisciplinary
- Disciplinary Knowledge (Biology) -> Question Development
- Statistics -> Exploration and Testing
- Computer Science -> Repeatable, Scalable, Reusable
- Code MUST be FREE OF ERROR
- Clean and Well documented (understandable)
- Modular - enhances creativity and scalability
Classwork to Professional Science
There is a difference between one-time “getting it to work” vs. professional science (publication)
How the Tools Fit Together
| Need | Tools |
|---|---|
| Observe -> Record Data -> Data Table | Notebooks |
| Code -> Document -> Comment (annotate) | R |
| Organize Project -> Version Control -> Share | Git/GitHub |
| Communicate | Quarto/Rmarkdown |
Open Source Tools you will learn in this course
- R [watch short podcast, up to 4:00]
- Command Line (UNIX/ Windows/ File Commands in your operating system)
- Understand your computer’s organization and filepaths
- More precisely control your files and information
- Install some command-line software
- Git/GitHub [course repo]
- Quarto/Rmarkdown [scientific and technical publishing system]
- and many more…
How to Succeed
- Practice
- Make errors – figure out how to fix them
- Fearlessly ask questions
- Trial and Error is critical to learning
- Validate – check that the answer is right
- When you are developing a script, go back and clean it up!
- Save the correct, good code, throw out the mistakes
- Document so that you can understand it 1 year from now
Course Topics
- Your Computer
- Where information is stored - FILEPATHS
- Your OS (Operating System)
- Git/GitHub
- R
- Making them talk to each other
- Coding Fundamentals
- Tour of Univariate + Multivariate Statistics
- Graphics
- Special Topics - Tell me your interests! [Google Form]
Software
- Do you have R installed and working? (R studio is optional)
- For a longer walk-through here is another resource: Introduction to R/Rstudio
- Git/GitHub: Please Install for next time. Letʻs follow Introduction to Git/GitHub
Learning R - A first session
- Think about how R works as you try out commands
- “Mistakes” are opportunities to learn how R works!
- Learning Language involves a lot of trial and error
- Don’t be afraid to try - poke it - it won’t break!
- https://www.r-project.org Go to manuals, click on An Introduction to R
- Follow Section 2.1
- input -> R -> output
- What came out? What does it tell you about the rules R follows?
- Computers only do Exactly what you tell them to do
- Jump to Appendix A - letʻs try to understand some rules of R together