| Lesson | Date | Theme | Topic | Links |
|---|---|---|---|---|
| 1 | Tue, 9/5 | Manage & Organize | Project managment and coding best practices | |
| 2 | Thu, 9/7 | Share & Collaborate | Using shell commands | |
| 3 | Tue, 9/12 | Share & Collaborate | Version control with git | |
| 4 | Thu, 9/14 | Share & Collaborate | Developing code on GitHub | |
| 5 | Tue, 9/19 | Share & Collaborate | Collaborating with GitHub | |
| 6 | Thu, 9/21 | Tidy & Wrangle | Data Manipulation | |
| 7 | Tue, 9/26 | Repeat & Reproduce | Intermediate R programming I | |
| 8 | Thu, 9/28 | Repeat & Reproduce | Intermediate R programming II | |
| 9 | Tue, 10/3 | Document & Publish | Documentation and literate programming | |
| 10 | Thu, 10/5 | Document & Publish | Getting credit for your hard work | |
| — | Tue, 10/17 | Help | Drop-in help session | — |
| — | Tue, 10/24 | Show & Tell | An opportunity for you to show off what you've learned | — |
Reproducibility and Data Science in R
Fall 2023
Welcome to the syllabus for the CCT Data Science fall workshop series: Reproducibility and Data Science in R. If you didn’t register for the course this year, sign up for our mailing list to be notified when enrollment opens for next year’s iteration and to be notified of our other monthly workshops.
Schedule (tentative)
We’ll meet on Tuesdays and Thursdays from 11am to 1pm via Zoom (link)
Code of Conduct
Our group’s mission is to enable scientists. This means treating people with respect and responding in a polite and helpful way.
Our group is committed to ensuring a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.
Examples of unacceptable behavior by members, collaborators, and contributors include the use of sexual language or imagery, derogatory comments or personal attacks, trolling, public or private harassment, insults, or other unprofessional conduct.
Read our full code of conduct and please report any violations or concerns to the course instructors or to Kristina Riemer (kristinariemer@arizona.edu).
Helpful Reads
This workshop series doesn’t have anything like “required reading”, but we think these books and websites are good companions.
Data analysis in R:
Best practices for reproducibility:
- Good enough practices in scientific computing
- Data organization in spreadsheets
- A beginner’s guide to conducting reproducible research
- The Turing Way: Guide for Reproducible Research
Version control:
Citation
BibTeX citation:
@online{scott2023,
author = {Scott, Eric and Diaz, Renata and Guo, Jessica and Riemer,
Kristina},
title = {Syllabus},
date = {2023},
url = {https://cct-datascience.github.io/repro-data-sci//},
doi = {10.5281/zenodo.8411612},
langid = {en}
}
For attribution, please cite this work as:
Scott, Eric, Renata Diaz, Jessica Guo, and Kristina Riemer. 2023.
“Syllabus.” Reproducibility & Data Science in R. 2023.
https://doi.org/10.5281/zenodo.8411612.