BIO 5100 Resources
R & Coding
How to install R, RStudio, and R packages
- Videos, links and explanation for how to install, and what is R, RStudio and R Packages.
Posit Recipes
- Some tasty R code for how to do a variety of tasks in R.
R Coding (Tidyverse) Style Guide
- General style guidance for writing clear and efficient R code.
- And additional style guidance can also be found here: https://r4ds.hadley.nz/workflow-style.html
Coding Club Tutorials
- A collection of coding, data science and statistics tutorials with examples, part of a free and self-paced online course: Data Science for Ecologists and Environmental Scientists!
- Tutorials include: R basics, Data manipulation & visualization, modelling, spatial data analysis, Reproducible research (e.g., using GitHub and connecting your RStudio to GitHub) and more.
Data Carpentry Workshops
- Data Carpentry develops workshops on the fundamental data skills needed to conduct research, and its lessons are domain specific (e.g., ecology, genomics, geospatial). Lessons are in the form of self-paced tutorials, and are designed for researchers to teach themselves basic concepts and skills for working with data.
- Ecology Workshop
- Genomics Workshop
Conding Club Tutorials
- A collection of coding, data science and statistics tutorials with examples, mostly with R, but also with python and other languages.
(Youtube Channel) R for the Rest of Us
- A great series of short videos on various R topics. I really like this one because through the videos he uses a lot of newer tidyverse functions so I’m learning new ways to do things.
(Youtube Channel Playlist) Introduction to Data Science in R
- Brief 3-8 min videos on various topics From the Duke Center for Computational Thinking covering topics like data manipulation with dplyr, data visualization with ggplot2, and using other functions from the tidyverse.
R Coding Cheat Sheets
- Cheat Sheets for writing various types of R code
- Starting out, I’d suggest downloading (and maybe printing out) these two cheat sheets: Data Visualization w/ ggplot2 & Data Transformation w/ dplyr
Tidy Data Tutor: visualize your dplyr analysis pipelines
- Visualizes dplyr code to help you understand what specific dplyr functions are doing with the data
How to convert .qmd Quarto file to .R Script
- If your adviser wants you to send them your code in a .R Script, but you originally wrote it in a .qmd file…
- An other option is the render your .qmd file to a word doc and email them that.
Coding with AI help
(video) How to use GitHub Copilot and ChatGPT in RStudio
- In this video, we learn how to use GitHub Copilot and ChatGPT directly from within RStudio. Specifically, we’ll go over all of the setup steps that are required for using 1) the RStudio Copilot integration, and 2) the chattr package which allows you to interact with Large Language Models (LLMs) like the OpenAI models.
GitHub Copilot in RStudio
Rtutor.ai
- An external (web based) AI R code writer, if you don’t want to use something integrated within R Studio (i.e., one of the options above) this can help you figure out example code for a task or help debug (find errors) in your code.
Quarto and Git/GitHub
quarto.org
- Main reference website
Awesome Quarto (Github site)
- A(n awesome) curated list of Quarto talks, tools, examples & articles
(Video) Getting started with Quarto
- This video walks you through creating documents, presentations, and websites and publishing with Quarto. The video features authoring Quarto documents with executable R code chunks using the RStudio Visual Editor (https://quarto.org/docs/visual-editor/).
Build Websites with Quarto (Posit overview site )
If you don’t want to learn about how to use Git and GitHub, hosting your Quarto website on Netlify for free is less steps (see this step-by-step tutorial for building a quarto website then hosting it on Netlify)
If you’d like to host your Quarto Website on GitHub for free (and you’ve never used Git or GitHub before) there are multiple steps to FIRST (1) getting Git Installed & your GitHub setup:
(Watch) What is Git (version control software)? (2 min video)
(Watch) What is GitHub (repository/folder hosting website)? (3 min video)
(Then follow this guide to) Setting up Git & GitHub linked w/ RStudio
AND/OR there’s a more in-depth guide (online book) to setting up Git & GitHub linked w/ RStudio: Happy Git and GitHub for the useR
but note: if you do follow this guide, initially skip the install a “Git Client” (other software to manage Git) section since you are going to use RStudio for this
THEN (2) Build your Quarto Website & host it online with GitHub Pages - can follow these step-by-step instructions (and template if you want) to building your website and hosting it on GitHub
And more guidance on details of building Quarto Websites here
And more here on hosting a website on GitHub Pages with the “Render to docs” method
And here’s another YouTube Video Playlist on creating Quarto Websites hosted on GitHub
Use GitHub as your Data Science Portfolio
GitHub is an online platform that allows users to store, manage,share, and collaborate on their (R and other languages) code. You could create your own account and use it to show-off your analysis code for your thesis. Many Research labs have GutHub accounts and use it to collaborate on code see an example. It can also be used to freely host website (see section above).
If you are going to use it, you need to FIRST get Git Installed & your GitHub account setup:
(Watch) What is Git (version control software)? (2 min video)
(Watch) What is GitHub (repository/folder hosting website)? (3 min video)
(Then follow this guide to) Setting up Git & GitHub linked w/ RStudio
- AND there’s a more in-depth guide (online book) to setting up Git & GitHub linked w/ RStudio: Happy Git and GitHub for the useR
Statistics
MarinStatsLectures Youtube Channel
- These R Programming and Statistics videos/tutorials were originally created for Master’s and PhD students at UBC by Professor Mike Marin and are now available to public. These videos provide a comprehensive and completely free set of tutorials for introductory and intermediate concepts in Statistics and Statistics with R.
Common statistical tests as linear models
- Reference website showing the linear models underlying common parametric and “non-parametric” traditional statistical tests.
Data Management
Openscapes - Guidance for data organization in spreadsheets
- Compiled (by openscapes.org) guidance from multiple resources for how to organize and format your data in spreadsheets so that it is ready to analyze in R and follows “tidy data” principles.
How to name things (files) - by Jenny Bryan
- Best practices for naming your files by Jenny Bryan (& she’s got lots of other great resources on her website)
Data Carpentry - Common Mistakes Storing Data in Spreadsheets
Misc.
Scientific writing resources
- Links to papers with advice on scientific writing, including specifics on how to write statistical methods and results sections
Connect your Google Scholar to CPP Library to access pdfs of journal articles
- Link your CPP library account to your google scholar
How to appropriately cite R and R packages
- A short primer from a popular R blog on how to cite R and R packages in your thesis, publications, etc.
Continue your learning
Tidy Tuesday Website
- The weekly
#TidyTuesdayevent people all over the world are doing! Every week they post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. Some people will then post what they come up with on Twitter. A good way to keep practicing your R skills after Bio 5100 is over, can make it weekly thing you do with fellow students…
prstatistics.com (statistics workshops)
- Company that offers ~1 week workshops on various specific biostatistics and R coding topics.
Highland Statistics (advanced statistics workshops and books)
- Company that offers “how to” textbooks and ~1 week workshops on advanced statistics topics (e.g., generalized linear models, mixed models).
Communities to join
R Ladies
- Attend events, or join a Local Chapter (e.g., R Ladies Pasadena,R Ladies Riverside)
Data Science Hangout
- Join a community of data scientists that share insights about what it means to be a responsible, effective, and inspiring leader.