R & Coding
- Videos, links and explanation for how to install, and what is R, RStudio and R Packages.
- Online tidyverse coding tutorials (from Posit, formerly RStudio)
- 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
- Visualizes dplyr code to help you understand what specific dplyr functions are doing with the data
- AI R code writer, can help you figure out what you need to do or debug your code
Quarto
- A(n awesome) curated list of Quarto talks, tools, examples & articles
Statistics
- Reference website showing the linear models underlying common parametric and “non-parametric” traditional statistical tests.
Data Management
- 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.
- Best practices for naming your files by Jenny Bryan (& she’s got lots of other great resources on her website)
Misc.
- Link your CPP library account to your google scholar
- A short primer from a popular R blog on how to cite R and R packages in your thesis, publications, etc.
Continue your learning
- The weekly
#TidyTuesday event 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…
- Company that offers “how to” textbooks and ~1 week workshops on advanced statistics topics (e.g., generalized linear models, mixed models).