Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
A cheatsheet for basic best practice in R programming
The GitHub repository wurli/r-best-practice provides a cheatsheet aimed at establishing basic best practices for R programming. Intended for developers new to R or those interested in improving their skills, it contains opinionated guidelines for good practices. The cheatsheet includes a variety of tips and is licensed under CC-BY-4.0, ensuring free use with credit. Originally developed for the UK Department for Education, it has been generalized for broader usage. Inspired by Posit's data science software cheatsheets, it encourages forking for personalized adaptation.
Go to Resource
ggplot2 styling
This content introduces the styling capabilities of the ggplot2 package, explaining how to apply various themes to enhance the visual appearance of plots. It covers the theme system in ggplot2, including new updates, and guides the reader through using pre-existing themes or creating custom theme functions. Different components like panels, axes, titles, and legends are addressed, and a practical example plot is provided. The article also mentions additional themes available through packages like cowplot, ggthemes, and tvthemes, which offer styles inspired by external sources or even TV shows.
Go to Resource
How to Get Good with R? | Credibly Curious
This blog post discusses tips and strategies for improving coding skills in R, as well as non-coding aspects like community involvement and continuous learning.
Go to Resource
JEFworks Lab style guide
Best practices for readable, sharable, and verifiable R code
Go to Resource
resouRces
This content encompasses a comprehensive list of R-related educational materials, packages, tutorials, and datasets with projected dates ranging up to the year 2025. It includes various titles that focus on learning R programming, data analysis, data visualization, geospatial mapping, and statistical methods. Significant emphasis is placed on resources for learning R, such as introductions to R, books, courses, and video tutorials. Additionally, specific packages for data wrangling, statistical modeling, and visualization are mentioned, indicating the evolution and specialization of R's ecosystem to cater to diverse data science needs.
Go to Resource
