6 R learning resources
Hypermynds uses R extensively for data analysis, automation, dashboards and internal tools. The resources below are useful references for building strong, maintainable R skills in our technical work.
The goal is not to study everything before contributing. Start from the core workflow, then use the more advanced material when you meet it in a real project.
6.1 Recommended learning path
- Learn the core data workflow: importing data, transforming it, visualizing it and writing reproducible analyses.
- Learn how R projects are organized, documented, tested and made reproducible.
- Learn Shiny and reporting tools when working on dashboards, apps or documents.
- Learn advanced language features such as R6 classes when a project uses more structured or stateful code.
6.2 Core R and data science
Most R work starts with scripts, functions, data frames and reproducible data analysis. These skills are the foundation for reading existing code, reviewing changes and building reliable internal tools.
- R for Data Science introduces the tidyverse, data transformation, visualization and reproducible analysis workflows.
- Tidyverse style guide defines practical conventions for readable, consistent R code.
6.3 Packages and project structure
R code that is used by more than one person should be easy to install, test and maintain. Package structure is useful even for internal code because it creates clear boundaries between functions, documentation, tests and dependencies.
- R Packages is the main reference for creating, documenting, testing and publishing R packages.
- renv explains how to manage project-level package dependencies reproducibly.
- testthat documents the standard testing framework used in many R projects.
6.4 Applications and reporting
Some Hypermynds work is delivered through dashboards, reports or long-form documentation rather than scripts alone. These tools help turn R analysis into interfaces and documents that other people can use, review and maintain.
- Mastering Shiny is the reference for building Shiny applications, from basic reactivity to production-quality structure.
- R Markdown: The Definitive Guide covers reports, documents and reproducible communication.
- bookdown explains how to build books and long-form documentation, including this handbook.
6.5 Advanced R topics
Not all R code is simple script-based analysis. Some projects use more structured patterns, especially when the code needs to manage state, communicate with external systems or expose reusable services.
R6 is R’s reference-based object-oriented system. It is useful when an object needs to keep internal state over time, such as an API client, a Shiny module controller or a long-running service. You do not need to use R6 everywhere, but you should be able to read it confidently when you find it in a project.
- Advanced R explains the language internals that matter when writing robust abstractions and debugging complex code.
- R6 classes covers object-oriented R with R6, including public methods, private fields and reference semantics.