moai-lang-r

To build a Shiny app for interactive data visualization, you use R's Shiny framework alongside ggplot2 and plotly. This Skill guides you through creating dynamic dashboards and web applications directly from your data analysis workflows.

The best way to do data wrangling in R using tidyverse is leveraging packages like dplyr, tidyr, and purrr. This approach provides robust patterns for filtering, mutating, and summarizing data efficiently for production-ready code.

Yes, you can generate reproducible reports with R Markdown and Quarto. This Skill supports both formats, allowing you to integrate R code, statistical modeling outputs, and data visualizations into dynamic, shareable documents.

Renv helps with reproducible research in R by managing project-specific package dependencies and environments. This Skill addresses using renv to ensure your data science workflows and statistical models remain consistent and production-ready across different setups.

You do not strictly need the full tidyverse package to create ggplot2 visualizations in R, but they are designed to work together. This Skill covers modern R patterns using both tidyverse for data manipulation and ggplot2 for compelling plots.

You should use R for data analysis when you need advanced statistical modeling, interactive web applications via Shiny, or reproducible research using Quarto. This Skill focuses on these R-specific strengths for comprehensive data science workflows.