moai-lang-r

Build reactive Shiny dashboards by constructing tidyverse data pipelines with dplyr and tidyr, then connecting them to Shiny reactive inputs and outputs. This Skill scaffolds the reactivity logic, caching, and async patterns for interactive data products.

Create publication-ready ggplot2 visualizations by applying custom themes, scales, and patchwork layouts. This Skill provides conventions for layering geoms, adjusting aesthetics, and assembling multi-panel figures to meet reproducible reporting standards.

Yes, renv and testthat are required to package R analysis for reproducible collaboration. renv manages project-specific library dependencies, while testthat validates core analysis logic through automated testing workflows.

You can use dbplyr to translate dplyr pipelines into database queries within Shiny applications. This Skill covers integrating dbplyr patterns for remote data sources alongside local tidyverse data wrangling and reactive UI components.

Shiny reactivity works by binding dplyr and purrr data summarization pipelines to reactive inputs, automatically recomputing when values change. This Skill structures the reactive graph, caching, and async patterns to handle data workflows efficiently.

Shiny async patterns prevent blocking during long computations but require careful reactive graph management to avoid race conditions. This Skill provides structured approaches for async caching and module isolation to maintain reliable reactivity.