moai-lang-r

Building a Shiny app involves defining UI inputs for date and category filters, then writing server logic to render reactive ggplot2 visualizations and export results to CSV.

Creating reproducible reports with R Markdown and Quarto involves embedding R code chunks directly into your document to execute data analysis and generate visualizations, producing shareable outputs that update automatically when data changes.

Manage R package dependencies for reproducible data science projects using renv, which snapshots package versions to ensure environments remain consistent across machines and over time.

Yes, this R data science workflow supports tidyverse, leveraging dplyr and tidyr for robust data manipulation pipelines alongside ggplot2 for creating compelling data visualizations.

To create a ggplot2 scatter plot from the mtcars dataset, load ggplot2, pass mtcars to ggplot, and add geom_point with aesthetics mapping mpg to the y-axis and wt to the x-axis.

Yes, you can use modern R patterns for interactive data analysis pipelines by combining tidyverse for data transformation, Shiny for interactivity, and Quarto for reporting.