Workflow Prompt

Use: Be sure you are working in a folder where you want the new package components to live. Type the following prompt into AI, substituting for your file xxx.R and desired base name yyy

Prompt: “follow the workflow prompt to refactor workflow = xxx.R into a modular R package structure with basename = yyy”.

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Goal: Refactor a workflow into a modular R package structure.

Instructions:

1. Set the workflow and basename variables from user input:

2. Understand the workflow: Read the workflow and understand what it does.

3. Extract Functions: Move all logical units (data processing, analysis, plotting) into R/[basename].R.
   • Document each function with Roxygen2 syntax (including @param, @return, and @export).
   • Use R native pipes (|>) and explicit namespace calls (e.g., dplyr::mutate).
   • Centralize shared constants (like coordinate maps) if not already in common.R.
4. Create Entry Script: Create an execution script inst/scripts/analyze_[basename].R.
   • Use it as a clean entry point that calls the functions defined in R/.
   • Handle environment setup, file paths, and high-level execution flow here.
5. Return S3 Objects: Refactor the main “run” function to return an S3 object of class [basename]_analysis.
   • Implement print, summary, and plot methods in R/[basename].R.
   • Move file-saving logic (write.csv, ggsave) out of the functions and into the entry script.
6. Verify Integration: Check package and scripts, correcting any issues that arise.
   • Verify documents with devtools::document().
   • Build the package.
   • Ask user whether or not to run the analyze_basename.R script to make sure it works.
7. Create Exploration Document: Create an interactive Shiny-based Quarto document inst/scripts/explore_[basename].qmd allowing for dynamic filtering and exploration.
   • Set server: shiny in the YAML header.
   • Use context: setup to load data via sysgenAnalysis::read_[basename]_analysis().
   • Use ::: {.panel-tabset} to organize sections into Summary, Tables, and Plots.
   • Use Shiny’s fluidRow and column within each tab to create a side-bar like experience for controls next to outputs.
   • Integrate downr::downloadUI and downr::downloadServer for exporting tables and plots.
   • This approach provides a premium, interactive experience while keeping rendering fast by loading pre-calculated CSVs.

Requested Files:

• R/[basename].R
• inst/scripts/analyze_[basename].R
• inst/scripts/explore_[basename].qmd