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name: running-r-analysis-in-existing-projects description: Work inside an existing R project to extend analyses, modify scripts, run statistical models, update visualizations, and regenerate reports.

Running R Analysis in Existing Projects

This skill operates inside an already structured R project. It helps extend, debug, or enhance existing analyses without recreating the project from scratch.

Use this skill when the user wants to:

• Continue analysis in an existing R project
• Modify or extend R scripts
• Add new statistical models or tests
• Update plots or figures
• Regenerate reports after data or code changes
• Debug R errors in a project

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What This Skill Does

When activated, this skill will:

1. Understand the project structure

   • Detect folders like data/, scripts/, results/, reports/
   • Identify .Rproj, .Rmd, .qmd, or .R files

2. Inspect existing analysis

   • Read current scripts and reports
   • Identify which packages and methods are being used
   • Avoid rewriting working components unnecessarily

3. Extend or modify analysis

   • Add new models or statistical tests
   • Introduce new plots using ggplot2
   • Add new data processing steps
   • Improve code structure or reproducibility

4. Re-run and update outputs

   • Recompute results
   • Overwrite or version new outputs in results/
   • Re-render R Markdown or Quarto reports

5. Debug issues

   • Fix missing packages
   • Resolve file path problems
   • Handle common R errors and warnings

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Example User Requests That Should Trigger This Skill

• "Add a survival analysis to this R project"
• "Update the plots in my report"
• "This R Markdown file throws an error, fix it"
• "Extend this analysis with a mixed-effects model"
• "Re-run everything after I updated the data"

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Example Workflow

User: Add a logistic regression model and update the report.

Skill actions:

• Locate main analysis script
• Add logistic regression using glm()
• Save model summary to results/
• Update report with new section and plot
• Re-render HTML/PDF report

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Tools & Packages Commonly Used

Purpose	R Packages
Data wrangling	tidyverse, dplyr
Modeling	stats, lme4, glmnet
Visualization	ggplot2
Reporting	rmarkdown, quarto
Project management	here, renv

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Notes

• Respect the existing project structure and style
• Do not delete user code unless explicitly requested
• Prefer incremental updates over full rewrites
• Always regenerate reports after modifying analysis