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πŸ¦€ ClawHub

Running R Analysis In Existing Projects

by @jackkuo666

Work inside an existing R project to extend analyses, modify scripts, run statistical models, update visualizations, and regenerate reports.

Versionv0.1.0
Downloads1,268
TERMINAL
clawhub install running-r-analysis-in-existing-projects

πŸ“– About This Skill


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

  • 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


    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"

  • 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

  • Tools & Packages Commonly Used

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


    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
  • πŸ“‹ Tips & Best Practices

  • 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