creating-r-research-projects

# Creating R Research Projects This skill helps create and manage a complete R-based research analysis workflow. It is designed for scientific computing, statistical modeling, bioinformatics, and data visualization tasks. Use this skill when the user wants to: - Analyze datasets using R - Perform statistical tests or modeling - Run bioinformatics or omics analysis in R - Generate plots, figures, or reports - Create a reproducible R project structure - Install and manage R package dependencies --- ## What This Skill Does When activated, this skill will: 1. **Create a structured R project** - `data/` for raw and processed data - `scripts/` for analysis code - `results/` for outputs - `reports/` for R Markdown or Quarto reports 2. **Set up environment** - Initialize `.Rproj` (if using RStudio) - Create `renv` environment for reproducibility - Install required CRAN/Bioconductor packages 3. **Generate analysis scripts** - Data loading and cleaning - Statistical analysis or modeling - Visualization with `ggplot2` - Save outputs (CSV, plots, model summaries) 4. **Create a report** - R Markdown / Quarto document - Includes methods, results, and figures - Render to HTML or PDF --- ## Example User Requests That Should Trigger This Skill - "Use R to analyze this CSV and generate plots" - "Run differential expression analysis in R" - "Create a statistical report for this dataset" - "Build an R project for microbiome analysis" - "Fit a regression model in R and summarize results" --- ## Example Workflow **User:** Analyze this gene expression dataset and produce figures. **Skill actions:** - Create project structure - Install `tidyverse`, `DESeq2`, `ggplot2` - Write analysis script - Generate PCA plot and volcano plot - Produce an HTML report --- ## Tools & Packages Commonly Used | Purpose | R Packages | |--------|------------| | Data wrangling | tidyverse, data.table | | Visualization | ggplot2, patchwork | | Statistics | stats, lme4, survival | | Bioinformatics | Bioconductor packages (DESeq2, edgeR, limma) | | Reporting | rmarkdown, quarto | | Reproducibility | renv | --- ## Notes - Prefer reproducible workflows (`renv`, scripted analysis) - Avoid interactive-only steps unless requested - All outputs should be saved to files, not just printed to console