- name
- creating-r-research-projects
- description
- Set up a reproducible R research workspace, install required packages, run statistical or bioinformatics analysis, and generate publication-ready reports and visualizations.
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:
- 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
- Set up environment
- Initialize .Rproj (if using RStudio) - Create renv environment for reproducibility - Install required CRAN/Bioconductor packages
- Generate analysis scripts
- Data loading and cleaning - Statistical analysis or modeling - Visualization with ggplot2 - Save outputs (CSV, plots, model summaries)
- 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