A comprehensive skill for automating reproducible R-based research workflows, from data cleaning to publication-ready reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install creating-r-research-projects
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install creating-r-research-projects using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Creating R Research Projects?
This skill empowers developers and researchers to automate the creation of robust R environments and research projects. It bridges the gap between raw data and scientific insights by standardizing the project structure and ensuring reproducibility through environment management. By leveraging Openclaw Skills, users can instantly scaffold projects that include data directories, analysis scripts, and high-quality reporting templates.
Whether you are conducting bioinformatics research, financial modeling, or general statistical analysis, this skill handles the boilerplate of package installation via renv and CRAN, allowing you to focus on the analysis itself. It is designed to integrate seamlessly into a professional research workflow, ensuring that every plot and model summary is saved and documented.
Creating R Research Projects Use Cases
- Automating differential expression analysis for bioinformatics and omics datasets.
- Generating reproducible statistical reports from CSV or Excel data sources.
- Building standardized R project structures for team-based scientific research.
- Visualizing complex datasets using ggplot2 and rendering publication-ready PDF or HTML reports.
- Managing complex R package dependencies across different research environments.
How Creating R Research Projects Works
- Initialize a standardized project directory structure including data, scripts, results, and reports folders.
- Configure the R environment by creating a .Rproj file and initializing renv for dependency isolation.
- Install necessary CRAN or Bioconductor packages required for the specific analysis task.
- Generate R scripts for data ingestion, cleaning, and core statistical modeling or analysis.
- Produce visualizations and summary outputs, saving them to dedicated results directories.
- Render a final R Markdown or Quarto report that synthesizes findings into a professional format.
Creating R Research Projects Setup
To begin using this research automation tool within the Openclaw Skills ecosystem, ensure you have R installed on your system.
# Ensure your R environment is ready for automation
# The skill will typically handle directory creation:
mkdir -p project_name/{data,scripts,results,reports}
The agent will automatically manage the initialization of renv and the installation of required libraries like tidyverse or DESeq2 depending on your specific research request.
Creating R Research Projects Data Schema & Taxonomy
The skill organizes research data and metadata using a clean, reproducible taxonomy:
| Directory | Content Type | Purpose |
|---|---|---|
/data |
Raw/Processed | Input data storage and source files |
/scripts |
.R Files | Core analysis and visualization logic |
/results |
.csv, .png, .rds | Output artifacts, figures, and saved model objects |
/reports |
.Rmd, .qmd | Narrative documentation and final rendered outputs |
renv.lock |
JSON | Metadata for package versioning and reproducibility |
Creating R Research Projects Advanced Features
- Multi-stage bioinformatics pipeline support using integrated Bioconductor workflows.
- Automated report rendering to multiple formats including HTML and PDF via Quarto or R Markdown.
- Strict environment version control for package dependencies using renv snapshots to ensure results are replicable.
- Support for high-performance data manipulation using data.table and tidyverse within the generated scripts.
- Modular script architecture that separates data cleaning from statistical modeling for easier debugging.
SKILL.md
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