fairenough

Convert tidy data into R packages with documentation websites and intelligent AI descriptions. fairenough prepares your data for publication in one click.

Why fairenough?

Fairenough transforms this:

my-data/
└── penguins.csv

Into this complete R package:

• Documentation: AI-generated variable descriptions and data dictionaries

• Website: Professional pkgdown site ready for deployment

• Citations: Properly formatted citation files with DOI support

• Validation: Data structure checks and format consistency

• Integration: Full R package that others can install and use

Demo: Palmerpenguins published with fairenough!

Installation

Prerequisites: R ≥ 4.1.0 and an API key for your preferred LLM provider

install.packages("pak")
pak::pkg_install("openwashdata/fairenough")

Quick Start

• Create a new project (or just an empty directory) containing you dataset(s)
• Start an R console at the project (or directory) path
• Run

library(fairenough)

fairenough()

That’s it! You’ll get a complete R package with:

• Documented datasets and variables

• Professional package website

• Citation files and README

• Proper R package structure

Quick Start with Ellmer

# 1. Set up your LLM chat object (see options below)
chat <- ellmer::chat_openai(model = "gpt-4o-mini", api_args = list(temperature = 0.3), api_key = "")

# 2. Place your CSV/Excel files in an empty directory and run
fairenough(chat)

• Running fairenough with an ellmer::chat object will generate descriptions for your dataset’s variables

• See all supported providers →

# OpenAI
chat <- ellmer::chat_openai()

# Anthropic Claude
chat <- ellmer::chat_anthropic()

# Local models via Ollama
chat <- ellmer::chat_ollama()

Getting Help

• Detailed documentation: Reference

• Issues & bugs: GitHub Issues

• Questions: GitHub Discussions

Contributing

We welcome contributions! Please see our contributing guidelines and note that this project follows conventional commits.

Development workflow:

# Test new functions
roxygen2::roxygenise(clean = TRUE)
devtools::load_all()

Architecture: Feature functions maintain consistency in supported formats and path handling. See R/utils.R for implementation details.

License

MIT

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Citation

Please cite with:

Text:

Massari N, Zhong M, Götschmann M, Walder C, Schöbitz L (2025). "fairenough: fairenough."
<https://github.com/openwashdata/fairenough>.

BibTeX:

@Misc{massari_etall:2025,
  title = {fairenough: fairenough},
  author = {Nicolo Massari and Mian Zhong and Margaux Götschmann and Colin Walder and Lars Schöbitz},
  year = {2025},
  url = {https://github.com/openwashdata/fairenough},
  abstract = {Convert tidy data into R packages with documentation websites and intelligent AI descriptions.},
  version = {0.1.0},
}