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Introductory guide to the art and science of data visualisation. Insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful.

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'Best Practices for Data Visualisation', published by the Royal Statistical Society

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This GitHub repository contains all the source files and code for our website at https://royal-statistical-society.github.io/datavisguide/. The site and its content are under development, and the editors welcome feedback and contributions.

To make a suggestion or ask a question

Please open up a discussion in the repository's Discussions section.

If you spot a bug or an error

Please either:

  1. Raise an issue in the repository's Issues section, or
  2. Fork this repository, edit the relevant file(s), and make a pull request against the main branch of this repository.

If you want to add a new feature or section to the guide

  1. Raise an issue in the repository's Issues section, tag it as an 'enhancement' and describe your proposed contribution.
  2. Fork this repository and create a new branch named, e.g., my-new-feature-or-section.
  3. Add content, code and files to your branch.
  4. Make a pull request against the main branch of this repository.

Proposals for new features or sections will be reviewed by the editorial team:

  • Andreas Krause

  • Nicola Rennie

  • Anna Britten

Reviewers may offer feedback or suggestions to contributors before agreeing to merge pull requests. The editorial team reserves the right to refuse to merge pull requests, and the decision of the editorial team is final.

Use of, and all contributions to, our website and repository are governed by our Code of Conduct.

Content created by the editorial team and published in this website and repository is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence, meaning it can be used and adapted for any purpose, provided attribution is given to the original authors. By contributing to this guide, contributors agree to licence their work under the same terms.

This site is built in Quarto. To get started using Quarto, visit https://quarto.org/docs/get-started/.

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Introductory guide to the art and science of data visualisation. Insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful.

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