{fig-alt="Logo for the TidyTuesday project, represented by the word TidyTuesday over a messy splash of black paint"}

TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!

TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R, Python, and other data-related topics, or to participate in a data-related book club!

Goals

Our over-arching goal for TidyTuesday is to provide real-world datasets so that people can learn to work with data.

• For 2024, our goal was to be used in at least 10 courses. Our survey indicates that we are used in at least 30 courses!
• For 2025, our goal was to crowdsource the curation of TidyTuesday datasets. We certainly didn't hit 100%, but we are grateful for the 51 pull requests, which included dataset submissions, improvements to welcome participants using Python and Julia, and other improvements to the project. We look forward to more community contributions in 2026!
• For 2026, our goal is to provide tools for easier and better dataset curation, leading to curation of 95% of 2027 datasets by the end of 2026.

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How to Participate

• Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data in R, Python, or Julia, or download the data directly from GitHub for use in your favorite data exploration tool.
• Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
• Create a visualization, a model, a Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, Julia, or another programming language.
  • Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit's PydyTuesday repo.
  • Deploy or share your work however you want! If you'd like a super easy way to publish your work, give Connect Cloud a try.
• Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
  • R; TidyTuesday originated in the #RStats community on social media. Add that hashtag if you explore TidyTuesday data in R!
  • Python: Add the #PydyTuesday hashtag so that Posit has the chance to highlight your work, too!
  • Julia: Add the #TidierTuesday hashtag if you want the Tidier Org to share your visuals!

You can also curate a dataset for a future TidyTuesday!

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DataSets

2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026

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Citing TidyTuesday

To cite the TidyTuesday repo/project in publications use:

Data Science Learning Community (2024). Tidy Tuesday: A weekly social data project. https://tidytues.day

A BibTeX entry for LaTeX users is

  @misc{tidytuesday, 
    title = {Tidy Tuesday: A weekly social data project}, 
    author = {Data Science Learning Community}, 
    url = {https://tidytues.day}, 
    year = {2024} 
  }

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Contributing

Please see our contributing guide for ways that you can help!