TidyTuesdayis a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!TidyTuesdayis organized by the R4DS Online Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!
Our over-arching goal for TidyTuesday is to make learning to work with data easier, by providing real-world datasets.
Our goal for 2023-2024 is to increase usage of #TidyTuesday within classrooms. We would like to be used in at least 10 courses by September 2024. If you are using TidyTuesday to teach data-related skills, please let us know!
- Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
- 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 shiny app, or some other piece of data-science-related output, using R or another programming language.
- Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
To cite the TidyTuesday repo/project in publications use:
R4DS Online Learning Community (2023). Tidy Tuesday: A weekly social data project. https://github.com/rfordatascience/tidytuesday.
A BibTeX entry for LaTeX users is
@misc{tidytuesday,
title = {Tidy Tuesday: A weekly social data project},
author = {R4DS Online Learning Community},
url = {https://github.com/rfordatascience/tidytuesday},
year = {2023}
}
Note: If you would like to cite the tidytuesdayR package, you should use citation("tidytuesdayR") instead.
TidyTuesday is built around open datasets that are found in the "wild" or submitted as Issues on our GitHub.
If you find a dataset that you think would be interesting, you can approach it through two ways:
- Find an interesting dataset
- Find a report, blog post, article, etc relevant to the data
- Submit the dataset as an Issue along with a link to the article (and, ideally, 2 images from the article, with alt text)
- Find an interesting dataset
- Find a report, blog post, article, etc relevant to the data (or create one yourself!)
- Let us know you've found something interesting and are working on it by filing an Issue on our GitHub
- Provide a link or the raw data and a cleaning script for the data
- Write a basic
readme.mdfile using a recentreadme.mdas a template. Make sure to give yourself credit!