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Logo for the TidyTuesday project, represented by the word TidyTuesday over a messy splash of black paint

A weekly social data project in R

A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.

Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format. The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community! As such we encourage everyone of all skills to participate!

We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions. While we understand that the two are related, the focus of this practice is purely on building skills with real-world data.

All data will be posted on the data sets page on Monday. It will include the link to the original article (for context) and to the data set.

We welcome all newcomers, enthusiasts, and experts to participate, but be mindful of a few things:

  1. The data set comes from the source article or the source that the article credits. Be mindful that the data is what it is and Tidy Tuesday is designed to help you practice data visualization and basic data wrangling in R.
  2. Again, the data is what it is! You are welcome to explore beyond the provided dataset, but the data is provided as a "toy" dataset to practice techniques on.
  3. This is NOT about criticizing the original article or graph. Real people made the graphs, collected or acquired the data! Focus on the provided dataset, learning, and improving your techniques in R.
  4. This is NOT about criticizing or tearing down your fellow #RStats practitioners or their code! Be supportive and kind to each other! Like other's posts and help promote the #RStats community!
  5. Use the hashtags #TidyTuesday and #RStats on Twitter if you create your own version and would like to share it.
  6. Include a picture of the visualisation when you post to Twitter.
  7. Include a copy of the code used to create your visualization when you post to Twitter. Comment your code wherever possible to help yourself and others understand your process!
  8. Focus on improving your craft, even if you end up with something simple!
  9. Give credit to the original data source whenever possible.

Submitting Datasets

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:

Two Ways to Contribute

  1. Submit the dataset as an Issue
    a. Find an interesting dataset
    b. Find a report, blog post, article etc relevant to the data
    c. Submit the dataset as an Issue along with a link to the article

  2. Create an entire TidyTuesday challenge!
    a. Find an interesting dataset
    b. Find a report, blog post, article etc relevant to the data (or create one yourself!)
    c. Let us know you've found something interesting and are working on it by filing an Issue on our GitHub
    d. Provide a link or the raw data and a cleaning script for the data
    e. Write a basic file using the minimal template below and make sure to give yourself credit! template


The data this week comes from [SOURCE_OF_DATA](URL_TO_DATA). 

This [ARTICLE_SOURCE](LINK_TO_ARTICLE) talks about SUBJECT TITLE in greater detail.

Credit: [YOUR NAME](Twitter handle or other social media profile)

Submitting Code Chunks

Want to submit a useful code-chunk? Please submit as a Pull Request and follow the guide.

Citing TidyTuesday

To cite the TidyTuesday repo/project in publications use:

Thomas Mock (2022). Tidy Tuesday: A weekly data project aimed at the R ecosystem.

A BibTeX entry for LaTeX users is

    title = {Tidy Tuesday: A weekly data project aimed at the R ecosystem}, 
    author = {Mock, Thomas}, 
    url = {}, 
    year = {2022} 

Note: If you would like to cite the tidytuesdayR package, you should use citation("tidytuesdayR") instead.

Please add alt text to your posts

Please add alt text (alternative text) to all of your posted graphics for #TidyTuesday.

Twitter provides guidelines for how to add alt text to your images.

The DataViz Society/Nightingale has an article on writing good alt text for plots/graphs.

Here’s a simple formula for writing alt text for data visualization:

Chart type

It’s helpful for people with partial sight to know what chart type it is and gives context for understanding the rest of the visual. Example: Line graph

Type of data

What data is included in the chart? The x and y axis labels may help you figure this out. Example: number of bananas sold per day in the last year

Reason for including the chart

Think about why you’re including this visual. What does it show that’s meaningful. There should be a point to every visual and you should tell people what to look for. Example: the winter months have more banana sales

Link to data or source

Don’t include this in your alt text, but it should be included somewhere in the surrounding text. People should be able to click on a link to view the source data or dig further into the visual. This provides transparency about your source and lets people explore the data. Example: Data from the USDA

Penn State has an article on writing alt text descriptions for charts and tables.

Charts, graphs and maps use visuals to convey complex images to users. But since they are images, these media provide serious accessibility issues to colorblind users and users of screen readers. See the examples on this page for details on how to make charts more accessible.

The {rtweet} package includes the ability to post tweets with alt text programmatically.

Need a reminder? There are extensions that force you to remember to add Alt Text to Tweets with media.


2018 | 2019 | 2020 | 2021 | 2022

Week Date Data Source Article
1 2022-01-04 Bring your own data to start 2022!
2 2022-01-11 Bee Colony losses USDA Bee Informed
3 2022-01-18 Chocolate Bar ratings Flavors of Cacao Will Canniford on Kaggle
4 2022-01-25 Board games Kaggle Alyssa Goldberg
5 2022-02-01 Dog breeds American Kennel Club Vox
6 2022-02-08 Tuskegee Airmen Commemorative Airforce (CAF) by way of the VA-TUG Wikipedia & Air Force Historical Research Agency
7 2022-02-15 #DuBoisChallenge2022 Anthony Starks Nightingale by DVS
8 2022-02-22 World Freedom index UN and Freedom House Freedom House
9 2022-03-01 Alternative Fuel Stations US DOT EIA
10 2022-03-08 Erasmus student mobility
11 2022-03-15 CRAN/BIOC Vignettes Robert Flight GitHub Robert Flight GitHub
12 2022-03-22 Baby names US babynames & nzbabynames Emily Kothe's nzbabynames vignette
13 2022-03-29 Collegiate Sports Budgets Equity in Athletics Data Analysis NPR
14 2022-04-05 Digital Publications Project Oasis Project Oasis Report
15 2022-04-12 Indoor Air Pollution
16 2022-04-19 Crossword Puzzles and Clues Towards Data Science
17 2022-04-26 Kaggle Hidden Gems Kaggle Kaggle - Notebooks of the Week
18 2022-05-03 Solar/Wind utilities Berkeley Lab Berkeley Lab report
19 2022-05-10 NYTimes best sellers Post45 Data Finding Trends in NY Times Best Sellers - Kailey Smith
20 2022-05-17 Eurovision Eurovision Tanya Shapiro
21 2022-05-24 Women's Rugby Women's Rugby - ScrumQueens ScrumQueens
22 2022-05-31 Company reputation poll Axios and Harris Poll The Harris Poll
23 2022-06-07 Pride Corporate Accountability Project Data For Progress Data For Progress
24 2022-06-14 US Drought report
25 2022-06-21 Juneteenth WEB DuBois style by Anthony Starks Isabella Benabaye's blog on Juneteenth
26 2022-06-28 UK Gender pay gap
27 2022-07-05 San Francisco Rentals Kate Pennington Matrix-Berkeley
28 2022-07-12 European flights Eurocontrol
29 2022-07-19 Technology Adoption
30 2022-07-26 Bring your own data None None
31 2022-08-02 Oregon Spotted Frog spotted frog data spotted-frog-article
32 2022-08-09 Ferris Wheels ferriswheels ferriswheels
33 2022-08-16 Open Source Psychometrics Open-Source Psychometrics Project Character Personality
34 2022-08-23 CHIP dataset CHIP Dataset arxiv paper
35 2022-08-30 Pell Grants US Dept of Education pell R package
36 2022-09-06 LEGO database rebrickable rebrickable
37 2022-09-13 Bigfoot Data.World Finding Bigfoot
38 2022-09-20 Hydro Wastewater plants Macedo et al, 2022 HydroWASTE v1.0
39 2022-09-27 Artists in the USA Artists in the Workforce
40 2022-10-04 Product Hunt products The Gamer and the Nihilist by Andrew Thompson

Useful links

Link Description
Link The R4DS Online Learning Community Website
Link The R for Data Science textbook
Link Carbon for sharing beautiful code pics
Link Post gist to Carbon from RStudio
Link Post to Carbon from RStudio
Link Join GitHub!
Link Basics of GitHub
Link Learn how to use GitHub with R
Link Save high-rez ggplot2 images

Useful data sources

Link Description
Link Data is Plural collection
Link BuzzFeedNews GitHub
Link The Economist GitHub
Link The fivethirtyeight data package
Link The Upshot by NY Times
Link The Baltimore Sun Data Desk
Link The LA Times Data Desk
Link Open News Labs
Link BBC Data Journalism team

Data Viz/Science Books

Only books available freely online are sourced here. Feel free to add to the list

Link Description
Link Fundamentals of Data Viz by Claus Wilke
Link The Art of Data Science by Roger D. Peng & Elizabeth Matsui
Link Tidy Text Mining by Julia Silge & David Robinson
Link Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
Link Data Visualization by Kieran Healy
Link ggplot2 cookbook by Winston Chang
Link BBC Data Journalism team