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2019-01-01 Delete readme.html Dec 31, 2018
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2019-01-22 add week 5 - dairy cows Jan 28, 2019
2019-01-29 add week 5 - dairy cows Jan 28, 2019
2019-02-05 add tech document Feb 4, 2019
2019-02-12 update climate_spending.csv to get rid of NAs Feb 11, 2019
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2019-02-26 Rename trains_raw <- to full_trains <- Feb 28, 2019
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2019-04-30 Update Apr 29, 2019
2019-05-07 add ed data May 6, 2019
2019-05-14 add UFO data Jun 24, 2019
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2019-05-28 add meteorites Jun 10, 2019
2019-06-04 add ramen ratings Jun 3, 2019
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2019-06-25 add UFO data Jun 24, 2019
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2019-08-06 add bob ross data Aug 5, 2019
2019-08-13 add roman data Aug 12, 2019
2019-08-20 fix typo Nov 25, 2019
2019-08-27 update read_delim() Aug 27, 2019
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2019-09-17 add national park visit data Sep 16, 2019
2019-09-24 update authors Sep 23, 2019
2019-10-01 Update Sep 30, 2019
2019-10-08 add lift data Oct 7, 2019
2019-10-15 add mtcars Oct 14, 2019
2019-10-22 Update Oct 21, 2019
2019-10-29 Update Oct 28, 2019
2019-11-05 2019-11-05 - fix acs children_data slice Nov 24, 2019
2019-11-12 Update Nov 11, 2019
2019-11-19 add week 47 Nov 18, 2019
2019-11-26 correct typo Nov 29, 2019
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2019-12-10 fix typo and data dictionary Dec 9, 2019
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2019-12-24 Update Dec 23, 2019 remove unneeded relative paths Jan 9, 2020

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 hashtag #TidyTuesday 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

Want to submit an interesting dataset? Please open an Issue and post a link to the article (or blogpost, etc) using the data, then we can discuss adding it to a future TidyTuesday Event!

Submitting Code Chunks

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




Week Date Data Source Article
1 2019-01-01 #Rstats & #TidyTuesday Tweets rtweet
2 2019-01-08 TV's Golden Age IMDb The Economist
3 2019-01-15 Space Launches JSR Launch Vehicle Database The Economist
4 2019-01-22 Incarceration Trends Vera Institute Vera Institute
5 2019-01-29 Dairy production & Consumption USDA NPR
6 2019-02-05 House Price Index & Mortgage Rates FreddieMac & FreddieMac Fortune
7 2019-02-12 Federal R&D Spending AAAS New York Times
8 2019-02-19 US PhD's Awarded NSF #epibookclub
9 2019-02-26 French Train Delays SNCF RTL - Today
10 2019-03-05 Women in the Workplace Census Bureau & Bureau of Labor Census Bureau
11 2019-03-12 Board Games Board Game Geeks fivethirtyeight
12 2019-03-19 Stanford Open Policing Project Stanford Open Policing Project
SOPP - arXiv:1706.05678
SOPP - arXiv:1706.05678
13 2019-03-26 Seattle Pet Names Curbed Seattle
14 2019-04-02 Seattle Bike Traffic Seattle Times
15 2019-04-09 Tennis Grand Slam Champions Wikipedia Financial Times
16 2019-04-16 The Economist Data Viz Mistakes The Economist The Economist
17 2019-04-23 Anime Data MyAnimeList MyAnimeList
18 2019-04-30 Chicago Bird Collisions Winger et al, 2019 Winger et al, 2019
19 2019-05-07 Global Student to Teacher Ratios UNESCO Center for Public Education
20 2019-05-14 Nobel Prize Winners Kaggle The Economist
21 2019-05-21 Global Plastic Waste Our World In Data Our World in Data
22 2019-05-28 Wine Ratings Kaggle Vivino
23 2019-06-04 Ramen Ratings Food Republic
24 2019-06-11 Meteorites NASA The Guardian - Meteorite map
25 2019-06-18 Christmas Bird Counts Bird Studies Canada Hamilton Christmas Bird Count
26 2019-06-25 Global UFO Sightings NUFORC Example Plots
27 2019-07-02 Media Franchise Revenues Wikipedia reddit/dataisbeautiful post
28 2019-07-09 Women's World Cup Wikipedia
29 2019-07-16 R4DS Membership R4DS Slack R4DS useR Presentation
30 2019-07-23 Wildlife Strikes FAA FAA
31 2019-07-30 Video Games Steam Spy Liza Wood
32 2019-08-06 Bob Ross paintings FiveThirtyEight FiveThirtyEight
33 2019-08-13 Roman Emperors Wikipedia / Zonination
34 2019-08-20 Nuclear Explosions SIPRI Our World in Data
35 2019-08-27 Simpsons Guest Stars Wikipedia Wikipedia
36 2019-09-03 Moore's Law Wikipedia Wikipedia
37 2019-09-10 Amusement Park Injuries & Saferparks Saferparks
38 2019-09-17 National Park Visits fivethirtyeight article
39 2019-09-24 School Diversity NCES Washington Post article
40 2019-10-01 All the Pizza Jared Lander & Ludmila Janda, Tyler Richards, DataFiniti Tyler Richards on TWD
41 2019-10-08 Powerlifting Elias Oziolor
42 2019-10-15 Car Fuel Economy EPA Ellis Hughes
43 2019-10-22 Horror movie ratings IMDB Stephen Follows
44 2019-10-29 NYC Squirrel Census Squirrel Census CityLab
45 2019-11-05 Bike & Walk Commutes ACS ACS
46 2019-11-12 CRAN Code CRAN Phillip Massicotte
47 2019-11-19 NZ Bird of the Year New Zealand Forest and Bird Org Dragonfly Data Science & Nathan Moore
48 2019-11-26 Student Loan Debt Department of Education Dignity and Debt
49 2019-12-03 Philly Parking Tickets Open Data Philly NBC Philadelphia
50 2019-12-10 Replicating plots in R Simply Statistics Rafael Irizarry
51 2019-12-17 Adoptable dogs Petfinder The Pudding
52 2019-12-24 Christmas Songs Billboard Top 100 A Dash of Data

Useful links

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

Useful data sources

Link Description
🔗 Data is Plural collection
🔗 BuzzFeedNews GitHub
🔗 The Economist GitHub
🔗 The fivethirtyeight data package
🔗 The Upshot by NY Times
🔗 The Baltimore Sun Data Desk
🔗 The LA Times Data Desk
🔗 Open News Labs
🔗 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
🔗 Fundamentals of Data Viz by Claus Wilke
🔗 The Art of Data Science by Roger D. Peng & Elizabeth Matsui
🔗 Tidy Text Mining by Julia Silge & David Robinson
🔗 Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
🔗 Data Visualization by Kieran Healy
🔗 ggplot2 cookbook by Winston Chang
🔗 BBC Data Journalism team
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