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GitHub - rfordatascience/tidytuesday: Official repo for the #tidytuesday project

 4 years ago
source link: https://github.com/rfordatascience/tidytuesday
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README.md

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 β€œtamed”, 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.

DataSets

2018

2019

Week Date Data Source Article 1 2019-01-01 #Rstats & #TidyTuesday Tweets rtweet stackoverflow.blog 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 seattle.gov Curbed Seattle 14 2019-04-02 Seattle Bike Traffic seattle.gov 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 TheRamenRater.com 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 data.world 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 reddit.com/r/dataisbeautiful 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 Data.world & Saferparks Saferparks 38 2019-09-17 National Park Visits Data.world 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 OpenPowerlifting.org Elias Oziolor 42 2019-10-15 Car Fuel Economy EPA Ellis Hughes 43 2019-10-22 Horror movie ratings IMDB Stephen Follows

Useful links

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

Useful data sources

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

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