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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

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're 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.


2018 | 2019 | 2020

Week Date Data Source Article
1 2019-12-31 Bring your own data from 2019!
2 2020-01-07 Australian Fires Bureau of Meteorology NY Times & BBC
3 2020-01-14 Passwords Knowledge is Beautiful Information is Beautiful
4 2020-01-21 Song Genres spotifyr Kaylin Pavlik
5 2020-01-28 San Francisco Trees SF Weekly
6 2020-02-04 NFL Attendance Pro Football Reference
7 2020-02-11 Hotel Bookings Antonio, Almeida, and Nunes, 2019 tidyverts
8 2020-02-18 Food's Carbon Footprint nu3 r-tastic by Kasia Kulma
9 2020-02-25 Measles Vaccination The Wallstreet Journal The Wall Street Journal
10 2020-03-03 NHL Goals Washington Post
11 2020-03-10 College Tuition, Diversity, and Pay
12 2020-03-17 The Office schrute The Pudding
13 2020-03-24 Traumatic Brain Injury CDC CDC Traumatic Brain Injury Report

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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