8d5b618 Dec 10, 2018
2 contributors

Users who have contributed to this file

@jthomasmock @xvrdm
107 lines (78 sloc) 16.2 KB

A weekly social data project in R

A weekly data project aimed at the R ecosystem. An emphasis will be placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem.

Join the R4DS online learning community in the weekly #TidyTuesday event! Every week we post a raw dataset, an original chart associated with that dataset, and ask you to apply your take on the chart. While the data set 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 Tidy Tuesday 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! 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!



Week Date Data Source Article
1 2018-04-02 US Tuition Costs
2 2018-04-09 NFL Positional Salaries
3 2018-04-16 Global Mortality
4 2018-04-23 Australian Salaries by Gender
5 2018-04-30 ACS Census Data (2015) , Kaggle No article
6 2018-05-07 Global Coffee Chains Starbucks: , Tim Horton: , Dunkin Donuts:
7 2018-05-14 Star Wars Survey fivethirtyeight package
8 2018-05-21 US Honey Production USDA, Bee Culture
9 2018-05-29 Comic book characters FiveThirtyEight package
10 2018-06-05 Biketown Bikeshare BiketownPDX Biketown cascadiaRconf/cRaggy
11 2018-06-12 FIFA World Cup Audience FiveThirtyEight package
12 2018-06-19 Hurricanes & Puerto Rico FiveThirtyEight package
13 2018-06-26 Alcohol Consumption FiveThirtyEight package
14 2018-07-03 Global Life Expectancy
15 2018-07-10 Craft Beer USA
16 2018-07-17 Exercise USA CDC CDC - National Health Statistics Reports
17 2018-07-23 p-hack-athon collaboration p-hack-athon
18 2018-07-31 Dallas Animal Shelter FY2017 Dallas OpenData Dallas OpenData FY2017 Summary
19 2018-08-07 Airline Safety FiveThirtyEight Package 538 - Airline Safety
20 2018-08-14 Russian Troll Tweets 538 - Russian Troll Tweets
21 2018-08-21 California Fires BuzzFeed News - California Fires, RMarkdown
22 2018-08-28 NFL Stats
23 2018-09-04 Fast Food Calories
24 2018-09-11 Cats vs Dogs (USA) Washington Post
25 2018-09-18 US Flights or Hypoxia
Soaring Society of America
SSA - Hypoxia
26 2018-09-25 Global Invasive Species Paini et al, 2016
Paini et al, 2016
27 2018-10-02 US Births fivethirtyeight package 538 - Births
28 2018-10-09 US Voter Turnout Star Tribune
29 2018-10-16 College Major & Income fivethirtyeight/ACS fivethirtyeight
30 2018-10-23 Horror Movie Profit fivethirtyeight
31 2018-10-30 R and R package downloads No Article
32 2018-11-06 US Wind Farm locations Wind Market Reports
33 2018-11-13 Malaria Data
Malaria Data Challenge malariaAtlas
34 2018-11-20 Thanksgiving Dinner or Transgender Day of Remembrance fivethirtyeight
35 2018-11-27 Baltimore Bridges Federal Highway Administration Baltimore Sun
36 2018-12-04 Medium Article Metadata TidyText package
37 2018-12-04 NYC Restaurant inspections NYC OpenData/NYC Health Department FiveThirtyEight

Useful links

The R4DS Online Learning Community

The R for Data Science textbook

Carbon lets you post beautiful code directly to Twitter!

Post to from RStudio

Another from within RStudio

We will use the fivethirtyeight package frequently for “tame" data

GitHub lets you host raw code for free!

A guide to getting started with GitHub

How to save high quality ggplot2 images

Makeover Monday

Data Is Plural

BuzzFeed Data