## 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!

# 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
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
--- | ---
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