TidyTuesday

A weekly social data project (in R)

A weekly project that builds off #makeovermonday style projects but 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!

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:

This is NOT about criticizing the original authors. They are people like you and me and they have feelings. Focus on the data, the charts and improving your own techniques.
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!
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.
Use the hashtag #TidyTuesday on Twitter if you create your own version and would like to share it.
Include a picture of the visualisation when you post to Twitter.
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!
Focus on improving your craft, even if you end up with someting simple! Make something quick, but purposeful!
Give credit to the original data source whenever possible.