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## 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.