This is a project inspired by the Tidy Tuesday project by the R for Data Science Community. Tidy Friday is a side project created by part of the research assistants of the World Bank's Development Impact Evaluation Unit with the purpose to bring datasets related to development economics to the tidyverse.
The general idea is to wrangle data and plot figures emphasizing topics such as:
- Education
- Health
- Demographic change
- Food and agriculture
- Violence
- Democracy
- Human Rights
- Poverty and economic development
- Environment
Following the spirit of the Tidy Tuesday project, we will post a raw dataset fortnightly, including the article or information related to the dataset, and ask you to explore it and come with creative ways and best practices to visualize the data. Therefore, the goal is to apply your R skills, get feedbacks, see what other people did, and connect with more people. We encourage you to post your work on twitter using the hashtags #TidyFriday and #RStats.
The datasets will be posted on the datasets page on Friday.
Below there are some notes that come from the Tidy Tuesday project that we think are important to consider when taking part of this project:
- 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.
- 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.
- 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.
- 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!
- 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 something simple!
- Give credit to the original data source whenever possible.
We also encourage you to bring your dataset related to development economics. If you want to share an interesting dataset, please open an Issue and post a link to the article using the data.
| Week | Date | Data | Source | Article |
|---|---|---|---|---|
| 1 | 2020-02-20 |
| 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 |
The Tidy Tuesday project compiled a list of very useful resources from data management to data visualization. We will be adding more links as we continue finding resources.
| 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 |
| 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 |