rfordatascience/tidytuesday
Tidytuesday
This repository provides a curated collection of weekly datasets designed for data visualization practice, data science education, and statistical analysis. It serves as a central source for cleaned and structured real-world data, allowing practitioners to focus on analysis and visualization without the need to scrape or clean raw files.
The project facilitates a community learning workflow where users can explore a wide variety of topics, ranging from global health spending and energy datasets to maritime logs and baby name popularity. Participants are encouraged to share their resulting visualizations and code on social networks to exchange techniques and feedback.
The collection is expanded through a community-driven curation process where users can suggest new data sources or submit curated datasets. The repository also provides access to educational resources covering topics such as text mining and geospatial computation.
Features
- Curated Data Collections - Provides a central source of cleaned and structured real-world data for visualization and statistical analysis.
- Curated Datasets - Provides a central source of cleaned and structured real-world datasets for statistical analysis and visualization.
- Data Collections & Datasets - Provides a vast collection of curated datasets for data science and visualization practice.
- Practice Datasets - Offers a rotating collection of curated datasets specifically for practicing data cleaning and visualization techniques.
- Dataset Digests - Provides a rotating weekly release of curated datasets for analysis practice.
- Data Retrieval Services - Offers access to detailed, multi-faceted curated datasets for statistical analysis and pattern exploration.
- AI and Data Science Education - Provides pedagogical resources and weekly challenges to improve skills in data science and geospatial computation.
- Dataset Curation - Vets dataset ideas against quality and legal standards through a formal submission process.
- Flat-File Storage - Stores datasets in portable CSV files for easy loading into statistical environments.
- Git-Based Dataset Distributions - Distributes curated data files through a version-controlled repository to ensure consistent access.
- Community Curation Workflows - Implements a human-in-the-loop review process for community-contributed datasets via pull requests.
- Dataset Contribution Workflows - Provides a structured workflow for submitting new curated data collections for future challenges.
- Community Learning Platforms - Facilitates a collaborative environment where practitioners share analysis results and code to exchange techniques.
- Educational Books - Provides a curated list of free online books on data visualization, text mining, and geospatial computation.
- Collaborative Learning Communities - Encourages users to share visualizations and code on social networks for peer learning.
- Chronological Data Releases - Organizes datasets into a chronological directory structure to facilitate weekly release cycles.
- Proposal Submission Workflows - Uses a structured process for users to propose and submit curated data for future challenges.
- Proposal Frameworks - Allows the community to propose new data sources via issues and pull requests.
- Data Processing and Analysis - Weekly data project for the R ecosystem.
- Data Science Tooling - Weekly data project for the R ecosystem.
- Learning Communities - Weekly collaborative data analysis and visualization practice project.
- Skill Development Resources - Hosts weekly data analysis challenges.
- Training Resources - Weekly data projects for the R ecosystem.
- Community Challenges - Weekly data visualization and analysis challenges.
