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Are you passionate about data pipelines, workflow management, and the R programming language? Join us for an exciting and collaborative hack session as we add R Support to Soorgeon!

Date: 21 July 2023 (Today!)
Time: 13h00 (GMT+2)
Location: Click here to join the meeting
Repo we'll be working in: https://github.com/booysej/soorgeon

About Soorgeon: Converts monolithic Jupyter notebooks 📙 into maintainable Ploomber pipelines. 📊
What to Expect: During this hack session, we'll come together as a community of R aficionados, and workflow enthusiasts to tackle the challenge of incorporating R support into Soorgeon. Whether you're a seasoned R developer or a workflow management enthusiast eager to contribute, this event is for you!

Agenda:

  1. Introduction to Soorgeon: Get an overview of Soorgeon.
  2. Identifying R Integration Points: Collaboratively brainstorm and identify key areas where R support can be seamlessly integrated within Ploomber and Soorgeon.
  3. Hands-on Coding: Split into teams or work individually to code and experiment with R integration. Share ideas, exchange knowledge, and collaborate to overcome challenges.
  4. Group Discussions and Demos: Reassemble to discuss progress, share experiences, and showcase your achievements. Learn from others and gain insights into different approaches to tackle the integration.
  5. Contributions and Next Steps: Learn how to contribute your work to the Ploomber and Soorgeon projects and discuss the roadmap for future R-related enhancements.

Prerequisites:

  • Basic familiarity with R programming is recommended.
  • Prior knowledge of Ploomber or Soorgeon is beneficial but not required.

Who Can Attend:

  • Data scientists, data engineers, and data enthusiasts.
  • R developers and programmers.
  • Anyone interested in workflow management and contributing to open-source projects.

Don't miss this opportunity to be part of a community-driven effort. Let's come together, share our expertise, and work towards creating a more powerful and inclusive ecosystem. RSVP now, and let's code together!

Machine Learning
Data Analytics
Programming in R
R-Ladies
Statistical Computing

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