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The package assertr maintained by Tony Fischetti, provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly.
The provided functionality is similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.

Contributed to assertr!

The assertr issue tracker has a few tickets that you could help with, please have a look.
You can also subscribe to be notified of new issues opened in this repository.

How to help?

Volunteer in an open issue, then once you get a green light, go ahead and start a PR!
This workflow will avoid duplicate work which could happen if several people start solving the same issue at the same time.

Thank you!

Thank you!
Interested in contributing in other ways to rOpenSci?
Do not miss our contributing guide.
Also stay tuned for more similar posts about maintainers’ specific call for contributions.
Last but not least, if you maintain an rOpenSci package and would like to put out such a call, get in touch with us.

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Continue reading: Help make assertr better! Come close issues

Long-term Implications and Future Developments

The ‘assertr’ package, maintained by Tony Fischetti, opens up a critical functionality for data analysis pipelines in R. By asserting conditions that need to be met, it allows pipelines to fail quickly when there are errors in the data. While comparison can be drawn to stopifnot(), the ease, friendliness and power of assertr stands out. By continuing to improve and expand the features of assertr, the data science community can look forward to a future where troubleshooting data errors becomes progressively more efficient.

Moreover, the call for contributors to assertr shows the potential for community-engaged growth. Open source projects such as this not only help the tool to evolve, but also foster a collaborative and inclusive environment within the data science community. The future holds great promise for such collaborative models in shaping the landscape of open science tools.

Actionable Advice

  1. Explore the ‘assertr’ package and familiarize yourself with its functionality. Identifying its strengths and weaknesses will not only improve your own data analysis workflows, but also give you the knowledge base to potentially contribute to its improvement.
  2. Consider contributing to the ‘assertr’ project. Not only will you help to improve a valuable tool but also gain experience collaborating in a open source project. Have a look at the open issues on the assertr issue tracker.
  3. Stay informed about R news and tutorials. Subscribing to daily updates from R-bloggers.com can be a good way to stay abreast of developments.
  4. If you’re looking to get more involved in the broader rOpenSci project, check out their contributing guide and be open to calls for contributions.

Final Thoughts

With open source tools like ‘assertr’, the future of data science looks exciting and inclusive. Whether you’re a data analyst seeking to improve your workflows, or a budding open source enthusiast looking for a project to contribute to, assertr offers valuable opportunity. By staying in tune with the developments in the open-source R community, we can not only improve our own skills, but contribute to the growth of these vital tools.

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