Event page: www.meetup.com...
*Talk*
Title: Reproducible computation at scale in R with targets
Abstract: Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the targets R package. targets resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and manages data storage for you. It surpasses the permanent limitations of its predecessor, drake, and provides increased efficiency and a smoother user experience. This talk demonstrates how to create and maintain a Bayesian model validation project using targets-powered automation.
Slides: wlandau.github...
Materials: github.com/wla...
**Speaker**: Will Landau
Will Landau received his PhD in Statistics at Iowa State University in 2016. His dissertation research introduced a novel fully Bayesian, hierarchical model-driven, GPU-accelerated approach to the analysis of heterosis gene expression data (Landau, Niemi, and Nettleton 2019). He currently works at Eli Lilly and Company, where he develops capabilities for clinical statisticians. Will is the creator and maintainer of rOpenSci’s drake R package.
**Code of Conduct**: github.com/laR...
*Schedule*
6:30 Open zoom
6:40-7:40 Presentation
~8:00 Virtual Social
*LA R Users Group*
Invite yourself to our Slack group: socalrug.herok...
Ask us any questions by email: larusers@gmail.com
Find our previous talks on GitHub: github.com/laR...
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Check out more events: socalr.org/
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