Will Landau - Reproducible Computation at Scale in R with Targets [Remote]

Talk delivered December 1, 2020. Visit https://nyhackr.org/ to learn more and follow https://twitter.com/nyhackr. This meetup is the kickoff to the second 2020 R week with The R Conference for Government & Public Sector on December 2-4. Much like our recent NYR, this is virtual, so anyone around the world can attend. Visit https://rstats.ai/gov/ to learn more and use code nyhackr for a 20% discount on tickets, including workshops.

Following up our earlier meetup about drake, we have its creator talking about its successor, targets.

About the Talk:
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.

About Will:
Will Landau works at Eli Lilly and Company, where he develops capabilities for clinical statisticians, and he is the creator and maintainer of the targets and drake R packages. Will earned his PhD in Statistics at Iowa State University in 2016, where his dissertation research applied Bayesian methods, hierarchical models, and GPU computing to the analysis of RNA-seq data.

Where to find Will:
https://twitter.com/wmlandau
https://wlandau.github.io/
https://www.linkedin.com/in/wlandau/

Thank you EcoHealth Alliance (https://www.ecohealthalliance.org/) for providing the Zoom link.

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