Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The targets R package keeps results up to date and reproducible while minimizing the number of expensive tasks that actually run. Targets learns how your pipeline fits together, skips costly runtime for steps that are already up to date, runs the rest with optional implicit parallel computing, abstracts files as R objects, and shows tangible evidence that the output matches the underlying code and data. In other words, the package saves time while increasing our ability to trust the conclusions of the research. Targets surpasses the most burdensome permanent limitations of its predecessor, drake, to achieve greater efficiency and provide a safer, smoother, friendlier user experience. This hands-on workshop teaches targets using a realistic case study. Participants begin with the R implementation of a machine learning project, convert the workflow into a targets-powered pipeline, and efficiently maintain the output as the code and data change. R proficiency: intermediate and above required.
Will Landau
Eli Lilly
Targets and Reproducible Pipelines
Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The targets R package keeps results up to date and reproducible while minimizing the number of expensive tasks that actually run. Targets learns how your pipeline fits together, skips costly runtime for steps that are already up to date, runs the rest with optional implicit parallel computing, abstracts files as R objects, and shows tangible evidence that the output matches the underlying code and data. In other words, the package saves time while increasing our ability to trust the conclusions of the research. Targets surpasses the most burdensome permanent limitations of its predecessor, drake, to achieve greater efficiency and provide a safer, smoother, friendlier user experience. This hands-on workshop teaches targets using a realistic case study. Participants begin with the R implementation of a machine learning project, convert the workflow into a targets-powered pipeline, and efficiently maintain the output as the code and data change. R proficiency intermediate and above required.