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The {targets} R package user manual

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Pipeline tools coordinate the pieces of computationally demanding analysis projects. The targets package is a Make-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. Data analysis can be slow. A round of scientific computation can take several minutes, hours, or even days to complete. After it finishes, if you update your code or data, your hard-earned results may no longer be valid. Unchecked, this invalidation creates chronic Sisyphean loop: Pipeline tools like GNU Make break the cycle. They watch the dependency graph of the whole workflow and skip steps, or “targets”, whose code, data, and upstream dependencies have not changed since the last

The {targets} R package user manual 1 Introduction Pipeline tools coordinate the pieces of computationally demanding analysis projects. The targets package is a Make -like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. 1.1 Motivation Data analysis can be

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