The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping computation for up-to-date targets so that unchanged parts of the workflow are not recomputed. Targets can represent files or R objects, and tracking file changes etc is incorporated.
Features
- Declarative “target” definitions of tasks: you declare what steps depend on what, and targets builds the dependency graph
- Skipping computation for up-to-date targets so that unchanged parts of workflow are not recomputed
- Parallel execution of targets where possible to speed up workflows
- Support for branching / dynamic target creation (e.g. looping over combinations, expanding pipelines dynamically)
- File abstractions: targets can represent files or R objects, and tracking file changes etc is incorporated
- Tools for reproducibility: recording metadata, settings, dependency tracking, manifesting that output corresponds to current code/data etc.
Project Samples
Categories
Data ScienceFollow targets
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