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The targets package is a Make-like pipeline tool for statistics and data science in R. With targets, you can maintain a reproducible workflow without repeating yourself. targets 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. An up-to-date targets pipeline is tangible evidence that the output aligns with the code and data, which substantiates trust in the results.

Philosophy

A pipeline is a computational workflow that does statistics, analytics, or data science. Examples include forecasting customer behavior, simulating a clinical trial, and detecting differential expression from genomics data. A pipeline contains tasks to prepare datasets, run models, and summarize results for a business deliverable or research paper. The methods behind these tasks are user-defined R functions that live in R scripts, ideally in a folder called "R/" in the project. The tasks themselves are called “targets”, and they run the functions and return R objects. The targets package orchestrates the targets and stores the output objects to make your pipeline efficient, painless, and reproducible.

Prerequisites

  1. Familiarity with the R programming language, covered in R for Data Science.
  2. Data science workflow management techniques.
  3. How to write functions to prepare data, analyze data, and summarize results in a data analysis project.

Installation

Type Source Command
Release CRAN install.packages("targets")
Development GitHub remotes::install_github("ropensci/targets")
Development rOpenSci install.packages("targets", repos = "https://dev.ropensci.org")

Get started in 4 minutes

The 4-minute video at https://vimeo.com/700982360 demonstrates the example pipeline used in the walkthrough and functions chapters of the user manual. Visit https://github.com/wlandau/targets-four-minutes for the code and https://rstudio.cloud/project/3946303 to try out the code in a browser (no download or installation required).

Usage

To create a pipeline of your own:

  1. Write R functions for a pipeline and save them to R scripts (ideally in the "R/" folder of your project).
  2. Call use_targets() to write key files, including the vital _targets.R file which configures and defines the pipeline.
  3. Follow the comments in _targets.R to fill in the details of your specific pipeline.
  4. Check the pipeline with tar_visnetwork(), run it with tar_make(), and read output with tar_read(). More functions are available.

Documentation

  • User manual: in-depth discussion about how to use targets.
  • Reference website: formal documentation of all user-side functions, the statement of need, and multiple design documents of the internal architecture.
  • Developer documentation: software design documents for developers contributing to the deep internal architecture of targets.

Apps

Deployment

Extending and customizing targets

Help

Code of conduct

Please note that this package is released with a Contributor Code of Conduct.

Citation

citation("targets")

To cite targets in publications use:

  Landau, W. M., (2021). The targets R package: a dynamic Make-like
  function-oriented pipeline toolkit for reproducibility and
  high-performance computing. Journal of Open Source Software, 6(57),
  2959, https://doi.org/10.21105/joss.02959

A BibTeX entry for LaTeX users is

  @Article{,
    title = {The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing},
    author = {William Michael Landau},
    journal = {Journal of Open Source Software},
    year = {2021},
    volume = {6},
    number = {57},
    pages = {2959},
    url = {https://doi.org/10.21105/joss.02959},
  }