targets

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.

Courses

• Official half-day interactive tutorial.

Recorded talks

English

• Get started with targets in 4 minutes (4:08)
• R/Medicine 2021 (15.33)
• R/Pharma 2020 (9:24)
• LA R Users Meetup, October 2020 (1:14:40)
• New York Open Statistical Programming Meetup, December 2020 (1:54:28)
• ds-incubator series, 2021
• Lille R User Group, June 2021 (45:54)

Español

• R-Ladies Barcelona, 2021-05-25 (1:25:12)

日本語

• Bio”Pack”athon, 2022-03-31 (1:04:10)

Example projects

• Four-minute example
• Minimal example
• Machine learning with Keras
• Validate a minimal Stan model
• Using Target Markdown and stantargets to validate a Bayesian longitudinal model for clinical trial data analysis
• Shiny app that runs a pipeline
• Deploy a pipeline to RStudio Connect

Apps

• tar_watch(): a built-in Shiny app to visualize progress while a pipeline is running. Available as a Shiny module via tar_watch_ui() and tar_watch_server().
• targetsketch: a Shiny app to help sketch pipelines (app, source).

Deployment

• https://solutions.rstudio.com/r/workflows/ explains how to deploy a pipeline to RStudio Connect (example code).
• tar_github_actions() sets up a pipeline to run on GitHub Actions. The minimal example demonstrates this approach.

Extending and customizing targets

• R Targetopia: a collection of R packages that extend targets. These packages simplify pipeline construction for specific fields of Statistics and data science.
• Target factories: a programming technique to write specialized interfaces for custom pipelines. Posts here and here describe how.

Help

• Post to the GitHub discussion forum to ask questions. To get the best help about a specific issue, create a reproducible example with targets::tar_reprex() or reprex::reprex().
• The RStudio Community forum is full of friendly enthusiasts of R and the tidyverse. Use the targets tag.
• Stack Overflow broadcasts to the entire open source community. Use the targets-r-package tag.

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},
  }