targets

The targets package is a Make-like pipeline toolkit 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, runs the necessary computation with implicit parallel computing, and abstracts files as R objects. A fully up-to-date targets pipeline is tangible evidence that the output aligns with the code and data, which substantiates trust in the results.

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 data analysis projects.

How to get started

1. Watch minutes 6 through 40 of the New York Open Statistical Programming Meetup from December 2020.
2. Read the short walkthrough chapter of the user manual.
3. Sign up for a free RStudio Cloud account and click here to open the walkthrough code. Experiment with functions tar_make() and tar_read().
4. Log into the cloud workspace of the official targets short course. Work through the exercises in R notebooks 1-functions.Rmd, 2-pipelines.Rmd, and 3-changes.Rmd.
5. Try out one of the other example projects linked from the reference website.

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")

Recorded talks

• 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

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.

Example projects

• Minimal example.
• Machine learning with Keras.
• Validating a Stan model.

Apps

• targets-shiny: a simple prototype of a Shiny app with a targets backend. Shows how to build powerful data pipelines inside 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).

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 issue tracker to elicit help from the maintainer.
• 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},
#>   }