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.

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.

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.

• 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

• 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.

• Official half-day interactive tutorial.

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

• 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).

• 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.

• 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.

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

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},
#>   }