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Pipeline tools coordinate the pieces of computationally demanding analysis projects. The targets package is a Make-like pipeline tool for statistics and data science in R. The package 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. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise.


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.


  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.


If you are using targets with crew for distributed computing, it is recommended to use nanonext version or higher, mirai version or higher, and crew version 0.2.0 or higher. If the latest CRAN releases are older, then you can install the development versions from R-universe.

install.packages("nanonext", repos = "")
install.packages("mirai", repos = "")
install.packages("crew", repos = "")

There are multiple ways to install the targets package itself, and both the latest release and the development version are available.

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

Get started in 4 minutes

The 4-minute video at demonstrates the example pipeline used in the walkthrough and functions chapters of the user manual. Visit for the code and to try out the code in a browser (no download or installation required).


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.



Please read the help guide to learn how best to ask for help using targets.



Extending and customizing targets

Code of conduct

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


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),

A BibTeX entry for LaTeX users is

    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 = {},