This vignette is a high-level overview of
targets and its educational materials. The goal is to summarize the major features of
targets and direct users to the appropriate resources. It explains how to get started, and then it briefly describes each chapter of the user manual.
targets R package is a Make-like pipeline toolkit for Statistics and data science in R.
targets accelerates analysis with easy-to-configure parallel computing, enhances reproducibility, and reduces the burdens of repeated computation and manual data micromanagement. 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.
The top of the reference website links to a number of materials to help new users start learning
targets. It lists online talks, tutorials, books, and workshops in the order that a new user should consume them. The rest of the main page outlines a more comprehensive list of resources.
The user manual starts with a walkthrough chapter, a short tutorial to quickly started with
targets using a simple example project. That project also has a repository with the source code and an RStudio Cloud workspace that lets you try out the workflow in a web browser. Sign up for a free RStudio Cloud account, click on the link, and try out functions
tar_read() in the R console.
The help guide explains how to best get help using
targets, including reproducible examples and where to post.
The debugging chapter describes two alternative built-in systems for troubleshooting errors. The first system uses workspaces, which let you load a target’s dependencies into you R session. This way is usually preferred, especially with large pipelines on computing clusters, but it still may require some manual work. The second system launches an interactive debugger while the pipeline is actually running, which may not be feasible in some situations, but can often help you reach the problem more quickly.
targets expects users to adopt a function-oriented style of programming. User-defined R functions are essential to express the complexities of data generation, analysis, and reporting. The user manual has a whole chapter dedicated to user-defined functions for data science, and it explains why they are important and how to use them in
The target construction chapter explains best practices for creating targets: what a good target should do, how much work a target should do, and guidelines for thinking about side effects and upstream dependencies (i.e. other targets and global objects).
The packages chapter explains best practices for working with packages in
targets: how to load them, how to work with packages as projects, target factories inside packages, and automatically invalidating targets based on changes inside one or more packages.
The projects chapter explains best practices for working with
targets-powered projects: the recommended file structure, recommended third-party tools, multi-project repositories, and interdependent projects.
The chapter at https://books.ropensci.org/targets/data.html describes how the targets package stores data, manages memory, allows you to customize the data processing model. When a target finishes running during
tar_make(), it returns an R object. Those return values, along with descriptive metadata, are saved to persistent storage so your pipeline stays up to date even after you exit R. By default, this persistent storage is a special
_targets/ folder created in your working directory by
tar_make(). However, you can also interact with files outside the data store and send target data to the cloud.
The chapter at https://books.ropensci.org/targets/literate-programming.html covers literate programming: how to render an R Markdown or Quarto report as part of a
targets pipeline. A report can depend on other targets and take advantage of long computation already completed upstream.
targets is capable of distributing the computation in a pipeline across multiple cores of a laptop or multiple jobs on a computing cluster. The orchestration and scaling mechanisms are automatic, and only high-level configuration is required. Visit https://books.ropensci.org/targets/crew.html to learn more. Configuration happens through the
crew package: https://wlandau.github.io/crew/. The appendix at https://books.ropensci.org/targets/hpc.html describes how to use
targets with legacy backends
https://books.ropensci.org/targets/performance.html explains how to monitor the progress of a running pipeline and optimize your pipeline for performance.
targets has easy-to-configure efficiency settings at the level of
Sometimes, a pipeline contains more targets than a user can comfortably type by hand. For projects with hundreds of targets, branching can make the _targets.R file more concise and easier to read and maintain. Dynamic branching is a way to create new targets while the pipeline is running, and it is best suited to iterating over a larger number of very similar tasks. The dynamic branching chapter outlines this functionality, including how to create branching patterns, different ways to iterate over data, and recommendations for batching large numbers of small tasks into a comfortably small number of dynamic branches.
Static branching is the act of defining a group of targets in bulk before the pipeline starts. Whereas dynamic branching uses last-minute dependency data to define the branches, static branching uses metaprogramming to modify the code of the pipeline up front. Whereas dynamic branching excels at creating a large number of very similar targets, static branching is most useful for smaller number of heterogeneous targets. Some users find it more convenient because they can use
tar_visnetwork() to check the correctness of static branching before launching the pipeline. Read more about it in the static branching chapter.