Reproducibility and efficiency are critical in data analysis workflows, particularly in complex projects with multiple interdependent tasks. The {targets} package in R offers a robust solution for orchestrating reproducible and scalable workflows. By defining and managing a directed acyclic graph (DAG) of tasks, {targets} ensures that each step in the workflow is executed in the correct order and only when necessary, saving time and computational resources. In this presentation, we will demonstrate how to build a reproducible workflow using {targets}. We will explore the core features of the package, including target definition, dependency management, and automated pipeline execution. Through practical examples, we will show how {targets} can be integrated with other R packages to handle data processing, modeling, and visualization seamlessly. Additionally, we will discuss practical tips for maintaining workflows, ensuring they remain efficient and manageable as project complexity grows. Whether you are new to {targets} or looking to enhance your workflow automation, this session will provide valuable insights and practical techniques to elevate your data analysis projects.
Kensuke Sasaki | Masahiko Takemura
Datak Inc | Datak Inc
Building a reproducible workflow using {targets}
Reproducibility and efficiency are critical in data analysis workflows, particularly in complex projects with multiple interdependent tasks. The  targets  package in R offers a robust solution for orchestrating reproducible and scalable workflows. By defining and managing a directed acyclic graph (DAG) of tasks,  targets  ensures that each step in the workflow is executed in the correct order and only when necessary, saving time and computational resources. In this presentation, we will demonstrate how to build a reproducible workflow using  targets . We will explore the core features of the package, including target definition, dependency management, and automated pipeline execution. Through practical examples, we will show how  targets  can be integrated with other R packages to handle data processing, modeling, and visualization seamlessly. Additionally, we will discuss practical tips for maintaining workflows, ensuring they remain efficient and manageable as project complexity grows. Whether you are new to  targets  or looking to enhance your workflow automation, this session will provide valuable insights and practical techniques to elevate your data analysis projects.