Docker is a powerful tool for enabling reproducible research, particularly in the R environment. As computational work becomes more integral to scientific research, computational reproducibility has become increasingly important. However, the complex and rapidly changing nature of computer environments makes it challenging to reproduce and extend such work. This paper explores common reasons why code developed for one research project cannot be successfully executed or extended by subsequent researchers. It reviews current approaches to these issues, including virtual machines and workflow systems, and their limitations. It then examines how Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. The paper illustrates this with several examples of Docker use with a focus on the R statistical environment. Docker provides a solution to the challenges of computational reproducibility by allowing researchers to create and share self-contained environments that include all necessary software and dependencies. This ensures that the same environment can be used to reproduce results, regardless of the underlying operating system or hardware. Docker images are versioned, making it easy to track changes and ensure consistency. Additionally, Docker supports modular reuse, allowing researchers to build upon existing images and extend them as needed. The paper also discusses the challenges of computational reproducibility, including "dependency hell," imprecise documentation, code rot, and barriers to adoption and reuse in existing solutions. It highlights how Docker addresses these challenges through its features such as versioning, modular reuse, portable environments, public repository for sharing, and versioning. The paper also discusses the importance of using Docker as a local development environment, allowing researchers to work with familiar tools while still benefiting from the reproducibility and portability features of Docker. In conclusion, Docker offers a promising solution to the challenges of computational reproducibility in scientific research. By providing a self-contained environment that includes all necessary software and dependencies, Docker ensures that results can be reproduced consistently across different platforms and environments. The paper emphasizes the importance of adopting Docker as a best practice for reproducible research, highlighting its ability to address the challenges of computational reproducibility in scientific communities.Docker is a powerful tool for enabling reproducible research, particularly in the R environment. As computational work becomes more integral to scientific research, computational reproducibility has become increasingly important. However, the complex and rapidly changing nature of computer environments makes it challenging to reproduce and extend such work. This paper explores common reasons why code developed for one research project cannot be successfully executed or extended by subsequent researchers. It reviews current approaches to these issues, including virtual machines and workflow systems, and their limitations. It then examines how Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. The paper illustrates this with several examples of Docker use with a focus on the R statistical environment. Docker provides a solution to the challenges of computational reproducibility by allowing researchers to create and share self-contained environments that include all necessary software and dependencies. This ensures that the same environment can be used to reproduce results, regardless of the underlying operating system or hardware. Docker images are versioned, making it easy to track changes and ensure consistency. Additionally, Docker supports modular reuse, allowing researchers to build upon existing images and extend them as needed. The paper also discusses the challenges of computational reproducibility, including "dependency hell," imprecise documentation, code rot, and barriers to adoption and reuse in existing solutions. It highlights how Docker addresses these challenges through its features such as versioning, modular reuse, portable environments, public repository for sharing, and versioning. The paper also discusses the importance of using Docker as a local development environment, allowing researchers to work with familiar tools while still benefiting from the reproducibility and portability features of Docker. In conclusion, Docker offers a promising solution to the challenges of computational reproducibility in scientific research. By providing a self-contained environment that includes all necessary software and dependencies, Docker ensures that results can be reproduced consistently across different platforms and environments. The paper emphasizes the importance of adopting Docker as a best practice for reproducible research, highlighting its ability to address the challenges of computational reproducibility in scientific communities.