An introduction to Docker for reproducible research, with examples from the R environment (1410.0846v1)

Abstract: As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology 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. I illustrate this with several examples of Docker use with a focus on the R statistical environment.

• The paper introduces Docker as a solution to dependency challenges in computational research, enhancing reproducibility in the R environment.
• It details how Docker encapsulates entire software setups, enabling version control and mitigating issues like code rot.
• Practical examples, including running RStudio Server in a container, illustrate Docker’s seamless integration into research workflows.

Introduction to Docker for Reproducible Research in the R Environment

In Carl Boettiger's paper, "An introduction to Docker for reproducible research, with examples from the R environment," the author elucidates the primary challenges in computational reproducibility and explores how Docker mitigates these issues, particularly within the R environment. Computational reproducibility remains a pressing concern in contemporary research, as the increasing complexity and evolution of software environments often obstruct the ability of researchers to replicate and extend previous work.

The paper systematically dissects the challenges and presents Docker as an effective tool for ensuring reproducibility. By leveraging Docker, researchers can achieve operating system (OS) level virtualization, cross-platform portability, component re-use, and versioning—all pivotal features for computational reproducibility.

Technical Challenges and Docker's Solutions

The paper addresses four significant technical challenges in reproducibility:

1. Dependency Hell: A prevalent issue where recreating an original computational environment becomes arduous due to varied software dependencies. Docker addresses this by encapsulating the entire software environment into Docker images, which ensures consistent setups across different machines.
2. Imprecise Documentation: Researchers often face difficulties due to insufficient or incorrect documentation. Dockerfiles, the scripts that define the content of Docker images, present a human-readable form of these dependencies, promoting precise and reproducible software environments.
3. Code Rot: Over time, dependencies may receive updates that could change or break the functionality of original software. Docker images can be versioned and archived to retain the exact environment used in the initial paper, allowing researchers to test if updates might have affected reproducibility.
4. Barriers to Adoption and Re-use: Existing virtual machines or workflow systems often entail substantial learning curves and integration costs. Docker offers a more streamlined, low-overhead approach that fits into local development practices, enhancing its usability and uptake among domain scientists.

Docker's Practical Implementations

Boettiger demonstrates the practical application of Docker through several use cases in the R environment. For instance, Docker can be employed to run RStudio Server inside a container, providing a familiar interface while ensuring the consistency of the computational environment. This setup allows researchers to perform their analyses on local machines while leveraging the reproducibility features of Docker.

This is achieved by using commands such as:

docker run -d -p 8787:8787 cboettig/ropensci

This command initiates an RStudio Server, accessible through a web browser, thus combining ease of use with robust environment management.

Implications and Future Developments

Docker provides a significant leap toward improved reproducibility for computational research. Its versioning system, combined with modularity, allows for the incremental building and sharing of reproducible computational environments. This can lead to broader adoption and greater collaboration between researchers, as reproducibility not only becomes easier to achieve but also integrated into daily workflow practices.

Looking ahead, further development might address current limitations such as reliance on the Linux kernel, potential security vulnerabilities, and enhancing the performance and integration on non-Linux systems like macOS and Windows. These advancements would facilitate even broader acceptance and utility across diverse research communities.

Best Practices and Recommendations

To maximize Docker's potential in reproducible research, Boettiger recommends several best practices:

• Utilize Docker containers from the development phase to encapsulate the computational environment.
• Develop Dockerfiles to script the setup, ensuring transparency and reusability.
• Include validation tests within Dockerfiles to confirm successful environment setups.
• Leverage Docker's modularity by using and building upon base images.
• Share Docker images and Dockerfiles through repositories like Docker Hub.
• Archive tarball snapshots of Docker images to maintain long-term reproducibility.

Conclusion

Boettiger's work underscores Docker's utility as an effective tool for addressing the perennial challenges of computational reproducibility. By facilitating consistent and portable software environments, Docker holds promise for transforming computational research practices, fostering greater reproducibility, collaboration, and efficiency. Its ease of use, combined with powerful reproducibility features, positions Docker as an indispensable tool for researchers across disciplines.