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What strategies can you use to manage dependencies in statistical programming for reproducible research?

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Reproducible research is the practice of conducting and reporting statistical analyses in a way that allows others to verify and reproduce the results. It is essential for ensuring the validity, transparency, and reliability of scientific findings. However, reproducible research can be challenging when you use statistical programming languages, such as R or Python, that depend on external packages, libraries, or frameworks. These dependencies can introduce variability and inconsistency in your code, data, and outputs, especially when they change over time or across different environments. How can you manage these dependencies and ensure that your code runs as expected and produces the same results every time? Here are some strategies that can help you achieve reproducible research with statistical programming.

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Mehdi Hamedi, MD

Psychiatrist, Board certified. Enthusiast in computational psychiatry, artificial intelligence, machine learning…
Zahra Amini

Machine Learning Lecturer | Interested in AI | Data Science
Brunella D'Anzi

Doctoral Student @ CERN | High Energy Physics

1 Use version control

Version control is a system that tracks and records the changes you make to your code and data over time. It allows you to create snapshots of your project at different stages, compare and merge different versions, and revert to previous states if needed. Version control also enables you to collaborate with other researchers and share your code and data in a consistent and organized way. You can use version control tools, such as Git or SVN, to manage your project files and store them in online repositories, such as GitHub or Bitbucket.

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Brunella D'Anzi

Doctoral Student @ CERN | High Energy Physics
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On Github you are able to create a version tag as well. It is free and you could give a restricted or open access to your files. This is very useful to share projects and knowledge among a community.

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Michael Abildskov Poulsen
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To manage dependencies in statistical programming for reproducible research, I utilize version control systems, like Git, extensively. This allows me to track and document changes in my code and dependency configurations over time. By using branches, I can experiment with different versions of dependencies without affecting the main project. I also commit requirements.txt or environment.yml files to my repository, specifying exact versions of libraries and tools used. This practice ensures that anyone who clones my repository can recreate the same environment, leading to more consistent and reproducible results.

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2 Document your dependencies

Documentation is the process of describing and explaining your code and data, as well as the assumptions, methods, and results of your analysis. Documentation helps you and others understand and reproduce your work, as well as identify and resolve any issues or errors. One of the most important aspects of documentation is listing and specifying your dependencies, such as the packages, libraries, or frameworks that you use in your code, along with their versions and sources. You can use comments, README files, or metadata files to document your dependencies and provide instructions on how to install and load them.

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Mehdi Hamedi, MD

Psychiatrist, Board certified. Enthusiast in computational psychiatry, artificial intelligence, machine learning, cognitive modeling
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Documentation of statistical programming must be done in detail and accurately. It must be understandable and readable by others who wants to work on the project. Otherwise it can be frustrating to understand. You must define why to choose the specific decision in the code. The cons and pros of your options must be mentioned in your document. In the statistical programming projects, consistent naming conventions must be used. file names, variable names, and ... must follow a standard and consistent naming conventions. If you’re using dates, make sure that your documentation is ISO 8601 Standard, which is an international standard covering the exchange of date and time-related data. In the start of each code file, provide a brief summary.

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3 Isolate your environment

Isolation is the practice of creating and using a separate and dedicated environment for your project, where you can install and manage your dependencies without affecting or being affected by other projects or system settings. Isolation helps you avoid conflicts and compatibility issues that may arise from different versions or configurations of your dependencies. You can use tools, such as virtualenv or conda for Python, or renv or packrat for R, to create and activate isolated environments for your project and install your dependencies there.

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Gonzalo Sanchez Pla

Data Science & Software Engineering
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To ensure reproducibility in computational tasks, it's important to manage dependencies and isolate environments. Crucially, this includes setting a "seed" for random number generation. A seed acts as a starting point, ensuring the same sequence of numbers is generated each time, vital for consistent results in simulations or statistical algorithms like Monte Carlo methods or machine learning models. Setting a seed guarantees that others can replicate your results exactly, a key aspect of credibility in scientific computing and data analysis.

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4 Freeze your dependencies

Freezing is the practice of locking and saving the exact versions and sources of your dependencies that you use in your project, so that they do not change or update automatically. Freezing helps you ensure that your code runs with the same dependencies every time, regardless of when or where you run it. You can use tools, such as pip freeze or conda list for Python, or sessionInfo() or devtools::session_info() for R, to generate and export a list of your dependencies with their versions and sources. You can also use tools, such as pip install -r requirements.txt or conda env create -f environment.yml for Python, or install.packages() or devtools::install_version() for R, to install and load your dependencies from a file.

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5 Test your code

Testing is the practice of checking and verifying that your code works as intended and produces the expected results. Testing helps you detect and correct any errors or bugs in your code, as well as ensure that your code is robust and reliable. You can use tools, such as pytest or unittest for Python, or testthat or RUnit for R, to write and run automated tests for your code, such as unit tests, integration tests, or regression tests. You can also use tools, such as Travis CI or GitHub Actions, to run your tests continuously and automatically every time you make changes to your code or data.

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6 Share your code and data

Sharing is the practice of making your code and data publicly available and accessible for others to review and reuse. Sharing helps you disseminate and communicate your research, as well as facilitate collaboration and feedback. You can use platforms, such as GitHub or Zenodo, to upload and publish your code and data, along with your documentation and dependencies. You can also use tools, such as Binder or Code Ocean, to create and share executable and interactive versions of your code and data, where others can run and reproduce your analysis online without installing anything.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Zahra Amini

Machine Learning Lecturer | Interested in AI | Data Science
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To ensure reproducible research in statistical programming, use version control (e.g., Git) for tracking code changes, document all dependencies with their versions, and isolate your environment using tools like virtualenv or conda. Freeze dependencies to maintain version consistency and regularly test your code using pytest or testthat. Share your work on platforms like GitHub. Enhance these steps with containerization (Docker), CI/CD pipelines for automated testing, dependency management tools, and regular updates. Conduct code reviews, use interactive notebooks (Jupyter), archive dependencies, and promote a culture of reproducibility through training. These practices guarantee robust and reliable statistical analyses.

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What strategies can you use to manage dependencies in statistical programming for reproducible research?

1

2

3

4

5

6

7

1 Use version control

2 Document your dependencies

3 Isolate your environment

4 Freeze your dependencies

5 Test your code

6 Share your code and data

7 Here’s what else to consider

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What strategies can you use to manage dependencies in statistical programming for reproducible research?

1

2

3

4

5

6

7

1 Use version control

2 Document your dependencies

3 Isolate your environment

4 Freeze your dependencies

5 Test your code

6 Share your code and data

7 Here’s what else to consider

Statistics

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