Reproducible Analytical Pipelines (RAP) using R

📘 Course Title: Reproducible Analytical Pipelines (RAP) using R

🚀 Course Headline: Automating the production of statistical reports using DataOps principles

🎉 What You'll Achieve:

• Identify Opportunities: Spot where Reproducible Analytical Pipelines (RAP) can streamline processes at your organization.
• Data Management: Derive the minimal tidy dataset necessary to produce all the figures, tables, and statistics from a chosen report.
• Version Control Mastery: Utilize basic git functionality for version control and track your progress with an audit trail.
• Collaborative Skills: Engage in Github collaboration using a standard workflow with pull requests for peer review to ensure consistent quality assurance.
• Software Development: Create an R package that encapsulates the business knowledge, reflecting reproducibility and quality assurance standards.
• Efficiency & Quality: Apply Open Source software development tools and principles, including functional programming, unit testing, continuous integration, and dependency management to enhance production time and the quality of your statistical reports.
• Time for Innovation: Free up time by automating routine tasks and focus on more intriguing challenges.

Course Description: In this comprehensive course, you'll dive into the world of Reproducible Analytical Pipelines (RAP) using R, leveraging DataOps principles to revolutionize how you produce statistical reports. By the end of this course, you will have a solid understanding of how to:

• Select and Define: Choose the right report within your organization that can benefit from automation and define the minimal dataset required for its production.
• Version Control: Learn the ins and outs of using git for version control, which will provide an essential audit trail of your work.
• Collaborative Workflow: Discover how to collaborate on Github effectively with a standardized workflow that includes pull requests for peer review, ensuring that each contribution to your project is vetted and meets high-quality standards.
• Software Craftsmanship: Develop an R package from the ground up, enshrining your organization's business knowledge in software that can be reused and maintained with ease.
• Quality Assurance: Implement best practices for Open Source software development, including functional programming, unit testing, continuous integration, and robust dependency management to ensure that your pipeline is reliable and maintainable.

🔍 Key Topics Covered:

• Introduction to Reproducible Analytical Pipelines (RAP)
• Tidy Data Principles
• Version Control with Git
• Github Collaboration & Workflow Best Practices
• Software Development with R
• Functional Programming in R
• Unit Testing and Quality Assurance
• Continuous Integration Strategies
• Dependency Management
• Packaging Your Solution for Production Use

Learning Outcomes: By the end of this course, you will have not only mastered the creation of reproducible analytical pipelines using R but also be equipped with the skills to apply DataOps principles in your organization. This will enable you to produce high-quality statistical reports efficiently and with confidence, allowing you to allocate more time to exploring new challenges and opportunities.

🎓 Who Should Take This Course?

• Data Analysts
• Statisticians
• R Programmers
• Business Analysts
• Anyone interested in streamlining their data analysis workflow through automation and reproducibility.

🛠️ Tools & Technologies:

• R programming language
• Git version control system
• Github for collaboration and code hosting
• RStudio (IDE for R)
• Continuous integration services (e.g., Travis CI, GitHub Actions)
• Package development tools (e.g., devtools, testthat)

DISCLAIMER: The views and opinions expressed in this course are those of the author and do not reflect the official policy or position of GDS or the UK Government. 📢

Join us on this journey to elevate your data analysis capabilities with Reproducible Analytical Pipelines (RAP) using R, and embrace the future of DataOps! 🚀✨