R Programming Language Certification Professional Standards & Competencies 2

Question 1

Which practice best demonstrates reproducible research in R?

Accepted Answer

Using R Markdown to combine code, output, and narrative

Answer

R Markdown integrates code, output, and documentation so analyses can be re-run and verified by others.

Question 2

A data scientist shares an R project with a colleague who cannot reproduce results due to different package versions. What is the best preventive measure?

Accepted Answer

Use renv to snapshot and restore the project library

Answer

renv creates a project-level lockfile recording exact package versions, enabling consistent environments across machines.

Question 3

When publishing a statistical analysis, which action upholds professional data integrity standards?

Accepted Answer

Reporting all conducted tests, including non-significant results

Answer

Reporting all analyses, including null results, prevents publication bias and upholds scientific integrity.

Question 4

Which versioning strategy is recommended for collaborative R projects to track code changes?

Accepted Answer

Git with a hosted repository such as GitHub or GitLab

Answer

Git provides full change history, branching, and collaboration features essential for professional R projects.

Question 5

What does the principle of 'separation of concerns' mean in the context of an R project?

Accepted Answer

Splitting data loading, processing, and reporting into distinct scripts or functions

Answer

Separation of concerns improves maintainability by assigning distinct responsibilities to different parts of the codebase.

R Programming Language Certification Practice Test

R Programming Language Certification Practice Test

R Programming Language Certification Professional Standards & Competencies 2