Building R Packages

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About this course: Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.

Created by:  Joh…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.

Created by:  Johns Hopkins University
  • Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences

    Colorado State University
Basic Info Course 3 of 5 in the Mastering Software Development in R Specialization Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.2 stars Average User Rating 4.2See what learners said Coursework

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Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus


WEEK 1


Getting Started with R Packages



1 video, 16 readings expand


  1. Video: Welcome to Building R Packages
  2. Reading: Before You Start
  3. Reading: Using Mac OS
  4. Reading: Using Windows
  5. Reading: Using Unix/Linux
  6. Reading: R packages
  7. Reading: Basic Structure of an R Package
  8. Reading: DESCRIPTION File
  9. Reading: NAMESPACE File
  10. Reading: Namespace Function Notation
  11. Reading: Loading and Attaching a Package Namespace
  12. Reading: The R Sub-directory
  13. Reading: The man Sub-directory
  14. Reading: Summary
  15. Reading: The devtools package
  16. Reading: Creating a Package
  17. Reading: Other Functions

Graded: R Package and devtools

WEEK 2


Documentation and Testing



14 readings expand


  1. Reading: Documentation
  2. Reading: Vignette's and README Files
  3. Reading: Knitr / Markdown
  4. Reading: Common knitr Options
  5. Reading: Help Files and roxygen2
  6. Reading: Common roxygen2 Tags
  7. Reading: Overview
  8. Reading: Data for Demos
  9. Reading: Internal Data
  10. Reading: Data Packages
  11. Reading: Summary
  12. Reading: Introduction
  13. Reading: The testthat Package
  14. Reading: Passing CRAN Checks

Graded: Documenting Code

WEEK 3


Licensing, Version Control, and Software Design



25 readings expand


  1. Reading: Overview
  2. Reading: The General Public License
  3. Reading: The MIT License
  4. Reading: The CC0 License
  5. Reading: Overview
  6. Reading: Paying it Forward
  7. Reading: Linus’s Law
  8. Reading: Hiring
  9. Reading: Summary
  10. Reading: Introduction
  11. Reading: git
  12. Reading: Initializing a git repository
  13. Reading: Committing
  14. Reading: Browsing History
  15. Reading: Linking local repo to GitHub repo
  16. Reading: Syncing RStudio and GitHub
  17. Reading: Issues
  18. Reading: Pull Request
  19. Reading: Merge Conflicts
  20. Reading: Introduction
  21. Reading: The Unix Philosophy
  22. Reading: Default Values
  23. Reading: Naming Things
  24. Reading: Playing Well With Others
  25. Reading: Summary

Graded: Testing, GitHub, and Open Source

WEEK 4


Continuous Integration and Cross Platform Development



13 readings expand


  1. Reading: Overview
  2. Reading: Web Services for Continuous Integration
  3. Reading: Using Travis
  4. Reading: Using AppVeyor
  5. Reading: Summary
  6. Reading: Introduction
  7. Reading: Handling Paths
  8. Reading: Saving Files & rappdirs
  9. Reading: rappdirs
  10. Reading: Options and Starting R
  11. Reading: Package Installation
  12. Reading: Environmental Attributes
  13. Reading: Summary

Graded: Building an R Package
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