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.
Este curso faz parte do Programa de cursos integrados Mastering Software Development in R
oferecido por
Informações sobre o curso
Habilidades que você terá
- Programming Tool
- Github
- Continuous Integration
- R Programming
oferecido por

Universidade Johns Hopkins
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.
Programa - O que você aprenderá com este curso
Getting Started with R Packages
Documentation and Testing
Licensing, Version Control, and Software Design
Continuous Integration and Cross Platform Development
Avaliações
- 5 stars52,07%
- 4 stars23,96%
- 3 stars13,82%
- 2 stars3,22%
- 1 star6,91%
Principais avaliações do BUILDING R PACKAGES
Very good course for intermediate/advanced R users. Sad that you are elegible to do assignments only if you pay.
Useful programming exercises to guide learning the basic elements of R packages. Also glad that I got my assignments graded within a week following submission (thought it would take much longer).
Overall, this was a good course to learn the intricacies of building R packages.
This is a critical skill and it's barely covered anywhere else. Thanks for making this course!
Sobre Programa de cursos integrados Mastering Software Development in R
R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.

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