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.
Building R Packages
Building R Packages
This course is part of Mastering Software Development in R Specialization
Instructors: Roger D. Peng, PhD
Sponsored by Coursera Learning Team
10,948 already enrolled
Gain insight into a topic and learn the fundamentals.
4.1
(223 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 20 hours
Learn at your own pace
97%
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Shareable certificate
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Assessments
2 assignments
Taught in English
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This course is part of the Mastering Software Development in R Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
What's included
1 video16 readings1 assignment
What's included
14 readings1 peer review
What's included
25 readings1 assignment
What's included
13 readings1 peer review
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3.7 (8 ratings)Offered by
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4.1
223 reviews
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Showing 3 of 223
M
5
Reviewed on Jun 11, 2022
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).
MM
5
Reviewed on Nov 16, 2020
This is an excellent course, a really good starting point to learn how to build R packages.
MA
5
Reviewed on Jan 16, 2017
Very good course for intermediate/advanced R users. Sad that you are elegible to do assignments only if you pay.
Recommended if you're interested in Data Science
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