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.
This course is part of the Mastering Software Development in R Specialization
Building R Packages
Offered By
About this Course
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Learner Career Outcomes
25%
started a new career after completing these courses
17%
got a tangible career benefit from this course
Skills you will gain
Programming ToolGithubContinuous IntegrationR Programming
Learner Career Outcomes
25%
started a new career after completing these courses
17%
got a tangible career benefit from this course
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Course 3 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 13 hours to complete
Suggested: 4 hours/week
English
Subtitles: English
Syllabus - What you will learn from this course
3 hours to complete
Getting Started with R Packages
3 hours to complete
1 video (Total 2 min), 16 readings, 1 quiz
1 video
16 readings
Before You Start10m
Using Mac OS10m
Using Windows10m
Using Unix/Linux10m
R packages10m
Basic Structure of an R Package10m
DESCRIPTION File10m
NAMESPACE File10m
Namespace Function Notation10m
Loading and Attaching a Package Namespace10m
The R Sub-directory10m
The man Sub-directory10m
Summary10m
The devtools package10m
Creating a Package10m
Other Functions10m
1 practice exercise
R Package and devtools20m
7 hours to complete
Documentation and Testing
7 hours to complete
14 readings, 1 quiz
14 readings
Documentation10m
Vignette's and README Files10m
Knitr / Markdown30m
Common knitr Options10m
Help Files and roxygen210m
Common roxygen2 Tags10m
Overview10m
Data for Demos10m
Internal Data10m
Data Packages10m
Summary10m
Introduction10m
The testthat Package10m
Passing CRAN Checks10m
5 hours to complete
Licensing, Version Control, and Software Design
5 hours to complete
25 readings, 1 quiz
25 readings
Overview10m
The General Public License10m
The MIT License10m
The CC0 License10m
Overview10m
Paying it Forward10m
Linus’s Law10m
Hiring10m
Summary10m
Introduction10m
git10m
Initializing a git repository10m
Committing10m
Browsing History10m
Linking local repo to GitHub repo10m
Syncing RStudio and GitHub10m
Issues10m
Pull Request10m
Merge Conflicts10m
Introduction10m
The Unix Philosophy10m
Default Values10m
Naming Things10m
Playing Well With Others10m
Summary10m
1 practice exercise
Testing, GitHub, and Open Source20m
6 hours to complete
Continuous Integration and Cross Platform Development
6 hours to complete
13 readings, 1 quiz
13 readings
Overview10m
Web Services for Continuous Integration10m
Using Travis10m
Using AppVeyor10m
Summary10m
Introduction10m
Handling Paths10m
Saving Files & rappdirs10m
rappdirs10m
Options and Starting R10m
Package Installation10m
Environmental Attributes10m
Summary10m
4.2
45 Reviews
Top reviews from Building R Packages
Fantastic course... Unfortunately, not too many people registered, it's tough to get your assignments graded. The program is the great continuation to the 10 course R data science specialization...
This is a critical skill and it's barely covered anywhere else. Thanks for making this course!
Instructors
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brooke Anderson
Assistant Professor, Environmental & Radiological Health SciencesColorado State University
About 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.
About the Mastering Software Development in R Specialization
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.
This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.
This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.