This course is part of the Mastering Software Development in R Specialization

4.1
stars
219 ratings

Roger D. Peng, PhD +1 more instructor

10,251 already enrolled

Offered By

Mastering Software Development in R SpecializationJohns Hopkins University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Course 3 of 5 in the

Mastering Software Development in R Specialization

Intermediate Level

Approx. 21 hours to complete

English

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Skills you will gain

Programming Tool
Github
Continuous Integration
R Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 5 in the

Mastering Software Development in R Specialization

Intermediate Level

Approx. 21 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

3.71/5 (8 Ratings)

Roger D. Peng, PhD

Associate Professor, Biostatistics

Bloomberg School of Public Health

1,487,287 Learners

37 Courses

Brooke Anderson

Assistant Professor, Environmental & Radiological Health Sciences

Colorado State University

71,430 Learners

5 Courses

Offered by

Johns Hopkins University

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Syllabus - What you will learn from this course

Content Rating97%(1,575 ratings)

Week

Week 1

3 hours to complete

Getting Started with R Packages

3 hours to complete

1 video (Total 2 min), 16 readings, 1 quiz

1 video

Welcome to Building R Packages2m

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 devtools30m

Week

Week 2

7 hours to complete

Documentation and Testing

7 hours to complete

14 readings

Week

Week 3

5 hours to complete

Licensing, Version Control, and Software Design

5 hours to complete

25 readings

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 Source30m

Week

Week 4

6 hours to complete

Continuous Integration and Cross Platform Development

6 hours to complete

13 readings

Reviews

4.1

60 reviews

5 stars
52.05%
4 stars
23.74%
3 stars
13.69%
2 stars
3.65%
1 star
6.84%

TOP REVIEWS FROM BUILDING R PACKAGES

by GPMar 12, 2022

The course delivered the basic goals of creating a package for the first time in R!

by DANov 1, 2019

very good, an interesting way of learning.

high-level examination

by KMJun 3, 2019

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...

by CBMar 29, 2017

This is a critical skill and it's barely covered anywhere else. Thanks for making this course!

View all reviews

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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Building R Packages

About this Course

Skills you will gain