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

4.1
stars
219 ratings

Roger D. Peng, PhD +1 more instructor

10,353 already enrolled

Offered By

Mastering Software Development in R SpecializationJohns Hopkins University

About this Course

2,340 recent views

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

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 5 in the

Mastering Software Development in R Specialization

Intermediate Level

Approx. 20 hours to complete

English

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

Try Coursera for Business

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 5 in the

Mastering Software Development in R Specialization

Intermediate Level

Approx. 20 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

1,499,292 Learners

37 Courses

Brooke Anderson

Assistant Professor, Environmental & Radiological Health Sciences

Colorado State University

71,914 Learners

5 Courses

Offered by

Johns Hopkins University

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Syllabus - What you will learn from this course

Content Rating97%(1,576 ratings)

Week1

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 Start10mUsing Mac OS10mUsing Windows10mUsing Unix/Linux10mR packages10mBasic Structure of an R Package10mDESCRIPTION File10mNAMESPACE File10mNamespace Function Notation10mLoading and Attaching a Package Namespace10mThe R Sub-directory10mThe man Sub-directory10mSummary10mThe devtools package10mCreating a Package10mOther Functions10m

1 practice exercise

R Package and devtools30m

Week2

Week 2

7 hours to complete

Documentation and Testing

7 hours to complete

14 readings

Week3

Week 3

5 hours to complete

Licensing, Version Control, and Software Design

5 hours to complete

25 readings

25 readings

Overview10mThe General Public License10mThe MIT License10mThe CC0 License10mOverview10mPaying it Forward10mLinus’s Law10mHiring10mSummary10mIntroduction10mgit10mInitializing a git repository10mCommitting10mBrowsing History10mLinking local repo to GitHub repo10mSyncing RStudio and GitHub10mIssues10mPull Request10mMerge Conflicts10mIntroduction10mThe Unix Philosophy10mDefault Values10mNaming Things10mPlaying Well With Others10mSummary10m

1 practice exercise

Testing, GitHub, and Open Source30m

Week4

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 MJun 10, 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).

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 XZApr 15, 2021

It teaches the up-to-date approaches, in a concise and also systematic way

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.

Build employee skills, drive business results

Discover Coursera for Business

Building R Packages

About this Course

Skills you will gain