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

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 20 hours to complete

English

Subtitles: English

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

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 20 hours to complete

English

Subtitles: English

Offered by

Johns Hopkins University logo

Johns Hopkins University

Syllabus - What you will learn from this course

Content RatingThumbs Up97%(1,289 ratings)Info
Week
1

Week 1

3 hours to complete

Getting Started with R Packages

3 hours to complete
1 video (Total 2 min), 16 readings, 1 quiz
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
Week
2

Week 2

7 hours to complete

Documentation and Testing

7 hours to complete
14 readings
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
Week
3

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 Source20m
Week
4

Week 4

6 hours to complete

Continuous Integration and Cross Platform Development

6 hours to complete
13 readings
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

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....
Mastering Software Development in R

Frequently Asked Questions

  • 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.

  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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