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Building R Packages

Approx. 21 hours to complete

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Course Summary

This course teaches students how to create and use R packages, which are collections of R functions, data, and compiled code.

Key Learning Points

Learn how to create your own R packages
Discover best practices for package development
Explore the benefits of using R packages
Gain hands-on experience through guided projects

Job Positions & Salaries of people who have taken this course might have

- USA: $113,000
- India: ₹1,122,000
- Spain: €40,000
- USA: $113,000
- India: ₹1,122,000
- Spain: €40,000
- USA: $65,000
- India: ₹475,000
- Spain: €30,000
- USA: $113,000
- India: ₹1,122,000
- Spain: €40,000
- USA: $65,000
- India: ₹475,000
- Spain: €30,000
- USA: $63,000
- India: ₹446,000
- Spain: €29,000

Learning Outcomes

Create and manage your own R packages
Implement best practices for package development
Use R packages to streamline your data analysis workflow

Prerequisites or good to have knowledge before taking this course

Basic knowledge of R programming
Familiarity with data analysis concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Guided projects
Video lectures
Quizzes and exercises

Similar Courses

Data Science Essentials
Data Visualization with ggplot2
R Programming

Related Education Paths

Related Books

Description

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.

Outline

Getting Started with R Packages
Welcome to Building R Packages
Before You Start
Using Mac OS
Using Windows
Using Unix/Linux
R packages
Basic Structure of an R Package
DESCRIPTION File
NAMESPACE File
Namespace Function Notation
Loading and Attaching a Package Namespace
The R Sub-directory
The man Sub-directory
Summary
The devtools package
Creating a Package
Other Functions
R Package and devtools

Documentation and Testing
Documentation
Vignette's and README Files
Knitr / Markdown
Common knitr Options
Help Files and roxygen2
Common roxygen2 Tags
Overview
Data for Demos
Internal Data
Data Packages
Summary
Introduction
The testthat Package
Passing CRAN Checks

Licensing, Version Control, and Software Design
Overview
The General Public License
The MIT License
The CC0 License
Overview
Paying it Forward
Linus’s Law
Hiring
Summary
Introduction
git
Initializing a git repository
Committing
Browsing History
Linking local repo to GitHub repo
Syncing RStudio and GitHub
Issues
Pull Request
Merge Conflicts
Introduction
The Unix Philosophy
Default Values
Naming Things
Playing Well With Others
Summary
Testing, GitHub, and Open Source

Continuous Integration and Cross Platform Development
Overview
Web Services for Continuous Integration
Using Travis
Using AppVeyor
Summary
Introduction
Handling Paths
Saving Files & rappdirs
rappdirs
Options and Starting R
Package Installation
Environmental Attributes
Summary

Summary of User Reviews

Discover the power of R packages with the Coursera course. Students have praised the course for its comprehensive coverage and hands-on approach. The course has received high ratings from users, making it a top choice for those looking to master R packages.

Key Aspect Users Liked About This Course

The course's comprehensive coverage and hands-on approach

Pros from User Reviews

Easy to follow and understand
Great for beginners and intermediate learners
Instructors are knowledgeable and engaging
Practical assignments that reinforce learning

Cons from User Reviews

Some users found the course too basic
Limited feedback on assignments
Not suitable for advanced learners
Some users found the course pacing too slow
Lack of real-world examples

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