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
Related Topics for further study
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
IntermediateCourse 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
- Data Science Professional Certificate
- Data Analysis and Interpretation Specialization
- Applied Data Science with Python Specialization
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 approachPros 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
Roger D. Peng, PhD
4.1 Raiting
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