Course Summary
This advanced R course covers advanced topics in R programming, including functional programming, debugging, and profiling. Students will also learn how to create efficient and reproducible code using R packages and tools.Key Learning Points
- Learn advanced R programming concepts such as functional programming and debugging
- Create efficient and reproducible code using R packages and tools
- Gain practical experience through hands-on coding projects
Related Topics for further study
Learning Outcomes
- Create efficient and reproducible code using R packages and tools
- Understand advanced R programming concepts such as functional programming and debugging
- Apply R programming skills to practical data science projects
Prerequisites or good to have knowledge before taking this course
- Proficiency in basic R programming concepts
- Familiarity with R packages and functions
Course Difficulty Level
AdvancedCourse Format
- Online
- Self-paced
- Project-based
Similar Courses
- Data Science: Foundations using R
- Applied Data Science with R
Related Education Paths
Notable People in This Field
- Hadley Wickham
- RStudio
Related Books
Description
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
Outline
- Welcome to Advanced R Programming
- Welcome to Advanced R Programming
- Syllabus
- Course Textbook: Mastering Software Development in R
- swirl Assignments
- Functions
- Control Structures Overview
- if-else
- for Loops
- Nested for loops
- next, break
- Summary
- Functions Overview
- Code
- Function interface
- Default values
- Re-factoring code
- Dependency Checking
- Vectorization
- Argument Checking
- R package
- When Should I Write a Function?
- Summary
- Functions: Lesson Choices
- Swirl Lesson
- Functional Programming
- What is Functional Programming?
- Core Functional Programming Functions
- Map
- Reduce
- Search
- Filter
- Compose
- Partial Application
- Side Effects
- Recursion
- Summary
- Expressions
- Environments
- Execution Environments
- What is an error?
- Generating Errors
- When to generate errors or warnings
- How should errors be handled?
- Summary
- Functional Programming: Lesson Choices
- Swirl Lesson
- Debugging and Profiling
- Debugging Overview
- traceback()
- Browsing a Function Environment
- Tracing Functions
- Using debug() and debugonce()
- recover()
- Final Thoughts on Debugging
- Summary
- Profiling Overview
- microbenchmark
- profvis
- Find out more
- Summary
- Non-standard evaluation
- Summary
- Debugging and Profiling
- Object-Oriented Programming
- OOP Overview
- Object Oriented Principles
- S3
- S4
- Reference Classes
- Summary
- Overview
- Reuse existing data structures
- Compose simple functions with the pipe
- Embrace functional programming
- Design for humans
Summary of User Reviews
Coursera's Advanced R course receives high praise from users. Learners appreciate the comprehensive coverage of advanced R topics and the practical exercises that help them apply their newfound knowledge. One standout aspect of the course is its clear and engaging instruction that makes even complex concepts easy to understand.Key Aspect Users Liked About This Course
The course provides comprehensive coverage of advanced R topics and practical exercises that help learners apply their new knowledge.Pros from User Reviews
- The course is well-structured and covers a wide range of advanced R topics.
- The practical exercises are challenging and help learners apply their knowledge to real-world problems.
- The instruction is clear and engaging, making even complex concepts easy to understand.
- The course provides valuable resources and support for learners, including a discussion forum and helpful feedback from instructors.
- The course is a great value for the money, offering high-quality instruction at an affordable price.
Cons from User Reviews
- Some learners felt that the course could benefit from more in-depth coverage of certain topics.
- The course requires a significant time commitment, which may be challenging for learners with busy schedules.
- Some learners felt that the course could benefit from more hands-on exercises and practical applications.
- The course may not be suitable for beginners or those with limited experience in R programming.
- The pace of the course can be fast at times, which may be challenging for some learners.
Roger D. Peng, PhD
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