John Hopkins University - Advanced R Programming
4.3 /5- Offered byCoursera
Advanced R Programming at Coursera Overview
Duration | 18 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced R Programming at Coursera Highlights
- 22% started a new career after completing these courses.
- 12% got a tangible career benefit from this course.
- Earn a certificate from the Johns Hopkins University upon completion of course.
Advanced R Programming at Coursera Course details
- 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.
Advanced R Programming at Coursera Curriculum
Welcome to Advanced R Programming
Welcome to Advanced R Programming
Syllabus
Course Textbook: Mastering Software Development in R
swirl Assignments
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
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
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
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