About this course: 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.

Created by: Johns Hopkins University

Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University

Basic Info	Course 2 of 5 in the Mastering Software Development in R Specialization
Level	Intermediate
Language	English, Subtitles: Chinese (Simplified)
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.4 stars Average User Rating 4.4See what learners said

Syllabus

WEEK 1

Welcome to Advanced R Programming

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.

1 video, 3 readings

Video: Welcome to Advanced R Programming
Reading: Syllabus
Reading: Course Textbook: Mastering Software Development in R
Reading: swirl Assignments

Functions

This module begins with control structures in R for controlling the logical flow of an R program. We then move on to functions, their role in R programming, and some guidelines for writing good functions.

17 readings

Reading: Control Structures Overview
Reading: if-else
Reading: for Loops
Reading: Nested for loops
Reading: next, break
Reading: Summary
Reading: Functions Overview
Reading: Code
Reading: Function interface
Reading: Default values
Reading: Re-factoring code
Reading: Dependency Checking
Reading: Vectorization
Reading: Argument Checking
Reading: R package
Reading: When Should I Write a Function?
Reading: Summary

Graded: Swirl Lesson

WEEK 2

Functional Programming

Functional programming is a key aspect of R and is one of R's differentiating factors as a data analysis language. Understanding the concepts of functional programming will help you to become a better data science software developer. In addition, we cover error and exception handling in R for writing robust code.

19 readings

Reading: What is Functional Programming?
Reading: Core Functional Programming Functions
Reading: Map
Reading: Reduce
Reading: Search
Reading: Filter
Reading: Compose
Reading: Partial Application
Reading: Side Effects
Reading: Recursion
Reading: Summary
Reading: Expressions
Reading: Environments
Reading: Execution Environments
Reading: What is an error?
Reading: Generating Errors
Reading: When to generate errors or warnings
Reading: How should errors be handled?
Reading: Summary

Graded: Swirl Lesson

WEEK 3

Debugging and Profiling

Debugging tools are useful for analyzing your code when it exhibits unexpected behavior. We go through the various debugging tools in R and how they can be used to identify problems in code. Profiling tools allow you to see where your code spends its time and to optimize your code for maximum efficiency.

15 readings

Reading: Debugging Overview
Reading: traceback()
Reading: Browsing a Function Environment
Reading: Tracing Functions
Reading: Using debug() and debugonce()
Reading: recover()
Reading: Final Thoughts on Debugging
Reading: Summary
Reading: Profiling Overview
Reading: microbenchmark
Reading: profvis
Reading: Find out more
Reading: Summary
Reading: Non-standard evaluation
Reading: Summary

Graded: Debugging and Profiling

WEEK 4

Object-Oriented Programming

Object oriented programming allows you to define custom data types or classes and a set of functions for handling that data type in a way that you define. R has a three different methods for implementing object oriented programming and we will cover them in this section.

11 readings

Reading: OOP Overview
Reading: Object Oriented Principles
Reading: S3
Reading: S4
Reading: Reference Classes
Reading: Summary
Reading: Overview
Reading: Reuse existing data structures
Reading: Compose simple functions with the pipe
Reading: Embrace functional programming
Reading: Design for humans

Graded: Functional and Object-Oriented Programming

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

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Creators

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Pricing

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Access to course materials	Available	Available
Access to graded materials	Not available	Available
Receive a final grade	Not available	Available
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Ratings and Reviews

Rated 4.4 out of 5 of 164 ratings

Excellent course for improving R programming skills. Highly recommended.

I think the course introduced some useful packages like purrr and concepts like map/reduce, but I think the explanation into the different OO systems could be improved. I struggled a lot with the final assignment and really depended on external sources of information to complete it.

Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

a little challenging but good for people who want to work on programming from the beginning