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	4.3 stars Average User Rating 4.3See 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

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

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How It Works

Coursework

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

Help from Your Peers

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

Certificates

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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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Ratings and Reviews

Rated 4.3 out of 5 of 316 ratings

Much of this course is well-constructed, well-communicated and clear. Sections from weeks 1-3 were attuned to my programming skill level and the material was communicated generally so that those with experience in other languages could also follow along easily. I had no technical problems with submitting assignments or quizzes and I found the assignments with swirl to be effective in reinforcing the material from the readings. My biggest criticism of this course is about Week 4's content and its respective part in the final assignment. The content in Week 4 concerns Object-Oriented programming and this material was really hard to absorb. It seemed to come out of left field and the readings did not feel as clear as they were in other sections. The readings essentially say "Here is what object-oriented programming allows you to do [create/manipulate classes and objects]" but then goes back on itself by recommending that you do not use object-oriented programming to create custom classes or data structures because the R community already knows what data structures they like. Personally, I had a lot of trouble understanding this content because it was so briefly explained. This did not help me by the time I had to complete the object-oriented programming portion of the final assignment. It is very clear from the class forums that other students have found that section of the assignment particularly difficult for years. Overall, this course was excellent in teaching functional programming principles and application, but the object-oriented section slightly spoiled my experience of the course.

It is a short and good R course. It goes right to the more advanced stuff. Even a long time user of R will learn new things. The final project is revised by the same students which in my opinion makes it more of a challenge. I've never wanted a homework to be as perfect as this one.

Great course. Challenging and I learn a lot of new techniques to improve my R skills.

Excellent subject matter. 4 stars instead of 5 is only because there was no video. I love the videos in the other courses in this track, since I am an auditory learner.