Advanced R Programming

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Advanced R Programming

This course is part of Mastering Software Development in R Specialization

Instructors: Roger D. Peng, PhD

32,812 already enrolled

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7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the Mastering Software Development in R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 7 modules in 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.

What's included

1 video3 readings

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.

What's included

17 readings

17 readingsTotal 138 minutes

Control Structures Overview2 minutes
if-else10 minutes
for Loops10 minutes
Nested for loops10 minutes
next, break10 minutes
Summary2 minutes
Functions Overview2 minutes
Code10 minutes
Function interface10 minutes
Default values10 minutes
Re-factoring code10 minutes
Dependency Checking10 minutes
Vectorization10 minutes
Argument Checking10 minutes
R package10 minutes
When Should I Write a Function?10 minutes
Summary2 minutes

What's included

1 assignment1 programming assignment

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.

What's included

19 readings

19 readingsTotal 174 minutes

What is Functional Programming?10 minutes
Core Functional Programming Functions10 minutes
Map10 minutes
Reduce10 minutes
Search10 minutes
Filter10 minutes
Compose10 minutes
Partial Application10 minutes
Side Effects10 minutes
Recursion10 minutes
Summary2 minutes
Expressions10 minutes
Environments10 minutes
Execution Environments10 minutes
What is an error?10 minutes
Generating Errors10 minutes
When to generate errors or warnings10 minutes
How should errors be handled?10 minutes
Summary2 minutes

What's included

1 assignment1 programming assignment

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.

What's included

15 readings1 assignment

15 readingsTotal 110 minutes

Debugging Overview2 minutes
traceback()10 minutes
Browsing a Function Environment10 minutes
Tracing Functions10 minutes
Using debug() and debugonce()10 minutes
recover()10 minutes
Final Thoughts on Debugging10 minutes
Summary2 minutes
Profiling Overview2 minutes
microbenchmark10 minutes
profvis10 minutes
Find out more10 minutes
Summary2 minutes
Non-standard evaluation10 minutes
Summary2 minutes

1 assignmentTotal 30 minutes

Debugging and Profiling30 minutes

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.

What's included

11 readings1 peer review

11 readingsTotal 86 minutes

OOP Overview2 minutes
Object Oriented Principles10 minutes
S310 minutes
S410 minutes
Reference Classes10 minutes
Summary2 minutes
Overview2 minutes
Reuse existing data structures10 minutes
Compose simple functions with the pipe10 minutes
Embrace functional programming10 minutes
Design for humans10 minutes

1 peer reviewTotal 240 minutes

Functional and Object-Oriented Programming240 minutes

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Instructors

Roger D. Peng, PhD

Johns Hopkins University

37 Courses1,691,611 learners

Brooke Anderson

Johns Hopkins University

5 Courses77,466 learners

Offered by

Johns Hopkins University

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