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
This course is part of Mastering Software Development in R Specialization
Instructors: Roger D. Peng, PhD
Access provided by Coursera Learning Team
32,010 already enrolled
(574 reviews)
Skills you'll gain
Details to know
Add to your LinkedIn profile
3 assignments
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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
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
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
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
Instructors
Offered by
Why people choose Coursera for their career
Learner reviews
574 reviews
- 5 stars
58.36%
- 4 stars
22.29%
- 3 stars
10.27%
- 2 stars
2.78%
- 1 star
6.27%
Showing 3 of 574
Reviewed on Dec 15, 2016
Good Course! But focus should be more on OOPs Concepts through video lectures to better understand it.
Reviewed on Jul 19, 2017
The last problem is unnecessarily difficult with little related teaching and learning material provided. Otherwise, the course is certainly well worth taking.
Reviewed on May 18, 2018
Awesome course! Learned a lot! It is mandatory if you would like to become an experienced R user, not just programmer!
Recommended if you're interested in Data Science
Johns Hopkins University
Johns Hopkins University
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy