Johns Hopkins University

Advanced R Programming

Roger D. Peng, PhD
Brooke Anderson

Instructors: Roger D. Peng, PhD

31,699 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.2

(573 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 18 hours
Learn at your own pace
91%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.2

(573 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 18 hours
Learn at your own pace
91%
Most learners liked this course

Details to know

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Assessments

3 assignments

Taught in English

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

Instructor ratings
3.8 (32 ratings)
Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,603,626 learners
Brooke Anderson
Johns Hopkins University
5 Courses75,127 learners

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Recommended if you're interested in Data Analysis

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4.2

573 reviews

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SB
4

Reviewed on Jul 17, 2018

AG
4

Reviewed on Dec 14, 2016

MF
5

Reviewed on Feb 22, 2017

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