Johns Hopkins University

Advanced R Programming

This course is part of Mastering Software Development in R Specialization

Taught in English

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Roger D. Peng, PhD
Brooke Anderson

Instructors: Roger D. Peng, PhD

31,493 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.2

(570 reviews)

|

91%

Intermediate level
Some related experience required
18 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.2

(570 reviews)

|

91%

Intermediate level
Some related experience required
18 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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
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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 quiz1 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 quiz1 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 quiz

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 Courses1,588,590 learners
Brooke Anderson
Johns Hopkins University
5 Courses74,619 learners

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4.2

570 reviews

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