Advanced R Programming

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 4.4 stars Average User Rating 4.4See what learners said 課程作業

每門課程都像是一本互動的教科書，具有預先錄製的視頻、測驗和項目。

來自同學的幫助

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證書

獲得正式認證的作業，並與朋友、同事和雇主分享您的成功。

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.

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 expand

1. Video: Welcome to Advanced R Programming
2. 閱讀: Syllabus
3. 閱讀: Course Textbook: Mastering Software Development in R
4. 閱讀: 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 expand

1. 閱讀: Control Structures Overview
2. 閱讀: if-else
3. 閱讀: for Loops
4. 閱讀: Nested for loops
5. 閱讀: next, break
6. 閱讀: Summary
7. 閱讀: Functions Overview
8. 閱讀: Code
9. 閱讀: Function interface
10. 閱讀: Default values
11. 閱讀: Re-factoring code
12. 閱讀: Dependency Checking
13. 閱讀: Vectorization
14. 閱讀: Argument Checking
15. 閱讀: R package
16. 閱讀: When Should I Write a Function?
17. 閱讀: 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 expand

1. 閱讀: What is Functional Programming?
2. 閱讀: Core Functional Programming Functions
3. 閱讀: Map
4. 閱讀: Reduce
5. 閱讀: Search
6. 閱讀: Filter
7. 閱讀: Compose
8. 閱讀: Partial Application
9. 閱讀: Side Effects
10. 閱讀: Recursion
11. 閱讀: Summary
12. 閱讀: Expressions
13. 閱讀: Environments
14. 閱讀: Execution Environments
15. 閱讀: What is an error?
16. 閱讀: Generating Errors
17. 閱讀: When to generate errors or warnings
18. 閱讀: How should errors be handled?
19. 閱讀: 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 expand

1. 閱讀: Debugging Overview
2. 閱讀: traceback()
3. 閱讀: Browsing a Function Environment
4. 閱讀: Tracing Functions
5. 閱讀: Using debug() and debugonce()
6. 閱讀: recover()
7. 閱讀: Final Thoughts on Debugging
8. 閱讀: Summary
9. 閱讀: Profiling Overview
10. 閱讀: microbenchmark
11. 閱讀: profvis
12. 閱讀: Find out more
13. 閱讀: Summary
14. 閱讀: Non-standard evaluation
15. 閱讀: 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 expand

1. 閱讀: OOP Overview
2. 閱讀: Object Oriented Principles
3. 閱讀: S3
4. 閱讀: S4
5. 閱讀: Reference Classes
6. 閱讀: Summary
7. 閱讀: Overview
8. 閱讀: Reuse existing data structures
9. 閱讀: Compose simple functions with the pipe
10. 閱讀: Embrace functional programming
11. 閱讀: Design for humans

Graded: Functional and Object-Oriented Programming