Этот курс входит в специализацию ''Специализация Mastering Software Development in R'
Advanced R Programming
от партнера
Об этом курсе
4.3
Оценки: 316
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.
Приобретаемые навыки
Logic ProgrammingR ProgrammingObject-Oriented Programming (OOP)Functional Programming
Программа курса: что вы изучите
Week
1Welcome 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 видео (всего 1 мин.), 3 материалов для самостоятельного изучения
1 видео
3 материала для самостоятельного изучения
Syllabus1мин
Course Textbook: Mastering Software Development in R1мин
swirl Assignments5мин
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 материалов для самостоятельного изучения, 1 тест
17 материала для самостоятельного изучения
Control Structures Overview2мин
if-else10мин
for Loops10мин
Nested for loops10мин
next, break10мин
Summary2мин
Functions Overview2мин
Code10мин
Function interface10мин
Default values10мин
Re-factoring code10мин
Dependency Checking10мин
Vectorization10мин
Argument Checking10мин
R package10мин
When Should I Write a Function?10мин
Summary2мин
1 практическое упражнение
Swirl Lessonмин
Week
2Functional 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 материалов для самостоятельного изучения, 1 тест
19 материала для самостоятельного изучения
What is Functional Programming?10мин
Core Functional Programming Functions10мин
Map10мин
Reduce10мин
Search10мин
Filter10мин
Compose10мин
Partial Application10мин
Side Effects10мин
Recursion10мин
Summary2мин
Expressions10мин
Environments10мин
Execution Environments10мин
What is an error?10мин
Generating Errors10мин
When to generate errors or warnings10мин
How should errors be handled?10мин
Summary2мин
1 практическое упражнение
Swirl Lesson30мин
Week
3Debugging 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 материалов для самостоятельного изучения, 1 тест
15 материала для самостоятельного изучения
Debugging Overview2мин
traceback()10мин
Browsing a Function Environment10мин
Tracing Functions10мин
Using debug() and debugonce()10мин
recover()10мин
Final Thoughts on Debugging10мин
Summary2мин
Profiling Overview2мин
microbenchmark10мин
profvis10мин
Find out more10мин
Summary2мин
Non-standard evaluation10мин
Summary2мин
1 практическое упражнение
Debugging and Profiling30мин
Week
4Object-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 материалов для самостоятельного изучения, 1 тест
11 материала для самостоятельного изучения
OOP Overview2мин
Object Oriented Principles10мин
S310мин
S410мин
Reference Classes10мин
Summary2мин
Overview2мин
Reuse existing data structures10мин
Compose simple functions with the pipe10мин
Embrace functional programming10мин
Design for humans10мин
4.3
Лучшие рецензии
автор: FZ•Jun 7th 2017
Very useful, I considered myself quite an advanced R user, but this class raised the level, especially with the R as OOB part. Good investment if you are not a beginner.
автор: JY•May 8th 2017
It is a good course that forced me to understand the s3 and s4 object of R and have gained an appreciation of "methods belonging to functions not belonging to objects".
Преподавателя
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brooke Anderson
Assistant Professor, Environmental & Radiological Health SciencesColorado State University
О 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.
О специализации ''Mastering Software Development in R'
This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.
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