À propos de ce cours : 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.
Créé par : Johns Hopkins University
Enseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
| Informations de base | Cours 2 de 5 dans Mastering Software Development in R Specialization |
| Niveau | Intermediate |
| Langue | English, Sous-titres : Chinese (Simplified) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 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 vidéo, 3 lectures
- Reading: Syllabus
- Reading: Course Textbook: Mastering Software Development in R
- Reading: 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 lectures
- Reading: Control Structures Overview
- Reading: if-else
- Reading: for Loops
- Reading: Nested for loops
- Reading: next, break
- Reading: Summary
- Reading: Functions Overview
- Reading: Code
- Reading: Function interface
- Reading: Default values
- Reading: Re-factoring code
- Reading: Dependency Checking
- Reading: Vectorization
- Reading: Argument Checking
- Reading: R package
- Reading: When Should I Write a Function?
- Reading: Summary
Noté: Swirl Lesson
SEMAINE 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 lectures
- Reading: What is Functional Programming?
- Reading: Core Functional Programming Functions
- Reading: Map
- Reading: Reduce
- Reading: Search
- Reading: Filter
- Reading: Compose
- Reading: Partial Application
- Reading: Side Effects
- Reading: Recursion
- Reading: Summary
- Reading: Expressions
- Reading: Environments
- Reading: Execution Environments
- Reading: What is an error?
- Reading: Generating Errors
- Reading: When to generate errors or warnings
- Reading: How should errors be handled?
- Reading: Summary
Noté: Swirl Lesson
SEMAINE 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 lectures
- Reading: Debugging Overview
- Reading: traceback()
- Reading: Browsing a Function Environment
- Reading: Tracing Functions
- Reading: Using debug() and debugonce()
- Reading: recover()
- Reading: Final Thoughts on Debugging
- Reading: Summary
- Reading: Profiling Overview
- Reading: microbenchmark
- Reading: profvis
- Reading: Find out more
- Reading: Summary
- Reading: Non-standard evaluation
- Reading: Summary
Noté: Debugging and Profiling
SEMAINE 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 lectures
- Reading: OOP Overview
- Reading: Object Oriented Principles
- Reading: S3
- Reading: S4
- Reading: Reference Classes
- Reading: Summary
- Reading: Overview
- Reading: Reuse existing data structures
- Reading: Compose simple functions with the pipe
- Reading: Embrace functional programming
- Reading: Design for humans
Noté: Functional and Object-Oriented Programming
FAQ
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Coursework
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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