Advanced R Programming

Go to Course: https://www.coursera.org/learn/advanced-r

Introduction

### Course Review: Advanced R Programming on Coursera If you're an R programmer looking to elevate your skills and create more robust and reusable data science tools, the **Advanced R Programming** course on Coursera is precisely what you need. This course dives deep into advanced R programming topics that are crucial for developing efficient data science solutions. Here’s a detailed review and recommendation for anyone considering enrolling. #### Overview The course is meticulously designed to cover essential advanced topics in R programming, focusing on concepts that will significantly enhance your coding skills and your ability to tackle complex data challenges. Key areas of study include: - **Functional Programming**: Discover how functional programming shapes R as a distinctive data analysis language and learn to write code that is reusable and reliable. - **Robust Error Handling**: Gain insights into error and exception handling to ensure your code is resilient against unforeseen issues. - **Object-Oriented Programming**: Understand how to define custom data types and functions to manipulate those types effectively, enhancing the modularity and organization of your code. - **Profiling and Benchmarking**: Learn how to identify performance bottlenecks in your code, allowing you to optimize execution and improve efficiency. - **Debugging Techniques**: Master various debugging tools available in R to diagnose and resolve unexpected behavior in your programs. - **Proper Design of Functions**: Strengthen your ability to create well-structured and user-friendly functions that encapsulate common data analysis tasks. Upon completing this course, you will not only be proficient in identifying and abstracting tasks but also capable of encapsulating them into user-friendly functions tailored to specific challenges in your organization. #### Syllabus Breakdown The course begins with introductory modules that set the stage for what advanced programming entails in R. 1. **Introduction to Control Structures and Functions**: You’ll start with the control structures that dictate logical flow in R and move on to crafting effective functions, learning the best practices for function writing. 2. **Deep Dive into Functional Programming**: The course extensively covers functional programming, revealing how it differentiates R from other programming languages. This module emphasizes the importance of writing clean and concise code that adheres to functional programming principles. 3. **Debugging and Profiling Tools**: Understanding how to debug and profile your code is critical. This module provides you with practical tools to assess and enhance code performance, ensuring you can build applications that not only work but do so efficiently. 4. **Object-Oriented Programming in R**: Learn about the three different methodologies R offers for object-oriented programming. You’ll gain the ability to define new data types and the specific functions that interact with those types, leading to better organized and more manageable code. #### Recommendation I highly recommend the **Advanced R Programming** course for intermediate R users and data scientists aiming to deepen their understanding of programming principles that can elevate their projects. The course is structured in a way that it builds upon foundational knowledge while pushing you toward more complex concepts. The practical applications and programming paradigms taught in this course are invaluable in real-world data science scenarios. Whether you work in academia, industry, or are a hobbyist interested in enhancing your skills, the tools and techniques provided in this course will prove indispensable. In conclusion, if you aspire to create custom software solutions and develop strong reusable code within your team, enrolling in **Advanced R Programming** on Coursera is a wise investment in your career and your proficiency in R programming. Don't miss out on the opportunity to harness the power of advanced R programming techniques! ### Enrollment Information To enroll in **Advanced R Programming**, visit Coursera’s website, where you can find the course offerings along with user reviews, financial aid options, and detailed schedules. Empower your programming skills today!

Syllabus

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.

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.

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.

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.

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.