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
1 video•Total 1 minute
- Welcome to Advanced R Programming•1 minute•Preview module
3 readings•Total 7 minutes
- Syllabus•1 minute
- Course Textbook: Mastering Software Development in R•1 minute
- swirl Assignments•5 minutes
I am a seasoned data scientist and programming enthusiast with extensive experience in R programming. My expertise is not just theoretical; I have hands-on experience developing powerful, robust, and reusable data science tools using R. Over the years, I've worked on various projects that required a deep understanding of advanced R programming concepts.
Now, let's delve into the key concepts mentioned in the article about the Advanced R Programming course:
-
Functional Programming in R:
- This involves treating functions as first-class citizens, allowing for a more modular and concise code structure. It enables a functional style of programming, focusing on the computation of values rather than the alteration of state.
-
Robust Error Handling:
- Robust error handling is crucial in any programming language, and in the context of R programming, it involves implementing strategies to gracefully handle errors, ensuring that your code remains resilient even in unexpected situations.
-
Object-Oriented Programming (OOP) in R:
- OOP is a paradigm that organizes code around objects and data rather than actions and logic. In R, this allows for the creation of classes and methods, enhancing code organization and reuse.
-
Profiling and Benchmarking:
- Profiling involves analyzing the performance of your code to identify bottlenecks and optimize its execution. Benchmarking is the process of comparing the performance of different pieces of code. Both are essential for writing efficient and optimized programs.
-
Debugging:
- Debugging is the process of identifying and fixing errors or bugs in your code. This can involve using tools to step through code execution, inspect variables, and understand the flow of the program.
-
Proper Design of Functions:
- Designing functions in a thoughtful and systematic manner is crucial for creating maintainable and scalable code. This includes considerations such as input validation, clear documentation, and adherence to best practices.
-
Custom Software Development for Unique Data Challenges:
- Addressing the specific data challenges of a given environment often requires the development of custom software. This involves leveraging the advanced programming concepts learned in the course to create tailored solutions.
-
Defining New Data Types in R:
- R allows the definition of custom data types, providing flexibility in handling unique data structures. This skill is particularly useful when working with specialized datasets.
-
Development of Functionality for Custom Data Types:
- Once new data types are defined, the ability to develop functionality specific to those types enhances the execution of data science tasks and promotes reusability within a team.
The course structure, including videos, readings, and assignments, indicates a comprehensive approach to mastering advanced R programming skills. It's designed to empower individuals to tackle complex data science challenges and contribute to the development of robust and efficient solutions.
