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R Programming Intermediate: Mastering Loops
Learn how to write loops in R, along with when and how to avoid themPreview the first lesson free — get full access to all 9 lessons.
Intermediate Provider : Tom Rosenwinkel 9 Lessons · 38m in English
Course Description
How can you reuse R code to avoid introducing bugs? How can you reuse R code without introducing inefficiencies in a serial loop? The R programming language has basic control flow like many other programming languages, which combined with functional programming concepts allows for modular code design. The R programming language also allows for vectorization and vectorized loop functions designed to parallelize operations to scale with data size and available computing resources. Learning how to use the best looping and vectorizing techniques is an essential skill for any R programmer, especially for large data processing projects. The topics discussed in this course will provide you with a solid foundation for writing scalable R code.
In these lessons, you will learn control flow in R, especially loops and loop-=like programming. We will explore conditional loops, structural loops, and conditional quitting. We will also discuss vectorizing functions and applying loop functions in order to avoid structural loops in R. By the end of these lessons, you will be able to write loops or use loop-like techniques to apply R functions for data processing tasks.
What You'll Learn
- Understand conditional loops, structural loops, and conditional quitting in R
- Explain when a loop is necessary and when it can be avoided
- Apply best practices for writing loops and loop-like functions in R
- Use vectorized and matrix operations to avoid structural loops
- Apply loop functions and split-and-apply techniques for data processing
- Write loops or loop-like techniques to apply R functions to data processing tasks
Key Takeaways
- R provides basic control flow that, combined with functional programming concepts, allows for modular code design.
- R supports vectorization and vectorized loop functions designed to parallelize operations and scale with data size and available computing resources.
- Choosing the best looping and vectorizing techniques is an essential skill for R programmers, especially for large data processing projects.
- Reusing R code helps avoid introducing bugs and inefficiencies in serial loops.
- The course covers conditional loops, structural loops, conditional quitting, vectorizing functions, and applying loop functions to avoid structural loops.
Frequently Asked Questions
Who is this course for?
It is for R programmers who want to master looping and vectorizing techniques, especially for large data processing projects.
What topics does this course cover?
It covers control flow in R including conditional loops, structural loops, and conditional quitting, as well as vectorizing functions and applying loop functions to avoid structural loops. Lessons include Repeat Loops, Vector Loops, Conditional Loops, Conditional Quitting, vectorized operations, matrix operations, loop functions, split and apply, and vectorizing functions.
What will I be able to do after completing this course?
By the end of these lessons, you will be able to write loops or use loop-like techniques to apply R functions for data processing tasks.
What skills does this course build?
It builds skills in flow control, nested loops, and vectorization.
Is this an intermediate-level course?
Yes, it is an intermediate R programming course focused on mastering loops, providing a solid foundation for writing scalable R code.
