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R Programming Intermediate
Learn how to write high-performing functional code in R for data analysisPreview the first lesson free — get full access to all 35 lessons.
Intermediate Provider : Tom Rosenwinkel 8 chapters · 35 Lessons · 2h 54m in English
Course Description
How can you write reusable code in R that will perform well as the datasets get larger? Programmers in R will often find themselves challenged when the code they wrote for one successful project can’t be applied to solve a new problem due to the size of the dataset. Organizations often struggle to leverage the computing resources available if the R code for a project is not scalable. The R programming language has powerful features for writing code that will scale to large datasets and take advantage of parallel computing resources. The topics discussed in this course will provide a foundation for future work in computational image enhancement or computer vision AI applications. The topics for this course will build on your understanding of the R language with a focus on performance for data processing. Writing scalable R code is an essential skill in many data and application projects.
In this course on R Programming, you will learn how to create R functional programming projects by writing source code that will scale to high performance in data analysis. We will explore data structures for base objects as well as learn how to create our own classes, rewrite code to take advantage of vectorization and parallel processing, and use benchmarking and profiling to measure performance gains. By the end of this course, you will be able to write R code that will perform data analysis and scale well with large datasets and available computing resources.
What You'll Learn
- Understand the base data structures in R and how classes and methods are created and applied
- Rewrite R code to take advantage of vectorization and parallel processing
- Measure R code performance using benchmarking and profiling tools
- Create your own S3 classes, methods, and inheritance for object-oriented programming
- Avoid loops using vectorized operations, matrix operations, and loop functions like split and apply
- Apply parallel computing with the Parallel package, mclapply, and parLapply to scale to large datasets
Key Takeaways
- The R programming language has powerful features for writing code that scales to large datasets and takes advantage of parallel computing resources.
- Writing scalable R code is an essential skill in many data and application projects.
- This course covers data structures for base objects, creating your own classes, vectorization, and parallel processing.
- Benchmarking and profiling tools are used to measure performance gains in R code.
- The topics build on an understanding of the R language with a focus on performance for data processing.
Frequently Asked Questions
Who is this course for?
It is for programmers in R who want to write reusable, scalable code that performs well as datasets get larger. It builds on an existing understanding of the R language.
What will I be able to do by the end of the course?
You will be able to write R code that performs data analysis and scales well with large datasets and available computing resources.
What topics does this course cover?
It covers base data structures and creating your own classes, rewriting code to take advantage of vectorization and parallel processing, and using benchmarking and profiling to measure performance gains.
What skills will I gain?
You will gain skills in Application Foundation Classes, Benchmarking, Data Profiling, and Object-Oriented Programming (OOP).
Does this course cover object-oriented programming in R?
Yes. It teaches how classes and methods are created and applied, including S3 classes, S3 methods, and S3 inheritance using the Sloop package.
