Advanced R

Advanced R

Public courses


- Anyone can join the training
- Course outline as presented on the website
- Small groups, 3-10 people

Private courses

Price set individually

- Training workshop just for your team
- You choose date and location of the training
- Course outline tailored to your needs

About the training

The training deals with the most important elements of programming which are crucial in creating apps and packages in R. More and more companies and institutions notice the potential of using R as a tool for data analysis. Our training enables you to work with R freely and discover the best programming techniques. It will make you a better and more skilled employee.

The training is conducted by data science experts using Live script method. We put great emphasis on the practical applicability thus during the training we constantly work with R. Instead of using presentations, we discuss all concepts while programming in R. It makes the learning process easier and much more efficient.

Who is this training for?

We created this training to answer the needs of our customers who are interested in programming and creating software in data analysis branch. If you are a scientist or an analyst who works with R and you are willing to develop your skills and gain useful knowledge about R, you should consider our training.

Data Analysts, Consultants, Statisticians, Engineers, Data Scientists

What will I learn?

  • Create new packages in R environment
  • Increase programming efficiency of your team
  • Create new R objects
  • Process data
  • Create new methods for existing functions
  • Learn how to create easy to read and optimal scripts
  • Generate automatic reports
  • Debug and handle errors in optimal way
  • Write C++ functions in R

Course outline

  1. R Environment
    • R
    • R language
    • Programming concepts in R
    • R system and S language
    • R IDE – Rstudio
  2. Programming in R
    • Names and objects
    • Functions and libraries
    • Help in R
    • CRAN Task View
  3. Programming elements
    • From script to function
    • Functions and functional programming paradigm
    • Debugging R code
    • Interactive debugging mode
    • Errors and warnings
    • Testing R programs
  4. Libraries in R
    • Components of R package
    • Concepts and tools for package development
    • Package development workflow
    • Documentation
    • Package testing
    • Compiling C++ functions and adding to package
  5. R objects
    • Function environment
    • Objects, names, references
    • Links
    • Importing/Exporting objects and data
  6. R built-in objects and data operations
    • Data types in R
    • Vectors
    • Vectorized functions
    • Data frame
    • Arithmetic and logical operators
    • Matrix and operations on matrices
    • Statistical distributions and simulations
    • Basic statistical measures
    • Statistical models
  7. String manipulation
    • String and R objects
    • String processing in R
      • Representation
      • Concatenating text and numerical output
      • String functions
    • Regular expressions in R
    • Examples
    • Perl integration in R
  8. Visualization
    • Basic plots with graphics package
    • Common data plots
    • Graphical parameters
    • Advanced visualizations with ggplot2
    • lattice package
  9. Object oriented programming in R
    • Classes
    • S3
      • Objects, functions, methods
      • Defining class and objects
      • Defining new methods and functions
      • Validating new objects
    • S4
      • Objects, functions, methods
      • Defining class and objects
      • Defining new methods and functions
      • Validating new objects
    • Reference classes
      • Defining new reference class
      • Creating new objects
  10. Methods and generic functions
    • Defining methods
    • Creating methods for existing functions
    • Generic functions
    • Methods in generic functions
  11. Benchmarking R code
    • What affects speed of execution
    • Code benchmarking
    • R performance
    • R implementation vs performance
  12. Profiling R code
    • Measuring performacne
    • Improving speed of execution
    • Code reorganization
    • Solutions
    • Vectorization
    • Avoiding copies
    • byte-code compilation
  13. Memory
    • Objects size
    • Memory usage and garbage collector
    • Profiling memory usage
  14. Developing C++ functions
    • Rcpp package
    • Basic objects in C++
    • Attributes and classes
    • Help functions – Rcpp sugar
    • Standard Template Library
  15. Integrating C++ with R
    • Calling functions in C++ from R
    • Data types in C++
    • C++ operations on data
  16. Parallel computing
    • Parallel computing in R –multiple core CPUs
    • Overview of R packages

Course Curriculum

Curriculum is empty


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