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.

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

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