R for data analytics

Data analytics for business intelligence starts with collecting, storing and cleverly summarizing enterprise data, which nowadays is generated by a diversity of data sources (click streams, social media, relational data, sensor data, ...)

A popular tool for this kind of analytics is R. Its popularity is partly explained because it's free open source software, but more importantly because an increasing number of add-on packages are made available which focus on particular use cases in this broad Data Science and Big Data universe.

This course will give you hands-on practice with R, both as a data analytics and graphical tool, and as a programming and scripting environment where you can let the system give you any possible insight into your data that you may want.


No public sessions are currently scheduled. We will be pleased to set up an on-site course or to schedule an extra public session (in case of a sufficient number of candidates). Interested? Please let us know.

Intended for

Whoever wants to start practising data analysis in a "data science" context: developers, data architects, marketeers, and anyone who needs to manipulate, visualize, or summarize their corporate data. This course is also a first introduction to the R programming language, so anyone who wants to start using R or one of its many packages is welcome.


This is a "beginners" course, so no technical background is required. Familiarity with the concepts of data stores and "big data" is of course advisable (see e.g. Big data architecture and infrastructure). Additionally, we expect that you are familiar with the concepts of a programming language (see e.g. Programming fundamentals).

Main topics

Part I - R fundamentals

  • Getting started
  • installing R (Linux / Windows / MAC)
  • getting to learn the command line interface and the Rstudio GUI
  • first steps with R: interactive commands; obtaining online help
  • basic concepts: expressions (numeric, textual); commands & functions; variables & assignment
  • R basics
  • "atomic" data types and how to write their constants: double (numeric), character, integer, logical
  • numeric and logical operators
  • the special values Inf, NaN, NA, and NULL
  • the vector type; operator "c()"; so-called coercing; vector operators
  • the "package" concept of R
  • CRAN and www.r-project.org
  • More "structural" data types
  • lists (hierarchical data) and matrices
  • Functions and attributes
  • positional and named parameters
  • creating your own functions
  • R scripts; the startup script; scope of variables; writing comments
  • dump, load, source and related commands
  • dir, ls, getwd and setwd
  • package loading, or using the "::" notation
  • control flow: if, while, for
  • the explicit "print" function; the "cat" function
  • other useful functions: length, names, dimnames, unlist, cbind, rbind, c, as.<type>, is.<type>, order(vector), ...

Part II -- Data analytics with R

  • Structured data
  • Objects and attributes
  • lists, names(), dimnames(), factors
  • reading / writing (structured) data from/to files: read.table; read.csv; readLines, write.csv, ...
  • how to be memory-efficient with large volumes of data
  • Data Frames
  • how to use a database as "back store"
  • Packages
  • how to install a (third party) R package
  • examples: the "stats" package and the "ggplot2" package
  • other useful packages: foreign (for reading/writing data of SAS, SPSS, dBase, etc.); XML; AER; tm; vcd; DBI; RODBC
  • Statistical techniques
  • Random Number Generators
  • sampling, summarizing: basic statistical terminology & techniques
  • examples from the "stats" package; the lm functions
  • plotting statistical graphs (scatter plots, histograms, trend lines, ...)

Training method

Classroom instruction, focused on practical examples and supported by extensive exercises and individual practice.


3 days.

Course leader

Peter Vanroose.