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, etc.).

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

In the UK this course is available for one-company, on-site presentations and for live presentation over the Internet, via the Virtual Classroom Environment service.Public presentations of this course run in Leuven in Belgium and in Woerden in Holland.

Classroom dates (High Wycombe) - click to book!

11 March 2019 6 May 2019

Objectives

On successful completion of this course you will be able to:

  • describe the concepts of R
  • install R
  • use R as a data analytics and graphical tool
  • program with R
  • fully exploit the functions and facilities of R.

Who Should Attend

Whoever wants to start practising data analysis in a "big data" 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.

Prerequisites

Familiarity with the concepts of data stores and "big data" is of course advisable, as well as some notions of statistics. Additionally, familiarity with a programming language is recommended.

Duration

3 days

Fee (per attendee)

£1425 (ex VAT)

Course Code

RDAA

Contents

Stage 1 - 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; getting 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; 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, and for the explicit "print" function; the "cat" function; other useful functions: length, names, dimnames, unlist, cbind, rbind, c, as., is., 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, etc.; 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 (3rd party) R package; example: the "stats" package; other useful packages: foreign (for reading/writing data of SAS, SPSS, dBase, etc.); XML; AER; tm; vcd; DBI.

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, etc.).


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