Course code Title Language Price # Unit Startdate Hour Enddate Location Signup
R003 Building data products with R English €425.00 1 Day(s) 06-12-2017 09u00 06-12-2017 Kontich Subscribe
R003 Building data products with R Dutch €425.00 1 Day(s) 13-06-2018 09u00 13-06-2018 Kontich Subscribe
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Building data products with R

Building data products with R


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R started as a statistical programming tool, as the open source version of S. Unlike SPSS it requires some programming skills, since it is no drag and drop tool.
The CRAN repository holds numerous R packages where you can find several prepackaged functions which can turn out to be handy in many situations.
In this course we will focus on R markdown to write documentation, R slidify to create presentations with R code and output and R shiny: a package to create interactive data products.
This course is the final part of our R track, for an introduction to R, we refer to the two predecessors: “Getting started with programming in R” and “Data science and prediction models in R”.
There is an optional part attached to this course: upon request we can handle the course material faster, with remaining time being used for an introduction on R with Big Data, namely by using the packages RHadoop and SparkR.

Essentially, this course helps you to get started with the following packages

- R markdown
- R slidify
- R shiny
- RHadoop & SparkR (optional)

Hands on exercises on all topics are offered

Learning objectives:
- Learning to create documentations with R code and results
- Making presentations in R with code and output
- Building interactive data products using R
- Optionally understanding the link between R and Big Data



CHAPTER 1: R markdown
- Writing R markdown
- Outputting in your desired format
- R markdown and its link with LaTeX

CHAPTER 2: R slidify
- Creating basic presentations in R
- R code and output in your presentation
- Styling your presentation

CHAPTER 3: R shiny
- Widgets
- Ui.R
- Server.R
- More help functions
- Building interactive data products yourself

CHAPTER 4 (optionally): R and Big Data
- R and Hadoop
- R and Spark
- Advantages and disadvantages of both techniques



- A good knowledge of R is required, if necessary we refer to “Getting started programming with R” and “Data science and prediction models with R”.
- Some knowledge of css will turn out handy to style your presentation, but it is not required



This course is aimed towards both developers and BI personnel willing to implement their own data products in R, or making presentations and writing documents with mathematical equations and output in it.