News

Lectures
Mondays 10.00  11.30 in 3029
Wednesdays 10.00  11.30 in 3029
Tutorial
Tuesdays 10.00  11.30 3029

Description

Graphical
analyses complement statistical analyses.
Their advantages lie in checking data quality, in
generating hypotheses (in contrast to testing
them) and in explaining model results.
Graphical methods are becoming increasingly
important for the analysis of large heterogenous
datasets, for which classical model assumptions
seldom hold. In this lecture we will discuss
graphical methods using examples from real
applications, including some from the web. R
will be used primarily (especially ggplot2) with
Mondrian for interactive graphics.

Literature

"Grammar
of Graphics" Lee Wilkinson (Springer)
"ggplot2" Hadley Wickham (Springer)
"Interactive Graphics for Data Analysis" Martin
Theus and Simon Urbanek (CRC Press)
"Graphics of Large Datasets" Antony Unwin, Martin
Theus and Heike Hofmann (Springer)
"Handbook of Data Visualization" (eds. Chen,
Härdle, Unwin) (Springer) 
Software 
R and Mondrian
will be used in this course.

Datasets (R/Mondrian format) 
Titanic
Sparpaket

RCode 
UnivariateContinuous
MultivariateContinuous
CategoricalData
SimulatingPlotsTitanic
