Archive for the ‘Fundamentals of Graphical Data Analysis’ Category

Understanding Area Based Plots: Mosaic Plots

Mosaic Plots are the swiss army knife of categorical data displays. Whereas bar charts are stuck in their univariate limits, mosaic plots and their variants open up the powerful visualization of multivariate categorical data. But let’s start with an introductory example. The Titanic data is still the most convincing application of mosaic plots, though many [...]

Mondrian Version 1.2 released

The new version (1.2) of Mondrian adds the following (significant) features: Scatterplotsmoother now includes “principle curves“, which are one of the nonlinear generalizations of principal components. All smoothers can be plotted for subgroups, which have a color assigned, “smoother by colors“. The color scheme has been refined once again, to make use of colors as [...]

Graphics *and* Statistics: The Facebook Map

There is this beautiful graph created by the facebook intern Paul Butler showing all (?) connections between facebook accounts: Paul’s article is called “Visualizing Friendships“, which I would more call “Visualizing connections between facebook accounts”, but that is probably a different matter. Although this is a beautiful piece of artwork, from a statistical point of [...]

Fundamentals: Learning to Cook

For those who are used to work with graphics on a regular basis, it is usually not a question, what plot (or combination of plots) to use when looking at particular data problems. Nevertheless, many statistically trained researcher and practitioners have a hard time to translate data problems into reasonable graphics (not to mention their [...]

Fundamentals: Scales imply Patterns

When it comes to graphing data in a chart, the scale of the data is the most important factor to determine which graphical representation might be useful. Please pardon me for the examples using the “Iris Data” and the “Titanic Data”; but these data sets are prototypes for multivariate continuous data and multivariate categorical data [...]