Visualization of data becomes more challenging as the dimensionality of the data increases,
impacting not only the display of the data itself but also the modeling results.
This paper discusses common visualization techniques for compositional data. None of them
seem to be well suited for changes in compositions that depend on either a metric covariate or a
factor. The clr-deviation chart as a chart with a factor or covariate as abscissa and all centered log
ratio-transformed component values superimposed on the ordinate axis is then introduced jointly
with the clr-component chart. The clr-deviation chart takes advantage of the sum-equals-zero
property of clr-transformed compositional data. It has some theoretical and practical advantages
over alternatives and one major disadvantage – an arbitrarily scaled ordinate axis; its properties
are discussed.
The usefulness of the methods are illustrated using an example analyzing the changes of proportions of the different diseases treated by hospitalization over a period of 13 years in Germany
