A marginal plot is a combination of a bivariate plot (typically a scatter plot) and one/two univariate plots (density, boxplot, dotplot, …). It is an interesting plot since you can inspect both the relationship between two variables and the distribution of each variable.
Even if there are several options to obtain a marginal plot, here I exploit ggside, an useful extension for ggplot2.
Note: ggside can be used also for adding univariate plot(s) to a generic ggplot.
If you are interested to customise a ggplot2 graph without going crazy in remembering all the technicalities for rotating labels, removing or rotaging axes, removing or changing appeareance of legends, change the text appearance, I suggest to inspect ggeasy. Documentation and tutorials are available on the ggeasy official github repository.
Instead (or maybe, at the same), if you are instead interested to master ggplot2 without shortcuts, please refer to the must-read book ggplot2: Elegant Graphic for Data Analysis, freely available online.
DESPOTA (DEndogram Slicing through a PermutatiOn Test Approach) is a novel approach exploiting permutation tests in order to automatically detect a partition among those embedded in a dendrogram. Unlike the traditional approach, DESPOTA includes in the search space also partitions not corresponding to horizontal cuts of the dendrogram.
The output of hierarchical clustering methods is typically displayed as a dendrogram describing a family of nested partitions. However, the exploitable partitions are usually restricted to those relying on horizontal cuts of the tree, missing the possibility to explore the whole set of partitions housed in the dendrogram.