Visualizations of High-Dimensional Data

A collection of various visualizations methods is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through the combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). The visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables, the Shepard density plot as well as the Bland-Altman plot are presented here. With regards to classified high-dimensional data several visualizations are presented, e.g. the heat map and silhouette plot. For a classification of countries, a map of the world or Germany can be visualized. More detailed explanations can be found in the book of Thrun, M.C.:"Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) . Furthermore, for categorical features the ABC analysis improved Pie charts, slope charts and fan plots are usable. Additionally, for measurements across a geographic area an easy to use function for a Choropleth map is presented here. The flagship of this package is the mirrored density plot (MD-plot) which is a PDE-optimized violin plot for either classified or non-classified, univariate or multivariate data. The MD-plot is an alternative for the box-and-whisker diagram (box plot) and bean plot.


Version 1.1.4 (CRAN, 2018-10-21) o DualAxisLineChart enables to visualize to lines in one plot overlaying them using ploty (e.g. two time series with two ranges of values) o Bugfix: PDEscatter now removes non-numeric values with na.rm=TRUE before xlim and ylim are defined o ProductRatioPlot: The plot is useful in the case where there are many instances of very small values, but a small number of very large ones o CrossTablePlot: Presents a heatmap with values and a cross table of given Data matrix of two features and a bin width or percentualized values o Update: plot3D function and documentation improved.

Version 1.1.3 (GitHub, 2018-07-07) o Improvement: MD-plot layout changes and plotting paramater added o InspectCorrelation now visualizes the density and calcluates the spearman correlation index as a shortcut to PDEscatter o Minor bugifx: MD-plot now uses ggExtra::rotateTextX() for better xaxis alignment of text

Version 1.1.2 (GitHub, 2018-07-02) o Bugfix: InspectDistances methods argument now passed on to parallelDist::parDist

Version 1.1.1 (CRAN 2018-06-30) o Improved visualization of MD-plot

Version 1.1.0 (GitHub) o Vignette generated o Bugfix: authors stated in Description regarding functions which were used in other dbt packages instead of this package.

Version 1.0.9 (GitHub) o ClassViolinPlot build on top of stat_pde_density for Data with Clustering o Bugfix in stat_pde_density for special case of one value o parallelDist integrated for faster distance computations

Version 1.0.8 (GitHub) o Bugfix in stat_pde_density for special case of one value o Minor bugfix in PDEscatter

Version 1.0.7 (GitHub) o MD-plot: stat_pde_density added in order to integrate concept with ggplot2

Version 1.0.1-1.0.6 (GitHub) o ClassBoxPlot function added for Data with Clustering o ClassPDEplot function added for Data with Clustering o ClassPDEplotMaxLikeli function added for Data with Clustering o Minor bugfix in InspectVariable function o Bugfix in internpiechart

Version 1.0.0 (CRAN, 2018-05-06) o Complete package generated

Reference manual

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1.1.5 by Michael Thrun, 16 days ago

Report a bug at

Browse source code at

Authors: Michael Thrun [aut, cre, cph] , Felix Pape [aut, rev] , Onno Hansen-Goos [ctr, ctb] , Alfred Ultsch [dtc, ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, AdaptGauss, ggplot2, sp

Suggests plyr, akima, ggmap, reshape2, plotrix, rworldmap, rgl, ABCanalysis, choroplethr, gplots, dplyr, R6, parallelDist, knitr, rmarkdown, vioplot, ggExtra, plotly, htmlwidgets, diptest, moments, signal, DatabionicSwarm

Linking to Rcpp, RcppArmadillo

System requirements: C++11

Suggested by DatabionicSwarm, GeneralizedUmatrix, ProjectionBasedClustering.

See at CRAN