R: Visualizations of High-Dimensional Data

DataVisualizations-package {DataVisualizations} | R Documentation

## Visualizations of High-Dimensional Data

### Description

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data 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 a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

### Details

For a brief introduction to DataVisualizations please see the vignette A Quick Tour in Data Visualizations.

Please see https://www.deepbionics.org/. Depending on the context please cite either [Thrun, 2018] regarding visualizations in the context of clustering or [Thrun/Ultsch, 2018] for other visualizations.

For the Mirrored Density Plot (MD plot) please cite [Thrun et al., 2020] and see the extensive vignette in https://md-plot.readthedocs.io/en/latest/index.html. The MD plot is also available in Python https://pypi.org/project/md-plot/

Index of help topics:

    ABCbarplot              Barplot with Sorted Data Colored by ABCanalysis
    AccountingInformation_PrimeStandard_Q3_2019
                            Accounting Information in the Prime Standard in
                            Q3 in 2019 (AI_PS_Q3_2019)
    BimodalityAmplitude     Bimodality Amplitude
    ChoroplethPostalCodesAndAGS_Germany
                            Postal Codes and AGS of Germany for a
                            Choropleth Map
    Choroplethmap           Plots the Choropleth Map
    ClassBoxplot            Creates Boxplot plot for all classes
    ClassMDplot             Class MDplot for Data w.r.t. all classes
    ClassPDEplot            PDE Plot for all classes
    ClassPDEplotMaxLikeli   Create PDE plot for all classes with maximum
                            likelihood
    Classplot               Classplot
    CombineCols             Combine vectors of various lengths
    Crosstable              Crosstable plot
    DataVisualizations-package
                            Visualizations of High-Dimensional Data
    DefaultColorSequence    Default color sequence for plots
    DensityScatter          Scatter Density Plot
    DualaxisClassplot       Dualaxis Classplot
    DualaxisLinechart       DualaxisLinechart
    Fanplot                 The fan plot
    FundamentalData_Q1_2018
                            Fundamental Data of the 1st Quarter in 2018
    GoogleMapsCoordinates   Google Maps with marked coordinates
    Heatmap                 Heatmap for Clustering
    HeatmapColors           Default color sequence for plots
    ITS                     Income Tax Share
    InspectBoxplots         Inspect Boxplots
    InspectCorrelation      Inspect the Correlation
    InspectDistances        Inspection of Distance-Distribution
    InspectScatterplots     Pairwise scatterplots and optimal histograms
    InspectStandardization
                            QQplot of Data versus Normalized Data
    InspectVariable         Visualization of Distribution of one variable
    JitterUniqueValues      Jitters Unique Values
    Lsun3D                  Lsun3D inspired by FCPS
    MAplot                  Minus versus Add plot
    MDplot                  Mirrored Density plot (MD-plot)
    MDplot4multiplevectors
                            Mirrored Density plot (MD-plot)for Multiple
                            Vectors
    MTY                     Muncipal Income Tax Yield
    OptimalNoBins           Optimal Number Of Bins
    PDEplot                 PDE plot
    PDEscatter              Scatter Density Plot
    ParetoDensityEstimation
                            Pareto Density Estimation V3
    ParetoRadius            ParetoRadius for distributions
    Piechart                The pie chart
    Pixelmatrix             Plot of a Pixel Matrix
    Plot3D                  3D plot of points
    PlotMissingvalues       Plot of the Amount Of Missing Values
    PlotProductratio        Product-Ratio Plot
    PmatrixColormap         P-Matrix colors
    QQplot                  QQplot with a Linear Fit
    ShepardDensityScatter   Shepard PDE scatter
    Sheparddiagram          Draws a Shepard Diagram
    SignedLog               Signed Log
    Silhouetteplot          Silhouette plot of classified data.
    Slopechart              Slope Chart
    SmoothedDensitiesXY     Smoothed Densities X with Y
    StatPDEdensity          Pareto Density Estimation
    Worldmap                plots a world map by country codes
    categoricalVariable     A categorical Feature.
    inPSphere2D             2D data points in Pareto Sphere
    stat_pde_density        Calculate Pareto density estimation for ggplot2
                            plots
    world_country_polygons
                            world_country_polygons
    zplot                   Plotting for 3 dimensional data

### Author(s)

Michael Thrun, Felix Pape, Onno Hansen-Goos, Alfred Ultsch

Maintainer: Michael Thrun <m.thrun@gmx.net>

### References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, doi: 10.1007/978-3-658-20540-9, 2018.

[Thrun/Ultsch, 2018] Thrun, M. C., & Ultsch, A. : Effects of the payout system of income taxes to municipalities in Germany, in Papiez, M. & Smiech,, S. (eds.), Proc. 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, pp. 533-542, Cracow: Foundation of the Cracow University of Economics, Cracow, Poland, 2018.

[Thrun et al., 2020] Thrun, M. C., Gehlert, T. & Ultsch, A.: Analyzing the Fine Structure of Distributions, PLoS ONE, Vol. 15(10), pp. 1-66, DOI 10.1371/journal.pone.0238835, 2020.

### Examples

    data("Lsun3D")
    Data=Lsun3D$Data

    Pixelmatrix(Data)

    InspectDistances(as.matrix(dist(Data)))

    data("ITS")
    data("MTY")
    Inds=which(ITS<900&MTY<8000)
    plot(ITS[Inds],MTY[Inds],main='Bimodality is not visible in normal scatter plot')

    PDEscatter(ITS[Inds],MTY[Inds],xlab = 'ITS in EUR',

    ylab ='MTY in EUR' ,main='Pareto Density Estimation indicates Bimodality' )

    MAlist=MAplot(ITS,MTY)

    data("Lsun3D")
    Cls=Lsun3D$Cls
    Data=Lsun3D$Data
    #clear cluster structure
    plot(Data[,1:2],col=Cls)
    #However, the silhouette plot does not indicate a very good clustering in cluster 1 and 2

    Silhouetteplot(Data,Cls = Cls)

    Heatmap(as.matrix(dist(Data)),Cls = Cls)

[Package DataVisualizations version 1.2.3 Index]