DataVisualizations (version 1.0.0)

DataVisualizations-package: The Data Visualizations Package

Description

'DataVisualizations' contains a collection of various visualizations methods. With regards to high-dimensional data the heat map and silhouette plot for grouped data, visualizations of the distribution of distances, the scatter-density plot for two variables, the Shepard density plot as well as the Bland-Altman plot are presented here. For a classification of countries, a map of the world or Germany can be visualized. 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through the combination of four methods. More detailed explanations can be found in the book of Thrun, M.C.:"Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>. Furthermore, for categorical features the ABCanalysis 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.

Details

Please see http://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.

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, https://doi.org/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, 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, Foundation of the Cracow University of Economics, Zakopane, Poland, 2018.

Examples

# NOT RUN {
data(categoricalVariable)
fanPlot(categoricalVariable)

pieChart(categoricalVariable)

data("Lsun3D")
Data=Lsun3D$Data
# }
# NOT RUN {
PixelMatrixPlot(Data)
# }
# NOT RUN {
# }
# NOT RUN {
InspectDistances(as.matrix(dist(Data)))
# }
# NOT RUN {
data("ITS")
data("MTY")
Inds=which(ITS<900&MTY<8000)
plot(ITS[Inds],MTY[Inds],main='Bimodality is not visible in normal scatter plot')
# }
# NOT RUN {
PDEscatter(ITS[Inds],MTY[Inds],xlab = 'ITS in EUR',

ylab ='MTY in EUR' ,main='Pareto Density Estimation indicates Bimodality' )
# }
# NOT RUN {
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
# }
# NOT RUN {
SilhouettePlot(Data,Cls = Cls)
# }
# NOT RUN {
Heatmap(as.matrix(dist(Data)),Cls = Cls)
# }