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DataVisualizations
Visualizations of High-Dimensional Data
View on CRAN: Click
here
Download and install DataVisualizations package within the R console
Install from CRAN:
install.packages("DataVisualizations")
Install from Github:
library("remotes")
install_github("cran/DataVisualizations") Install by package version:
library("remotes")
install_version("DataVisualizations", "1.3.3") Attach the package and use:
library("DataVisualizations")
Maintained by
Michael Thrun
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.DataVisualizations . https://github.com/Mthrun/DataVisualizations/issues . DataVisualizations citation info . DataVisualizations results . DataVisualizations.pdf . DataVisualizations_1.3.3.tar.gz . DataVisualizations_1.3.3.zip . DataVisualizations_1.3.3.zip . DataVisualizations_1.3.3.zip . DataVisualizations_1.3.3.tgz . DataVisualizations_1.3.3.tgz . DataVisualizations_1.3.3.tgz . DataVisualizations_1.3.3.tgz . DataVisualizations_1.3.3.tgz . DataVisualizations_1.3.3.tgz . DataVisualizations archive . https://CRAN.R-project.org/package=DataVisualizations .
First Published: 2018-01-30
Latest Update: 2023-10-10
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, . 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) .
How to cite:
Michael Thrun (2018). DataVisualizations: Visualizations of High-Dimensional Data. R package version 1.3.3, https://cran.r-project.org/web/packages/DataVisualizations. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.9.2 (2018-01-30 14:15), 1.0.0 (2018-05-06 17:34), 1.1.1 (2018-06-30 18:09), 1.1.4 (2018-10-22 07:40), 1.1.5 (2019-02-02 14:13), 1.1.6 (2019-03-08 13:43), 1.1.8 (2019-08-20 13:40), 1.1.9 (2019-11-10 16:40), 1.1.10 (2020-02-02 18:10), 1.1.11 (2020-02-13 18:50), 1.1.12 (2020-05-17 09:40), 1.2.0 (2020-08-04 18:34), 1.2.1 (2020-10-28 10:10), 1.2.2 (2021-01-12 18:10), 1.2.3 (2022-05-21 12:50), 1.3.0 (2023-06-06 23:20), 1.3.1 (2023-07-18 21:40), 1.3.2 (2023-10-10 10:10)
Other packages that cited DataVisualizations R package
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Other R packages that DataVisualizations depends,
imports, suggests or enhances
Complete documentation for DataVisualizations
Functions, R codes and Examples using
the DataVisualizations R package
Some associated functions: ABCbarplot . AccountingInformation_PrimeStandard_Q3_2019 . BimodalityAmplitude . ChoroplethPostalCodesAndAGS_Germany . Choroplethmap . ClassBoxPlot . ClassMDplot . ClassPDEplot . ClassPDEplotMaxLikeli . Classplot . CombineCols . Crosstable . DataVisualizations-package . DefaultColorSequence . DensityScatter . DualaxisClassplot . DualaxisLinechart . Fanplot . FundamentalData_Q1_2018 . GoogleMapsCoordinates . Heatmap . HeatmapColors . ITS . InspectBoxplots . InspectCorrelation . InspectDistances . InspectScatterplots . InspectStandardization . InspectVariable . JitterUniqueValues . Lsun3D . MAplot . MDplot . MDplot4multiplevectors . MTY . OptimalNoBins . PDEplot . PDEscatter . ParetoDensityEstimation . ParetoRadius . Piechart . Pixelmatrix . Plot3D . PlotMissingvalues . PlotProductratio . PmatrixColormap . QQplot . ShepardDensityScatter . Sheparddiagram . SignedLog . Silhouetteplot . Slopechart . SmoothedDensitiesXY . StatPDEdensity . Worldmap . categoricalVariable . inPSphere2D . stat_pde_density . world_country_polygons . zplot .
Some associated R codes: ABCbarplot.R . BimodalityAmplitude.R . Choroplethmap.R . ClassBoxPlot.R . ClassMDplot.R . ClassPDEplot.R . ClassPDEplotMaxLikeli.R . Classplot.R . CombineCols.R . Crosstable.R . DensityScatter.R . DrawWorldWithCls.R . DualaxisClassplot.R . DualaxisLinechart.R . Fanplot.R . GoogleMapsCoordinates.R . Heatmap.R . InspectBoxplots.R . InspectCorrelation.R . InspectDistances.R . InspectScatterplots.R . InspectStandardization.R . InspectVariable.R . JitterUniqueValues.R . MAplot.R . MDplot.R . MDplot4multiplevectors.R . OptimalNoBins.R . PDEplot.R . PDEscatter.R . ParetoDensityEstimation.R . ParetoRadius.R . Piechart.R . Pixelmatrix.R . Plot3D.R . PlotMissingvalues.R . PlotProductratio.R . QQplot.R . RcppExports.R . ShepardDensityScatter.R . Sheparddiagram.R . SignedLog.R . Silhouetteplot.R . Slopechart.R . SmoothedDensitiesXY.R . Worldmap.R . bimodal.R . checkCls.R . checkFeature.R . inPSphere2D.R . internpiechart.R . smooth1D.R . stat_pde_density.R . zplot.R . Full DataVisualizations package functions and examples
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