CRAN/E | DataVisualizations

DataVisualizations

Visualizations of High-Dimensional Data

Installation

About

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.

Citation DataVisualizations citation info
www.deepbionics.org/
Bug report File report

Key Metrics

Version 1.3.2
R ≥ 3.5
Published 2023-10-10 188 days ago
Needs compilation? yes
License GPL-3
CRAN checks DataVisualizations results

Downloads

Yesterday 9
Last 7 days 83 -60%
Last 30 days 601 -22%
Last 90 days 2.513 +0%
Last 365 days 10.444 +14%

Maintainer

Maintainer

Michael Thrun

m.thrun@gmx.net

Authors

Michael Thrun

aut / cre / cph

Felix Pape

aut / rev

Onno Hansen-Goos

ctr / ctb

Quirin Stier

ctb / rev

Hamza Tayyab

ctr / ctb

Luca Brinkmann

ctr / ctb

Dirk Eddelbuettel

ctr

Craig Varrichio

ctr

Alfred Ultsch

dtc / ctb / ctr

Material

README
NEWS
Reference manual
Package source

Vignettes

A Quick Tour in Data Visualizations

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

DataVisualizations archive

Depends

R ≥ 3.5

Imports

Rcpp ≥ 0.12.12
ggplot2
sp
pracma
reshape2

Suggests

plyr
MBA
ggmap
plotrix
rworldmap
rgl
ABCanalysis
choroplethr
dplyr
R6
parallelDist
knitr ≥ 1.12
rmarkdown ≥ 0.9
vioplot
ggExtra
plotly
htmlwidgets
diptest
moments
signal
ggrepel
MASS
ROCit
ScatterDensity ≥ 0.0.3
colorspace
viridis

LinkingTo

Rcpp
RcppArmadillo

Reverse Imports

AdaptGauss
FCPS
opGMMassessment
pguIMP
Umatrix

Reverse Suggests

DatabionicSwarm
DRquality
GeneralizedUmatrix
ProjectionBasedClustering
ScatterDensity