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explor

explor is an R package to allow interactive exploration of multivariate analysis results.

For now on, it is usable the following types of analyses :

AnalysisFunctionPackageNotes
Principal Component AnalysisPCAFactoMineR-
Correspondance AnalysisCAFactoMineR-
Multiple Correspondence AnalysisMCAFactoMineR-
Principal Component Analysisdudi.pcaade4Qualitative supplementary variables are ignored
Correspondance Analysisdudi.coaade4-
Multiple Correspondence Analysisdudi.acmade4Quantitative supplementary variables are ignored
Specific Multiple Correspondance AnalysisspeMCAGDAtools-
Multiple Correspondance AnalysismcaMASSQuantitative supplementary variables are not supported
Principal Component AnalysisprincompstatsSupplementary variables are ignored
Principal Component AnalysisprcompstatsSupplementary variables are ignored
Correspondance Analysistextmodel_caquanteda.textmodelsOnly coordinates are available

Features

For each type of analysis, explor launches a shiny interactive Web interface which is displayed inside RStudio or in your system Web browser. This interface provides both numerical results as dynamic tables (sortable and searchable thanks to the DT package) and interactive graphics thanks to the scatterD3 package. You can zoom, drag labels, hover points to display tooltips, hover legend items to highlights points, and the graphics are fully updatable with animations which can give some visual clues. You can also export the current plot as an SVG file or get the R code to reproduce it later in a script or document.

Here is a preview of what you will get. Note that the interface is available both in english and french, depending on your locale :

Installation

To get the stable version from CRAN :

install.packages("explor")

To install the latest dev version from GitHub :

install.packages("remotes")  # If necessary
remotes::install_github("juba/scatterD3")
remotes::install_github("juba/explor")

Usage

Usage is very simple : you just apply the explor function to the result of one of the supported analysis functions.

Example with a principal correspondence analysis from FactoMineR::PCA :

library(FactoMineR)
library(explor)

data(decathlon)
pca <- PCA(decathlon[,1:12], quanti.sup = 11:12, graph = FALSE)
explor(pca)

Example with a multiple correspondence analysis from FactoMineR::MCA:

data(hobbies)
mca <- MCA(hobbies[1:1000,c(1:8,21:23)],quali.sup = 9:10, quanti.sup = 11, ind.sup = 1:100)
explor(mca)

Documentation and localization

Two vignettes are provided for more detailed documentation :

Depending on your system locale settings, the interface is displayed either in english or in french (other languages can be easily added).

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Version

Install

install.packages('explor')

Monthly Downloads

1,503

Version

0.3.10

License

GPL (>= 3)

Maintainer

Last Published

April 29th, 2023

Functions in explor (0.3.10)

ggvar

Graphical representation of the variables (columnss) of a multivariate analysis
MCA_var_plot

Interactive MCA variables plot
PCA_ind_plot

Interactive PCA indivuals plot
prepare_results

Analysis results preparation
ggind

Graphical representation of indivduals (rows) of a multivariate analysis
CA_var_plot

Interactive CA variables plot
PCA_var_plot

Interactive PCA variables plot
explor

Interface for analysis results exploration
speMCA_varsup

Compute supplementary variables data for a GDAtools::speMCA result
MCA_biplot

Interactive MCA biplot
MCA_ind_plot

Interactive MCA indivuals plot