Interactive Interfaces for Results Exploration

Shiny interfaces and graphical functions for multivariate analysis results exploration.


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 AnalysisspeMCAGDAtoolsSupplementary variables are not supported
Multiple Correspondance AnalysismcaMASSQuantitative supplementary variables are not supported
Principal Component AnalysisprincompstatsSupplementary variables are ignored
Principal Component AnalysisprcompstatsSupplementary variables are ignored

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 :

To get the stable version from CRAN :

install.packages("explor")

To install the latest dev version from GitHub :

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

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)

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).

News

  • Add a "Get R code" button which allows to get the R code to reproduce the displayed plot (minus custom labels positions)
  • Add support for princomp and prcomp
  • Add support for MASS::mca
  • Add support for GDAtools::speMCA
  • Qualitative supplementary variables are now displayed with FactoMineR::PCA results
  • explor.MCA now works if MCA has been called with an excl argument
  • Code refactoring
  • Bugfix : core dump in explor.MCA when only one supplementary qualitative variable
  • Bugfix : No variable plot when missing scale argument in dudi.pca
  • Fix test failing with next testthat version
  • Add ability to select points with lasso
  • Add ellipses to color mapping variables
  • Add ability to color individual points according to one of the qualitative variables in MCA
  • Add control to hide Rows or Columns in CA plot and tables
  • Add the ability to change point size and sizes range in variables plots for CA and MCA.
  • First version

Reference manual

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install.packages("explor")

0.3.1 by Julien Barnier, 2 months ago


https://github.com/juba/explor


Report a bug at https://github.com/juba/explor/issues


Browse source code at https://github.com/cran/explor


Authors: Julien Barnier [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports shiny, DT, dplyr, tidyr, ggplot2, highr, scatterD3

Suggests FactoMineR, ade4, GDAtools, MASS, testthat, knitr, rmarkdown


See at CRAN