Interfaces for Multivariate Analysis in R
Latest commit f6ec0f4 Dec 15, 2016 @juba Version bump


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

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

Analysis Function Package Notes
Principal Component Analysis PCA FactoMineR -
Correspondance Analysis CA FactoMineR -
Multiple Correspondence Analysis MCA FactoMineR -
Principal Component Analysis dudi.pca ade4 Qualitative supplementary variables are ignored
Correspondance Analysis dudi.coa ade4 -
Multiple Correspondence Analysis dudi.acm ade4 Quantitative supplementary variables are ignored
Specific Multiple Correspondance Analysis speMCA GDAtools Supplementary variables are not supported
Multiple Correspondance Analysis mca MASS Quantitative supplementary variables are not supported
Principal Component Analysis princomp stats Supplementary variables are ignored
Principal Component Analysis prcomp stats Supplementary 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 :


To install the latest dev version from GitHub :

install.packages("devtools")  # If necessary


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 :


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

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

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

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).