explor is an R package to allow interactive exploration of multivariate analysis results.
For now on, it is usable the following types of analyses :
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 - 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 Correspondance Analysis textmodel_ca quanteda.textmodels Only coordinates are available FeaturesFor 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 :
example InstallationTo 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 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).
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