explor

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

For now on, the following analyses are supported :

Analysis Function Package Notes
Principal component analysis PCA FactoMineR Qualitative supplementary variables are ignored
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

The philosophy behind explor is to only be an exploration interface which doesn’t really do anything by itself : analysis and computations are made in your R script, and explor only helps you visualizing the results. As such it can not disrupt code execution and reproducibility.

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 a series of tabs with interactive data and graphics.

These data and graphics are displayed with several “interactive” features. Numerical results are shown as dynamic tables which are sortable and searchable thanks to the DT package. Most graphics are generated with the scatterD3 package which provides the following features :

Usage

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

FactoMineR functions

Supported FactoMineR functions should work “out of the box”. Just pass the result object to explor().

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

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

Example with a simple correspondence analysis from FactoMiner::CA :

data(children)
res.ca <- CA(children, row.sup = 15:18, col.sup = 6:8)
explor(res.ca)

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

library(FactoMineR)
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)

ade4 functions

ade4 functions should also work by directly passing the object result to explor().

For example, to visualize a simple PCA results :

library(ade4)
data(deug)
pca <- dudi.pca(deug$tab, scale = TRUE, scannf = FALSE, nf = 5)
explor(pca)

There’s a bit more work to be done if you want to display supplementary elements, as ade4 don’t include them directly in the results analysis.

For a principal component analysis, you have to compute supplementary individuals (resp. variables) results with suprow (resp. supcol) and add them manually as a supi (resp. supv) element of your result object.

Here is an example of how to do this :

data(deug)
d <- deug$tab
sup_var <- d[-(1:10), 8:9]
sup_ind <- d[1:10, -(8:9)]
pca <- dudi.pca(d[-(1:10), -(8:9)], scale = TRUE, scannf = FALSE, nf = 5)
## Supplementary individuals
supi <- suprow(pca, sup_ind)
pca$supi <- supi$lisup
## Supplementary variables
supv <- supcol(pca, dudi.pca(sup_var, scale = TRUE, scannf = FALSE)$tab)
pca$supv <- supv$cosup
explor(pca)

You have to do the same thing for supplementary elements in a multiple correspondence analysis, with a slightly more complicated computation for supplementary variables :

data(banque)
d <- banque[-(1:100),-(19:21)]
ind_sup <- banque[1:100, -(19:21)]
var_sup <- banque[-(1:100),19:21]
acm <- dudi.acm(d, scannf = FALSE, nf = 5)
## Supplementary variables
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)$cosup
colw <- acm$cw*ncol(d)
X <- acm.disjonctif(ind_sup)
X <- t(t(X)/colw) - 1
X <- data.frame(X)
## Supplementary individuals
acm$supi <- suprow(acm, X)$lisup
explor(acm)

For simple correspondence analysis, you can add supplementary rows or columns by adding their coordinates to supr and supc elements of your result object :

data(bordeaux)
tab <- bordeaux
row_sup <- tab[5,-4]
col_sup <- tab[-5,4]
coa <- dudi.coa(tab[-5,-4], nf = 5, scannf = FALSE)
coa$supr <- suprow(coa, row_sup)$lisup
coa$supc <- supcol(coa, col_sup)$cosup
explor(coa)

Feedback

explor is quite a young package, so there certainly are bugs or problems. Thanks for reporting them by mail or by opening an issue on GitHub