Interactive MCA/PCA results exploration with explor

Julien Barnier



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 -
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

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.


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

prcomp, princomp and MASS::mca

To visualize and explore these functions results, just pass the result object to explor().

Here is an example for a sample PCA with princomp :

pca <- princomp(USArrests, cor = TRUE)

explor supports the visualization of supplementary individuals whose scores have been computed with predict. You just have to add them as a supi element to your result object.

Here is an example with prcomp :

pca <- prcomp(USArrests[6:50,], scale. = TRUE)
pca$supi <- predict(pca, USArrests[1:5,])

For MASS::mca, explor() also supports qualitative supplementary variables. You must include their predicted coordinates to a supv element. It’s also best to manually add row names to the supidata, if any :

mca <- MASS::mca(farms[4:20, 2:4], nf = 11)
supi_df <- farms[1:3, 2:4]
supi <- predict(mca, supi_df, type="row")
rownames(supi) <- rownames(supi_df)
mca$supi <- supi
mca$supv <- predict(mca, farms[4:20, 1, drop=FALSE], type="factor")

Note that the results of these three functions are quite limited : they provide variables and individuals coordinates, but no contributions or squared cosinus.

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 :

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

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

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

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)

ade4 functions

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

For example, to visualize a simple PCA results :

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

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 :

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

You have to do the same thing for supplementary elements in a multiple correspondence analysis :

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)
## Supplementary individuals
acm$supi <- suprow(acm, ind_sup)

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

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

GDAtools functions

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

For example, to visualize a speMCA results :

mca <- speMCA(Music[,1:5], excl = c(3, 6, 9, 12, 15))

To display supplementary individuals, you have to compute their data with the indsup function, and add them manually as a supi element of your result object :

mca <- speMCA(Music[3:nrow(Music), 1:5], excl = c(3, 6, 9, 12, 15))
mca$supi <- indsup(mca, Music[1:2, 1:5])

To display supplementary variables, you have to compute their data with the speMCA_varsup function and add them manually as a supv element of your result object :

mca <- speMCA(Music[3:nrow(Music), 1:4], excl = c(3, 6, 9, 12))
mca$supi <- indsup(mca, Music[1:2, 1:4])
mca$supv <- speMCA_varsup(mca, Music[3:nrow(Music), 5:6])

Exporting Plots

explor provides two different ways to export the displayed plots.

SVG export

To save the displayed plot as an SVG file, click on the Export to SVG button in the bottom of the left sidebar, or choose Export to SVG in the gear menu.

SVG is a vector graphics format, editable with softwares like Inkscape.

This SVG export may cause issues when used inside RStudio. As a workaround, you can open explor in a browser (with Open in Browser icon) before exporting.

R code

Another way is to get the R code which allows to generate the current plot. This code can then be used in a script or a Rmarkdown document.

To do this, click on the Get R code button on the bottom of the left sidebar. A modal dialog should show up with the R code that you can then copy/paste.

Please note that this R code keeps track of the current plot zooming, but not of any custom label positioning. If you want to keep those, you have to first save them in a CSV file with Export labels positions gear menu entry. Then, in your R script, read this file in an object with read.csv and pass this object to the export_labels_positions argument in the generated code :

labels <- read.csv("position_labels.csv")
res <- explor::prepare_results(mca)
explor::MCA_var_plot(res, xax = 1, yax = 2,
    var_sup = TRUE, , var_lab_min_contrib = 0,
    col_var = "Variable", symbol_var = "Type",
    size_var = NULL, size_range = c(10, 300),
    labels_size = 10, point_size = 56,
    transitions = TRUE, labels_positions = labels)


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