The package Factoshiny

A beautiful graph tells more than a lengthy speech!!

It is crucial to improve the graphs obtained by any Principal Component Methods (PCA, CA, MCA, MFA, ...). Factoshiny allows you to easily improve these graphs interactively.

Why using Factoshiny?

  • This user-friendly interface allows you to parametrize the methods and to modify the graphical options
  • You do not need to know how to program
  • You modify the graphical options and see instantly how the graphs are improved
  • The results (graphs and indicators) are updated automatically
  • You can download the plots as well as the lines of code to redo the analysis
  • You can save and then reuse the object resulting from Factoshiny in order to further modify the graphs. The interface is re-opened as it was when you left it and you can modify the parameters of the method or the graphical options.

How to use Factoshiny?

Visualize this video to see how to use Factoshiny.


Tree ways to use Factoshiny:

  • Simply choose the Factoshiny method and your dataset, and then parametrize the method and construct the graphs interactively. For instance, in PCA: library(Factoshiny)
  • You can first perform the analysis with FactoMineR, and then use Factoshiny on the FactoMineR output to construct the graphs:
  • library(FactoMineR)
    res.pca = PCA(decathlon, quanti.sup=11:12,quali.sup=13)
    resshiny = PCAshiny(res.pca)
  • You can open again a Factoshiny object: resshiny = PCAshiny(resshiny)