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.

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)
    PCAshiny(Mydata)
  • You can first perform the analysis with FactoMineR, and then use Factoshiny on the FactoMineR output to construct the graphs:
    • library(FactoMineR)
    data(decathlon)
    res.pca = PCA(decathlon, quanti.sup=11:12,quali.sup=13)
    library(Factoshiny)
    resshiny = PCAshiny(res.pca)
  • You can open again a Factoshiny object: resshiny = PCAshiny(resshiny)
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