Mixed data arise when observations are described by a mixture of numerical
and categorical variables. The R package PCAmixdata extends standard
multivariate analysis methods to incorporate this type of data. The key
techniques/methods included in the package are principal component analysis for
mixed data (PCAmix), varimax-like orthogonal rotation for PCAmix, and multiple
factor analysis for mixed multi-table data. This paper gives a synthetic
presentation of the three algorithms with details to help the user understand
graphical and numerical outputs of the corresponding R functions. The three
main methods are illustrated on a real dataset composed of four data tables
characterizing living conditions in different municipalities in the Gironde
region of southwest France