  Mixed data arise when observations are described by a mixture of numerical
and categorical variables. The R package PCAmixdata extends to this type of
data standard multivariate analysis methods which allow description,
exploration and visualization of the 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 proposes a unified mathematical presentation
of the different methods with common notations, as well as providing a
summarised presentation of the three algorithms, with details to help the user
understand graphical and numerical outputs of the corresponding R functions.
This then allows the user to easily provide relevant interpretations of the
results obtained. 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.
