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.

This paper proposes an original data mining method for unsupervised learning, replacing traditional factor analysis with a system of variable clustering. Clustering of variables aims to group together variables that are strongly related to each other, i.e. containing the same information. We recently proposed the ClustOfVar method, specifically devoted to variable clustering, regardless of whether the variables are numeric or categorical in nature. It simultaneously provides homogeneous clusters of variables and their corresponding synthetic variables that can be read as a kind of gradient. In this algorithm, the homogeneity criterion of a cluster is defined by the squared Pearson correlation for the numeric variables and by the correlation ratio for the categorical variables. This method was tested on categorical data relating to French farmers and their perception of the environment. The use of synthetic variables provided us with an original approach of identifying the way farmers reconfigured the questions put to them.