Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency.
First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. See, for example:
Lebart, L. and Piron, M. and Morineau, A.  (2006).
Statistique Exploratoire Multidimensionnelle, Dunod, Paris.
In order to permit to have different weights of the elements to be clustered, the function 'kmeansW', programmed in C++, is included. It is a modification of 'kmeans'.
Some graphical functions include the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' and 'ggrepel' packages to avoid  the super-position of the labels.

Automatic Description of Factorial Analysis
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
FactoMineR (>= 1.33), stats, methods, graphics, rrcov,
rmarkdown, parallel

FactoMineR, stats, methods, graphics, rrcov, rmarkdown,
parallel

FactoMineR, stats, methods, graphics, rrcov, rmarkdown,
parallel, ggplot2

FactoMineR, stats, methods, graphics, rmarkdown, parallel,
ggplot2

François Husson, Sébastien Lê, Jérémy Mazet
an R package for exploratory data analysis

Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet

Multivariate Exploratory Data Analysis and Data Mining with R

Exploratory data analysis methods such as principal component methods and clustering

Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods such as principal component methods and clustering.