decathlon

<p>The data used here refer to athletes' performance during two sporting events.</p>

datasets

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Francois Husson

FactoMineR

Multivariate Exploratory Data Analysis and Data Mining

decathlon function

<p>A data frame with 41 rows and 13 columns: the first ten columns corresponds to 
the performance of the athletes for the 10 events of the decathlon. 
The columns 11 and 12 correspond respectively to the rank and the points obtained. 
The last column is a categorical variable
corresponding to the sporting event (2004 Olympic Game or 2004 Decastar)</p>

decathlon: Performance in decathlon (data)

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