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Van der Sterr Building, Department Statistics, Room 2058
Biplots are useful when visualizing multivariate data. It can, however, sometimes be challenging to interpret, for example when the axes and points cause overcrowding of the plot. This overcrowding is often due to the presence of many variables, highly correlated variables, or merely data sets with a large number of observations. In this paper improvements to the biplot are made to address these shortcomings. These improvements include: i) the automatic parallel translation, or “explosion", of axes, ii) the use of densities on the axes to improve interpretation and representation of large data sets, and iii) introducing interactive biplots via the use of the Plotly package in R. These improvements result in a better composition of the plot to make it seem less crowded, more easily interpretable, offer additional information that can get lost in the case of a high volume of data, and allowing the user to inspect the biplot element-wise.