  Ranked data is commonly used in research across many fields of study
including medicine, biology, psychology, and economics. One common statistic
used for analyzing ranked data is Kendall's {\tau} coefficient, a
non-parametric measure of rank correlation which describes the strength of the
association between two monotonic continuous or ordinal variables. While the
mathematics involved in calculating Kendall's {\tau} is well-established, there
are relatively few graphing methods available to visualize the results. Here,
we describe a visualization method and provide an interactive app for graphing
Kendall's {\tau} which uses a series of rigid Euclidean transformations along a
Cartesian plane to map rank pairs into discrete quadrants. The resulting graph
provides a visualization of rank correlation which helps display the proportion
of concordant and discordant pairs. Moreover, this method highlights other key
features of the data which are not represented by Kendall's {\tau} alone but
may nevertheless be meaningful, such as the relationship between discrete pairs
of observations. We demonstrate the effectiveness of our approach through
several examples and compare our results to other visualization methods.
