Rug plot

A rug plot is a data visualization tool primarily used in statistics and data analysis. It's a simple yet informative way to display the distribution of a single numerical variable. A rug plot consists of short, vertical lines (or "ticks") drawn along the x-axis (for a horizontal rug plot) or y-axis (for a vertical rug plot), with each tick representing a single observation in the dataset.

The primary purpose of a rug plot is to provide a visual indication of the density of data points. Where the ticks are closely clustered together, it suggests a higher concentration of data values in that region. Conversely, areas with sparsely distributed ticks indicate regions with fewer observations.

Rug plots are often used in conjunction with other visualizations, such as histograms, kernel density estimations (KDEs), or scatter plots. By adding a rug plot to these more complex visualizations, it allows the viewer to simultaneously observe the overall distribution (e.g., through a histogram) and the individual data points. This can be especially helpful in identifying outliers or understanding the shape of the distribution more intuitively.

Compared to histograms or KDEs, rug plots offer a more direct representation of the raw data. While histograms group data into bins and KDEs estimate a continuous density function, rug plots show the exact location of each data point, preserving more information about the original dataset. However, rug plots can become less effective with very large datasets, where the ticks may overlap and obscure the underlying patterns. In such cases, other visualization techniques may be more suitable.