For working with circular (or directional) data, the circular package provides a data type to hold values that represent positions across a circle. The circular package comes with basic plotting functions, but compared to ggplot2 it lacks a lot of flexibility. GGplot2 supports circular coordinate systems (using coord_polar()). However, bringing these two together can actually be quite complex, because ordinary stats like stats_density() will not work correctly with circular data.

GGcircular tries to make workig with such data easier while alowing you to use the flexibility of ggplot2. It introduces a number of new geoms and stats:

• geom_point_circular() for drawing circular data points
• stats_density_circular(): for drawing circular densities
• stats_mean_circular(): for drawing circular means and mean resultant lengths
• annotation_axis_circular() for drawing axis and decoration

# Create a von-Mises distirbuted sample with M=0 and ϰ=2
data_example <- data.frame(x = circular::rvonmises(100,0,2, control.circular = list(units = "degrees")))

# Plot points, density and mean arrow
ggplot(data = data_example, mapping = aes(x = x)) + 
  coord_polar() +
  geom_point_circular() +
  stat_density_circular() +
  stat_mean_circular() +
  annotation_axis_circular(unit="degrees") +
  theme_circular()