Tools for visualising densities, both discrete (binned histograms) and continuous (smooth density estimates).
All methods accept weights.
A discrete density is described by a tiling of the interval (1d) or plane (2d), along with a count of observations in each tile.
Binning data in 1d and 2d is tedious and tricky if you want to correctly deal with floating point (FP) issues. The `bin' package provides a fast and convenient interface for break calculation and binning in 1d and 2d.
bin_interval
: interval bins (1d)bin_rect
: rectangular bins (2d) bin_hex
: hexagonal bins (2d)A discrete density is described by a function that maps location on the real interval or plane to the density at that location.
This package also provides two methods for continuous density estimation, kernel_density
and local_density
. local_density
uses local regression as implemented in the locfit
package, and provides a large number of options to control the output. However, it can be slow, so kernel_density
provides a faster implementation that offers less control.
kernel_density_1d
kernel_density_2d
local_density_1d
local_density_2d
Both functions work with either 1d or 2d data, and both share a grid
argument which specifies the locations where densities should be computed. This adds flexibility, allowing the function to be used to display the density over a regular grid, or just where the data points lie.
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