ggincerta

Overview

ggincerta is an extension of ggplot2 that introduces new layers and scales for visualizing spatial uncertainty, and can be extended to more general bivariate schemes. It reimplements the visualization methods for four types of maps introduced in the Vizumap package in a way that fully aligns with the grammar of graphics and integrates seamlessly with the ggplot2 ecosystem, making the visualization process more flexible and convenient.

Installation

# Install the release from CRAN:
install.packages("ggincerta")

# Install the development version from GitHub:
# install.packages("pak")
pak::pak("maggiexma/ggincerta")

Usage

The example dataset included in ggincerta is an sf object adapted from the nc shapefile in the sf package. It contains two simulated columns value and sd, which are mainly used in example maps to demonstrate how to visualize visualize regional uncertainty alongside average estimates. For more details about the description and design of four map types, see https://doi.org/10.1002/sta4.150.

library(ggincerta)
#> Loading required package: ggplot2

The ggincerta package defines two layer functions geom_sf_pixel() and geom_sf_glyph() for creating pixel and glyph maps, respectively. They can be used in the same way as other geom_*() functions in ggplot2, like geom_point().

ggplot(nc) + geom_sf_pixel(mapping = aes(v1 = value, v2 = sd))

Two variables v1 and v2, are required in the mapping for a glyph map. They are visually transformed into a pixel representation, where pixel values are sampled from a specified probability distribution parameterized by v1 and v2 for each region and then mapped to the colour aesthetic. A greater variation in pixel colors within a region indicates higher uncertainty.

ggplot(nc) + geom_sf_glyph(mapping = aes(v1 = value, v2 = sd))

Glyph maps are essentially centroid maps that require two guides: v1 is conventionally mapped to the colour aesthetic and shown with a continuous colourbar, while v2 is mapped to the rotation angle of glyph.

The designs of both the bivariate map and the exceedance probability map are primarily reflected in their colour mapping schemes. The scale function scale_*_bivariate() discretizes each variable into n_breaks bins, maps these bins to corresponding colour ramps, and generates a colour grid in which each cell represents the joint bin from the two variables.

ggplot(nc) + geom_sf(aes(fill = duo(value, sd)))

This scale can be applied to data types other than sf objects.

ggplot(anscombe, aes(x1, x2)) +
  geom_point(aes(color = duo(y1, y2)), size = 6)

Like scale_*_bivariate(), scale_*_exceed() in ggincerta can be applied to data types beyond sf. Note that it currently computes exceedance probabilities under a normal distribution only, taking the first variable as mean and the second as standard deviation with a default threshold of 1.64. To change the threshold, call scale_*_exceed() explicitly and pass the desired arguments.

ggplot(nc) + geom_sf(aes(fill = duo_exceed(value, sd)))

ggplot(anscombe, aes(x1, x2)) +
  geom_point(aes(color = duo_exceed(y1, y2)), size = 6)