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Title:

Extend 'ggplot2' with Layers and Scales for Spatial Uncertainty Visualization

Version:

0.1.0

Description:

Provide specialized 'ggplot2' layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

Imports:

dplyr, grDevices, grid, gtable, rlang, scales, sf, stats, withr

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.2

Suggests:

testthat (≥ 3.0.0), vdiffr

Config/testthat/edition:

Depends:

ggplot2 (≥ 3.5.0), R (≥ 4.1.0)

LazyData:

true

URL:

https://github.com/maggiexma/ggincerta

BugReports:

https://github.com/maggiexma/ggincerta/issues

NeedsCompilation:

Packaged:

2025-11-07 13:13:56 UTC; maxueqi

Author:

Xueqi Ma [aut, cre, cph], Emi Tanaka

[aut, ths], Weihao Li [ths], Quan Vu [ths], Francis Hui [ths]

Maintainer:

Xueqi Ma <maggiexma07@gmail.com>

Repository:

CRAN

Date/Publication:

2025-11-11 22:00:02 UTC

ggincerta: Extend 'ggplot2' with Layers and Scales for Spatial Uncertainty Visualization

Description

Provide specialized 'ggplot2' layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

Author(s)

Maintainer: Xueqi Ma maggiexma07@gmail.com [copyright holder]

Authors:

Emi Tanaka dr.emi.tanaka@gmail.com (ORCID) [thesis advisor]

Other contributors:

Weihao Li [thesis advisor]
Quan Vu [thesis advisor]
Francis Hui [thesis advisor]

Bivariate color scales

Description

⁠scale_*_bivariate⁠ creates a bivariate palette by mixing two colour ramps, then implements the mapping by binning the variables v1, v2 and assigning each bin combination to a colour.

Usage

ScaleBivariate

scale_fill_bivariate(
  name1 = NULL,
  name2 = NULL,
  colors = c("gold", "red4"),
  n_breaks = 4,
  breaks = c("quantile", "equal"),
  flip = c("none", "vertical", "horizontal", "both"),
  guide_size = 1.5,
  na.value = NA,
  na.translate = TRUE,
  aesthetics = "fill",
  ...
)

scale_color_bivariate(
  name1 = NULL,
  name2 = NULL,
  colors = c("gold", "red4"),
  n_breaks = 4,
  breaks = c("quantile", "equal"),
  flip = c("none", "vertical", "horizontal", "both"),
  guide_size = 1.5,
  na.value = NA,
  na.translate = TRUE,
  aesthetics = "colour",
  ...
)

scale_colour_bivariate(
  name1 = NULL,
  name2 = NULL,
  colors = c("gold", "red4"),
  n_breaks = 4,
  breaks = c("quantile", "equal"),
  flip = c("none", "vertical", "horizontal", "both"),
  guide_size = 1.5,
  na.value = NA,
  na.translate = TRUE,
  aesthetics = "colour",
  ...
)

Arguments

name1, name2

Optional names for v1 and v2. Used as axis titles in the legend. If NULL, the default, the names are taken from the mapping.

colors

A character vector of length two specifying the colors for the bivariate palette.

n_breaks

An integer guiding the number of bins for each variable.

breaks

Method used to bin the variables: "quantile" (the default) or "equal".

flip

Method used to flip the legend: "none" (the default), "vertical", "horizontal", or "both".

guide_size

A numeric value controlling the size of the legend graphic, in centimeters.

na.value

If na.translate = TRUE, what aesthetic value should the missing values be displayed as? Does not apply to position scales where NA is always placed at the far right.

na.translate

Unlike continuous scales, discrete scales can easily show missing values, and do so by default. If you want to remove missing values from a discrete scale, specify na.translate = FALSE.

aesthetics

The names of the aesthetics that this scale works with.

...

Other arguments passed to ggplot2::discrete_scale().

Format

An object of class ScaleBivariate (inherits from ScaleDiscrete, Scale, ggproto, gg) of length 5.

Value

A ScaleBivariate ggproto object.

Examples

# Create a bivariate fill scale
sc <- scale_fill_bivariate()
class(sc)
sc$palette(9)

# Basic bivariate map
p <- ggplot(nc) + geom_sf(aes(fill = duo(value, sd)))

# Customize axis labels
p + scale_fill_bivariate(name1 = 'var1', name2 = 'var2')

Exceedance probability colour scales

Description

⁠scale_*_exceed⁠ computes exceedance probabilities from a specified distribution and maps onto a continuous gradient colour scale.

Usage

ScaleExceed

scale_fill_exceed(
  name = NULL,
  palette = "Oranges",
  type = "seq",
  direction = 1,
  dist_fun = NULL,
  threshold = 1.64,
  limits = c(0, 1),
  na.value = NA,
  guide = "colourbar",
  aesthetics = "fill",
  ...
)

scale_colour_exceed(
  name = NULL,
  palette = "Oranges",
  type = "seq",
  direction = 1,
  dist_fun = NULL,
  threshold = 1.64,
  limits = c(0, 1),
  na.value = NA,
  guide = "colourbar",
  aesthetics = "colour",
  ...
)

Arguments

name

The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the legend title will be omitted.

palette

A palette function that when called with a numeric vector with values between 0 and 1 returns the corresponding output values (e.g., scales::pal_area()).

type

One of "seq" (sequential), "div" (diverging) or "qual" (qualitative)

direction

Sets the order of colours in the scale. If 1, the default, colours are as output by RColorBrewer::brewer.pal(). If -1, the order of colours is reversed.

dist_fun

A function used to compute the exceedance probability. If NULL (the default), a normal distribution with stats::pnorm() is used.

threshold

A numeric value specifying the threshold q in the exceedance probability expression P(X > q).

limits

One of:

NULL to use the default scale range
A numeric vector of length two providing limits of the scale. Use NA to refer to the existing minimum or maximum
A function that accepts the existing (automatic) limits and returns new limits. Also accepts rlang lambda function notation. Note that setting limits on positional scales will remove data outside of the limits. If the purpose is to zoom, use the limit argument in the coordinate system (see coord_cartesian()).

na.value

Missing values will be replaced with this value.

guide

A function used to create a guide or its name. See guides() for more information.

aesthetics

The names of the aesthetics that this scale works with.

...

Other arguments passed to ggplot2::continuous_scale().

Format

An object of class ScaleExceed (inherits from ScaleContinuous, Scale, ggproto, gg) of length 2.

Value

A ScaleExceed ggproto object.

Examples

# Create an exceedance probability scale
sc <- scale_fill_exceed()
class(sc)

# Basic bivariate map
p <- ggplot(nc) + geom_sf(aes(fill = duo_exceed(value, sd)))

Generate glyph maps on sf objects

Description

geom_sf_glyph() adds a glyph map layer based on simple feature (sf) objects to a ggplot. A glyph map is essentially a centroid-based map, where each region is represented by a rotated glyph, and the rotation angle indicates the value of v2 specified in the mapping.

Usage

StatGlyph

geom_sf_glyph(
  mapping = NULL,
  data = NULL,
  size = 70,
  style = "icone",
  max_v2 = NULL,
  position = "identity",
  show.legend = TRUE,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes(). v1 and v2 are required, which are the variables used for glyph fill and rotation, respectively.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

size

A positive numeric scaling factor controlling glyph size. Larger values produce smaller glyphs.

style

Either "icone" or "semi". Controls the glyph shape.

max_v2

Numeric value setting the upper limit for v2.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

You can also set this to one of "polygon", "line", and "point" to override the default legend.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. annotation_borders().

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

Format

An object of class StatGlyph (inherits from StatSf, Stat, ggproto, gg) of length 3.

Value

A list of ggplot2 layer objects.

Glyph map layer contents

The layer returned by geom_sf_glyph() actually contains two scales, corresponding to the two variables specified in the mapping. Therefore, modifying the scale for v1 will trigger a warning indicating that the scale for fill is being replaced.

Examples

# Basic glyph map
p <- ggplot(nc) + geom_sf_glyph(mapping = aes(v1 = value, v2 = sd))
p1 <- ggplot(nc) + geom_sf_glyph(mapping = aes(v1 = value, v2 = sd), style = "semi")

# Customize labels and theme
p + labs(title = "glyph map on nc") + theme(legend.position = "left", legend.box = "horizontal")

# Replacing the internal fill scale triggers a message
# ("Scale for fill is already present. Adding another scale for fill...")
p + scale_fill_distiller(palette = "Blues")

Generate pixel maps on sf objects

Description

geom_sf_pixel() adds a pixel map layer based on simple feature (sf) objects to a ggplot. In a pixel map, each region is divided into small pixels, with colours mapped from values sampled from distribution specified.

Usage

StatPixel

geom_sf_pixel(
  mapping = NULL,
  data = NULL,
  n = 40,
  distribution = "uniform",
  seed = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes(). v1 and v2 are required, which are the variables used as the parameters in the sampling distribution.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

n

integer of length 1 or 2, number of grid cells in x and y direction (columns, rows)

distribution

Sampling distribution: "uniform"(the default) or, "normal".

"uniform" treats v1 as the centre and uniformly samples within the range ⁠(v1 - v2, v1 + v2)⁠.
"normal" treats v1 as the mean and v2 as the standard deviation.

seed

Random seed used to ensure reproducibility of the sampling process.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

You can also set this to one of "polygon", "line", and "point" to override the default legend.

inherit.aes

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

Format

An object of class StatPixel (inherits from StatSf, Stat, ggproto, gg) of length 3.

Value

A list of ggplot2 layer objects.

Pixel map layer contents

The layer returned by geom_sf_pixel() internally includes a scale object created by scale_fill_distiller(). Therefore, modifying the scale will trigger a message indicating that the scale for fill is being replaced.

Examples

# Basic pixel map
p <- ggplot(nc) + geom_sf_pixel(mapping = aes(v1 = value, v2 = sd), n = 20)

# Replacing the internal fill scale triggers a message
# ("Scale for fill is already present. Adding another scale for fill...")
p + scale_fill_distiller(palette = "Blues")

Format input and assign the "map" class

Description

duo() and duo_exceed() create paired mapping objects that combine two variables, record their names, and assign the bivariate/exceed class as an attribute for use in aesthetic mappings.

Usage

duo(v1, v2)

duo_exceed(estimate, error)

Arguments

v1, v2

Input variables for duo().

estimate, error

Input variables for duo_exceed() representing the point estimate and its uncertainty.

Value

A list-like object containing pairs of values from the two variables, with attributes storing the variable names and the class.

Examples

value <- nc$value
sd <- nc$sd
res <- duo(value, sd)
res_exceed <- duo_exceed(value, sd)
class(res); class(res_exceed)
attr(res, "vars"); attr(res_exceed, "vars")

North Carolina SIDS data

Description

The dataset nc is derived from the North Carolina shapefile (nc.shp) included in the sf package. Two random variables, value and sd, have been added for demonstration purposes.

Further details about the original data can be found in the spdep package vignette.

Usage

nc

Format

A sf object.

Examples

head(nc)

plot(sf::st_geometry(nc))

ggincerta: Extend 'ggplot2' with Layers and Scales for Spatial Uncertainty Visualization

Description

Author(s)

See Also

Bivariate color scales

Description

Usage

Arguments

Format

Value

See Also

Examples

Exceedance probability colour scales

Description

Usage

Arguments

Format

Value

Examples

Generate glyph maps on sf objects

Description

Usage

Arguments

Format

Value

Glyph map layer contents

Examples

Generate pixel maps on sf objects

Description

Usage

Arguments

Format

Value

Pixel map layer contents

Examples

Format input and assign the "map" class

Description

Usage

Arguments

Value

Examples

North Carolina SIDS data

Description

Usage

Format

Examples