Help for package gggda

Title:

A 'ggplot2' Extension for Geometric Data Analysis

Version:

0.1.1

Description:

A variety of multivariable data summary statistics and constructions have been proposed, either to generalize univariable analogs or to exploit multivariable properties. Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value–decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. 'gggda' extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data.

Depends:

R (≥ 3.3.0), ggplot2

Imports:

rlang, tidyr, dplyr, magrittr, scales, labeling, ddalpha

Suggests:

gridExtra, MASS, Hmisc, tibble, mlpack, testthat, knitr, rmarkdown

License:

GPL-3

Encoding:

UTF-8

URL:

https://github.com/corybrunson/gggda, https://corybrunson.github.io/gggda/

BugReports:

https://github.com/corybrunson/gggda/issues

RoxygenNote:

7.3.2

Collate:

'aaa-.r' 'aes-coord.r' 'coord-rect.r' 'depth-median.r' 'geom-axis.r' 'geom-bagplot.r' 'key-intervals.r' 'geom-intervals.r' 'geom-isoline.r' 'geom-rule.r' 'geom-text-radiate.r' 'geom-utils.r' 'geom-vector.r' 'gggda.r' 'peel.r' 'stat-peel.r' 'stat-depth.r' 'stat-bagplot.r' 'stat-center.r' 'stat-cone.r' 'stat-referent.r' 'stat-rule.r' 'stat-scale.r' 'stat-spantree.r' 'utils.r'

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2025-07-18 23:29:34 UTC; jasoncorybrunson

Author:

Jason Cory Brunson

[aut, cre], Emily Paul [ctb], John Gracey [aut]

Maintainer:

Jason Cory Brunson <cornelioid@gmail.com>

Repository:

CRAN

Date/Publication:

2025-07-19 23:50:02 UTC

gggda package

Description

This is a ggplot2 extension for multivariate and geometric data analysis.

Acknowledgments

This package was spun off from ordr. Many users have identified problems and suggested improvements.

Development benefitted from the use of equipment and the support of colleagues at UConn Health and at UF Health.

Author(s)

Maintainer: Jason Cory Brunson cornelioid@gmail.com (ORCID)

Authors:

John Gracey jbgracey6@gmail.com

Other contributors:

Emily Paul erpb.71@gmail.com [contributor]

Multidimensional coordinate mappings

Description

Allow stat layers to receive a sequence of positional variables rather than only x and y.

Usage

aes_coord(.data, prefix)

get_aes_coord(data)

aes_c(...)

Arguments

.data, data

A data frame. .data stands in for the data passed to ggplot2::ggplot(), while data is expected to have been pre-processed before being passed to a ⁠Stat*$compute_*()⁠ function.

prefix

A regular expression used to identify the coordinate columns of .data.

...

objects to be concatenated. All NULL entries are dropped before method dispatch unless at the very beginning of the argument list.

Details

These functions coordinate (pun intended) the use of more than two positional variables in plot layers. Pass multidimensional coordinates to a stat via mapping = aes_coord(...) and reconcile the recovered coordinates with x and y (which are overridden if present) in ⁠Stat*$compute_*()⁠; see the StatChull source code for an example. Use aes_c() to concatenate aesthetic mappings.

Value

A list with class uneval. Components of the list are either quosures or constants.

Cartesian coordinates and plotting window with fixed aspect ratios

Description

Geometric data analysis often requires that coordinates lie on the same scale. The coordinate system CoordRect, alias CoordSquare, provides control of both coordinate and window aspect ratios.

Usage

coord_rect(
  ratio = 1,
  window_ratio = ratio,
  xlim = NULL,
  ylim = NULL,
  expand = TRUE,
  clip = "on"
)

Arguments

ratio

aspect ratio, expressed as y / x

window_ratio

aspect ratio of plotting window

xlim, ylim

Limits for the x and y axes.

expand

If TRUE, the default, adds a small expansion factor to the limits to ensure that data and axes don't overlap. If FALSE, limits are taken exactly from the data or xlim/ylim.

clip

Should drawing be clipped to the extent of the plot panel? A setting of "on" (the default) means yes, and a setting of "off" means no. In most cases, the default of "on" should not be changed, as setting clip = "off" can cause unexpected results. It allows drawing of data points anywhere on the plot, including in the plot margins. If limits are set via xlim and ylim and some data points fall outside those limits, then those data points may show up in places such as the axes, the legend, the plot title, or the plot margins.

Value

A Coord ggproto object.

Examples

# ensures that the resolutions of the axes and the dimensions of the plotting
# window respect the specified aspect ratios
p <- ggplot(mtcars, aes(mpg, hp/10)) + geom_point()
p + coord_rect(ratio = 1)
p + coord_rect(ratio = 1, window_ratio = 2)
p + coord_rect(ratio = 1, window_ratio = 1/2)
p + coord_rect(ratio = 5)
p + coord_rect(ratio = 1/5)
p + coord_rect(xlim = c(15, 30))
p + coord_rect(ylim = c(15, 30))

# square (even excluding some geometric constructions)
p + coord_square(xlim = c(0, 30), ylim = c(20, 40))

Depth median

Description

Compute the depth median of a data set.

Usage

depth_median(x, notion = "zonoid", ...)

Arguments

x

Matrix of data whose depth median is to be calculated; see ddalpha::depth.().

notion

The name of the depth notion (shall also work with a user-defined depth function named "depth.<name>").

...

Additional parameters passed to the depth functions.

Details

This function is called internally by stat_bagplot() and can be passed to stat_center() but is also exported directly for data analysis.

Value

A one-row matrix of depth median coordinates.

Examples

# sample median
iris %>% 
  subset(select = -Species) %>% 
  depth_median()
# groupwise medians
iris %>% 
  split(~ Species) %>% 
  lapply(subset, select = -Species) %>% 
  lapply(depth_median) %>% 
  simplify2array() %>% t() %>% as.data.frame()

Key drawing functions for bivariate intervals.

Description

These key drawing functions supplement those built into ggplot2 for legend glyphs suitable to bivariate line-ranges and point-ranges.

Usage

draw_key_line(data, params, size)

draw_key_crosslines(data, params, size)

draw_key_crosspoint(data, params, size)

Arguments

data

A single row data frame containing the scaled aesthetics to display in this key

params

A list of additional parameters supplied to the geom.

size

Width and height of key in mm.

Details

draw_key_line() is a horizontal counterpart to ggplot2::draw_key_vline(). draw_key_crosslines() superimposes these two keys, and draw_key_crosspoint() additionally superimposes an oversized ggplot2::draw_key_point().

Value

A grid grob.

Axes through or offset from the origin

Description

geom_axis() renders lines through or orthogonally translated from the origin and the position of each case or variable.

Usage

geom_axis(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  axis_labels = TRUE,
  axis_ticks = TRUE,
  axis_text = TRUE,
  by = NULL,
  num = NULL,
  tick_length = 0.025,
  text_dodge = 0.03,
  label_dodge = 0.03,
  ...,
  axis.colour = NULL,
  axis.color = NULL,
  axis.alpha = NULL,
  label.angle = 0,
  label.colour = NULL,
  label.color = NULL,
  label.alpha = NULL,
  tick.linewidth = 0.25,
  tick.colour = NULL,
  tick.color = NULL,
  tick.alpha = NULL,
  text.size = 2.6,
  text.angle = 0,
  text.hjust = 0.5,
  text.vjust = 0.5,
  text.family = NULL,
  text.fontface = NULL,
  text.colour = NULL,
  text.color = NULL,
  text.alpha = NULL,
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

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)).

stat

The statistical transformation to use on the data for this layer. When using a ⁠geom_*()⁠ function to construct a layer, the stat argument can be used the override the default coupling between geoms and stats. The stat argument accepts the following:

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

axis_labels, axis_ticks, axis_text

Logical; whether to include labels, tick marks, and text value marks along the axes.

by, num

Intervals between elements or number of elements; specify only one.

tick_length

Numeric; the length of the tick marks, as a proportion of the minimum of the plot width and height.

text_dodge

Numeric; the orthogonal distance of tick mark text from the axis, as a proportion of the minimum of the plot width and height.

label_dodge

Numeric; the orthogonal distance of the axis label from the axis, as a proportion of the minimum of the plot width and height.

...

Additional arguments passed to ggplot2::layer().

axis.colour, axis.color, axis.alpha

Default aesthetics for axes. Set to NULL to inherit from the data's aesthetics.

label.angle, label.colour, label.color, label.alpha

Default aesthetics for labels. Set to NULL to inherit from the data's aesthetics.

tick.linewidth, tick.colour, tick.color, tick.alpha

Default aesthetics for tick marks. Set to NULL to inherit from the data's aesthetics.

text.size, text.angle, text.hjust, text.vjust, text.family, text.fontface, text.colour, text.color, text.alpha

Default aesthetics for tick mark labels. Set to NULL to inherit from the data's aesthetics.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

check_overlap

If TRUE, text that overlaps previous text in the same layer will not be plotted. check_overlap happens at draw time and in the order of the data. Therefore data should be arranged by the label column before calling geom_text(). Note that this argument is not supported by geom_label().

na.rm

Passed to ggplot2::layer().

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. It can also be a named logical vector to finely select the aesthetics to display.

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. borders().

Details

Axes are lines that track the values of linear variables across a plot. Multivariate scatterplots may include more axes than plotting dimensions, in which case the plot may display only a fraction of the total variation in the data.

Gower & Hand (1996) recommend using axes to represent numerical variables in biplots. Consequently, Gardner & le Roux (2002) refer to these as Gower biplots.

Axes positioned orthogonally at the origin are a ubiquitous feature of scatterplots and used both to recover variable values from case markers (prediction) and to position new case markers from variables (interpolation). When they are not orthogonal, these two uses conflict, so interpolative versus predictive axes must be used appropriately.

Value

A ggproto layer.

Aesthetics

geom_axis() understands the following aesthetics (required aesthetics are in bold):

x
y
lower
upper
yintercept or xintercept or xend and yend
linetype
linewidth
size
hjust
vjust
colour
alpha
label
family
fontface
center, scale
group

References

Gower JC & Hand DJ (1996) Biplots. Chapman & Hall, ISBN: 0-412-71630-5.

Gardner S, le Roux N (2002) "Biplot Methodology for Discriminant Analysis Based upon Robust Methods and Principal Curves". Classification, Clustering, and Data Analysis: Recent Advances and Applications: 169–176. https://link.springer.com/chapter/10.1007/978-3-642-56181-8_18

Examples

# stack loss gradient
stackloss %>% 
  lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) %>% 
  coef() %>% 
  as.list() %>% as.data.frame() %>% 
  subset(select = c(Air.Flow, Water.Temp, Acid.Conc.)) ->
  coef_data
# gradient axis with respect to two predictors
scale(stackloss, scale = FALSE) %>% 
  ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
  coord_square() +
  geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) + 
  scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
  geom_axis(data = coef_data)
# unlimited axes with window forcing
stackloss_centered <- scale(stackloss, scale = FALSE)
stackloss_centered %>% 
  ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
  coord_square() +
  geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) + 
  scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
  stat_rule(
    geom = "axis", data = coef_data,
    referent = stackloss_centered,
    fun.lower = function(x) minpp(x, p = 1),
    fun.upper = function(x) maxpp(x, p = 1),
    fun.offset = function(x) minabspp(x, p = 1)
  )
# NB: `geom_axis(stat = "rule")` would fail to pass positional aesthetics.

# eigen-decomposition of covariance matrix
ability.cov$cov %>% 
  cov2cor() %>% 
  eigen() %>% getElement("vectors") %>% 
  as.data.frame() %>% 
  transform(test = rownames(ability.cov$cov)) ->
  ability_cor_eigen
# test axes in best-approximation space
ability_cor_eigen %>% 
  transform(E3 = ifelse(V3 > 0, "rise", "fall")) %>% 
  ggplot(aes(V1, V2, color = E3)) +
  coord_square() +
  geom_axis(aes(label = test), text.color = "black", text.alpha = .5) +
  expand_limits(x = c(-1, 1), y = c(-1, 1))

Bagplots

Description

Render bagplots from tagged data comprising medians, hulls, contours, and outlier specifications.

Usage

geom_bagplot(
  mapping = NULL,
  data = NULL,
  stat = "bagplot",
  position = "identity",
  ...,
  bag.linewidth = sync(),
  bag.linetype = sync(),
  bag.colour = "black",
  bag.color = NULL,
  bag.fill = sync(),
  bag.alpha = NA,
  median.shape = 21L,
  median.stroke = sync(),
  median.size = 5,
  median.colour = sync(),
  median.color = NULL,
  median.fill = "white",
  median.alpha = NA,
  fence.linewidth = 0.25,
  fence.linetype = 0L,
  fence.colour = sync(),
  fence.color = NULL,
  fence.fill = sync(),
  fence.alpha = 0.25,
  outlier.shape = sync(),
  outlier.stroke = sync(),
  outlier.size = sync(),
  outlier.colour = sync(),
  outlier.color = NULL,
  outlier.fill = NA,
  outlier.alpha = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

...

Additional arguments passed to ggplot2::layer().

bag.linetype, bag.linewidth, bag.colour, bag.color, bag.fill, bag.alpha

Default aesthetics for bags. Set to sync() to inherit from the data's aesthetics or to NULL to use the data's aesthetics.

median.shape, median.stroke, median.size, median.colour, median.color, median.fill, median.alpha

Default aesthetics for medians. Set to sync() to inherit from the data's aesthetics or to NULL to use the data's aesthetics.

fence.linetype, fence.linewidth, fence.colour, fence.color, fence.fill, fence.alpha

Default aesthetics for fences. Set to sync() to inherit from the data's aesthetics or to NULL to use the data's aesthetics.

outlier.shape, outlier.stroke, outlier.size, outlier.colour, outlier.color, outlier.fill, outlier.alpha

Default aesthetics for outliers. Set to sync() to inherit from the data's aesthetics or to NULL to use the data's aesthetics.

na.rm

Passed to ggplot2::layer().

show.legend

inherit.aes

Details

geom_bagplot() is designed to pair with stat_bagplot(), analogously to the pairing of ggplot2::geom_boxplot() with ggplot2::stat_boxplot().

Because the optional components are more expensive to compute in this setting, they are controlled by parameters passed to the stat. Auxiliary aesthetics like median.colour are available that override auxiliary defaults, and these in turn override the standard defaults. Auxiliary defaults also take effect when auxiliary aesthetics are passed NULL, so that stat_bagplot() and geom_bagplot() have the same default behavior. Pass sync() (instead of NULL, as in ggplot2::geom_boxplot()) to synchronize an auxiliary aesthetic with its standard counterpart.

WARNING: The trade-off between precision and runtime is greater for depth estimation than for density estimation. At the resolution of the default 100 \times 100 grid, basic examples may vary noticeably when starting from different random seeds.

Value

A ggproto layer.

Aesthetics

geom_bagplot() understands the following aesthetics (required aesthetics are in bold):

x
y
component
linewidth
linetype
colour
fill
alpha
shape
stroke
size
group

Examples

# Motor Trends base plot with factorized cylinder counts
p <- mtcars %>% 
  transform(cyl = factor(cyl)) %>% 
  ggplot(aes(x = wt, y = disp)) +
  theme_bw()
# basic bagplot
p + geom_bagplot()
# group by cylinder count
p + geom_bagplot(
  fraction = 0.4, coef = 1.2,
  aes(fill = cyl, linetype = cyl, color = cyl)
)
# using normally unmapped aesthetics
p + geom_bagplot(
  fraction = 0.4, coef = 1.2,
  aes(fill = cyl, linetype = cyl, color = cyl),
  median.color = "black",
  fence.linetype = sync(), fence.colour = "black",
  outlier.shape = "asterisk", outlier.colour = "black"
)

Isolines (contour lines)

Description

geom_isoline() renders isolines along row or column axes.

Usage

geom_isoline(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  isoline_text = TRUE,
  by = NULL,
  num = NULL,
  text_dodge = 0.03,
  ...,
  text.size = 3,
  text.angle = 0,
  text.colour = NULL,
  text.color = NULL,
  text.alpha = NULL,
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

isoline_text

Logical; whether to include text value marks along the isolines.

by, num

Intervals between elements or number of elements; specify only one.

text_dodge

Numeric; the orthogonal distance of the text from the axis or isoline, as a proportion of the minimum of the plot width and height.

...

Additional arguments passed to ggplot2::layer().

text.size, text.angle, text.colour, text.color, text.alpha

Default aesthetics for tick mark labels. Set to NULL to inherit from the data's aesthetics.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

check_overlap

na.rm

Passed to ggplot2::layer().

show.legend

inherit.aes

Details

Isolines are topographical features that separate a plot into regions in which a gradient of interest falls within a specified range. Greenacre (2010) uses them effectively to assist with the projection of markers onto axes.

Value

A ggproto layer.

Aesthetics

geom_isoline() understands the following aesthetics (required aesthetics are in bold):

x
y
colour
alpha
linewidth
linetype
center, scale
hjust
vjust
family
fontface
group

References

Greenacre MJ (2010) Biplots in Practice. Fundacion BBVA, ISBN: 978-84-923846. https://www.fbbva.es/microsite/multivariate-statistics/biplots.html

Examples

# stack loss gradient
stackloss %>% 
  lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) %>% 
  coef() %>% 
  as.list() %>% as.data.frame() %>% 
  subset(select = c(Air.Flow, Water.Temp, Acid.Conc.)) ->
  coef_data
# isolines along strongest predictors
scale(stackloss, scale = FALSE) %>% 
  ggplot(aes(x = Water.Temp, y = Air.Flow)) +
  coord_square() +
  geom_point(aes(size = stack.loss)) + scale_size_area() +
  geom_isoline(data = coef_data)

Intervals depicting ranges, usually about center points

Description

geom_lineranges() renders horizontal and vertical intervals for a specified subject or variable; geom_pointranges() additionally renders a point at their crosshairs.

Usage

geom_lineranges(
  mapping = NULL,
  data = NULL,
  stat = "center",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_pointranges(
  mapping = NULL,
  data = NULL,
  stat = "center",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

...

Additional arguments passed to ggplot2::layer().

na.rm

Passed to ggplot2::layer().

show.legend

inherit.aes

Details

The geom_*ranges() layers are shortcuts for equivalently-specified pairs of horizontal and vertical ggplot2::geom_*range() layers. Rather than ggplot2::stat_identity(), they default to stat_center(), so that in practice the summary values do not need to be manually passed.

Value

A ggproto layer.

Aesthetics

geom_lineranges() and geom_pointranges() understand the following aesthetics (required aesthetics are in bold):

x
xmin
xmax
y
ymin
ymax'
alpha
colour
linewidth
linetype
size
group

Examples

ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(alpha = .25) +
  geom_lineranges()

if (require(Hmisc)) {
  ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
    geom_point(alpha = .25) +
    geom_pointranges(fun.data = mean_sdl, shape = "circle open")
}

mpg %>% 
  aggregate(
    x = cbind(displ, hwy) ~ 0,
    FUN = function(z) c(min = min(z), med = median(z), max = max(z))
  ) %>% 
  do.call(what = data.frame) %>% 
  ggplot(aes(displ.med, hwy.med)) +
  geom_pointranges(
    stat = "identity",
    aes(xmin = displ.min, xmax = displ.max, ymin = hwy.min, ymax = hwy.max)
  ) +
  geom_point(data = mpg, aes(displ, hwy), alpha = .5)

Rulers through or offset from the origin

Description

geom_rule() renders segments through or orthogonally translated from the origin.

Usage

geom_rule(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  axis_labels = TRUE,
  axis_ticks = TRUE,
  axis_text = TRUE,
  by = NULL,
  num = NULL,
  snap_rule = TRUE,
  tick_length = 0.025,
  text_dodge = 0.03,
  label_dodge = 0.03,
  ...,
  axis.colour = NULL,
  axis.color = NULL,
  axis.alpha = NULL,
  label.angle = 0,
  label.colour = NULL,
  label.color = NULL,
  label.alpha = NULL,
  tick.linewidth = 0.25,
  tick.colour = NULL,
  tick.color = NULL,
  tick.alpha = NULL,
  text.size = 2.6,
  text.angle = 0,
  text.hjust = 0.5,
  text.vjust = 0.5,
  text.family = NULL,
  text.fontface = NULL,
  text.colour = NULL,
  text.color = NULL,
  text.alpha = NULL,
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

axis_labels, axis_ticks, axis_text

Logical; whether to include labels, tick marks, and text value marks along the axes.

by, num

Intervals between elements or number of elements; specify only one.

snap_rule

Logical; whether to snap rule segments to grid values.

tick_length

Numeric; the length of the tick marks, as a proportion of the minimum of the plot width and height.

text_dodge

Numeric; the orthogonal distance of tick mark text from the axis, as a proportion of the minimum of the plot width and height.

label_dodge

Numeric; the orthogonal distance of the axis label from the axis, as a proportion of the minimum of the plot width and height.

...

Additional arguments passed to ggplot2::layer().

axis.colour, axis.color, axis.alpha

Default aesthetics for axes. Set to NULL to inherit from the data's aesthetics.

label.angle, label.colour, label.color, label.alpha

Default aesthetics for labels. Set to NULL to inherit from the data's aesthetics.

tick.linewidth, tick.colour, tick.color, tick.alpha

Default aesthetics for tick marks. Set to NULL to inherit from the data's aesthetics.

text.size, text.angle, text.hjust, text.vjust, text.family, text.fontface, text.colour, text.color, text.alpha

Default aesthetics for tick mark labels. Set to NULL to inherit from the data's aesthetics.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

check_overlap

na.rm

Passed to ggplot2::layer().

show.legend

inherit.aes

Details

As implemented here, a rule is just an axis that has a fixed range, usually the limits of the data. geom_rule() defaults to stat = "identity" to avoid the problem of failing to pass referent data to the referential stat_rule(). Therefore, the user must provide the lower and upper aesthetics, which are used as euclidean lengths in the plotting window. Meanwhile, stat_rule() defaults to geom = "rule"; see stat_rule() for details on this pairing.

Value

A ggproto layer.

Aesthetics

geom_rule() understands the following aesthetics (required aesthetics are in bold):

x
y
lower
upper
yintercept or xintercept or xend and yend
linetype
linewidth
size
hjust
vjust
colour
alpha
label
family
fontface
center, scale
group

Examples

USJudgeRatings %>% 
  subset(select = -c(1, 12)) %>% 
  dist(method = "maximum") %>% 
  cmdscale() %>% 
  as.data.frame() %>% 
  setNames(c("PCo1", "PCo2")) %>% 
  transform(name = rownames(USJudgeRatings)) ->
  judge_mds
USJudgeRatings %>% 
  subset(select = c(CONT, RTEN)) %>% 
  setNames(c("contacts", "recommendation")) ->
  judge_meta
lm(as.matrix(judge_meta) ~ as.matrix(judge_mds[, seq(2)])) %>% 
  getElement("coefficients") %>% 
  unname() %>% t() %>% as.data.frame() %>% 
  setNames(c("Intercept", "PCo1", "PCo2")) %>% 
  transform(variable = names(judge_meta)) ->
  judge_lm
ggplot(judge_mds, aes(x = PCo1, y = PCo2)) +
  coord_equal() +
  theme_void() +
  geom_text(aes(label = name), size = 3) +
  stat_rule(
    data = judge_lm, referent = judge_mds,
    aes(center = Intercept, label = variable)
  )

Text radiating outward from the origin

Description

geom_text_radiate() is adapted from ggbiplot() in the off-CRAN extensions of the same name (Vu, 2014; Telford, 2017; Gegzna, 2018). It renders text at specified positions and angles that radiate out from the origin. This layer and its associated ggproto are deprecated.

Usage

geom_text_radiate(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. Cannot be jointy specified with nudge_x or nudge_y. 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().
A string nameing 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.

...

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.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

check_overlap

na.rm

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

show.legend

inherit.aes

Value

A ggproto layer.

Aesthetics

geom_text_radiate() understands the following aesthetics (required aesthetics are in bold):

x
y
label
alpha
angle
colour
family
fontface
hjust
lineheight
size
vjust
group

References

Vincent Q. Vu (2014). ggbiplot: A 'ggplot2' based biplot. R package version 0.55. https://github.com/vqv/ggbiplot, experimental branch

Richard J Telford (2017). ggbiplot: A 'ggplot2' based biplot. R package version 0.6. https://github.com/richardjtelford/ggbiplot (fork), experimental branch

Vilmantas Gegzna (2018). ggbiplot: A 'ggplot2' based biplot. R package version 0.55. https://github.com/forked-packages/ggbiplot (fork), experimental branch

Vectors from the origin

Description

geom_vector() renders arrows from the origin to points, optionally with text radiating outward.

Usage

geom_vector(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = default_arrow,
  lineend = "round",
  linejoin = "mitre",
  vector_labels = TRUE,
  ...,
  label.colour = NULL,
  label.color = NULL,
  label.alpha = NULL,
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

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.

arrow

Specification for arrows, as created by grid::arrow(), or else NULL for no arrows.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

vector_labels

Logical; whether to include labels radiating outward from the vectors.

...

Additional arguments passed to ggplot2::layer().

label.colour, label.color, label.alpha

Default aesthetics for labels. Set to NULL to inherit from the data's aesthetics.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

check_overlap

na.rm

Passed to ggplot2::layer().

show.legend

inherit.aes

Details

Vectors are positions relative to some common reference point, in this case the origin; they comprise direction and magnitude. Vectors are usually represented with arrows rather than markers (points).

Vectors are commonly used to represent numerical variables in biplots, as by Gabriel (1971) and Greenacre (2010). Gardner & le Roux (2002) refer to these as Gabriel biplots. This layer, with optional radiating text labels, is adapted from ggbiplot() in the off-CRAN extensions of the same name (Vu, 2014; Telford, 2017; Gegzna, 2018).

Value

A ggproto layer.

Aesthetics

geom_vector() understands the following aesthetics (required aesthetics are in bold):

x
y
alpha
colour
linetype
label
size
angle
hjust
vjust
family
fontface
lineheight
group

References

Gabriel KR (1971) "The biplot graphic display of matrices with application to principal component analysis". Biometrika 58(3), 453–467. doi:10.1093/biomet/58.3.453

Greenacre MJ (2010) Biplots in Practice. Fundacion BBVA, ISBN: 978-84-923846. https://www.fbbva.es/microsite/multivariate-statistics/biplots.html

Vincent Q. Vu (2014). ggbiplot: A 'ggplot2' based biplot. R package version 0.55. https://github.com/vqv/ggbiplot, experimental branch

Richard J Telford (2017). ggbiplot: A 'ggplot2' based biplot. R package version 0.6. https://github.com/richardjtelford/ggbiplot (fork), experimental branch

Vilmantas Gegzna (2018). ggbiplot: A 'ggplot2' based biplot. R package version 0.55. https://github.com/forked-packages/ggbiplot (fork), experimental branch

Examples

# multidimensional scaling of covariances
ability.cov$cov %>% 
  cov2cor() %>%
  eigen() %>% getElement("vectors") %>% 
  as.data.frame() %>% 
  transform(test = rownames(ability.cov$cov)) ->
  ability_cor_eigen
ability_cor_eigen %>% 
  ggplot(aes(-V1, V2, label = test)) +
  coord_square() + theme_void() +
  geom_vector(check_overlap = TRUE) +
  scale_y_continuous(expand = expansion(mult = .2)) +
  ggtitle("Ability and intelligence test covariances")
# multidimensional scaling of correlations
ability.cov$cov %>% 
  eigen() %>% getElement("vectors") %>% 
  as.data.frame() %>% 
  transform(test = rownames(ability.cov$cov)) ->
  ability_cor_eigen
ability_cor_eigen %>% 
  ggplot(aes(-V1, -V2, label = test)) +
  coord_square() + theme_void() +
  geom_vector(check_overlap = TRUE) +
  expand_limits(x = c(-1, 1), y = c(-1, 1)) +
  ggtitle("Ability and intelligence test covariances")

ggproto classes created and adapted for gggda

Description

gggda introduces several ggproto classes for coordinate systems, statistical transformations, and geometric constructions specific to multivariate analysis or following geometric data analysis principles.

Details

The new ggprotos are inspired by two entangled but distinct threads in multivariate data visualization: First, several geometric constructions have been proposed to generalize both numeric and graphical summaries of univariate data to the bivariate setting. Among these are various "peeling" procedures like that by successive convex hulls, which generalize the concept of rank (Green, 1981); and the depth-based bag-and-bolster plot, designed as a bivariate analog of the box-and-whisker plot (Rousseeuw &al, 1999). Second, the use of biplots to visualize singular value–decomposed data benefits from being able to encode variables with different graphical elements than the markers used to encode cases—for example, vectors (arrows emanating from the origin; Gabriel, 1971), calibrated axes (Gower & Hand, 1996), and prediction regions (Gardner & le Roux, 2002).

References

Green PJ (1981) "Peeling Bivariate Data". Interpreting Multivariate Data Chapter 1, 3–19. John Wiley & Sons, Ltd, ISBN 978-0-471-28039-2.

Rousseeuw PJ, Ruts I, & Tukey JW (1999) "The Bagplot: A Bivariate Boxplot". The American Statistician, 53(4): 382–387. doi:10.1080/00031305.1999.10474494

Gabriel KR (1971) "The biplot graphic display of matrices with application to principal component analysis". Biometrika 58(3), 453–467. doi:10.1093/biomet/58.3.453

Gower JC & Hand DJ (1996) Biplots. Chapman & Hall, ISBN: 0-412-71630-5.

Bivariate data peelings

Description

Use convex hulls (and eventually other peelings) to order bivariate data.

Usage

peel_hulls(
  x,
  y = NULL,
  num = NULL,
  by = 1L,
  breaks = c(0.5),
  cut = c("above", "below")
)

Arguments

x, y

coordinate vectors of points. This can be specified as two vectors x and y, a 2-column matrix x, a list x with two components, etc, see xy.coords.

num

A positive integer; the number of hulls to peel. Pass Inf for all hulls.

by

A positive integer; with what frequency to include consecutive hulls, pairs with num.

breaks

A numeric vector of fractions (between 0 and 1) of the data to contain in each hull; overridden by num.

cut

Character; one of "above" and "below", indicating whether each hull should contain at least or at most breaks of the data, respectively.

Details

Methods for peeling bivariate data into concentric tiers generalize the univariate concept of rank to separate core versus peripheral cases (Green, 1981).

The code for peeling convex hulls was adapted from plothulls() in the aplpack package. Other peeling options should be implemented soon.

Value

A matrix with some or all of the following columns:

x,y: original coordinates
i: position in input matrix or vectors
hull: index of hull, starting from outermost
frac: value of breaks used to determine hull
prop: proportion of data within hull

References

Green PJ (1981) "Peeling Bivariate Data". Interpreting Multivariate Data Chapter 1, 3–19. John Wiley & Sons, Ltd, ISBN 978-0-471-28039-2.

Examples

x <- mtcars$disp; y <- mtcars$mpg

# all hulls
peel_hulls(x, y, num = Inf)

# every third hull
peel_hulls(x, y, num = Inf, by = 3)

# tertile hulls, cut below
peel_hulls(x, y, breaks = seq(0, 1, length.out = 4))

# tertile hulls, cut above
peel_hulls(x, y, breaks = seq(0, 1, length.out = 4), cut = "below")

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr: %>%

Bagplots

Description

Construct medians, bags, fences, and outlier specifications for bagplots.

Usage

stat_bagplot(
  mapping = NULL,
  data = NULL,
  geom = "bagplot",
  position = "identity",
  fraction = 0.5,
  coef = 3,
  median = TRUE,
  fence = TRUE,
  outliers = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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.

geom

The geometric object to use to display the data for this layer. When using a ⁠stat_*()⁠ function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

fraction

Fraction of the data to include in the bag.

coef

Scale factor of the fence relative to the bag.

median, fence, outliers

Logical indicators whether to include median, fence, and outliers in the composite output.

show.legend

inherit.aes

...

Arguments passed on to stat_depth

notion: Character; the name of the depth function (passed to ddalpha::depth.()).
notion_params: List of additional parameters passed via ... to ddalpha::depth.().

Details

A bagplot comprises a single, often filled, depth contour (the "bag") overlaid with the hull of its union with the data points contained in its scaled expansion from the depth median (the "fence") and a scatterplot of outliers beyond the fence (the "loop"). Rousseeuw &al (1999) suggest the term "bag-and-bolster plot" evocative of the "box-and-whisker plot".

While the depth median can be obtained using stat_center(), the data depth values used to compute it are also used to demarcate the bag, so it is implemented separately in StatBagplot$compute_group() for efficiency.

stat_bagplot() is designed to pair with geom_bagplot(), analogously to the pairing of ggplot2::stat_boxplot() with ggplot2::geom_boxplot(). In particular, GeomBagplot is the only ggproto that recognizes the computed variable component, used by StatBagplot to separate data for the four bagplot elements.

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

Some transformations (e.g. stat_center()) commute with projection to the lower (1 or 2)-dimensional biplot space. If they detect aesthetics of the form ⁠..coord[0-9]+⁠, then ..coord1 and ..coord2 are converted to x and y while any remaining are ignored.

Other transformations (e.g. stat_spantree()) yield different results in a lower-dimensional biplot when they are computed before versus after projection. If the stat layer detects these aesthetics, then the transformation is performed before projection, and the results in the first two dimensions are returned as x and y.

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

component: the component of the composite plot; used internally

References

Rousseeuw PJ, Ruts I, & Tukey JW (1999) "The Bagplot: A Bivariate Boxplot". The American Statistician, 53(4): 382–387. doi:10.1080/00031305.1999.10474494

Examples

# petroleum rock base plot
p <- ggplot(rock, aes(area, shape, size = peri)) + theme_bw()
# scatterplot
p + geom_point()
# NB: Non-standard aesthetics are handled as in version > 3.5.1; see:
# https://github.com/tidyverse/ggplot2/issues/6191
# custom bag fraction, coefficient, and aesthetics
p + stat_bagplot(fraction = .4, coef = 1.5,
                 outlier_gp = list(shape = "asterisk"))
# invisible fence
p + stat_bagplot(fence = FALSE)

Centers and spreads for bivariate data

Description

Centers and spreads for bivariate data

Usage

stat_center(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.min = NULL,
  fun.max = NULL,
  fun.ord = NULL,
  fun.args = list()
)

stat_star(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.ord = NULL,
  fun.args = list()
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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

inherit.aes

...

Additional arguments passed to ggplot2::layer().

fun.data

A function that is given the complete data and should return a data frame with variables ymin, y, and ymax.

fun.center

Deprecated alias to fun.

fun.min, fun, fun.max

Alternatively, supply three individual functions that are each passed a vector of values and should return a single number.

fun.ord

Alternatively to the ggplot2::stat_summary_bin() parameters, supply a summary function that takes a matrix as input and returns a named column summary vector. Overridden by fun.data and fun, cannot be used together with fun.min and fun.max.

fun.args

Optional additional arguments passed on to the functions.

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

⁠xmin,ymin,xmax,ymax⁠: results of ⁠fun.min,fun.max⁠ applied to ⁠x,y⁠

Examples

ggplot(mpg, aes(x = displ, y = cty, shape = drv)) +
  geom_point() +
  stat_center(fun = "median", size = 5, alpha = .5)

ggplot(mpg, aes(x = displ, y = cty, shape = drv, linetype = drv)) +
  stat_center(size = 3) +
  stat_star()

Convex hulls and hull peelings

Description

Restrict planar data to the boundary points of its convex hull, or of nested convex hulls containing specified fractions of points.

Usage

stat_chull(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_peel(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  num = NULL,
  by = 1L,
  breaks = c(0.5),
  cut = c("above", "below"),
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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

inherit.aes

...

Additional arguments passed to ggplot2::layer().

num

A positive integer; the number of hulls to peel. Pass Inf for all hulls.

by

A positive integer; with what frequency to include consecutive hulls, pairs with num.

breaks

A numeric vector of fractions (between 0 and 1) of the data to contain in each hull; overridden by num.

cut

Character; one of "above" and "below", indicating whether each hull should contain at least or at most breaks of the data, respectively.

Details

As used in a ggplot2 vignette, stat_chull() restricts a dataset with x and y variables to the points that lie on its convex hull.

Building on this, stat_peel() returns hulls from a convex hull peeling: a subset of sequentially removed hulls containing specified fractions of the data.

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

hull: the position of breaks that defines each hull
frac: the value of breaks that defines each hull
prop: the actual proportion of data within each hull

References

Barnett V (1976) "The Ordering of Multivariate Data". Journal of the Royal Statistical Society: Series A (General), 139(3): 318–344. doi:10.2307/2344839

Examples

ggplot(USJudgeRatings, aes(x = INTG, y = PREP)) +
  geom_point() +
  stat_chull(alpha = .5)
ggplot(USJudgeRatings, aes(x = INTG, y = PREP)) +
  stat_peel(
    aes(alpha = after_stat(hull)),
    breaks = seq(.1, .9, .2)
  )
ggplot(USJudgeRatings, aes(x = INTG, y = PREP)) +
  stat_peel(
    aes(alpha = after_stat(hull)),
    num = 6, by = 2, color = "black"
  )

# specify fractions of points
ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Species)) +
  stat_peel(aes(fill = Species, alpha = after_stat(frac)),
            breaks = seq(.1, .9, .2)) +
  scale_alpha_continuous(trans = scales::reverse_trans()) +
  geom_point()
# specify number of peels
ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Species)) +
  stat_peel(fill = "transparent", num = 3) +
  geom_point()
# mapping to opacity overrides transparency
ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Species)) +
  stat_peel(aes(alpha = after_stat(hull)), fill = "transparent", num = 3) +
  geom_point()

Conical hull

Description

Restrict planar data to the points that lie on its conical hull.

Usage

stat_cone(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  origin = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

origin

Logical; whether to include the origin with the transformed data. Defaults to FALSE.

show.legend

inherit.aes

...

Additional arguments passed to ggplot2::layer().

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Examples

mtcars$name <- rownames(mtcars)
ggplot(mtcars, aes(wt, mpg, label = name)) +
  geom_text(size = 3) +
  stat_cone()
ggplot(mtcars, aes(hp, mpg, label = name)) +
  geom_text(size = 3) +
  stat_cone(origin = TRUE, linetype = "dotted")

Depth estimates and contours

Description

Estimate data depth using ddalpha::depth.().

Usage

stat_depth(
  mapping = NULL,
  data = NULL,
  geom = "contour",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_depth_filled(
  mapping = NULL,
  data = NULL,
  geom = "contour_filled",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

contour

If TRUE, contour the results of the depth estimation.

contour_var

Character string identifying the variable to contour by. Can be one of "depth" or "ndepth". See the section on computed variables for details.

notion

Character; the name of the depth function (passed to ddalpha::depth.()).

notion_params

List of additional parameters passed via ... to ddalpha::depth.().

n

Number of grid points in each direction.

show.legend

inherit.aes

...

Arguments passed on to ggplot2::geom_contour

bins

Number of contour bins. Overridden by breaks.

binwidth

The width of the contour bins. Overridden by bins.

breaks

One of:

Numeric vector to set the contour breaks
A function that takes the range of the data and binwidth as input and returns breaks as output. A function can be created from a formula (e.g. ~ fullseq(.x, .y)).

Overrides binwidth and bins. By default, this is a vector of length ten with pretty() breaks.

Details

Depth is an extension of the univariate notion of rank to bivariate (and sometimes multivariate) data (Rousseeuw &al, 1999). It comes in several flavors and is the basis for bagplots.

stat_depth() is adapted from ggplot2::stat_density_2d() and returns depth values over a grid in the same format, so it is neatly paired with ggplot2::geom_contour().

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

stat_depth() and stat_depth_filled() compute different variables depending on whether contouring is turned on or off. With contouring off (contour = FALSE), both stats behave the same, and the following variables are provided:

depth: the depth estimate
ndepth: depth estimate, scaled to a maximum of 1

With contouring on (contour = TRUE), either ggplot2::stat_contour() or ggplot2::stat_contour_filled() is run after the depth estimate has been obtained, and the computed variables are determined by these stats.

References

Rousseeuw PJ, Ruts I, & Tukey JW (1999) "The Bagplot: A Bivariate Boxplot". The American Statistician, 53(4): 382–387. doi:10.1080/00031305.1999.10474494

Examples

# base Motor Trends plot
b <- ggplot(mtcars, aes(wt, disp)) + geom_point()

# depth raster
b + geom_raster(stat = "depth", aes(fill = after_stat(depth)))
# depth grid
b + stat_depth(
  geom = "point", contour = FALSE,
  aes(size = after_stat(depth)), n = 20
)

# depth contours
b + geom_contour(stat = "depth", contour = TRUE)
# depth bands
b + geom_contour_filled(stat = "depth_filled", contour = TRUE, alpha = .75)
# contours colored by group
b + stat_depth(aes(color = factor(cyl)))
# custom depth notion
b + stat_depth(
  aes(color = factor(cyl)),
  notion = "halfspace", notion_params = list(exact = TRUE)
)

# contours faceted by group
b + stat_depth_filled(alpha = .75) +
  facet_wrap(facets = vars(factor(cyl)))
# scaled to the unit interval
b + stat_depth_filled(contour_var = "ndepth", alpha = .75) +
  facet_wrap(facets = vars(factor(cyl)))

Transformations with respect to reference data

Description

Compute statistics with respect to a reference data set with shared positional variables.

Usage

stat_referent(
  mapping = NULL,
  data = NULL,
  geom = "blank",
  position = "identity",
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

## S3 method for class 'LayerRef'
ggplot_add(object, plot, ...)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

referent

The reference data set; see Details.

show.legend

inherit.aes

...

Additional arguments passed to ggplot2::layer().

object

An object to add to the plot

plot

The ggplot object to add object to

Details

Often in geometric data analysis a statistical transformation applied to data X will also depend on data Y, for example when drawing the projections of vectors X onto vectors Y. The stat layer stat_referent() accepts Y as an argument to the referent parameter and pre-processes them using the existing positional aesthetic mappings to x and y.

The ggproto can be used as a parent to more elaborate statistical transformations, or the stat can be paired with geoms that expect the referent parameter and use it to position their transformations of X. It pairs by default to ⁠[ggplot2::geom_blank()]⁠ so as to prevent possibly confusing output.

Value

A ggproto layer.

Examples

# simplify the Motor Trends data to two predictors legible at aspect ratio 1
mtcars %>%
  transform(hp00 = hp/100) %>%
  subset(select = c(mpg, hp00, wt)) ->
  subcars
# compute the gradient of `mpg` against these two predictors
lm(mpg ~ hp00 + wt, subcars) %>%
  coefficients() %>%
  as.list() %>% as.data.frame() ->
  grad
# use the gradient as a reference (to no effect in this basic ggproto)
ggplot(subcars, aes(x = hp00, y = wt)) +
  coord_equal() +
  geom_point() +
  stat_referent(referent = grad)
ggplot(subcars, aes(x = hp00, y = wt)) +
  coord_equal() +
  stat_referent(geom = "point", referent = grad)

Construct limited rules offset from the origin

Description

Determine axis limits and offset vectors from reference data.

Usage

stat_rule(
  mapping = NULL,
  data = NULL,
  geom = "rule",
  position = "identity",
  fun.lower = "minpp",
  fun.upper = "maxpp",
  fun.offset = "minabspp",
  fun.args = list(),
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

minpp(x, p = 0.1)

maxpp(x, p = 0.1)

minabspp(x, p = 0.1)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

fun.lower, fun.upper, fun.offset

Functions used to determine the limits of the rules and the translations of the axes from the projections of referent onto the axes and onto their normal vectors.

fun.args

Optional additional arguments passed on to the functions.

referent

The reference data set; see Details.

show.legend

inherit.aes

...

Additional arguments passed to ggplot2::layer().

x

A numeric vector.

p

A numeric value; the proportion of a range used as a buffer.

Details

Biplots with several axes can become cluttered and illegible. When this happens, Gower, Gardner–Lubbe, & le Roux (2011) recommend to translate the axes to a new point of intersection away from the origin, adjusting the axis markers accordingly. Then the axes converge in a region of the plot offset from most position markers or other elements. An alternative solution, implemented in the bipl5 package (https://github.com/RuanBuys/bipl5), is to translate each axis orthogonally away from the origin, which preserves the axis markers. This is the technique implemented here.

Separately, axes that fill the plotting window are uninformative when they exceed the range of the plotted position markers projected onto them. They may even be misinformative, suggesting that linear relationships extrapolate outside the data range. In these cases, Gower and Harding (1988) recommend using finite ranges determined by the data projection onto each axis.

Three functions control these operations: fun.offset computes the orthogonal distance of each axis from the origin, and fun.lower and fun.upper compute the distance along each axis of the endpoints to the (offset) origin. Both functions depend on what position data is to be offset from or limited to, which must be passed manually to the referent parameter.

Value

A ggproto layer.

Referential stats

This statistical transformation is done with respect to reference data passed to referent (ignored if NULL, the default, possibly resulting in empty output). See stat_referent() for more details. This relies on a sleight of hand through a new undocumented LayerRef class and associated ggplot2::ggplot_add() method. As a result, only layers constructed using this ⁠stat_*()⁠ shortcut will pass the necessary positional aesthetics to the ⁠$setup_params()⁠ step, making them available to pre-process referent data.

The biplot shortcuts automatically substitute the complementary matrix factor for referent = NULL and will use an integer vector to select a subset from this factor. These uses do not require the mapping passage.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

axis: unique axis identifier (integer)
⁠lower,upper⁠: distances to endpoints from origin (before offset)
⁠yintercept,xintercept⁠: intercepts (possibly Inf) of offset axis

References

Gower JC, Gardner–Lubbe S, & le Roux NJ (2011) Understanding Biplots. Wiley, ISBN: 978-0-470-01255-0. https://www.wiley.com/go/biplots

Gower JC & Harding SA (1988) "Nonlinear biplots". Biometrika 75(3): 445–455. doi:10.1093/biomet/75.3.445

Examples

# stack loss gradient
stackloss %>% 
  lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) %>% 
  coef() %>% 
  as.list() %>% as.data.frame() %>% 
  subset(select = c(Air.Flow, Water.Temp, Acid.Conc.)) ->
  coef_data
# gradient rule with respect to two predictors
stackloss_centered <- scale(stackloss, scale = FALSE)
stackloss_centered %>% 
  ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
  coord_square() +
  geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) + 
  scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
  stat_rule(
    geom = "axis",
    data = coef_data,
    referent = stackloss_centered,
    fun.offset = function(x) minabspp(x, p = .5)
  )

Multiply artificial coordinates by a scale factor

Description

This is a simple stat that applies a constant scale factor to both positional coordinates. It can be handy in tandem with secondary axes.

Usage

stat_scale(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  mult = 1
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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

inherit.aes

...

Additional arguments passed to ggplot2::layer().

mult

Numeric value used to scale the coordinates.

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Calculate a minimum spanning tree among cases or variables

Description

This stat layer identifies the n-1 pairs among n points that form a minimum spanning tree, then calculates the segments between these poirs in the two dimensions x and y.

Usage

stat_spantree(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  engine = "mlpack",
  method = "euclidean",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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.

geom

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

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.

engine

A single character string specifying the package implementation to use; "mlpack", "vegan", or "ade4".

method

Passed to stats::dist() if engine is "vegan" or "ade4", ignored if "mlpack".

show.legend

inherit.aes

...

Additional arguments passed to ggplot2::layer().

Details

A minimum spanning tree (MST) on the point cloud X is a minimal connected graph on X with the smallest possible sum of distances (or dissimilarities) between linked points. These layers call stats::dist() to calculate a distance/dissimilarity object and an engine from mlpack, vegan, or ade4 to calculate the MST. The result is formatted with position aesthetics readable by ggplot2::geom_segment().

An MST calculated on x and y reflects the distances among the points in X in the reduced-dimension plane of the biplot. In contrast, one calculated on the full set of coordinates reflects distances in higher-dimensional space. Plotting this high-dimensional MST on the 2-dimensional biplot provides a visual cue as to how faithfully two dimensions can encapsulate the "true" distances between points (Jolliffe, 2002).

Value

A ggproto layer.

Multidimensional position aesthetics

This statistical transformation is compatible with the convenience function aes_coord().

A small number of transformations (stat_rule()) are incompatible with these aesthetics but will accept aes_coord() without warning.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

⁠xend,yend,x,y⁠: endpoints of tree branches (segments)

References

Jolliffe IT (2002) Principal Component Analysis, Second Edition. Springer Series in Statistics, ISSN 0172-7397. doi:10.1007/b98835 https://link.springer.com/book/10.1007/b98835

Examples

eurodist %>% 
  cmdscale(k = 6) %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "city") ->
  euro_mds
ggplot(euro_mds, aes(V1, V2, label = city)) +
  stat_spantree() +
  geom_label(alpha = .25)
ggplot(euro_mds, aes_c(aes_coord(euro_mds, "V"), aes(label = city))) +
  stat_spantree() +
  geom_label(aes(x = V1, y = V2), alpha = .25)

A synchronization flag

Description

The inert function sync() operates analogously to ggplot2::waiver() to indicate that an auxiliary aesthetic should be synchronized to a standard aesthetic (when it is mapped from data). is.sync() reports whether an object is of this class.

Usage

sync()

is.sync(x)

Arguments

x

An object to test

Value

sync() returns an empty list with the "sync" class; is.sync() returns a logical value of whether x inherits this class.

gggda package

Description

Acknowledgments

Author(s)

See Also

Multidimensional coordinate mappings

Description

Usage

Arguments

Details

Value

See Also

Cartesian coordinates and plotting window with fixed aspect ratios

Description

Usage

Arguments

Value

Examples

Depth median

Description

Usage

Arguments

Details

Value

Examples

Key drawing functions for bivariate intervals.

Description

Usage

Arguments

Details

Value

See Also

Axes through or offset from the origin

Description

Usage

Arguments

Details

Value

Aesthetics

References

See Also

Examples

Bagplots

Description

Usage

Arguments

Details

Value

Aesthetics

See Also

Examples

Isolines (contour lines)

Description

Usage

Arguments

Details

Value

Aesthetics

References

See Also

Examples

Intervals depicting ranges, usually about center points

Description

Usage

Arguments

Details

Value

Aesthetics

See Also

Examples

Rulers through or offset from the origin

Description

Usage

Arguments

Details

Value

Aesthetics

See Also

Examples

Text radiating outward from the origin