Package 'ggmulti'

Title: High Dimensional Data Visualization
Description: It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Authors: Zehao Xu [aut, cre], R. Wayne Oldford [aut]
Maintainer: Zehao Xu <[email protected]>
License: GPL-2
Version: 1.0.6
Built: 2024-11-04 04:50:23 UTC
Source: https://github.com/z267xu/ggmulti

Help Index

Layers for serial axes coordinate

Description

Project the regular geom layers onto the serial axes coordinate.

Usage

add_serialaxes_layers(layer, plot, object, axes)

Arguments

layer

a layer object
plot

a ggplot object
object

some parameters used to modify this serial axes ggplot object (i.e. axes.sequence, ...)
axes

canvas sequence axes

Details

The class is determined by layers you add. For example, you want to add a boxplot layer on serial axes coordinate. By the ggplot syntax, it should be ggplot(data, mapping) + geom_boxplot() + coord_serialaxes() To make it work, object add_serialaxes_layers.GeomBoxplot must be created. In this function, some computations will be applied.

Radial axes

Description

A radial (spider) coordinate. A wrapper of the function coord_polar() by forcing it linear.

Usage

coord_radial(theta = "x", start = 0, direction = 1, clip = "on")

Arguments

theta

variable to map angle to (x or y)
start

Offset of starting point from 12 o'clock in radians. Offset is applied clockwise or anticlockwise depending on value of direction.
direction

1, clockwise; -1, anticlockwise
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. For details, please see coord_cartesian().

Details

The serial histogram and serial density cannot be applied on a radial coordinate yet.

Examples

if(require("dplyr")) {
ggplot(NBAstats2021, mapping = aes(colour = Playoff)) +
  geom_serialaxes(
    axes.sequence = c("PTS", "OPTS", "3PM", "O3PM", "PTS"),
      scaling = "variable"
    ) +
  coord_radial() +
  scale_x_continuous(
    breaks = 1:5,
    labels = c("Points",
               "Oppo Points",
               "3P Made",
               "Oppo 3P Made",
               "Points Per Game")) +
  scale_y_continuous(labels = NULL) +
  facet_wrap(~CONF)
  }

Serial axes coordinates

Description

It is mainly used to visualize the high dimensional data set either on the parallel coordinate or the radial coordinate.

Usage

coord_serialaxes(
  axes.layout = c("parallel", "radial"),
  scaling = c("data", "variable", "observation", "none"),
  axes.sequence = character(0L),
  positive = TRUE,
  ...
)

Arguments

axes.layout

Serial axes layout, either "parallel" or "radial".
scaling

One of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
axes.sequence

A vector with variable names that defines the axes sequence.
positive

If y is set as the density estimate, where the smoothed curved is faced to, right (positive) or left (negative) as vertical layout; up (positive) or down (negative) as horizontal layout?
...

other arguments used to modify layers

Details

Serial axes coordinate system (parallel or radial) is different from the Cartesian coordinate system or its transformed system (say polar in ggplot2) since it does not have a formal transformation (i.e. in polar coordinate system, "x = rcos(theta)", "y = rsin(theta)"). In serial axes coordinate system, mapping aesthetics does not really require "x" or "y". Any "non-aesthetics" components passed in the mapping system will be treated as an individual axis.

To project a common geom layer on such serialaxes, users can customize function add_serialaxes_layers.

Value

a ggproto object

Potential Risk

In package ggmulti, the function ggplot_build.gg is provided. At the ggplot construction time, the system will call ggplot_build.gg first. If the plot input is not a CoordSerialaxes coordinate system, the next method ggplot_build.ggplot will be called to build a "gg" plot; else some geometric transformations will be applied first, then the next method ggplot_build.ggplot will be executed. So, the potential risk is, if some other packages e.g. foo, also provide a function ggplot_build.gg that is used for their specifications but the namespace is beyond the ggmulti (ggmulti:::ggplot_build.gg is covered), error may occur. If so, please consider using the geom_serialaxes.

Examples

if(require("dplyr")) {
# Data
nba <- NBAstats2021 %>%
  mutate(
    dPTS = PTS - OPTS,
    dREB = REB - OREB,
    dAST = AST - OAST,
    dTO = TO - OTO
  )
# set sequence by `axes.sequence`
p <- ggplot(nba,
            mapping = aes(
              dPTS = dPTS,
              dREB = dREB,
              dAST = dAST,
              dTO = dTO,
              colour = Win
            )) +
       geom_path(alpha = 0.2) +
       coord_serialaxes(axes.layout = "radial") +
       scale_color_gradient(low="blue", high="red")
p
# quantile layer
p + geom_quantiles(quantiles = c(0.5),
                   colour = "green", linewidth = 1.2)

# facet
p +
  facet_grid(Playoff ~ CONF)
}

Transformation Coefficients

Description

The dimension of the original data set is n\*p. It can be projected onto a n\*k space. The functions below are to provide such transformations, e.g. the Andrews coefficient (a Fourier transformation) and the Legendre polynomials.

Usage

andrews(p = 4, k = 50 * (p - 1), ...)

legendre(p = 4, k = 50 * (p - 1), ...)

Arguments

p

The number of dimensions
k

The sequence length
...

Other arguments passed on to methods. Mainly used for customized transformation function

Value

A list contains two named components

  1. vector: A length k vector (define the domain)

  2. matrix: A p\*k transformed coefficient matrix

References

Andrews, David F. "Plots of high-dimensional data." Biometrics (1972): 125-136.

Abramowitz, Milton, and Irene A. Stegun, eds. "Chapter 8" Handbook of mathematical functions with formulas, graphs, and mathematical tables. Vol. 55. US Government printing office, 1948.

Examples

x <- andrews(p = 4)
dat <- iris[, -5]
proj <- t(as.matrix(dat) %*% x$matrix)
matplot(x$vector, proj,
        type = "l", lty = 1,
        col = "black",
        xlab = "x",
        ylab = "Andrews coefficients",
        main = "Iris")

More general smoothed density estimates

Description

Computes and draws kernel density estimate. Compared with geom_density(), it provides more general cases that accepting x and y. See details

Usage

geom_density_(
  mapping = NULL,
  data = NULL,
  stat = "density_",
  position = "identity_",
  ...,
  scale.x = NULL,
  scale.y = c("data", "group", "variable"),
  as.mix = FALSE,
  positive = TRUE,
  prop = 0.9,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_density_(
  mapping = NULL,
  data = NULL,
  geom = "density_",
  position = "stack_",
  ...,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  na.rm = FALSE,
  orientation = NA,
  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)).
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
scale.x

A sorted length 2 numerical vector representing the range of the whole data will be scaled to. The default value is (0, 1).
scale.y

one of data and group to specify.

Type Description
data (default) The density estimates are scaled by the whole data set
group The density estimates are scaled by each group

If the scale.y is data, it is meaningful to compare the density (shape and area) across all groups; else it is only meaningful to compare the density within each group. See details.
as.mix

Logical. Within each group, if TRUE, the sum of the density estimate area is mixed and scaled to maximum 1. The area of each subgroup (in general, within each group one color represents one subgroup) is proportional to the count; if FALSE the area of each subgroup is the same, with maximum 1. See details.
positive

If y is set as the density estimate, where the smoothed curved is faced to, right ('positive') or left ('negative') as vertical layout; up ('positive') or down ('negative') as horizontal layout?
prop

adjust the proportional maximum height of the estimate (density, histogram, ...).
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom, stat

Use to override the default connection between geom_density() and stat_density().
bw

The smoothing bandwidth to be used. If numeric, the standard deviation of the smoothing kernel. If character, a rule to choose the bandwidth, as listed in stats::bw.nrd().
adjust

A multiplicate bandwidth adjustment. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.
kernel

Kernel. See list of available kernels in density().
n

number of equally spaced points at which the density is to be estimated, should be a power of two, see density() for details
trim

If FALSE, the default, each density is computed on the full range of the data. If TRUE, each density is computed over the range of that group: this typically means the estimated x values will not line-up, and hence you won't be able to stack density values. This parameter only matters if you are displaying multiple densities in one plot or if you are manually adjusting the scale limits.

Details

The x (or y) is a group variable (categorical) and y (or x) is the target variable (numerical) to be plotted. If only one of x or y is provided, it will treated as a target variable and ggplot2::geom_density will be executed.

There are four combinations of scale.y and as.mix.

scale.y = "group" and as.mix = FALSE

The density estimate area of each subgroup (represented by each color) within the same group is the same.

scale.y = "group" and as.mix = TRUE

The density estimate area of each subgroup (represented by each color) within the same group is proportional to its own counts.

scale.y = "data" and as.mix = FALSE

The sum of density estimate area of all groups is scaled to maximum of 1. and the density area for each group is proportional to the its count. Within each group, the area of each subgroup is the same.

scale.y = "data" and as.mix = TRUE

The sum of density estimate area of all groups is scaled to maximum of 1 and the area of each subgroup (represented by each color) is proportional to its own count.

See vignettes[https://great-northern-diver.github.io/ggmulti/articles/histogram-density-.html] for more intuitive explanation.

Orientation

This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation parameter, which can be either "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.

See Also

geom_density, geom_hist_

Examples

if(require(dplyr)) {
  mpg %>%
    dplyr::filter(drv != "f") %>%
    ggplot(mapping = aes(x = drv, y = cty, fill = factor(cyl))) +
    geom_density_(alpha = 0.1)

  # only `x` or `y` is provided
  # that would be equivalent to call function `geom_density()`
  diamonds %>%
    dplyr::sample_n(500) %>%
    ggplot(mapping = aes(x = price)) +
    geom_density_()

  # density and boxplot
  # set the density estimate on the left
  mpg %>%
    dplyr::filter(drv != "f") %>%
    ggplot(mapping = aes(x = drv, y = cty,
                         fill = factor(cyl))) +
    geom_density_(alpha = 0.1,
                  scale.y = "group",
                  as.mix = FALSE,
                  positive = FALSE) +
    geom_boxplot()

  # x as density
  set.seed(12345)
  suppressWarnings(
    diamonds %>%
      dplyr::sample_n(500) %>%
      ggplot(mapping = aes(x = price, y = cut, fill = color)) +
      geom_density_(orientation = "x", prop = 0.25,
                    position = "stack_",
                    scale.y = "group")
  )
}
# settings of `scale.y` and `as.mix`

ggplots <- lapply(list(
                      list(scale.y = "data", as.mix = TRUE),
                      list(scale.y = "data", as.mix = FALSE),
                      list(scale.y = "group", as.mix = TRUE),
                      list(scale.y = "group", as.mix = FALSE)
                    ),
                   function(vars) {
                     scale.y <- vars[["scale.y"]]
                     as.mix <- vars[["as.mix"]]
                     ggplot(mpg,
                            mapping = aes(x = drv, y = cty, fill = factor(cyl))) +
                       geom_density_(alpha = 0.1, scale.y = scale.y, as.mix = as.mix) +
                       labs(title = paste("scale.y =", scale.y),
                            subtitle = paste("as.mix =", as.mix))
                   })
suppressWarnings(
  gridExtra::grid.arrange(grobs = ggplots)
)

More general histogram

Description

More general histogram (geom_histogram) or bar plot (geom_bar). Both x and y could be accommodated. See details

Usage

geom_hist_(
  mapping = NULL,
  data = NULL,
  stat = "hist_",
  position = "stack_",
  ...,
  scale.x = NULL,
  scale.y = c("data", "group", "variable"),
  as.mix = FALSE,
  binwidth = NULL,
  bins = NULL,
  positive = TRUE,
  prop = 0.9,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_histogram_(
  mapping = NULL,
  data = NULL,
  stat = "bin_",
  position = "stack_",
  ...,
  scale.x = NULL,
  scale.y = c("data", "group"),
  as.mix = FALSE,
  positive = TRUE,
  prop = 0.9,
  binwidth = NULL,
  bins = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_bar_(
  mapping = NULL,
  data = NULL,
  stat = "count_",
  position = "stack_",
  ...,
  scale.x = NULL,
  scale.y = c("data", "group"),
  positive = TRUE,
  prop = 0.9,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_hist_(
  mapping = NULL,
  data = NULL,
  geom = "bar_",
  position = "stack_",
  ...,
  binwidth = NULL,
  bins = NULL,
  center = NULL,
  boundary = NULL,
  breaks = NULL,
  closed = c("right", "left"),
  pad = FALSE,
  width = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_bin_(
  mapping = NULL,
  data = NULL,
  geom = "bar_",
  position = "stack_",
  ...,
  binwidth = NULL,
  bins = NULL,
  center = NULL,
  boundary = NULL,
  breaks = NULL,
  closed = c("right", "left"),
  pad = FALSE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_count_(
  mapping = NULL,
  data = NULL,
  geom = "bar_",
  position = "stack_",
  ...,
  width = NULL,
  na.rm = FALSE,
  orientation = NA,
  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)).
position

Position adjustment, either as a string, or the result of a call to a position adjustment function. Function geom_hist_ and geom_histogram_ understand stack_ (stacks bars on top of each other), or dodge_ and dodge2_ (overlapping objects side-to-side) instead of stack, dodge or dodge2
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
scale.x

A sorted length 2 numerical vector representing the range of the whole data will be scaled to. The default value is (0, 1).
scale.y

one of data and group to specify.

Type Description
data (default) The density estimates are scaled by the whole data set
group The density estimates are scaled by each group

If the scale.y is data, it is meaningful to compare the density (shape and area) across all groups; else it is only meaningful to compare the density within each group. See details.
as.mix

Logical. Within each group, if TRUE, the sum of the density estimate area is mixed and scaled to maximum 1. The area of each subgroup (in general, within each group one color represents one subgroup) is proportional to the count; if FALSE the area of each subgroup is the same, with maximum 1. See details.
binwidth

The width of the bins. Can be specified as a numeric value or as a function that calculates width from unscaled x. Here, "unscaled x" refers to the original x values in the data, before application of any scale transformation. When specifying a function along with a grouping structure, the function will be called once per group. The default is to use the number of bins in bins, covering the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.
bins

Number of bins. Overridden by binwidth. Defaults to 30.
positive

If y is set as the density estimate, where the smoothed curved is faced to, right ('positive') or left ('negative') as vertical layout; up ('positive') or down ('negative') as horizontal layout?
prop

adjust the proportional maximum height of the estimate (density, histogram, ...).
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom, stat

Use to override the default connection between geom_hist_()/geom_histogram_()/geom_bar_() and stat_hist_()/stat_bin_()/stat_count_().
center, boundary

bin position specifiers. Only one, center or boundary, may be specified for a single plot. center specifies the center of one of the bins. boundary specifies the boundary between two bins. Note that if either is above or below the range of the data, things will be shifted by the appropriate integer multiple of binwidth. For example, to center on integers use binwidth = 1 and center = 0, even if 0 is outside the range of the data. Alternatively, this same alignment can be specified with binwidth = 1 and boundary = 0.5, even if 0.5 is outside the range of the data.
breaks

Alternatively, you can supply a numeric vector giving the bin boundaries. Overrides binwidth, bins, center, and boundary.
closed

One of "right" or "left" indicating whether right or left edges of bins are included in the bin.
pad

If TRUE, adds empty bins at either end of x. This ensures frequency polygons touch 0. Defaults to FALSE.
width

Bar width. By default, set to 90% of the resolution() of the data.

Details

x (or y) is a group variable (categorical) and y (or x) a target variable (numerical) to be plotted. If only one of x or y is provided, it will treated as a target variable and ggplot2::geom_histogram will be executed. Several things should be noticed:

1. If both x and y are given, they can be one discrete one continuous or two discrete. But they cannot be two continuous variables (which one will be considered as a group variable?).

2. geom_hist_ is a wrapper of geom_histogram_ and geom_count_. Suppose the y is our interest (x is the categorical variable), geom_hist_() can accommodate either continuous or discrete y. While, geom_histogram_() only accommodates the continuous y and geom_bar_() only accommodates the discrete y.

3. There are four combinations of scale.y and as.mix.

scale.y = "group" and as.mix = FALSE

The density estimate area of each subgroup (represented by each color) within the same group is the same.

scale.y = "group" and as.mix = TRUE

The density estimate area of each subgroup (represented by each color) within the same group is proportional to its own counts.

scale.y = "data" and as.mix = FALSE

The sum of density estimate area of all groups is scaled to maximum of 1. and the density area for each group is proportional to the its count. Within each group, the area of each subgroup is the same.

scale.y = "data" and as.mix = TRUE

The sum of density estimate area of all groups is scaled to maximum of 1 and the area of each subgroup (represented by each color) is proportional to its own count.

See vignettes[https://great-northern-diver.github.io/ggmulti/articles/histogram-density-.html] for more intuitive explanation. Note that, if it is a grouped bar chart (both x and y are categorical), parameter 'as.mix' is meaningless.

Orientation

This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation parameter, which can be either "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.

See Also

geom_histogram, geom_density_

Examples

if(require(dplyr) && require(tidyr)) {

  # histogram
  p0 <- mpg %>%
    dplyr::filter(manufacturer %in% c("dodge", "ford", "toyota", "volkswagen")) %>%
    ggplot(mapping = aes(x = manufacturer, y = cty))
  p0 + geom_hist_()

  ## set position
  #### default is "stack_"
  p0 + geom_hist_(mapping = aes(fill = fl))
  #### "dodge_"
  p0 + geom_hist_(position = "dodge_",
                  mapping = aes(fill = fl))
  #### "dodge2_"
  p0 + geom_hist_(position = "dodge2_",
                  mapping = aes(fill = fl))

  # bar chart
  mpg %>%
    ggplot(mapping = aes(x = drv, y = class)) +
    geom_hist_(orientation = "y")

  # scale.y as "group"
  p <- iris %>%
    tidyr::pivot_longer(cols = -Species,
                        names_to = "Outer sterile whorls",
                        values_to = "x") %>%
    ggplot(mapping = aes(x = `Outer sterile whorls`,
                         y = x, fill = Species)) +
    stat_hist_(scale.y = "group",
               prop = 0.6,
               alpha = 0.5)
  p
  # with density on the left
  p + stat_density_(scale.y = "group",
                    prop = 0.6,
                    alpha = 0.5,
                    positive = FALSE)

  ########### only `x` or `y` is provided ###########
  # that would be equivalent to call function
  # `geom_histogram()` or `geom_bar()`
  ### histogram
  diamonds %>%
    dplyr::sample_n(500) %>%
    ggplot(mapping = aes(x = price)) +
    geom_hist_()
  ### bar chart
  diamonds %>%
    dplyr::sample_n(500) %>%
    ggplot(mapping = aes(x = cut)) +
    geom_hist_()
}

Add image glyphs on scatter plot

Description

Each point glyph can be an image (png, jpeg, etc) object.

Usage

geom_image_glyph(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  images,
  imagewidth = 1.2,
  imageheight = 0.9,
  interpolate = TRUE,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
images

a list of images (a raster object, bitmap image). If not provided, a point visual (geom_point()) will be displayed.
imagewidth

Numerical; width of image
imageheight

Numerical; height of image
interpolate

A logical value indicating whether to linearly interpolate the image (the alternative is to use nearest-neighbour interpolation, which gives a more blocky result). See rasterGrob.
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. 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().

Value

a geom layer

Aesthetics

geom_..._glyph() understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • alpha

  • colour

  • fill

  • group

  • size

  • linetype

  • shape

  • stroke

The size unit is cm

Note that the shape and stroke do not have real meanings unless the essential argument images is missing. If so, a point visual will be displayed with corresponding shape and stroke.

See Also

geom_serialaxes_glyph, geom_polygon_glyph

Examples

# image glyph
if(require("png")) {
img_path <- list.files(file.path(find.package(package = 'ggmulti'),
                                 "images"),
                       full.names = TRUE)
Raptors <- png::readPNG(img_path[2L])
Warriors <- png::readPNG(img_path[3L])

pg <- ggplot(data = data.frame(x = 1:2, y = rep(1, 2)),
       mapping = aes(x = x, y = y)) +
  geom_image_glyph(images = list(Raptors,
                                 Warriors),
                   imagewidth = rep(1.2, 2),
                   imageheight = c(0.9, 1.2)) +
  coord_cartesian(xlim = extendrange(c(1,2)))
pg
# query the images (a numerical array)
build <- ggplot2::ggplot_build(pg)
# `imageRaptors` and `imageWarriors` are three dimensional
# arrays (third dimension specifying the plane)
imageRaptors <- build$data[[1]]$images[[1]]
imageWarriors <- build$data[[1]]$images[[2]]

if(require("grid")) {
grid.newpage()
grid.raster(imageRaptors)
grid.newpage()
grid.raster(imageWarriors)
}

# THIS IS SLOW
mercLogo <- png::readPNG(img_path[1L])

p <- ggplot(mapping = aes(x = hp, y = mpg)) +
       geom_point(
         data = mtcars[!grepl("Merc", rownames(mtcars)), ],
         color = "skyblue") +
       geom_image_glyph(
         data = mtcars[grepl("Merc", rownames(mtcars)), ],
         images = mercLogo,
         imagewidth = 1.5
       )
p

}

Add polygon glyphs on scatter plot

Description

Each point glyph can be a polygon object. We provide some common polygon coords in polygon_glyph. Also, users can customize their own polygons.

Usage

geom_polygon_glyph(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  polygon_x,
  polygon_y,
  linewidth = 1,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
polygon_x

nested list of x-coordinates of polygons, one list element for each scatterplot point. If not provided, a point visual (geom_point()) will be displayed.
polygon_y

nested list of y-coordinates of polygons, one list element for each scatterplot point. If not provided, a point visual (geom_point()) will be displayed.
linewidth

line width of the "glyph" object
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. 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().

Value

a geom layer

Aesthetics

geom_..._glyph() understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • alpha

  • colour

  • fill

  • group

  • size

  • linetype

  • shape

  • stroke

The size unit is cm

Note that the shape and stroke do not have real meanings unless the essential argument polygon_x or polygon_y is missing. If so, a point visual will be displayed with corresponding shape and stroke.

See Also

geom_serialaxes_glyph, geom_image_glyph

Examples

# polygon glyph
p <- ggplot(data = data.frame(x = 1:4, y = 1:4),
            mapping = aes(x = x, y = y)) +
  geom_polygon_glyph(polygon_x = list(x_star, x_cross, x_hexagon, x_airplane),
                     polygon_y = list(y_star, y_cross, y_hexagon, y_airplane),
                     colour = 'black', fill = 'red')
p

# the coords of each polygons can be achieved by calling function `ggplot_build`
build <- ggplot2::ggplot_build(p)
polygon_x <- build$data[[1]]$polygon_x
polygon_y <- build$data[[1]]$polygon_y

Add quantile layers on serial axes coordinate

Description

In ggplot2, geom_quantile() is used to fit a quantile regression to the data and draws the fitted quantiles with lines. However, geom_quantiles() is mainly used to draw quantile lines on serial axes. See examples

Usage

geom_quantiles(
  mapping = NULL,
  data = NULL,
  stat = "quantile",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 10,
  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, as a string.
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
lineend

Line end style (round, butt, square).
linejoin

Line join style (round, mitre, bevel).
linemitre

Line mitre limit (number greater than 1).
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. 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().

See Also

geom_serialaxes_quantile

Examples

p <- ggplot(iris,
            mapping = aes(
              Sepal.Length = Sepal.Length,
              Sepal.Width = Sepal.Width,
              Petal.Length = Petal.Length,
              Petal.Width = Petal.Width
            )
  ) +
  geom_path(alpha = 0.2)  +
  coord_serialaxes(scaling = "variable")
p + geom_quantiles(colour = c("red", "green", "blue"),
                   quantiles = c(0.25, 0.5, 0.75),
                   linewidth = 2)

Serial axes layer

Description

Draw a serial axes layer, parallel axes under Cartesian system and radial axes under Polar system. It only takes the "widens" data. Each non-aesthetics component defined in the mapping aes() will be treated as an axis.

Usage

geom_serialaxes(
  mapping = NULL,
  data = NULL,
  stat = "serialaxes",
  position = "identity",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_serialaxes(
  mapping = NULL,
  data = NULL,
  geom = "serialaxes",
  position = "identity",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  axes.position = NULL,
  scaling = c("data", "variable", "observation", "none"),
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_dotProduct(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  scaling = c("data", "variable", "observation", "none"),
  transform = andrews,
  na.rm = FALSE,
  orientation = NA,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
axes.sequence

A vector to define the axes sequence. In serial axes coordinate, the sequence can be either determined in mapping (function aes()) or by axes.sequence. The only difference is that the mapping aesthetics will omit the duplicated axes (check examples in geom_serialaxes).
merge

Should axes.sequence be merged with mapping aesthetics as a single mapping uneval object?
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
axes.position

A numerical vector to determine the axes sequence position; the length should be the same with the length of axes.sequence (or mapping aesthetics, see examples).
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
transform

A transformation function, can be either andrews, legendre or some other customized transformation functions.

Details

The difference between the "lengthens" data and "widens" data can be found in Tidy Data. How to transform one to the other is explained in tidyr

See Also

coord_serialaxes, geom_serialaxes_density, geom_serialaxes_quantile, geom_serialaxes_hist

Andrews plot andrews, Legendre polynomials legendre

Examples

# parallel coordinate
p <- ggplot(NBAstats2021,
            mapping = aes(FGA = FGA,
                          `3PA` = `3PA`,
                          FTA = FTA,
                          OFGA = OFGA,
                          O3PA = O3PA,
                          OFTA = OFTA,
                          colour = CONF))

# Teams in West are more likely to make 3-point field goals.
# Besides, they have a better performance in restricting opponents
# to make 3-point field goals.
p +
  geom_serialaxes(scaling = "variable",
                  alpha = 0.4,
                  linewidth = 3) +
  scale_x_continuous(breaks = 1:6,
                     labels = c("FGA", "3PA", "FTA",
                                "OFGA", "O3PA", "OFTA")) +
  scale_y_continuous(labels = NULL)

# andrews plot
p + geom_serialaxes(stat = "dotProduct",
                    scaling = "variable",
                    transform = andrews) # default

# Legendre polynomials
p + geom_serialaxes(stat = "dotProduct",
                    scaling = "variable",
                    transform = legendre)


############# Determine axes sequence
# 1. set the duplicated axes by mapping aesthetics
ggplot(iris, mapping = aes(Sepal.Length = Sepal.Length,
                           Sepal.Width = Sepal.Width,
                           Sepal.Length = Sepal.Length,
                           Sepal.Width = Sepal.Width,
                           colour = Species)) +
  # only two axes, duplicated axes are removed
  geom_serialaxes()

# 2. set the duplicated axes by axes.sequence
ggplot(iris, mapping = aes(colour = Species)) +
  geom_serialaxes(
    axes.sequence = c("Sepal.Length", "Sepal.Width",
                      "Sepal.Length", "Sepal.Width"))

Smoothed density estimates for "widens" data under serial axes coordinate

Description

Computes and draws kernel density estimates on serial axes coordinate for each non-aesthetics component defined in the mapping aes().

Usage

geom_serialaxes_density(
  mapping = NULL,
  data = NULL,
  stat = "serialaxes_density",
  position = "identity_",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  scale.y = c("data", "group"),
  as.mix = TRUE,
  positive = TRUE,
  prop = 0.9,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_serialaxes_density(
  mapping = NULL,
  data = NULL,
  geom = "serialaxes_density",
  position = "stack_",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  axes.position = NULL,
  scaling = c("data", "variable", "observation", "none"),
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  na.rm = FALSE,
  orientation = NA,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
axes.sequence

A vector to define the axes sequence. In serial axes coordinate, the sequence can be either determined in mapping (function aes()) or by axes.sequence. The only difference is that the mapping aesthetics will omit the duplicated axes (check examples in geom_serialaxes).
merge

Should axes.sequence be merged with mapping aesthetics as a single mapping uneval object?
scale.y

one of data and group to specify.

Type Description
data (default) The density estimates are scaled by the whole data set
group The density estimates are scaled by each group

If the scale.y is data, it is meaningful to compare the density (shape and area) across all groups; else it is only meaningful to compare the density within each group. See details.
as.mix

Logical. Within each group, if TRUE, the sum of the density estimate area is mixed and scaled to maximum 1. The area of each subgroup (in general, within each group one color represents one subgroup) is proportional to the count; if FALSE the area of each subgroup is the same, with maximum 1. See details.
positive

If y is set as the density estimate, where the smoothed curved is faced to, right ('positive') or left ('negative') as vertical layout; up ('positive') or down ('negative') as horizontal layout?
prop

adjust the proportional maximum height of the estimate (density, histogram, ...).
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
axes.position

A numerical vector to determine the axes sequence position; the length should be the same with the length of axes.sequence (or mapping aesthetics, see examples).
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
bw

The smoothing bandwidth to be used. If numeric, the standard deviation of the smoothing kernel. If character, a rule to choose the bandwidth, as listed in stats::bw.nrd().
adjust

A multiplicate bandwidth adjustment. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.
kernel

Kernel. See list of available kernels in density().
n

number of equally spaced points at which the density is to be estimated, should be a power of two, see density() for details
trim

If FALSE, the default, each density is computed on the full range of the data. If TRUE, each density is computed over the range of that group: this typically means the estimated x values will not line-up, and hence you won't be able to stack density values. This parameter only matters if you are displaying multiple densities in one plot or if you are manually adjusting the scale limits.

See Also

geom_density_, geom_serialaxes, geom_serialaxes_quantile, geom_serialaxes_hist

Examples

p <- ggplot(iris, mapping = aes(Sepal.Length = Sepal.Length,
                                Sepal.Width = Sepal.Width,
                                Petal.Length = Petal.Length,
                                Petal.Width = Petal.Width,
                                colour = Species,
                                fill = Species)) +
       geom_serialaxes(alpha = 0.2) +
       geom_serialaxes_density(alpha = 0.5) +
       scale_x_continuous(breaks = 1:4,
                          labels = colnames(iris)[-5]) +
       scale_y_continuous(labels = NULL) +
       xlab("variable") +
       ylab("") +
       theme(axis.text.x = element_text(angle = 45, vjust = 0.5))
p

Add serial axes glyphs on scatter plot

Description

To visualize high dimensional data on scatterplot. Each point glyph is surrounded by a serial axes (parallel axes or radial axes) object.

Usage

geom_serialaxes_glyph(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  serialaxes.data,
  axes.sequence = character(0L),
  scaling = c("data", "variable", "observation", "none"),
  axes.layout = c("parallel", "radial"),
  andrews = FALSE,
  show.axes = FALSE,
  show.enclosing = FALSE,
  linewidth = 1,
  axescolour = "black",
  bboxcolour = "black",
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
serialaxes.data

a serial axes numerical data set. If not provided, a point visual (geom_point()) will be displayed.
axes.sequence

A vector to define the axes sequence. In serial axes coordinate, the sequence can be either determined in mapping (function aes()) or by axes.sequence. The only difference is that the mapping aesthetics will omit the duplicated axes (check examples in geom_serialaxes).
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
axes.layout

either "radial" or "parallel"
andrews

Logical; Andrew's plot (a Fourier transformation)
show.axes

boolean to indicate whether axes should be shown or not
show.enclosing

boolean to indicate whether enclosing should be shown or not
linewidth

line width of the "glyph" object
axescolour

axes color
bboxcolour

bounding box color
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. 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().

Value

a geom layer

Aesthetics

geom_..._glyph() understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • alpha

  • colour

  • fill

  • group

  • size

  • linetype

  • shape

  • stroke

The size unit is cm

Note that the shape and stroke do not have real meanings unless the essential argument serialaxes.data is missing. If so, a point visual will be displayed with corresponding shape and stroke.

See Also

geom_polygon_glyph, geom_image_glyph

Examples

# serial axes glyph
p <- ggplot(data = iris,
            mapping = aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
  geom_serialaxes_glyph(serialaxes.data = iris[, -5],
                        axes.layout = "radial")
p

Histogram for "widens" data under serial axes coordinate

Description

Computes and draws histogram on serial axes coordinate for each non-aesthetics component defined in the mapping aes().

Usage

geom_serialaxes_hist(
  mapping = NULL,
  data = NULL,
  stat = "serialaxes_hist",
  position = "stack_",
  ...,
  axes.sequence = character(0L),
  axes.position = NULL,
  merge = TRUE,
  scale.y = c("data", "group"),
  as.mix = TRUE,
  positive = TRUE,
  prop = 0.9,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_serialaxes_hist(
  mapping = NULL,
  data = NULL,
  geom = "serialaxes_hist",
  position = "stack_",
  ...,
  axes.sequence = character(0L),
  scaling = c("data", "variable", "observation", "none"),
  axes.position = NULL,
  binwidth = NULL,
  bins = NULL,
  center = NULL,
  boundary = NULL,
  breaks = NULL,
  closed = c("right", "left"),
  pad = FALSE,
  width = NULL,
  na.rm = FALSE,
  orientation = NA,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
axes.sequence

A vector to define the axes sequence. In serial axes coordinate, the sequence can be either determined in mapping (function aes()) or by axes.sequence. The only difference is that the mapping aesthetics will omit the duplicated axes (check examples in geom_serialaxes).
axes.position

A numerical vector to determine the axes sequence position; the length should be the same with the length of axes.sequence (or mapping aesthetics, see examples).
merge

Should axes.sequence be merged with mapping aesthetics as a single mapping uneval object?
scale.y

one of data and group to specify.

Type Description
data (default) The density estimates are scaled by the whole data set
group The density estimates are scaled by each group

If the scale.y is data, it is meaningful to compare the density (shape and area) across all groups; else it is only meaningful to compare the density within each group. See details.
as.mix

Logical. Within each group, if TRUE, the sum of the density estimate area is mixed and scaled to maximum 1. The area of each subgroup (in general, within each group one color represents one subgroup) is proportional to the count; if FALSE the area of each subgroup is the same, with maximum 1. See details.
positive

If y is set as the density estimate, where the smoothed curved is faced to, right ('positive') or left ('negative') as vertical layout; up ('positive') or down ('negative') as horizontal layout?
prop

adjust the proportional maximum height of the estimate (density, histogram, ...).
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
binwidth

The width of the bins. Can be specified as a numeric value or as a function that calculates width from unscaled x. Here, "unscaled x" refers to the original x values in the data, before application of any scale transformation. When specifying a function along with a grouping structure, the function will be called once per group. The default is to use the number of bins in bins, covering the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.
bins

Number of bins. Overridden by binwidth. Defaults to 30.
center, boundary

bin position specifiers. Only one, center or boundary, may be specified for a single plot. center specifies the center of one of the bins. boundary specifies the boundary between two bins. Note that if either is above or below the range of the data, things will be shifted by the appropriate integer multiple of binwidth. For example, to center on integers use binwidth = 1 and center = 0, even if 0 is outside the range of the data. Alternatively, this same alignment can be specified with binwidth = 1 and boundary = 0.5, even if 0.5 is outside the range of the data.
breaks

Alternatively, you can supply a numeric vector giving the bin boundaries. Overrides binwidth, bins, center, and boundary.
closed

One of "right" or "left" indicating whether right or left edges of bins are included in the bin.
pad

If TRUE, adds empty bins at either end of x. This ensures frequency polygons touch 0. Defaults to FALSE.
width

Bar width. By default, set to 90% of the resolution() of the data.

See Also

geom_hist_, geom_serialaxes, geom_serialaxes_quantile, geom_serialaxes_density

Examples

p <- ggplot(NBAstats2021,
            mapping = aes(`FG%` = `FG%`,
                          `3P%` = `3P%`,
                          `FT%` = `FT%`,
                          `OFG%` = `OFG%`,
                          `O3P%` = `O3P%`,
                          `OFT%` = `OFT%`,
                          colour = Playoff,
                          fill = Playoff)) +
            geom_serialaxes(alpha = 0.2,
                            scaling = "variable") +
            geom_serialaxes_hist(alpha = 0.5,
                                 prop = 0.7,
                                 scaling = "variable") +
            scale_x_continuous(breaks = 1:6,
                               labels = c("FG", "3P", "FT",
                                          "OFG", "O3P", "OFT")) +
            scale_y_continuous(labels = NULL) +
            xlab("variable") +
            ylab("") +
            theme(axis.text.x = element_text(angle = 45, vjust = 0.5))
p

Quantile layer for serial axes coordinate

Description

Draw a quantile layer for serial axes coordinate. Don't be confused with geom_quantile() which is a quantile regression. See examples.

Usage

geom_serialaxes_quantile(
  mapping = NULL,
  data = NULL,
  stat = "serialaxes",
  position = "identity",
  ...,
  quantiles = seq(0, 1, 0.25),
  axes.sequence = character(0L),
  merge = TRUE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_serialaxes_quantile(
  mapping = NULL,
  data = NULL,
  geom = "serialaxes_quantile",
  position = "identity",
  ...,
  axes.sequence = character(0L),
  merge = TRUE,
  quantiles = seq(0, 1, 0.25),
  scaling = c("data", "variable", "observation", "none"),
  axes.position = NULL,
  na.rm = FALSE,
  orientation = NA,
  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, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.
quantiles

numeric vector of probabilities with values in [0,1]. (Values up to 2e-14 outside that range are accepted and moved to the nearby endpoint.)
axes.sequence

A vector to define the axes sequence. In serial axes coordinate, the sequence can be either determined in mapping (function aes()) or by axes.sequence. The only difference is that the mapping aesthetics will omit the duplicated axes (check examples in geom_serialaxes).
merge

Should axes.sequence be merged with mapping aesthetics as a single mapping uneval object?
na.rm

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

The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
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().
geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
axes.position

A numerical vector to determine the axes sequence position; the length should be the same with the length of axes.sequence (or mapping aesthetics, see examples).

See Also

geom_density_, geom_serialaxes, geom_serialaxes_density, geom_serialaxes_hist

Examples

# lower quantile, median and upper quantile
p <- ggplot(iris, mapping = aes(Sepal.Length = Sepal.Length,
                                Sepal.Width = Sepal.Width,
                                Petal.Length = Petal.Length,
                                Petal.Width = Petal.Width)) +
       geom_serialaxes(stat = "dotProduct") +
       geom_serialaxes_quantile(stat = "dotProduct",
                                quantiles = c(0.25, 0.5, 0.75),
                                colour = c("red", "blue", "green"))
p

Base Geom ggproto classes for ggplot2

Description

All geom_ functions (like geom_point) return a layer that contains a Geom object (like GeomPoint). The Geom object is responsible for rendering the data in the plot. Each of the Geom objects is a ggproto object, descended from the top-level Geom, and each implements various methods and fields. Compared to Stat and Position, Geom is a little different because the execution of the setup and compute functions is split up. setup_data runs before position adjustments, and draw_layer is not run until render time, much later. This means there is no setup_params because it's hard to communicate the changes.

Usage

GeomDensity_

GeomBar_

GeomImageGlyph

GeomPolygonGlyph

GeomQuantiles

GeomSerialaxesDensity

GeomSerialAxesGlyph

GeomSerialaxesHist

GeomSerialaxesQuantile

GeomSerialaxes

Format

An object of class GeomDensity_ (inherits from GeomRibbon, Geom, ggproto, gg) of length 6.

An object of class GeomBar_ (inherits from GeomBar, GeomRect, Geom, ggproto, gg) of length 4.

An object of class GeomImageGlyph (inherits from Geom, ggproto, gg) of length 7.

An object of class GeomPolygonGlyph (inherits from Geom, ggproto, gg) of length 7.

An object of class GeomQuantiles (inherits from GeomQuantile, GeomPath, Geom, ggproto, gg) of length 1.

An object of class GeomSerialaxesDensity (inherits from GeomDensity_, GeomRibbon, Geom, ggproto, gg) of length 2.

An object of class GeomSerialAxesGlyph (inherits from Geom, ggproto, gg) of length 7.

An object of class GeomSerialaxesHist (inherits from GeomBar_, GeomBar, GeomRect, Geom, ggproto, gg) of length 2.

An object of class GeomSerialaxesQuantile (inherits from GeomPath, Geom, ggproto, gg) of length 4.

An object of class GeomSerialaxes (inherits from GeomPath, Geom, ggproto, gg) of length 3.

scale data

Description

It is mainly used in serial axes

Usage

get_scaledData(
  data,
  sequence = NULL,
  scaling = c("data", "variable", "observation", "none"),
  displayOrder = NULL,
  reserve = FALSE,
  as.data.frame = FALSE
)

Arguments

data

A data frame
sequence

vector with variable names that defines the axes sequence. If NULL, it will be set as the column names automatically.
scaling

one of data, variable, observation or none (not suggested the layout is the same with data) to specify how the data is scaled.
displayOrder

the order of the display
reserve

If TRUE, return the variables not shown in sequence as well; else only return the variables defined in sequence.
as.data.frame

Return a matrix or a data.frame

NBA 30 Teams Statistics in 20-21 Regular Season

Description

A dataset containing the statistics (e.g. Points Per Game, Average Field Goals Made, etc) of 30 NBA Teams in 2020-2021 regular season

Format

A data frame with 30 rows (teams) and 42 variables:

Team

Team Names.

CONF

Factor; Conference of Teams (West or East).

DIV

Factor; Division of Teams.

Playoff

Factor; Whether Teams are in (0 or 1) Playoffs.

PTS

Points Per Game.

FGM

Average Field Goals Made.

FGA

Average Field Goals Attempted.

FG%

Field Goal Percentage.

3PM

Average 3-Point Field Goals Made.

3PA

Average 3-Point Field Goals Attempted.

3P%

3-Point Field Goal Percentage.

FTM

Average Free Throws Made.

FTA

Average Free Throws Attempted.

FT%

Free Throw Percentage.

OR

Offensive Rebounds Per Game.

DR

Defensive Rebounds Per Game.

REB

Rebounds Per Game.

AST

Assists Per Game.

STL

Steals Per Game.

BLK

Blocks Per Game.

TO

Turnovers Per Game.

PF

Fouls Per Game.

OPTS

Opponent Points Per Game.

OFGM

Opponent Average Field Goals Made.

OFGA

Opponent Average Field Goals Attempted.

OFG%

Opponent Field Goal Percentage.

O3PM

Opponent Average 3-Point Field Goals Made.

O3PA

Opponent Average 3-Point Field Goals Attempted.

O3P%

Opponent 3-Point Field Goal Percentage.

OFTM

Opponent Average Free Throws Made.

OFTA

Opponent Average Free Throws Attempted.

OFT%

Opponent Free Throw Percentage.

OOR

Opponent Offensive Rebounds Per Game.

ODR

Opponent Defensive Rebounds Per Game.

OREB

Opponent Rebounds Per Game.

OAST

Opponent Assists Per Game.

OSTL

Opponent Steals Per Game.

OBLK

Opponent Blocks Per Game.

OTO

Opponent Turnovers Per Game.

OPF

Opponent Fouls Per Game.

Win

Win Games in Regular Season.

Lose

Loss Games in Regular Season.

Author(s)

Zehao Xu

Source

https://www.espn.com/nba/stats/team/_/season/2021

Polygon glyph coordinates

Description

polygon coordinates scaled to (0, 1)

Usage

x_star

y_star

x_cross

y_cross

x_hexagon

y_hexagon

x_airplane

y_airplane

x_maple

y_maple

Format

An object of class numeric of length 10.

An object of class numeric of length 10.

An object of class numeric of length 12.

An object of class numeric of length 12.

An object of class numeric of length 6.

An object of class numeric of length 6.

An object of class numeric of length 32.

An object of class numeric of length 32.

An object of class numeric of length 26.

An object of class numeric of length 26.

See Also

geom_polygon_glyph

Examples

if(require("grid")) {
  library(grid)
  grid.newpage()
  grid.polygon(x=(x_star + 1)/2,
               y=(y_star + 1)/2)
  grid.newpage()
  grid.polygon(x=(x_cross + 1)/2,
               y=(y_cross + 1)/2)
  grid.newpage()
  grid.polygon(x=(x_hexagon + 1)/2,
               y=(y_hexagon + 1)/2)
  grid.newpage()
  grid.polygon(x=(x_airplane + 1)/2,
               y=(y_airplane + 1)/2)
  grid.newpage()
  grid.polygon(x=(x_maple + 1)/2,
               y=(y_maple + 1)/2)
}

Dodge overlapping objects side-to-side

Description

Dodging preserves the vertical position of an geom while adjusting the horizontal position. position_dodge_() dodges bars side by side but conditional on locations.

Usage

position_dodge_(width = NULL, preserve = c("total", "single"))

position_dodge2_(
  width = NULL,
  preserve = c("total", "single"),
  padding = 0.1,
  reverse = FALSE
)

Arguments

width

Dodging width, when different to the width of the individual elements. This is useful when you want to align narrow geoms with wider geoms. See the examples.
preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?
padding

Padding between elements at the same position. Elements are shrunk by this proportion to allow space between them. Defaults to 0.1.
reverse

If TRUE, will reverse the default stacking order. This is useful if you're rotating both the plot and legend.

Details

It is built based on position_dodge, but used for multiple locations, such as geom_hist_() or geom_density_(). Check examples to see the difference.

See Also

See geom_hist_ and geom_serialaxes_hist for more examples.

Other position adjustments for multiple locations: position_identity_, position_stack_, position_fill_

Parent: position_dodge

Examples

if(require(dplyr)) {
p <- iris %>%
  tidyr::pivot_longer(cols = -Species,
                      names_to = "Outer sterile whorls",
                      values_to = "values") %>%
  ggplot(data,
         mapping = aes(x = `Outer sterile whorls`,
                       y = values,
                       fill = Species))

p + geom_hist_(position = position_dodge_())
}


# all bins are shifted on the left
p +
  geom_hist_(position = position_dodge())

Don't adjust position

Description

Don't adjust position

Usage

position_identity_()

See Also

Other position adjustments for multiple locations: position_stack_, position_fill_, position_dodge_, position_dodge2_

Stack overlapping objects on top of each another

Description

position_stack_ stacks bars on top of each other, conditional on locations.

Usage

position_stack_(vjust = 1, reverse = FALSE)

position_fill_(vjust = 1, reverse = FALSE)

Arguments

vjust

Vertical adjustment for geoms that have a position (like points or lines), not a dimension (like bars or areas). Set to 0 to align with the bottom, 0.5 for the middle, and 1 (the default) for the top.
reverse

If TRUE, will reverse the default stacking order. This is useful if you're rotating both the plot and legend.

Details

It is built based on position_stack, but used for multiple locations, such as geom_hist_ or geom_density_. Rather than stack everything on top of each other, position_stack_ stacks bars based on locations. Check examples to see the difference.

See Also

See geom_hist_, geom_density_, geom_serialaxes_density and geom_serialaxes_hist for more examples.

Other position adjustments for multiple locations: position_identity_, position_dodge_, position_dodge2_

Parent: position_stack

Examples

p <- ggplot(iris,
       mapping = aes(Sepal.Length = Sepal.Length,
                     Sepal.Width = Sepal.Width,
                     Petal.Length = Petal.Length,
                     Petal.Width = Petal.Width,
                     colour = Species))
p +
 geom_serialaxes_density(position = position_stack_())


p +
  geom_serialaxes_density(position = position_stack())

Base Position ggproto classes for ggplot2

Description

All position_ functions (like position_dodge) return a Position object (like PositionDodge). The Position object is responsible for adjusting the position of overlapping geoms. The way that the position_ functions work is slightly different from the geom_ and stat_ functions, because a position_ function actually "instantiates" the Position object by creating a descendant, and returns that. Each of the Position objects is a ggproto object, descended from the top-level Position.

Usage

PositionDodge_

PositionDodge2_

PositionIdentity_

PositionStack_

PositionFill_

Format

An object of class PositionDodge_ (inherits from PositionDodge, Position, ggproto, gg) of length 2.

An object of class PositionDodge2_ (inherits from PositionDodge2, PositionDodge, Position, ggproto, gg) of length 2.

An object of class PositionIdentity_ (inherits from PositionIdentity, Position, ggproto, gg) of length 3.

An object of class PositionStack_ (inherits from PositionStack, Position, ggproto, gg) of length 3.

An object of class PositionFill_ (inherits from PositionStack_, PositionStack, Position, ggproto, gg) of length 2.

Base Stat ggproto classes for ggplot2

Description

All stat_ functions (like stat_bin()) return a layer that contains a Stat object (like StatBin). The Stat object is responsible for rendering the data in the plot. Each of the Stat objects is a ggproto object, descended from the top-level Stat, and each implements various methods and fields.

Usage

StatDensity_

StatHist_

StatBin_

StatCount_

StatSerialaxesDensity

StatSerialaxesHist

StatSerialaxes

StatDotProduct

Format

An object of class StatDensity_ (inherits from StatDensity, Stat, ggproto, gg) of length 4.

An object of class StatHist_ (inherits from StatBin, Stat, ggproto, gg) of length 4.

An object of class StatBin_ (inherits from StatHist_, StatBin, Stat, ggproto, gg) of length 2.

An object of class StatCount_ (inherits from StatHist_, StatBin, Stat, ggproto, gg) of length 2.

An object of class StatSerialaxesDensity (inherits from StatDensity, Stat, ggproto, gg) of length 4.

An object of class StatSerialaxesHist (inherits from StatBin, Stat, ggproto, gg) of length 4.

An object of class StatSerialaxes (inherits from Stat, ggproto, gg) of length 6.

An object of class StatDotProduct (inherits from StatSerialaxes, Stat, ggproto, gg) of length 4.