Package 'ggsc'

Two-Dimensional Weighted Kernel Density Estimation And Mapping the Result To Original Dimension

Description

Two-Dimensional Weighted Kernel Density Estimation And Mapping the Result To Original Dimension

Usage

CalWkdeCpp(x, w, l, h, adjust = 1, n = 400L)

Arguments

x	The 2-D coordinate matrix
w	The weighted sparse matrix, the number columns the same than the number rows than x.
l	The limits of the rectangle covered by the grid as c(xl, xu, yl, yu)
h	The vector of bandwidths for x and y directions, defaults to normal reference bandwidth (see bandwidth.nrd), A scalar value will be taken to apply to both directions (see ks::hpi).
adjust	numeric value to adjust to bandwidth, default is 1.
n	number of grid points in the two directions, default is 400.

---

Key drawing functions

Description

Each Geom has an associated function that draws the key when the geom needs to be displayed in a legend. These are the options built into ggplot2.

Usage

draw_key_scattermore2(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.

Value

A grid grob.

---

geom_scattermore2

Description

this add the background colour for the geom_scattermore

Usage

geom_scattermore2(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  interpolate = FALSE,
  pointsize = 0,
  pixels = c(512, 512),
  gap_colour = "white",
  gap_alpha = 1,
  bg_line_width = 0.3,
  gap_line_width = 0.1
)

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.
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().
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). Default FALSE, passed to rasterGrob.
pointsize	Radius of rasterized point. Use ‘0’ for single pixels (fastest).
pixels	Vector with X and Y resolution of the raster, default c(512,512).
gap_colour	colour of gap background between the bottom background and top point point layer, default is white.
gap_alpha	numeric the transparency of gap background colour, default is 1.
bg_line_width	numeric the line width of background point layer, default is 0.3.
gap_line_width	numeric the line width of gap between the background and top point point layer, default is .1.

Details

• colour the colour of point, default is black.

• bg_colour the colour of background point, default is NA.

• alpha the transparency of colour, default is 1.

• subset subset the data frame which meet conditions to display.

Value

polygonal point layer

Aesthetics

geom_scattermore2() understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• bg_colour

• colour

• fill

• group

• shape

• size

• stroke

• subset

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Author(s)

Shuangbin Xu

Examples

library(ggplot2)
ggplot(iris,
 aes(x= Sepal.Length, y = Petal.Width, color=Species, bg_colour=Species)
) + 
geom_scattermore2(pointsize=4, gap_line_width = .1, bg_line_width = .3)

---

sc_dim

Description

sc_dim

Usage

sc_dim(
  object,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  geom = sc_geom_point,
  ...
)

## S4 method for signature 'Seurat'
sc_dim(
  object,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  geom = sc_geom_point,
  ...
)

## S4 method for signature 'SingleCellExperiment'
sc_dim(
  object,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  geom = sc_geom_point,
  ...
)

Arguments

object	Seurat object or SingleCellExperiment object
dims	selected dimensions (must be a two-length vector) that are used in visualization
reduction	reduction method, default is NULL and will use the default setting store in the object
cells	selected cells to plot (default is all cells)
slot	slot to pull expression data from (e.g., 'count' or 'data')
mapping	aesthetic mapping, the x and y is set internally, other character of geometric layer, such as color, size, alpha or (shape when geom = geom_point) can be set manually.
geom	the function of geometric layer, default is sc_geom_point, other geometric layer, such as geom_point also works.
...	additional parameters pass to geom_scattermore2(). bg_colour the colour of background point, default is NA. this character also can be set in mappint. gap_colour the colour of gap background, default is 'white'. bg_line_width the line width of background point, default is .3. gap_line_width the gap line width of background point, default is .1. alpha the transparency of colour, default is 1. subset subset the data frame which meet conditions to display. this should be set in mapping.

Value

dimension reduction plot

See Also

geom_scattermore;

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP', mapping = aes(colour = Cell_Cycle))
p2 <- sc_dim(sce, reduction = 'UMAP')
f1 <- p1 + sc_dim_geom_label()
f2 <- p2 + 
      sc_dim_geom_label(
        geom = shadowtext::geom_shadowtext,
        color='black',
        bg.color='white'
      )

---

sc_dim_count

Description

sc_dim_count

Usage

sc_dim_count(sc_dim_plot)

Arguments

sc_dim_plot

dimension reduction plot of single cell data

Value

a bar plot to present the cell numbers of different clusters

See Also

sc_dim()

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p <- sc_dim(sce, reduction = 'UMAP')
p1 <- sc_dim_count(p)

---

sc_dim_geom_ellipse

Description

sc_dim_geom_ellipse

Usage

sc_dim_geom_ellipse(mapping = NULL, level = 0.95, ...)

Arguments

mapping	aesthetic mapping
level	the level at which to draw an ellipse
...	additional parameters pass to the stat_ellipse

Value

layer of ellipse

See Also

stat_ellipse;

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP', mapping = aes(colour = Cell_Cycle))
p2 <- sc_dim(sce, reduction = 'UMAP')
f1 <- p1 + sc_dim_geom_ellipse()

---

sc_dim_geom_feature

Description

sc_dim_geom_feature

Usage

sc_dim_geom_feature(
  object,
  features,
  dims = c(1, 2),
  ncol = 3,
  ...,
  .fun = function(.data) dplyr::filter(.data, .data$value > 0)
)

Arguments

object	Seurat or SingleCellExperiment object
features	selected features (i.e., genes)
dims	selected dimensions (must be a two-length vector) that are used in visualization
ncol	number of facet columns if 'length(features) > 1'
...	additional parameters pass to 'scattermore::geom_scattermore()'
.fun	user defined function that will be applied to selected features (default is to filter out genes with no expression values)

Value

layer of points for selected features

See Also

sc_feature()

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP')
set.seed(123)
genes <- rownames(sce) |> sample(6)
f1 <- p1 + 
      sc_dim_geom_feature(
        object = sce, 
        features = genes
      )

---

sc_dim_geom_label

Description

sc_dim_geom_label

Usage

sc_dim_geom_label(geom = ggplot2::geom_text, ...)

Arguments

geom	geometric layer (default: geom_text) to display the lables
...	additional parameters pass to the geom

Value

layer of labels

See Also

sc_dim_geom_label()

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP', mapping = aes(colour = Cell_Cycle))
p2 <- sc_dim(sce, reduction = 'UMAP')
f1 <- p1 + sc_dim_geom_label()

---

sc_dim_geom_subset

Description

sc_dim_geom_subset

Usage

sc_dim_geom_sub(mapping = NULL, subset, .column = "ident", ...)

Arguments

mapping	aesthetic mapping
subset	subset of clusters to be displayed
.column	which column represents cluster (e.g., 'ident')
...	additional parameters pass to sc_geom_point

Value

plot with a layer of specified clusters

See Also

sc_dim_geom_sub

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP')
f1 <- p1 + sc_dim_geom_sub(subset = c(1, 2), .column = 'label')

---

sc_dim_sub

Description

sc_dim_sub

Usage

sc_dim_sub(subset, .column = "ident")

Arguments

subset	subset of clusters to be displayed
.column	which column represents cluster (e.g., 'ident')

Value

update plot with only subset displayed

See Also

sc_dim

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
p1 <- sc_dim(sce, reduction = 'UMAP')
f1 <- p1 + sc_dim_sub(subset = c(1, 2), .column = 'label')

---

sc_dot

Description

sc_dot

Usage

sc_dot(
  object,
  features,
  group.by = NULL,
  split.by = NULL,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  scale = TRUE,
  scale.by = "radius",
  scale.min = NA,
  scale.max = NA,
  cluster.idents = FALSE,
  ...
)

## S4 method for signature 'Seurat'
sc_dot(
  object,
  features,
  group.by = NULL,
  split.by = NULL,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  scale = TRUE,
  scale.by = "radius",
  scale.min = NA,
  scale.max = NA,
  cluster.idents = FALSE,
  ...
)

## S4 method for signature 'SingleCellExperiment'
sc_dot(
  object,
  features,
  group.by = NULL,
  split.by = NULL,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  scale = TRUE,
  scale.by = "radius",
  scale.min = NA,
  scale.max = NA,
  cluster.idents = FALSE,
  ...
)

Arguments

object	Seurat or SingleCellExperiment object
features	selected features
group.by	grouping factor
split.by	additional split factor
cols	colors of the points
col.min	minimum scaled averaged expression threshold
col.max	maximum scaled averaged expression threshold
dot.min	the threshold of percentage of cells for the the smallest dot
dot.scale	Scaling factor for size of points
slot	slot to pull expression data from (e.g., 'count' or 'data')
.fun	user defined function that will be applied to selected features (default is NULL and there is no data operation)
mapping	aesthetic mapping
scale	whether to scale the expression value (default to TRUE)
scale.by	scale the size of the points by size or radius
scale.min	lower limit of scaling
scale.max	upper limit of scaling
cluster.idents	Order identities by hierarchical clusters based on average expression and perventage of expression (default is FALSE)
...	additional parameters pass to 'ggplot2::geom_point()'

Value

dot plot to visualize feature expression distribution

See Also

DotPlot;

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
set.seed(123)
genes <- rownames(sce) |> sample(6) 
sc_dot(sce, genes[1:5], 'Treatment', slot = 'logcounts')

---

sc_feature

Description

sc_feature

Usage

sc_feature(
  object,
  features,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  ncol = 3,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  ...
)

## S4 method for signature 'Seurat'
sc_feature(
  object,
  features,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  ncol = 3,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  ...
)

## S4 method for signature 'SingleCellExperiment'
sc_feature(
  object,
  features,
  dims = c(1, 2),
  reduction = NULL,
  cells = NULL,
  slot = "data",
  mapping = NULL,
  ncol = 3,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  ...
)

Arguments

object	Seurat object
features	selected features (i.e., genes)
dims	selected dimensions (must be a two-length vector) that are used in visualization
reduction	reduction method, default is NULL and will use the default setting store in the object
cells	selected cells to plot (default is all cells)
slot	slot to pull expression data from (e.g., 'count' or 'data')
mapping	aesthetic mapping
ncol	number of facet columns if 'length(features) > 1'
density	whether plot the 2D weighted kernel density, default is FALSE.
grid.n	number of grid points in the two directions to estimate 2D weighted kernel density, default is 100.
joint	whether joint the multiple features with joint.fun, default is FALSE.
joint.fun	how to joint the multiple features if joint=TRUE, default is prod.
common.legend	whether to use facet_wrap to display the multiple features, default is TRUE.
...	additional parameters pass to 'scattermore::geom_scattermore()' bg_colour the colour of background point, default is NA. this character also can be set in mappint. gap_colour the colour of gap background, default is 'white'. bg_line_width the line width of background point, default is .3. gap_line_width the gap line width of background point, default is .1. alpha the transparency of colour, default is 1. subset subset the data frame which meet conditions to display. this should be set in mapping.

Value

dimension reduction plot colored by selected features

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runTSNE(sce, assay.type = 'logcounts')
set.seed(123)
genes <- rownames(sce) |> sample(6)
p1 <- sc_feature(sce, genes[1], slot='logcounts', reduction = 'TSNE')
p2 <- sc_feature(sce, genes, slot='logcounts', reduction = 'TSNE')
f1 <- sc_dim(sce, slot='logcounts', reduction = 'TSNE') +
      sc_dim_geom_feature(sce, genes[1], color='black')
f2 <- sc_dim(sce, alpha=.3, slot='logcounts', reduction = 'TSNE') +
    ggnewscale::new_scale_color() +
    sc_dim_geom_feature(sce, genes, mapping=aes(color=features)) +
    scale_color_viridis_d()
p1 + p2 + f1 + f2

---

sc_geom_point

Description

sc_geom_point

Usage

sc_geom_point(mapping = NULL, ...)

Arguments

mapping	aesthetic mapping
...	additional parameters pass to 'scattermore::geom_scattermore()'

Value

layer of points

See Also

sc_dim() and sc_feature()

Examples

library(ggplot2)
ggplot(iris,
 aes(x= Sepal.Length, y = Petal.Width, color=Species)
) + 
sc_geom_point()

---

sc_spatial

Description

sc_spatial

Usage

sc_spatial(
  object,
  features = NULL,
  sample.id = NULL,
  image.id = NULL,
  slot = "data",
  plot.pie = FALSE,
  pie.radius.scale = 0.3,
  image.plot = TRUE,
  image.first.operation = "rotate",
  image.rotate.degree = NULL,
  image.mirror.axis = NULL,
  remove.point = FALSE,
  mapping = NULL,
  ncol = 6,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  pointsize = 5,
  ...
)

## S4 method for signature 'Seurat'
sc_spatial(
  object,
  features = NULL,
  sample.id = NULL,
  image.id = NULL,
  slot = "data",
  plot.pie = FALSE,
  pie.radius.scale = 0.3,
  image.plot = TRUE,
  image.first.operation = "rotate",
  image.rotate.degree = NULL,
  image.mirror.axis = NULL,
  remove.point = FALSE,
  mapping = NULL,
  ncol = 6,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  pointsize = 5,
  ...
)

## S4 method for signature 'SingleCellExperiment'
sc_spatial(
  object,
  features = NULL,
  sample.id = NULL,
  image.id = NULL,
  slot = "data",
  plot.pie = FALSE,
  pie.radius.scale = 0.3,
  image.plot = TRUE,
  image.first.operation = "rotate",
  image.rotate.degree = NULL,
  image.mirror.axis = NULL,
  remove.point = FALSE,
  mapping = NULL,
  ncol = 6,
  density = FALSE,
  grid.n = 100,
  joint = FALSE,
  joint.fun = prod,
  common.legend = TRUE,
  pointsize = 5,
  ...
)

Arguments

object	Seurat object
features	selected features to be visualized
sample.id	the index name of sample id, which only work with SingleCellExperiment or SpatialExperiment.
image.id	the index name of image id, which only work with SingleCellExperiment or SpatialExperiment.
slot	if plotting a feature, which data will be used (e.g., 'data', 'counts'), the assay name if object is SingleCellExperiment or SpatialExperiment.
plot.pie	logical whether plot the features with pie, default is FALSE.
pie.radius.scale	numeric scale to the radius of pie only work with plot.pie=TRUE, default is 0.3.
image.plot	whether to display the issue image as background.
image.first.operation	character which the first operation to image, 'rotate' or 'mirror', default is 'rotate'.
image.rotate.degree	integer the degree to ratate image, default is NULL.
image.mirror.axis	character the direction to mirror the image, default is 'h'.
remove.point	whether to remove the spot points, it is nice if your just view the issue image, default is FALSE.
mapping	aesthetic mapping, default is NULL.
ncol	integer number of facet columns if 'length(features) > 1', default is 6.
density	whether plot the 2D weighted kernel density, default is FALSE.
grid.n	number of grid points in the two directions to estimate 2D weighted kernel density, default is 100.
joint	whether joint the multiple features with joint.fun, default is FALSE.
joint.fun	how to joint the multiple features if joint = TRUE, default is prod.
common.legend	whether to use facet_wrap to display the multiple features, default is TRUE.
pointsize	the size of point, default is 5.
...	additional parameters, see also geom_scattermore2(). bg_colour the colour of background point, default is NA. this character also can be set in mappint. gap_colour the colour of gap background, default is 'white'. bg_line_width the line width of background point, default is .3. gap_line_width the gap line width of background point, default is .1. alpha the transparency of colour, default is 1. subset subset the data frame which meet conditions to display. this should be set in mapping.

Value

ggplot object

Examples

## Not run: 
library(STexampleData)
# create ExperimentHub instance
eh <- ExperimentHub()
# query STexampleData datasets
myfiles <- query(eh, "STexampleData")
spe <- myfiles[["EH7538"]]
spe <- spe[, colData(spe)$in_tissue == 1]
set.seed(123)
genes <- rownames(spe) |> sample(6) 
p <- sc_spatial(spe, features = genes, 
                image.rotate.degree = -90, 
                image.mirror.axis = NULL, 
                ncol = 3)

## End(Not run)

---

sc_violin

Description

sc_violin

Usage

sc_violin(
  object,
  features,
  cells = NULL,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  ncol = 3,
  ...
)

## S4 method for signature 'Seurat'
sc_violin(
  object,
  features,
  cells = NULL,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  ncol = 3,
  ...
)

## S4 method for signature 'SingleCellExperiment'
sc_violin(
  object,
  features,
  cells = NULL,
  slot = "data",
  .fun = NULL,
  mapping = NULL,
  ncol = 3,
  ...
)

Arguments

object	Seurat object
features	selected features
cells	selected cells to plot (default is all cells)
slot	slot to pull expression data from (e.g., 'count' or 'data')
.fun	user defined function that will be applied to selected features (default is NULL and there is no data operation)
mapping	aesthetic mapping
ncol	number of facet columns if 'length(features) > 1'
...	additional parameters pass to 'ggplot2::geom_geom_violin()'

Value

violin plot to visualize feature expression distribution

See Also

geom_violin;

Examples

library(scuttle)
library(scater)
library(scran)
library(ggplot2)
sce <- mockSCE()
sce <- logNormCounts(sce)
clusters <- clusterCells(sce, assay.type = 'logcounts')
colLabels(sce) <- clusters
sce <- runUMAP(sce, assay.type = 'logcounts')
set.seed(123)
genes <- rownames(sce) |> sample(6) 
sc_violin(sce, genes[1], slot = 'logcounts')
sc_violin(sce, genes[1], slot = 'logcounts',
     .fun=function(d) dplyr::filter(d, value > 0)
     ) +
     ggforce::geom_sina(size=.1)
sc_violin(sce, genes, slot = 'logcounts') +
  theme(axis.text.x = element_text(angle=45, hjust=1))

---

Create your own discrete scale

Description

Create your own discrete scale

Usage

scale_bg_colour_identity(
  name = waiver(),
  ...,
  guide = "none",
  aesthetics = "bg_colour"
)

scale_bg_colour_manual(
  ...,
  values,
  aesthetics = "bg_colour",
  breaks = waiver(),
  na.value = "grey50"
)

Arguments

...	Arguments passed on to ggplot2::discrete_scale, ggplot2::discrete_scale scale_name The name of the scale that should be used for error messages associated with this scale. palette A palette function that when called with a single integer argument (the number of levels in the scale) returns the values that they should take (e.g., scales::pal_hue()). labels One of: NULL for no labels waiver() for the default labels computed by the transformation object A character vector giving labels (must be same length as breaks) An expression vector (must be the same length as breaks). See ?plotmath for details. A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation. limits One of: NULL to use the default scale values A character vector that defines possible values of the scale and their order A function that accepts the existing (automatic) values and returns new ones. Also accepts rlang lambda function notation. na.translate Unlike continuous scales, discrete scales can easily show missing values, and do so by default. If you want to remove missing values from a discrete scale, specify na.translate = FALSE. drop Should unused factor levels be omitted from the scale? The default, TRUE, uses the levels that appear in the data; FALSE uses all the levels in the factor. call The call used to construct the scale for reporting messages. super The super class to use for the constructed scale
name	The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the legend title will be omitted.
guide	A function used to create a guide or its name. See guides() for more information.
aesthetics	The names of the aesthetics that this scale works with.
values	a set of aesthetic values to map data values to. If this is a named vector, then the values will be matched based on the names. If unnamed, values will be matched in order (usually alphabetical) with the limits of the scale. Any data values that don't match will be given na.value.
breaks	One of: NULL for no breaks waiver() for the default breaks (the scale limits) A character vector of breaks A function that takes the limits as input and returns breaks as output. Also accepts rlang lambda function notation.
na.value	If na.translate = TRUE, what aesthetic value should the missing values be displayed as? Does not apply to position scales where NA is always placed at the far right.

Value

bg_colour scale constructor

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