Package 'see'

Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
Description: Provides plotting utilities supporting packages in the 'easystats' ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.
Authors: Daniel Lüdecke [aut, ctb] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Dominique Makowski [aut, inv] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Indrajeet Patil [aut, cre] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>, @mattansb), Brenton M. Wiernik [aut, ctb] , Philip Waggoner [aut, ctb] , Jeffrey R. Stevens [ctb] , Matthew Smith [rev] (@SmithMatt90), Jakob Bossek [rev] (@BossekJakob)
Maintainer: Indrajeet Patil <[email protected]>
License: MIT + file LICENSE
Version: 0.8.4.7
Built: 2024-07-05 05:27:19 UTC
Source: https://github.com/easystats/see

Help Index

Complete figure with its attributes

Description

The data_plot() function usually stores information (such as title, axes labels, etc.) as attributes, while add_plot_attributes() adds this information to the plot.

Usage

add_plot_attributes(x)

Arguments

x

An object.

Examples

library(rstanarm)
library(bayestestR)
library(see)
library(ggplot2)

model <- suppressWarnings(stan_glm(
  Sepal.Length ~ Petal.Width + Species + Sepal.Width,
  data = iris,
  chains = 2, iter = 200, refresh = 0
))

result <- bayestestR::hdi(model, ci = c(0.5, 0.75, 0.9, 0.95))
data <- data_plot(result, data = model)

p <- ggplot(
  data,
  aes(x = x, y = y, height = height, group = y, fill = fill)
) +
  ggridges::geom_ridgeline_gradient()

p
p + add_plot_attributes(data)

Extract blue-brown colors as hex codes

Description

Can be used to get the hex code of specific colors from the blue-brown color palette. Use bluebrown_colors() to see all available colors.

Usage

bluebrown_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

bluebrown_colors()

bluebrown_colors("blue", "brown")

Radar coordinate system

Description

Add a radar coordinate system useful for radar charts.

Usage

coord_radar(theta = "x", start = 0, direction = 1, ...)

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
...

Other arguments to be passed to ggproto.

Examples

library(ggplot2)

# Create a radar/spider chart with ggplot:
data(iris)
data <- aggregate(iris[-5], list(Species = iris$Species), mean)
data <- datawizard::data_to_long(
  data,
  c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
)

ggplot(data, aes(x = name, y = value, color = Species, group = Species)) +
  geom_polygon(fill = NA, linewidth = 2) +
  coord_radar(start = -pi / 4)

Prepare objects for plotting or plot objects

Description

data_plot() extracts and transforms an object for plotting, while plot() visualizes results of functions from different packages in easystats-project. See the documentation for your object's class:

Usage

data_plot(x, ...)

## S3 method for class 'compare_performance'
data_plot(x, data = NULL, ...)

Arguments

x

An object.
...

Arguments passed to or from other methods.
data

The original data used to create this object. Can be a statistical model.

Details

data_plot() is in most situation not needed when the purpose is plotting, since most plot()-functions in see internally call data_plot() to prepare the data for plotting.

Many plot()-functions have a data-argument that is needed when the data or model for plotting can't be retrieved via data_plot(). In such cases, plot() gives an error and asks for providing data or models.

Most plot()-functions work out-of-the-box, i.e. you don't need to do much more than calling ⁠plot(<object>)⁠ (see 'Examples'). Some plot-functions allow to specify arguments to modify the transparency or color of geoms, these are shown in the 'Usage' section.

See Also

Package-Vignettes

Examples

library(bayestestR)
library(rstanarm)

model <<- suppressWarnings(stan_glm(
  Sepal.Length ~ Petal.Width * Species,
  data = iris,
  chains = 2, iter = 200, refresh = 0
))

x <- rope(model, verbose = FALSE)
plot(x)

x <- hdi(model)
plot(x) + theme_modern()

x <- p_direction(model, verbose = FALSE)
plot(x)

model <<- suppressWarnings(stan_glm(
  mpg ~ wt + gear + cyl + disp,
  chains = 2,
  iter = 200,
  refresh = 0,
  data = mtcars
))
x <- equivalence_test(model, verbose = FALSE)
plot(x)

Extract Flat UI colors as hex codes

Description

Can be used to get the hex code of specific colors from the Flat UI color palette. Use flat_colors() to see all available colors.

Usage

flat_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

flat_colors()

flat_colors("dark red", "teal")

Add dot-densities for binary y variables

Description

Add dot-densities for binary y variables

Usage

geom_binomdensity(data, x, y, scale = "auto", ...)

Arguments

data

A dataframe.
x, y

Characters corresponding to the x and y axis. Note that y must be a variable with two unique values.
scale

Character specifying method of scaling the dot-densities. Can be: 'auto' (corresponding to the square root of the proportion), 'proportion', 'density' or a custom list with values for each factor level (see examples).
...

Other arguments passed to ggdist::geom_dots.

Examples

library(ggplot2)
library(see)

data <- iris[1:100, ]

ggplot() +
  geom_binomdensity(data,
    x = "Sepal.Length",
    y = "Species",
    fill = "red",
    color = NA
  )

# Different scales
data[1:70, "Species"] <- "setosa" # Create unbalanced proportions

ggplot() +
  geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "auto")
ggplot() +
  geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "density")
ggplot() +
  geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "proportion")
ggplot() +
  geom_binomdensity(data,
    x = "Sepal.Length", y = "Species",
    scale = list("setosa" = 0.4, "versicolor" = 0.6)
  )

Create ggplot2 geom(s) from a list

Description

These helper functions are built on top of ggplot2::layer() and can be used to add geom(s), whose type and content are specified as a list.

Usage

geom_from_list(x, ...)

geoms_from_list(x, ...)

Arguments

x

A list containing:

  • a geom type (e.g. geom = "point"),

  • a list of aesthetics (e.g. aes = list(x = "mpg", y = "wt")),

  • some data (e.g. data = mtcars),

  • and some other parameters.

For geoms_from_list() ("geoms" with an "s"), the input must be a list of lists, ideally named ⁠"l1", "l2", "l3"⁠, etc.
...

Additional arguments passed to ggplot2::layer().

Examples

library(ggplot2)

# Example 1 (basic geoms and labels) --------------------------
l1 <- list(
  geom = "point",
  data = mtcars,
  aes = list(x = "mpg", y = "wt", size = "hp", color = "hp"),
  show.legend = c("size" = FALSE)
)
l2 <- list(
  geom = "labs",
  title = "A Title"
)

ggplot() +
  geom_from_list(l1) +
  geom_from_list(l2)

ggplot() +
  geoms_from_list(list(l1 = l1, l2 = l2))

# Example 2 (Violin, boxplots, ...) --------------------------
l1 <- list(
  geom = "violin",
  data = iris,
  aes = list(x = "Species", y = "Sepal.Width")
)
l2 <- list(
  geom = "boxplot",
  data = iris,
  aes = list(x = "Species", y = "Sepal.Width"),
  outlier.shape = NA
)
l3 <- list(
  geom = "jitter",
  data = iris,
  width = 0.1,
  aes = list(x = "Species", y = "Sepal.Width")
)

ggplot() +
  geom_from_list(l1) +
  geom_from_list(l2) +
  geom_from_list(l3)

# Example 3 (2D density) --------------------------
ggplot() +
  geom_from_list(list(
    geom = "density_2d", data = iris,
    aes = list(x = "Sepal.Width", y = "Petal.Length")
  ))
ggplot() +
  geom_from_list(list(
    geom = "density_2d_filled", data = iris,
    aes = list(x = "Sepal.Width", y = "Petal.Length")
  ))
ggplot() +
  geom_from_list(list(
    geom = "density_2d_polygon", data = iris,
    aes = list(x = "Sepal.Width", y = "Petal.Length")
  ))
ggplot() +
  geom_from_list(list(
    geom = "density_2d_raster", data = iris,
    aes = list(x = "Sepal.Width", y = "Petal.Length")
  )) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0))

# Example 4 (facet and coord flip) --------------------------

ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
  geom_point() +
  geom_from_list(list(geom = "hline", yintercept = 2)) +
  geom_from_list(list(geom = "coord_flip")) +
  geom_from_list(list(geom = "facet_wrap", facets = "~ Species", scales = "free"))

# Example 5 (theme and scales) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
  geom_point() +
  geom_from_list(list(geom = "scale_color_viridis_d", option = "inferno")) +
  geom_from_list(list(geom = "theme", legend.position = "top"))

ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
  geom_point() +
  geom_from_list(list(geom = "scale_color_material_d", palette = "rainbow")) +
  geom_from_list(list(geom = "theme_void"))

# Example 5 (Smooths and side densities) --------------------------

ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
  geom_from_list(list(geom = "point")) +
  geom_from_list(list(geom = "smooth", color = "red")) +
  geom_from_list(list(aes = list(x = "Sepal.Length"), geom = "ggside::geom_xsidedensity")) +
  geom_from_list(list(geom = "ggside::scale_xsidey_continuous", breaks = NULL))

Better looking points

Description

Somewhat nicer points (especially in case of transparency) without outline strokes (borders, contours) by default.

Usage

geom_point2(..., stroke = 0, shape = 16)

geom_jitter2(..., size = 2, stroke = 0, shape = 16)

geom_pointrange2(..., stroke = 0)

geom_count2(..., stroke = 0)

geom_count_borderless(..., stroke = 0)

geom_point_borderless(...)

geom_jitter_borderless(...)

geom_pointrange_borderless(...)

Arguments

...

Other arguments to be passed to ggplot2::geom_point(), ggplot2::geom_jitter(), ggplot2::geom_pointrange(), or ggplot2::geom_count().
stroke

Stroke thickness.
shape

Shape of points.
size

Size of points.

Note

The color aesthetics for geom_point_borderless() is "fill", not "color". See 'Examples'.

Examples

library(ggplot2)
library(see)

normal <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
  geom_point(size = 8, alpha = 0.3) +
  theme_modern()

new <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
  geom_point2(size = 8, alpha = 0.3) +
  theme_modern()

plots(normal, new, n_columns = 2)

ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
  geom_point_borderless(size = 4) +
  theme_modern()

theme_set(theme_abyss())
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
  geom_point_borderless(size = 4)

Pool ball points

Description

Points labelled with the observation name.

Usage

geom_poolpoint(
  label,
  size_text = 3.88,
  size_background = size_text * 2,
  size_point = size_text * 3.5,
  ...
)

geom_pooljitter(
  label,
  size_text = 3.88,
  size_background = size_text * 2,
  size_point = size_text * 3.5,
  jitter = 0.1,
  ...
)

Arguments

label

Label to add inside the points.
size_text

Size of text.
size_background

Size of the white background circle.
size_point

Size of the ball.
...

Other arguments to be passed to geom_point.
jitter

Width and height of position jitter.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
  geom_poolpoint(label = rownames(iris)) +
  scale_color_flat_d() +
  theme_modern()


ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
  geom_pooljitter(label = rownames(iris)) +
  scale_color_flat_d() +
  theme_modern()

Half-violin Half-dot plot

Description

Create a half-violin half-dot plot, useful for visualising the distribution and the sample size at the same time.

Usage

geom_violindot(
  mapping = NULL,
  data = NULL,
  trim = TRUE,
  scale = c("area", "count", "width"),
  show.legend = NA,
  inherit.aes = TRUE,
  dots_size = 0.7,
  dots_color = NULL,
  dots_fill = NULL,
  binwidth = 0.05,
  position_dots = ggplot2::position_nudge(x = -0.025, y = 0),
  ...,
  size_dots = dots_size,
  color_dots = dots_color,
  fill_dots = dots_fill
)

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)).
trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.
scale

if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width.
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().
binwidth

When method is "dotdensity", this specifies maximum bin width. When method is "histodot", this specifies bin width. Defaults to 1/30 of the range of the data
position_dots

Position adjustment for dots, either as a string, or the result of a call to a position adjustment function.
...

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

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.

  • When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.

  • Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.

  • The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

size_dots, dots_size

Size adjustment for dots.
color_dots, dots_color

Color adjustment for dots.
fill_dots, dots_fill

Fill adjustment for dots.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violindot() +
  theme_modern()

Half-violin plot

Description

Create a half-violin plot.

Usage

geom_violinhalf(
  mapping = NULL,
  data = NULL,
  stat = "ydensity",
  position = "dodge",
  trim = TRUE,
  flip = FALSE,
  scale = c("area", "count", "width"),
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

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

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

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

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

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

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

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

position

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

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.
flip

Should the half-violin plot switch directions? By default, this is FALSE and all half-violin geoms will have the flat-side on facing leftward. If flip = TRUE, then all flat-sides will face rightward. Optionally, a numeric vector can be supplied indicating which specific geoms should be flipped. See examples for more details.
scale

if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width.
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().
...

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

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.

  • When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.

  • Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.

  • The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violinhalf() +
  theme_modern() +
  scale_fill_material_d()

# To flip all half-violin geoms, use `flip = TRUE`:
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violinhalf(flip = TRUE) +
  theme_modern() +
  scale_fill_material_d()

# To flip the half-violin geoms for the first and third groups only
# by passing a numeric vector
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violinhalf(flip = c(1, 3)) +
  theme_modern() +
  scale_fill_material_d()

Golden Ratio

Description

Returns the golden ratio (1.618034...). Useful to easily obtain golden proportions, for instance for a horizontal figure, if you want its height to be 8, you can set its width to be golden_ratio(8).

Usage

golden_ratio(x = 1)

Arguments

x

A number to be multiplied by the golden ratio. The default (x = 1) returns the value of the golden ratio.

Examples

golden_ratio()
golden_ratio(10)

Extract material design colors as hex codes

Description

Can be used to get the hex code of specific colors from the material design color palette. Use material_colors() to see all available colors.

Usage

material_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

material_colors()

material_colors("indigo", "lime")

Extract Metro colors as hex codes

Description

Can be used to get the hex code of specific colors from the Metro color palette. Use metro_colors() to see all available colors.

Usage

metro_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

metro_colors()

metro_colors("dark red", "teal")

Extract Okabe-Ito colors as hex codes

Description

Can be used to get the hex code of specific colors from the Okabe-Ito palette. Use okabeito_colors() to see all available colors.

Usage

okabeito_colors(..., original_names = FALSE, black_first = FALSE, amber = TRUE)

oi_colors(..., original_names = FALSE, black_first = FALSE, amber = TRUE)

Arguments

...

Character names of colors.
original_names

Logical. Should the colors be named using the original names used by Okabe and Ito (2008), such as "vermillion" (TRUE), or simplified names, such as "red" (FALSE, default)? Only used if no colors are specified (to see all available colors).
black_first

Logical. Should black be first (TRUE) or last (FALSE, default) in the color palette? Only used if no colors are specified (to see all available colors).
amber

If amber color should replace yellow in the palette.

Value

A character vector with color-codes.

Examples

okabeito_colors()

okabeito_colors(c("red", "light blue", "orange"))

okabeito_colors(original_names = TRUE)

okabeito_colors(black_first = TRUE)

Blue-brown design color palette

Description

The palette based on blue-brown colors.

Usage

palette_bluebrown(palette = "contrast", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_bluebrown().

Color palettes from https://www.color-hex.com/

Description

This function downloads a requested color palette from https://www.color-hex.com/. This website provides a large number of user-submitted color palettes.

Usage

palette_colorhex(palette = 1014416, reverse = FALSE, ...)

Arguments

palette

The numeric code for a palette at https://www.color-hex.com/. For example, 1014416 for the Josiah color palette (number 1014416).
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_colorhex().

Note

The default Josiah color palette (number 1014416) is available without an internet connection. All other color palettes require an internet connection to download and access.

Flat UI color palette

Description

The palette based on Flat UI.

Usage

palette_flat(palette = "contrast", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_flat().

Material design color palette

Description

The palette based on material design colors.

Usage

palette_material(palette = "contrast", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_material().

Metro color palette

Description

The palette based on Metro colors.

Usage

palette_metro(palette = "complement", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_metro().

Okabe-Ito color palette

Description

The palette based proposed by Okabe and Ito (2008).

Usage

palette_okabeito(palette = "full_amber", reverse = FALSE, order = 1:9, ...)

palette_oi(palette = "full_amber", reverse = FALSE, order = 1:9, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
order

A vector of numbers from 1 to 9 indicating the order of colors to use (default: 1:9)
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_material().

References

Okabe, M., & Ito, K. (2008). Color universal design (CUD): How to make figures and presentations that are friendly to colorblind people. https://jfly.uni-koeln.de/color/#pallet (Original work published 2002)

Pizza color palette

Description

The palette based on authentic neapolitan pizzas.

Usage

palette_pizza(palette = "margherita", reverse = FALSE, ...)

Arguments

palette

Pizza type. Can be "margherita" (default), "margherita_crust", "diavola" or "diavola_crust".
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_pizza().

See design color palette

Description

See design color palette

Usage

palette_see(palette = "contrast", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_see().

Social color palette

Description

The palette based Social colors.

Usage

palette_social(palette = "complement", reverse = FALSE, ...)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
...

Additional arguments to pass to colorRampPalette().

Details

This function is usually not called directly, but from within scale_color_social().

Extract pizza colors as hex codes

Description

Extract pizza colors as hex codes

Usage

pizza_colors(...)

Arguments

...

Character names of pizza ingredients.

Value

A character vector with color-codes.

Plot tabulated data.

Description

Plot tabulated data.

Usage

## S3 method for class 'dw_data_tabulates'
plot(
  x,
  label_values = TRUE,
  show_na = c("if_any", "always", "never"),
  na_label = "(Missing)",
  error_bar = TRUE,
  ci = 0.95,
  fill_col = "#87CEFA",
  color_error_bar = "#607B8B",
  ...
)

## S3 method for class 'dw_data_tabulate'
plot(
  x,
  label_values = TRUE,
  show_na = c("if_any", "always", "never"),
  na_label = "(Missing)",
  error_bar = TRUE,
  ci = 0.95,
  fill_col = "#87CEFA",
  color_error_bar = "#607B8B",
  ...
)

Arguments

x

Object created by datawizard::data_tabulate().
label_values

Logical. Should values and percentages be displayed at the top of each bar.
show_na

Should missing values be dropped? Can be "if_any" (default) to show the missing category only if any missing values are present, "always" to always show the missing category, or "never" to never show the missing category.
na_label

The label given to missing values when they are shown.
error_bar

Logical. Should error bars be displayed? If TRUE, confidence intervals computed using the Wilson method are shown. See Brown et al. (2001) for details.
ci

Confidence Interval (CI) level. Defaults to 0.95 (⁠95%⁠).
fill_col

Color to use for category columns (default: "#87CEFA").
color_error_bar

Color to use for error bars (default: "#607B8B").
...

Unused

References

Brown, L. D., Cai, T. T., & Dasgupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science, 16(2), 101-133. doi:10.1214/ss/1009213286

Plot method for Bayes Factors for model comparison

Description

The plot() method for the bayestestR::bayesfactor_models() function. These plots visualize the posterior probabilities of the compared models.

Usage

## S3 method for class 'see_bayesfactor_models'
plot(
  x,
  n_pies = c("one", "many"),
  value = c("none", "BF", "probability"),
  sort = FALSE,
  log = FALSE,
  prior_odds = NULL,
  ...
)

Arguments

x

An object.
n_pies

Number of pies.
value

What value to display.
sort

The behavior of this argument depends on the plotting contexts.

  • Plotting model parameters: If NULL, coefficients are plotted in the order as they appear in the summary. Setting sort = "ascending" or sort = "descending" sorts coefficients in ascending or descending order, respectively. Setting sort = TRUE is the same as sort = "ascending".

  • Plotting Bayes factors: Sort pie-slices by posterior probability (descending)?

log

Logical that decides whether to display log-transformed Bayes factors.
prior_odds

An optional vector of prior odds for the models. See BayesFactor::priorOdds. As the size of the pizza slices corresponds to posterior probability (which is a function of prior probability and the Bayes Factor), custom prior_odds will change the slices' size.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(bayestestR)
library(see)

lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)

result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)

plot(result, n_pies = "one", value = "probability", sort = TRUE) +
  scale_fill_pizza(reverse = TRUE)

plot(result, n_pies = "many", value = "BF", log = TRUE) +
  scale_fill_pizza(reverse = FALSE)

Plot method for Bayes Factors for a single parameter

Description

The plot() method for the bayestestR::bayesfactor_parameters() function.

Usage

## S3 method for class 'see_bayesfactor_parameters'
plot(
  x,
  size_point = 2,
  rope_color = "#0171D3",
  rope_alpha = 0.2,
  show_intercept = FALSE,
  ...
)

Arguments

x

An object.
size_point

Numeric specifying size of point-geoms.
rope_color

Character specifying color of ROPE ribbon.
rope_alpha

Numeric specifying transparency level of ROPE ribbon.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Plot method for multicollinearity checks

Description

The plot() method for the performance::check_collinearity() function.

Usage

## S3 method for class 'see_check_collinearity'
plot(
  x,
  data = NULL,
  colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
  size_point = 3.5,
  size_line = 0.8,
  size_title = 12,
  size_axis_title = base_size,
  base_size = 10,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
size_point

Numeric specifying size of point-geoms.
size_line

Numeric value specifying size of line geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_collinearity(m)
result
plot(result)

Plot method for classifying the distribution of a model-family

Description

The plot() method for the performance::check_distribution() function.

Usage

## S3 method for class 'see_check_distribution'
plot(x, size_point = 2, panel = TRUE, ...)

Arguments

x

An object.
size_point

Numeric specifying size of point-geoms.
panel

Logical, if TRUE, plots are arranged as panels; else, single plots are returned.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)
m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_distribution(m)
result
plot(result)

Plot method for (non-)constant error variance checks

Description

The plot() method for the performance::check_heteroscedasticity() function.

Usage

## S3 method for class 'see_check_heteroscedasticity'
plot(
  x,
  data = NULL,
  size_point = 2,
  size_line = 0.8,
  size_title = 12,
  size_axis_title = base_size,
  base_size = 10,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
size_point

Numeric specifying size of point-geoms.
size_line

Numeric value specifying size of line geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- performance::check_heteroscedasticity(m)
result
plot(result, data = m) # data required for pkgdown

Plot method for homogeneity of variances checks

Description

The plot() method for the performance::check_homogeneity() function.

Usage

## S3 method for class 'see_check_homogeneity'
plot(x, data = NULL, ...)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)

model <<- lm(len ~ supp + dose, data = ToothGrowth)
result <- check_homogeneity(model)
result
plot(result)

Plot method for checking model assumptions

Description

The plot() method for the performance::check_model() function. Diagnostic plots for regression models.

Usage

## S3 method for class 'see_check_model'
plot(
  x,
  style = theme_lucid,
  colors = NULL,
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  n_columns = 2,
  ...
)

Arguments

x

An object.
style

A ggplot2-theme.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
type

Plot type for the posterior predictive checks plot. Can be "density" (default), "discrete_dots", "discrete_interval" or "discrete_both" (the ⁠discrete_*⁠ options are appropriate for models with discrete - binary, integer or ordinal etc. - outcomes).
n_columns

Number of columns to align plots.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

library(performance)

model <- lm(qsec ~ drat + wt, data = mtcars)
plot(check_model(model))

Plot method for check model for (non-)normality of residuals

Description

The plot() method for the performance::check_normality() function.

Usage

## S3 method for class 'see_check_normality'
plot(
  x,
  type = c("qq", "pp", "density"),
  data = NULL,
  size_line = 0.8,
  size_point = 2,
  size_title = 12,
  size_axis_title = base_size,
  base_size = 10,
  alpha = 0.2,
  dot_alpha = 0.8,
  colors = c("#3aaf85", "#1b6ca8"),
  detrend = TRUE,
  method = "ell",
  ...
)

Arguments

x

An object.
type

Character vector, indicating the type of plot. Options are "qq" (default) for quantile-quantile (Q-Q) plots, "pp" for probability-probability (P-P) plots, or "density" for density overlay plots.
data

The original data used to create this object. Can be a statistical model.
size_line

Numeric value specifying size of line geoms.
size_point

Numeric specifying size of point-geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
alpha

Numeric value specifying alpha level of the confidence bands.
dot_alpha

Numeric value specifying alpha level of the point geoms.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
detrend

Logical that decides if Q-Q and P-P plots should be de-trended (also known as worm plots).
method

The method used for estimating the qq/pp bands. Default to "ell" (equal local levels / simultaneous testing - recommended). Can also be one of "pointwise" or "boot" for pointwise confidence bands, or "ks" or "ts" for simultaneous testing. See qqplotr::stat_qq_band() for details.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

library(performance)

m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_normality(m)
plot(result)


plot(result, type = "qq", detrend = TRUE)

Plot method for checking outliers

Description

The plot() method for the performance::check_outliers() function.

Usage

## S3 method for class 'see_check_outliers'
plot(
  x,
  size_text = 3.5,
  size_line = 0.8,
  size_title = 12,
  size_axis_title = base_size,
  base_size = 10,
  dot_alpha = 0.8,
  colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
  rescale_distance = TRUE,
  type = c("dots", "bars"),
  show_labels = TRUE,
  ...
)

Arguments

x

An object.
size_text

Numeric value specifying size of text labels.
size_line

Numeric value specifying size of line geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
dot_alpha

Numeric value specifying alpha level of the point geoms.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
rescale_distance

Logical. If TRUE, distance values are rescaled to a range from 0 to 1. This is mainly due to better catch the differences between distance values.
type

Character vector, indicating the type of plot. Options are "dots" (default) for a scatterplot of leverage (hat) values versus residuals, with Cook's Distance contours for evaluating influential points, or "bars" for a bar chart of (rescaled) outlier statistic values for each data point. Only used for outlier plots of fitted models; for outlier plots of raw data values, type = "bars" is always used.
show_labels

Logical. If TRUE, text labels are displayed.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)
data(mtcars)
mt1 <- mtcars[, c(1, 3, 4)]
mt2 <- rbind(
  mt1,
  data.frame(mpg = c(37, 40), disp = c(300, 400), hp = c(110, 120))
)
model <- lm(disp ~ mpg + hp, data = mt2)
plot(check_outliers(model))

Plot method for comparison of model parameters

Description

The plot() method for the parameters::compare_parameters() function.

Usage

## S3 method for class 'see_compare_parameters'
plot(
  x,
  show_intercept = FALSE,
  size_point = 0.8,
  size_text = NA,
  dodge_position = 0.8,
  sort = NULL,
  n_columns = NULL,
  show_labels = FALSE,
  ...
)

Arguments

x

An object.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
size_point

Numeric specifying size of point-geoms.
size_text

Numeric value specifying size of text labels.
dodge_position

Numeric value specifying the amount of "dodging" (spacing) between geoms.
sort

The behavior of this argument depends on the plotting contexts.

  • Plotting model parameters: If NULL, coefficients are plotted in the order as they appear in the summary. Setting sort = "ascending" or sort = "descending" sorts coefficients in ascending or descending order, respectively. Setting sort = TRUE is the same as sort = "ascending".

  • Plotting Bayes factors: Sort pie-slices by posterior probability (descending)?

n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
show_labels

Logical. If TRUE, text labels are displayed.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- parameters::compare_parameters(lm1, lm2, lm3)
plot(result)

Plot method for comparing model performances

Description

The plot() method for the performance::compare_performance() function.

Usage

## S3 method for class 'see_compare_performance'
plot(x, size_line = 1, ...)

Arguments

x

An object.
size_line

Numeric value specifying size of line geoms.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_performance(lm1, lm2, lm3)
result
plot(result)

Plot method for effect size tables

Description

The plot() method for the effectsize::effectsize() function.

Usage

## S3 method for class 'see_effectsize_table'
plot(x, ...)

Arguments

x

An object.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)

Plot method for (conditional) equivalence testing

Description

The plot() method for the bayestestR::equivalence_test() function.

Usage

## S3 method for class 'see_equivalence_test_effectsize'
plot(x, ...)

## S3 method for class 'see_equivalence_test'
plot(
  x,
  rope_color = "#0171D3",
  rope_alpha = 0.2,
  show_intercept = FALSE,
  n_columns = 1,
  ...
)

## S3 method for class 'see_equivalence_test_lm'
plot(
  x,
  size_point = 0.7,
  rope_color = "#0171D3",
  rope_alpha = 0.2,
  show_intercept = FALSE,
  n_columns = 1,
  ...
)

Arguments

x

An object.
...

Arguments passed to or from other methods.
rope_color

Character specifying color of ROPE ribbon.
rope_alpha

Numeric specifying transparency level of ROPE ribbon.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
size_point

Numeric specifying size of point-geoms.

Value

A ggplot2-object.

Examples

library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)

Plot method for estimating contrasts

Description

The plot() method for the modelbased::estimate_contrasts() function.

Usage

## S3 method for class 'see_estimate_contrasts'
plot(x, data = NULL, ...)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(modelbased)

model <- lm(Sepal.Width ~ Species, data = iris)
contrasts <- estimate_contrasts(model)
means <- estimate_means(model)
plot(contrasts, means)

Plot method for density estimation of posterior samples

Description

The plot() method for the bayestestR::estimate_density() function.

Usage

## S3 method for class 'see_estimate_density'
plot(
  x,
  stack = TRUE,
  show_intercept = FALSE,
  n_columns = 1,
  priors = FALSE,
  priors_alpha = 0.4,
  posteriors_alpha = 0.7,
  size_line = 0.9,
  size_point = 2,
  centrality = "median",
  ci = 0.95,
  ...
)

Arguments

x

An object.
stack

Logical. If TRUE, densities are plotted as stacked lines. Else, densities are plotted for each parameter among each other.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
priors

Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
priors_alpha

Numeric value specifying alpha for the prior distributions.
posteriors_alpha

Numeric value specifying alpha for the posterior distributions.
size_line

Numeric value specifying size of line geoms.
size_point

Numeric specifying size of point-geoms.
centrality

Character specifying the point-estimate (centrality index) to compute. Can be "median", "mean" or "MAP".
ci

Numeric value of probability of the CI (between 0 and 1) to be estimated. Default to 0.95.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- estimate_density(m)
plot(result)

Plot method for uncertainty or credible intervals

Description

The plot() method for the bayestestR::hdi() and related function.

Usage

## S3 method for class 'see_hdi'
plot(
  x,
  data = NULL,
  show_intercept = FALSE,
  show_zero = TRUE,
  show_title = TRUE,
  n_columns = 1,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
show_zero

Logical. If TRUE, will add a vertical (dotted) line at 0.
show_title

Logical. If TRUE, will show the title of the plot.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- bayestestR::hdi(m)
result
plot(result)

Plot method for numbers of clusters to extract or factors to retain

Description

The plot() method for the parameters::n_factors() and parameters::n_clusters()

Usage

## S3 method for class 'see_n_factors'
plot(x, data = NULL, type = c("bar", "line", "area"), size = 1, ...)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
type

Character vector, indicating the type of plot. Options are three different shapes to illustrate the degree of consensus between dimensionality methods for each number of factors; "bar" (default) for a bar chart, "line" for a horizontal point and line chart, or "area" for an area chart (frequency polygon).
size

Depending on type, a numeric value specifying size of bars, lines, or segments.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

data(mtcars)
result <- parameters::n_factors(mtcars, type = "PCA")
result

plot(result) # type = "bar" by default
plot(result, type = "line")
plot(result, type = "area")

Plot method for probability of direction

Description

The plot() method for the bayestestR::p_direction() function.

Usage

## S3 method for class 'see_p_direction'
plot(
  x,
  data = NULL,
  show_intercept = FALSE,
  priors = FALSE,
  priors_alpha = 0.4,
  n_columns = 1,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
priors

Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
priors_alpha

Numeric value specifying alpha for the prior distributions.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- p_direction(m)
plot(result)

Plot method for plotting p-functions (aka consonance functions)

Description

The plot() method for the parameters::p_function ().

Usage

## S3 method for class 'see_p_function'
plot(
  x,
  colors = c("black", "#1b6ca8"),
  size_point = 1.2,
  size_line = c(0.7, 0.9),
  size_text = 3,
  line_alpha = 0.15,
  show_labels = TRUE,
  n_columns = NULL,
  show_intercept = FALSE,
  ...
)

Arguments

x

An object returned by parameters::p_function ().
colors

Character vector of length two, indicating the colors (in hex-format) used when only one parameter is plotted, resp. when panels are plotted as facets.
size_point

Numeric specifying size of point-geoms.
size_line

Numeric value specifying size of line geoms.
size_text

Numeric value specifying size of text labels.
line_alpha

Numeric value specifying alpha of lines indicating the emphasized compatibility interval levels (see ?parameters::p_function).
show_labels

Logical. If TRUE, text labels are displayed.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(parameters)
model <- lm(Sepal.Length ~ Species + Sepal.Width + Petal.Length, data = iris)
result <- p_function(model)
plot(result, n_columns = 2, show_labels = FALSE)

result <- p_function(model, keep = "Sepal.Width")
plot(result)

Plot method for practical significance

Description

The plot() method for the bayestestR::p_significance() function.

Usage

## S3 method for class 'see_p_significance'
plot(
  x,
  data = NULL,
  show_intercept = FALSE,
  priors = FALSE,
  priors_alpha = 0.4,
  n_columns = 1,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
priors

Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
priors_alpha

Numeric value specifying alpha for the prior distributions.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- p_significance(m)
plot(result)

Plot method for Model Parameters from Bayesian Meta-Analysis

Description

The plot() method for the parameters::model_parameters() function when used with brms-meta-analysis models.

Usage

## S3 method for class 'see_parameters_brms_meta'
plot(
  x,
  size_point = 2,
  size_line = 0.8,
  size_text = 3.5,
  posteriors_alpha = 0.7,
  rope_alpha = 0.15,
  rope_color = "cadetblue",
  normalize_height = TRUE,
  show_labels = TRUE,
  ...
)

Arguments

x

An object.
size_point

Numeric specifying size of point-geoms.
size_line

Numeric value specifying size of line geoms.
size_text

Numeric value specifying size of text labels.
posteriors_alpha

Numeric value specifying alpha for the posterior distributions.
rope_alpha

Numeric specifying transparency level of ROPE ribbon.
rope_color

Character specifying color of ROPE ribbon.
normalize_height

Logical. If TRUE, height of mcmc-areas is "normalized", to avoid overlap. In certain cases when the range of a posterior distribution is narrow for some parameters, this may result in very flat mcmc-areas. In such cases, set normalize_height = FALSE.
show_labels

Logical. If TRUE, text labels are displayed.
...

Arguments passed to or from other methods.

Details

Colors of density areas and errorbars

To change the colors of the density areas, use scale_fill_manual() with named color-values, e.g. scale_fill_manual(values = c("Study" = "blue", "Overall" = "green")). To change the color of the error bars, use scale_color_manual(values = c("Errorbar" = "red")).

Show or hide estimates and CI

Use show_labels = FALSE to hide the textual output of estimates and credible intervals.

Value

A ggplot2-object.

Examples

library(parameters)
library(brms)
library(metafor)
data(dat.bcg)

dat <- escalc(
  measure = "RR",
  ai = tpos,
  bi = tneg,
  ci = cpos,
  di = cneg,
  data = dat.bcg
)
dat$author <- make.unique(dat$author)

# model
set.seed(123)
priors <- c(
  prior(normal(0, 1), class = Intercept),
  prior(cauchy(0, 0.5), class = sd)
)
model <- suppressWarnings(
  brm(yi | se(vi) ~ 1 + (1 | author), data = dat, refresh = 0, silent = 2)
)

# result
mp <- model_parameters(model)
plot(mp)

Plot method for describing distributions of vectors

Description

The plot() method for the parameters::describe_distribution() function.

Usage

## S3 method for class 'see_parameters_distribution'
plot(
  x,
  dispersion = FALSE,
  dispersion_alpha = 0.3,
  dispersion_color = "#3498db",
  dispersion_style = c("ribbon", "curve"),
  size_bar = 0.7,
  highlight = NULL,
  highlight_color = NULL,
  ...
)

Arguments

x

An object.
dispersion

Logical. If TRUE, a range of dispersion for each variable to the plot will be added.
dispersion_alpha

Numeric value specifying the transparency level of dispersion ribbon.
dispersion_color

Character specifying the color of dispersion ribbon.
dispersion_style

Character describing the style of dispersion area. "ribbon" for a ribbon, "curve" for a normal-curve.
size_bar

Size of bar geoms.
highlight

A vector with names of categories in x that should be highlighted.
highlight_color

A vector of color values for highlighted categories. The remaining (non-highlighted) categories will be filled with a lighter grey.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(parameters)
set.seed(333)
x <- sample(1:100, 1000, replace = TRUE)
result <- describe_distribution(x)
result
plot(result)

Plot method for model parameters

Description

The plot() method for the parameters::model_parameters() function.

Usage

## S3 method for class 'see_parameters_model'
plot(
  x,
  show_intercept = FALSE,
  size_point = 0.8,
  size_text = NA,
  sort = NULL,
  n_columns = NULL,
  type = c("forest", "funnel"),
  weight_points = TRUE,
  show_labels = FALSE,
  show_estimate = TRUE,
  show_interval = TRUE,
  show_density = FALSE,
  log_scale = FALSE,
  ...
)

## S3 method for class 'see_parameters_sem'
plot(
  x,
  data = NULL,
  component = c("regression", "correlation", "loading"),
  type = component,
  threshold_coefficient = NULL,
  threshold_p = NULL,
  ci = TRUE,
  size_point = 22,
  ...
)

Arguments

x

An object.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
size_point

Numeric specifying size of point-geoms.
size_text

Numeric value specifying size of text labels.
sort

The behavior of this argument depends on the plotting contexts.

  • Plotting model parameters: If NULL, coefficients are plotted in the order as they appear in the summary. Setting sort = "ascending" or sort = "descending" sorts coefficients in ascending or descending order, respectively. Setting sort = TRUE is the same as sort = "ascending".

  • Plotting Bayes factors: Sort pie-slices by posterior probability (descending)?

n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
type

Character indicating the type of plot. Only applies for model parameters from meta-analysis objects (e.g. metafor).
weight_points

Logical. If TRUE, for meta-analysis objects, point size will be adjusted according to the study-weights.
show_labels

Logical. If TRUE, text labels are displayed.
show_estimate

Should the point estimate of each parameter be shown? (default: TRUE)
show_interval

Should the compatibility interval(s) of each parameter be shown? (default: TRUE)
show_density

Should the compatibility density (i.e., posterior, bootstrap, or confidence density) of each parameter be shown? (default: FALSE)
log_scale

Should exponentiated coefficients (e.g., odds-ratios) be plotted on a log scale? (default: FALSE)
...

Arguments passed to or from other methods.
data

The original data used to create this object. Can be a statistical model.
component

Character indicating which component of the model should be plotted.
threshold_coefficient

Numeric, threshold at which value coefficients will be displayed.
threshold_p

Numeric, threshold at which value p-values will be displayed.
ci

Logical, whether confidence intervals should be added to the plot.

Value

A ggplot2-object.

Examples

library(parameters)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- model_parameters(m)
result
plot(result)

Plot method for principal component analysis

Description

The plot() method for the parameters::principal_components() function.

Usage

## S3 method for class 'see_parameters_pca'
plot(
  x,
  type = c("bar", "line"),
  size_text = 3.5,
  text_color = "black",
  size = 1,
  show_labels = TRUE,
  ...
)

Arguments

x

An object.
type

Character vector, indicating the type of plot. Options are three different shapes to represent component loadings; "bar" (default) for a horizontal bar chart, or "line" for a horizontal point and line chart.
size_text

Numeric value specifying size of text labels.
text_color

Character specifying color of text labels.
size

Depending on type, a numeric value specifying size of bars, lines, or segments.
show_labels

Logical. If TRUE, text labels are displayed.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(parameters)
data(mtcars)
result <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
result
plot(result)

Plot method for simulated model parameters

Description

The plot() method for the parameters::simulate_parameters() function.

Usage

## S3 method for class 'see_parameters_simulate'
plot(
  x,
  data = NULL,
  stack = TRUE,
  show_intercept = FALSE,
  n_columns = NULL,
  normalize_height = FALSE,
  size_line = 0.9,
  posteriors_alpha = 0.7,
  centrality = "median",
  ci = 0.95,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
stack

Logical. If TRUE, densities are plotted as stacked lines. Else, densities are plotted for each parameter among each other.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
normalize_height

Logical. If TRUE, height of density-areas is "normalized", to avoid overlap. In certain cases when the range of a distribution of simulated draws is narrow for some parameters, this may result in very flat density-areas. In such cases, set normalize_height = FALSE.
size_line

Numeric value specifying size of line geoms.
posteriors_alpha

Numeric value specifying alpha for the posterior distributions.
centrality

Character specifying the point-estimate (centrality index) to compute. Can be "median", "mean" or "MAP".
ci

Numeric value of probability of the CI (between 0 and 1) to be estimated. Default to 0.95.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(parameters)
m <<- lm(mpg ~ wt + cyl + gear, data = mtcars)
result <- simulate_parameters(m)
result
plot(result)

Plot method for ROC curves

Description

The plot() method for the performance::performance_roc() function.

Usage

## S3 method for class 'see_performance_roc'
plot(x, ...)

Arguments

x

An object.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(performance)
data(iris)
set.seed(123)
iris$y <- rbinom(nrow(iris), size = 1, .3)

folds <- sample(nrow(iris), size = nrow(iris) / 8, replace = FALSE)
test_data <- iris[folds, ]
train_data <- iris[-folds, ]

model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
result <- performance_roc(model, new_data = test_data)
result
plot(result)

Plot method for check model for (non-)normality of residuals

Description

The plot() method for the performance::check_residuals() resp. performance::simulate_residuals() function.

Usage

## S3 method for class 'see_performance_simres'
plot(
  x,
  size_line = 0.8,
  size_point = 2,
  size_title = 12,
  size_axis_title = base_size,
  base_size = 10,
  alpha = 0.2,
  dot_alpha = 0.8,
  colors = c("#3aaf85", "#1b6ca8"),
  detrend = FALSE,
  transform = NULL,
  style = theme_lucid,
  ...
)

Arguments

x

An object.
size_line

Numeric value specifying size of line geoms.
size_point

Numeric specifying size of point-geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
alpha

Numeric value specifying alpha level of the confidence bands.
dot_alpha

Numeric value specifying alpha level of the point geoms.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
detrend

Logical that decides if Q-Q and P-P plots should be de-trended (also known as worm plots).
transform

Function to transform the residuals. If NULL (default), no transformation is applied and uniformly distributed residuals are expected. See argument quantileFuntion in ?DHARMa:::residuals.DHARMa for more details.
style

A ggplot2-theme.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

data(Salamanders, package = "glmmTMB")
model <- glmmTMB::glmmTMB(
  count ~ mined + spp + (1 | site),
  family = poisson(),
  data = Salamanders
)
simulated_residuals <- performance::simulate_residuals(model)
plot(simulated_residuals)

# or
simulated_residuals <- performance::simulate_residuals(model)
result <- performance::check_residuals(simulated_residuals)
plot(result)

Plot method for point estimates of posterior samples

Description

The plot() method for the bayestestR::point_estimate().

Usage

## S3 method for class 'see_point_estimate'
plot(
  x,
  data = NULL,
  size_point = 2,
  size_text = 3.5,
  panel = TRUE,
  show_labels = TRUE,
  show_intercept = FALSE,
  priors = FALSE,
  priors_alpha = 0.4,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
size_point

Numeric specifying size of point-geoms.
size_text

Numeric value specifying size of text labels.
panel

Logical, if TRUE, plots are arranged as panels; else, single plots are returned.
show_labels

Logical. If TRUE, the text labels for the point estimates (i.e. "Mean", "Median" and/or "MAP") are shown. You may set show_labels = FALSE in case of overlapping labels, and add your own legend or footnote to the plot.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
priors

Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
priors_alpha

Numeric value specifying alpha for the prior distributions.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- point_estimate(m, centrality = "median")
result
plot(result)

Plot method for Region of Practical Equivalence

Description

The plot() method for the bayestestR::rope().

Usage

## S3 method for class 'see_rope'
plot(
  x,
  data = NULL,
  rope_alpha = 0.5,
  rope_color = "cadetblue",
  show_intercept = FALSE,
  n_columns = 1,
  ...
)

Arguments

x

An object.
data

The original data used to create this object. Can be a statistical model.
rope_alpha

Numeric specifying transparency level of ROPE ribbon.
rope_color

Character specifying color of ROPE ribbon.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
n_columns

For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- rope(m)
result
plot(result)

Plot method for support intervals

Description

The plot() method for the bayestestR::si().

Usage

## S3 method for class 'see_si'
plot(
  x,
  si_color = "#0171D3",
  si_alpha = 0.2,
  show_intercept = FALSE,
  support_only = FALSE,
  ...
)

Arguments

x

An object.
si_color

Character specifying color of SI ribbon.
si_alpha

Numeric value specifying Transparency level of SI ribbon.
show_intercept

Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
support_only

Logical. Decides whether to plot only the support data, or show the "raw" prior and posterior distributions? Only applies when plotting bayestestR::si().
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- si(m, verbose = FALSE)
result
plot(result)

Multiple plots side by side

Description

A wrapper around patchwork to plot multiple figures side by side on the same page.

Usage

plots(
  ...,
  n_rows = NULL,
  n_columns = NULL,
  guides = NULL,
  tags = FALSE,
  tag_prefix = NULL,
  tag_suffix = NULL,
  tag_sep = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  theme = NULL
)

Arguments

...

Multiple ggplots or a list containing ggplot objects
n_rows

Number of rows to align plots.
n_columns

Number of columns to align plots.
guides

A string specifying how guides should be treated in the layout. 'collect' will collect shared guides across plots, removing duplicates. 'keep' will keep guides alongside their plot. 'auto' will inherit from a higher patchwork level (if any). See patchwork::plot_layout() for details.
tags

Add tags to your subfigures. Can be NULL to omit (default) or a character vector containing tags for each plot. Automatic tags can also be generated with '1' for Arabic numerals, 'A' for uppercase Latin letters, 'a' for lowercase Latin letters, 'I' for uppercase Roman numerals, and 'i' for lowercase Roman numerals. For backwards compatibility, can also be FALSE (equivalent to NULL), NA (equivalent to NULL), or TRUE (equivalent to 'A').
tag_prefix, tag_suffix

Text strings that should appear before or after the tag.
tag_sep

Text string giving the separator to use between different tag levels.
title, subtitle, caption

Text strings to use for the various plot annotations to add to the composed patchwork.
theme

A ggplot theme specification to use for the plot. Only elements related to titles, caption, and tags, as well as plot margin and background, are used.

Details

See the patchwork documentation for more advanced control of plot layouts.

Examples

library(ggplot2)
library(see)

p1 <- ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point()
p2 <- ggplot(mtcars, aes(x = mpg)) +
  geom_density()
p3 <- ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar() +
  scale_x_discrete("cyl")

plots(p1, p2)
plots(p1, p2, n_columns = 2, tags = "A")
plots(
  p1, p2, p3,
  n_columns = 1, tags = c("Fig. 1", "Fig. 2", "Fig. 3"),
  title = "The surprising truth about mtcars"
)

Plot method for posterior predictive checks

Description

The plot() method for the performance::check_predictions() function.

Usage

## S3 method for class 'see_performance_pp_check'
print(
  x,
  size_line = 0.5,
  size_point = 2,
  size_bar = 0.7,
  size_axis_title = base_size,
  size_title = 12,
  base_size = 10,
  line_alpha = 0.15,
  style = theme_lucid,
  colors = unname(social_colors(c("green", "blue"))),
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  x_limits = NULL,
  ...
)

## S3 method for class 'see_performance_pp_check'
plot(
  x,
  size_line = 0.5,
  size_point = 2,
  size_bar = 0.7,
  size_axis_title = base_size,
  size_title = 12,
  base_size = 10,
  line_alpha = 0.15,
  style = theme_lucid,
  colors = unname(social_colors(c("green", "blue"))),
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  x_limits = NULL,
  ...
)

Arguments

x

An object.
size_line

Numeric value specifying size of line geoms.
size_point

Numeric specifying size of point-geoms.
size_bar

Size of bar geoms.
base_size, size_axis_title, size_title

Numeric value specifying size of axis and plot titles.
line_alpha

Numeric value specifying alpha of lines indicating yrep.
style

A ggplot2-theme.
colors

Character vector of length two, indicating the colors (in hex-format) for points and line.
type

Plot type for the posterior predictive checks plot. Can be "density" (default), "discrete_dots", "discrete_interval" or "discrete_both" (the ⁠discrete_*⁠ options are appropriate for models with discrete - binary, integer or ordinal etc. - outcomes).
x_limits

Numeric vector of length 2 specifying the limits of the x-axis. If not NULL, will zoom in the x-axis to the specified limits.
...

Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

library(performance)

model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
check_predictions(model)

# dot-plot style for count-models
d <- iris
d$poisson_var <- rpois(150, 1)
model <- glm(
  poisson_var ~ Species + Petal.Length + Petal.Width,
  data = d,
  family = poisson()
)
out <- check_predictions(model)
plot(out, type = "discrete_dots")

Blue-brown color palette

Description

A blue-brown color palette. Use scale_color_bluebrown_d() for discrete categories and scale_color_bluebrown_c() for a continuous scale.

Usage

scale_color_bluebrown(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_bluebrown_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_bluebrown_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_bluebrown(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_bluebrown_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_bluebrown_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_bluebrown(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_bluebrown_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_bluebrown_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_bluebrown_d()

Color palettes from color-hex

Description

This function creates color scales based on palettes from https://www.color-hex.com/. This website provides a large number of user-submitted color palettes. This function downloads a requested color palette from https://www.color-hex.com/. and creates a {ggplot2} color scale from the provided hex codes.

Use scale_color_colorhex_d for discrete categories and scale_color_colorhex_c for a continuous scale.

Usage

scale_color_colorhex(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_colorhex_d(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_colorhex_c(
  palette = 1014416,
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_colorhex(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_colorhex_c(
  palette = 1014416,
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_colorhex_d(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_colorhex(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_colorhex_d(
  palette = 1014416,
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_colorhex_c(
  palette = 1014416,
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

The numeric code for a palette at https://www.color-hex.com/. For example, 1014416 for the Josiah color palette (number 1014416).
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Note

The default Josiah color palette (number 1014416) is available without an internet connection. All other color palettes require an internet connection to download and access.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_color_colorhex_d(palette = 1014416)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_colorhex_d(palette = 1014416)

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_colorhex_c(palette = 1014416)

Flat UI color palette

Description

The palette based on Flat UI. Use scale_color_flat_d for discrete categories and scale_color_flat_c for a continuous scale.

Usage

scale_color_flat(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_flat_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_flat_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_flat(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_flat_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_flat_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_flat(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_flat_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_flat_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments passed to discrete_scale() when discrete is TRUE or to scale_color_gradientn() when discrete is FALSE.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_flat_d()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_flat_d(palette = "ice")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_flat_c(palette = "rainbow")

Material design color palette

Description

The palette based on material design colors. Use scale_color_material_d() for discrete categories and scale_color_material_c() for a continuous scale.

Usage

scale_color_material(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_material_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_material_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_material(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_material_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_material_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_material(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_material_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_material_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_material_d()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_material_d(palette = "ice")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_material_c(palette = "rainbow")

Metro color palette

Description

The palette based on Metro Metro colors. Use scale_color_metro_d for discrete categories and scale_color_metro_c for a continuous scale.

Usage

scale_color_metro(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_metro_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_metro_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_metro(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_metro_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_metro_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_metro(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_metro_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_metro_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_metro_d()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_metro_d(palette = "ice")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_metro_c(palette = "rainbow")

Okabe-Ito color palette

Description

The Okabe-Ito color palette was proposed by Okabe and Ito (2008) as a qualitative color palette that is accessible to people with a variety of forms of color vision deficiency. In addition to being accessible, it includes 9 vivid colors that are readily nameable and include colors that correspond to major primary and secondary colors (e.g., red, yellow, blue).

Usage

scale_color_okabeito(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "color",
  ...
)

scale_fill_okabeito(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "fill",
  ...
)

scale_colour_okabeito(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "color",
  ...
)

scale_colour_oi(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "color",
  ...
)

scale_color_oi(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "color",
  ...
)

scale_fill_oi(
  palette = "full",
  reverse = FALSE,
  order = 1:9,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
reverse

Boolean indicating whether the palette should be reversed.
order

A vector of numbers from 1 to 9 indicating the order of colors to use (default: 1:9)
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Details

The Okabe-Ito palette is included in the base R grDevices::palette.colors(). These functions make this palette easier to use with ggplot2.

The original Okabe-Ito palette's "yellow" color is "#F0E442". This color is very bright and often does not show up well on white backgrounds (see here) for a discussion of this issue). Accordingly, by default, this function uses a darker more "amber" color for "yellow" ("#F5C710"). This color is the "yellow" color used in base R >4.0's default color palette. The palettes "full" and "black_first" use this darker yellow color. For the original yellow color suggested by Okabe and Ito ("#F0E442"), use palettes "full_original" or "black_first_original".

The Okabe-Ito palette is only available as a discrete palette. For color-accessible continuous variables, consider the viridis palettes.

References

Okabe, M., & Ito, K. (2008). Color universal design (CUD): How to make figures and presentations that are friendly to colorblind people. https://jfly.uni-koeln.de/color/#pallet (Original work published 2002)

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_okabeito()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_oi(palette = "black_first")

# for the original brighter yellow color suggested by Okabe and Ito
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_oi(palette = "full")

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_oi(order = c(1, 5, 6, 2, 4, 3, 7))

Pizza color palette

Description

The palette based on authentic neapolitan pizzas. Use scale_color_pizza_d() for discrete categories and scale_color_pizza_c() for a continuous scale.

Usage

scale_color_pizza(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_pizza_d(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_pizza_c(
  palette = "margherita",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_pizza(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_pizza_c(
  palette = "margherita",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_pizza_d(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_pizza(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_pizza_d(
  palette = "margherita",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_pizza_c(
  palette = "margherita",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Pizza type. Can be "margherita" (default), "margherita_crust", "diavola" or "diavola_crust".
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_pizza_d()

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_pizza_c()

See color palette

Description

The See color palette. Use scale_color_see_d() for discrete categories and scale_color_see_c() for a continuous scale.

Usage

scale_color_see(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_see_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_see_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_see(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_see_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_see_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_see(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_see_d(
  palette = "contrast",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_see_c(
  palette = "contrast",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_see_d()

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
  geom_point() +
  theme_abyss() +
  scale_colour_see(palette = "light")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_see_c(palette = "rainbow")

Social color palette

Description

The palette based Social colors. Use scale_color_social_d for discrete categories and scale_color_social_c for a continuous scale.

Usage

scale_color_social(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_social_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_color_social_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_social(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_social_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_colour_social_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "color",
  ...
)

scale_fill_social(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_social_d(
  palette = "complement",
  discrete = TRUE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

scale_fill_social_c(
  palette = "complement",
  discrete = FALSE,
  reverse = FALSE,
  aesthetics = "fill",
  ...
)

Arguments

palette

Character name of palette. Depending on the color scale, can be "full", "ice", "rainbow", "complement", "contrast", "light" (for dark themes), "black_first", full_original, or black_first_original.
discrete

Boolean indicating whether color aesthetic is discrete or not.
reverse

Boolean indicating whether the palette should be reversed.
aesthetics

A vector of names of the aesthetics that this scale should be applied to (e.g., c('color', 'fill')).
...

Additional arguments to pass to colorRampPalette().

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_social_d()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_social_d(palette = "ice")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_social_c(palette = "rainbow")

Extract See colors as hex codes

Description

Can be used to get the hex code of specific colors from the See color palette. Use see_colors() to see all available colors.

Usage

see_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

see_colors()

see_colors("indigo", "lime")

Extract Social colors as hex codes

Description

Can be used to get the hex code of specific colors from the Social color palette. Use social_colors() to see all available colors.

Usage

social_colors(...)

Arguments

...

Character names of colors.

Value

A character vector with color-codes.

Examples

social_colors()

social_colors("dark red", "teal")

Abyss theme

Description

A deep dark blue theme for ggplot.

Usage

theme_abyss(
  base_size = 11,
  base_family = "",
  plot.title.size = 15,
  plot.title.face = "plain",
  plot.title.space = 20,
  plot.title.position = "plot",
  legend.position = "right",
  axis.title.space = 20,
  legend.title.size = 13,
  legend.text.size = 12,
  axis.title.size = 13,
  axis.title.face = "plain",
  axis.text.size = 12,
  axis.text.angle = NULL,
  tags.size = 15,
  tags.face = "bold"
)

Arguments

base_size

base font size, given in pts.
base_family

base font family
plot.title.size

Title size in pts. Can be "none".
plot.title.face

Title font face ("plain", "italic", "bold", "bold.italic").
plot.title.space

Title spacing.
plot.title.position

Alignment of the plot title/subtitle and caption. The setting for plot.title.position applies to both the title and the subtitle. A value of "panel" (the default) means that titles and/or caption are aligned to the plot panels. A value of "plot" means that titles and/or caption are aligned to the entire plot (minus any space for margins and plot tag).
legend.position

the default position of legends ("none", "left", "right", "bottom", "top", "inside")
axis.title.space

Axis title spacing.
legend.title.size

Legend elements text size in pts.
legend.text.size

Legend elements text size in pts. Can be "none".
axis.title.size

Axis title text size in pts.
axis.title.face

Axis font face ("plain", "italic", "bold", "bold.italic").
axis.text.size

Axis text size in pts.
axis.text.angle

Rotate the x axis labels.
tags.size

Tags text size in pts.
tags.face

Tags font face ("plain", "italic", "bold", "bold.italic").

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point(color = "white") +
  theme_abyss()

Blackboard dark theme

Description

A modern, sleek and dark theme for ggplot.

Usage

theme_blackboard(
  base_size = 11,
  base_family = "",
  plot.title.size = 15,
  plot.title.face = "plain",
  plot.title.space = 20,
  plot.title.position = "plot",
  legend.position = "right",
  axis.title.space = 20,
  legend.title.size = 13,
  legend.text.size = 12,
  axis.title.size = 13,
  axis.title.face = "plain",
  axis.text.size = 12,
  axis.text.angle = NULL,
  tags.size = 15,
  tags.face = "bold"
)

Arguments

base_size

base font size, given in pts.
base_family

base font family
plot.title.size

Title size in pts. Can be "none".
plot.title.face

Title font face ("plain", "italic", "bold", "bold.italic").
plot.title.space

Title spacing.
plot.title.position

Alignment of the plot title/subtitle and caption. The setting for plot.title.position applies to both the title and the subtitle. A value of "panel" (the default) means that titles and/or caption are aligned to the plot panels. A value of "plot" means that titles and/or caption are aligned to the entire plot (minus any space for margins and plot tag).
legend.position

the default position of legends ("none", "left", "right", "bottom", "top", "inside")
axis.title.space

Axis title spacing.
legend.title.size

Legend elements text size in pts.
legend.text.size

Legend elements text size in pts. Can be "none".
axis.title.size

Axis title text size in pts.
axis.title.face

Axis font face ("plain", "italic", "bold", "bold.italic").
axis.text.size

Axis text size in pts.
axis.text.angle

Rotate the x axis labels.
tags.size

Tags text size in pts.
tags.face

Tags font face ("plain", "italic", "bold", "bold.italic").

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point(color = "white") +
  theme_blackboard()

Lucid theme

Description

A light, clear theme for ggplot.

Usage

theme_lucid(
  base_size = 11,
  base_family = "",
  plot.title.size = 12,
  plot.title.face = "plain",
  plot.title.space = 15,
  plot.title.position = "plot",
  legend.position = "right",
  axis.title.space = 10,
  legend.title.size = 11,
  legend.text.size = 10,
  axis.title.size = 11,
  axis.title.face = "plain",
  axis.text.size = 10,
  axis.text.angle = NULL,
  tags.size = 11,
  tags.face = "plain"
)

Arguments

base_size

base font size, given in pts.
base_family

base font family
plot.title.size

Title size in pts. Can be "none".
plot.title.face

Title font face ("plain", "italic", "bold", "bold.italic").
plot.title.space

Title spacing.
plot.title.position

Alignment of the plot title/subtitle and caption. The setting for plot.title.position applies to both the title and the subtitle. A value of "panel" (the default) means that titles and/or caption are aligned to the plot panels. A value of "plot" means that titles and/or caption are aligned to the entire plot (minus any space for margins and plot tag).
legend.position

the default position of legends ("none", "left", "right", "bottom", "top", "inside")
axis.title.space

Axis title spacing.
legend.title.size

Legend elements text size in pts.
legend.text.size

Legend elements text size in pts. Can be "none".
axis.title.size

Axis title text size in pts.
axis.title.face

Axis font face ("plain", "italic", "bold", "bold.italic").
axis.text.size

Axis text size in pts.
axis.text.angle

Rotate the x axis labels.
tags.size

Tags text size in pts.
tags.face

Tags font face ("plain", "italic", "bold", "bold.italic").

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point(color = "white") +
  theme_lucid()

The easystats' minimal theme

Description

A modern, sleek and elegant theme for ggplot.

Usage

theme_modern(
  base_size = 11,
  base_family = "",
  plot.title.size = 15,
  plot.title.face = "plain",
  plot.title.space = 20,
  plot.title.position = "plot",
  legend.position = "right",
  axis.title.space = 20,
  legend.title.size = 13,
  legend.text.size = 12,
  axis.title.size = 13,
  axis.title.face = "plain",
  axis.text.size = 12,
  axis.text.angle = NULL,
  tags.size = 15,
  tags.face = "bold"
)

Arguments

base_size

base font size, given in pts.
base_family

base font family
plot.title.size

Title size in pts. Can be "none".
plot.title.face

Title font face ("plain", "italic", "bold", "bold.italic").
plot.title.space

Title spacing.
plot.title.position

Alignment of the plot title/subtitle and caption. The setting for plot.title.position applies to both the title and the subtitle. A value of "panel" (the default) means that titles and/or caption are aligned to the plot panels. A value of "plot" means that titles and/or caption are aligned to the entire plot (minus any space for margins and plot tag).
legend.position

the default position of legends ("none", "left", "right", "bottom", "top", "inside")
axis.title.space

Axis title spacing.
legend.title.size

Legend elements text size in pts.
legend.text.size

Legend elements text size in pts. Can be "none".
axis.title.size

Axis title text size in pts.
axis.title.face

Axis font face ("plain", "italic", "bold", "bold.italic").
axis.text.size

Axis text size in pts.
axis.text.angle

Rotate the x axis labels.
tags.size

Tags text size in pts.
tags.face

Tags font face ("plain", "italic", "bold", "bold.italic").

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  theme_modern()

Themes for radar plots

Description

theme_radar() is a light, clear theme for ggplot radar-plots, while theme_radar_dark() is a dark variant of theme_radar().

Usage

theme_radar(
  base_size = 11,
  base_family = "",
  plot.title.size = 12,
  plot.title.face = "plain",
  plot.title.space = 15,
  plot.title.position = "plot",
  legend.position = "right",
  axis.title.space = 15,
  legend.title.size = 11,
  legend.text.size = 10,
  axis.title.size = 11,
  axis.title.face = "plain",
  axis.text.size = 10,
  axis.text.angle = NULL,
  tags.size = 11,
  tags.face = "plain"
)

theme_radar_dark(
  base_size = 11,
  base_family = "",
  plot.title.size = 12,
  plot.title.face = "plain",
  plot.title.space = 15,
  legend.position = "right",
  axis.title.space = 15,
  legend.title.size = 11,
  legend.text.size = 10,
  axis.title.size = 11,
  axis.title.face = "plain",
  axis.text.size = 10,
  axis.text.angle = NULL,
  tags.size = 11,
  tags.face = "plain"
)

Arguments

base_size

base font size, given in pts.
base_family

base font family
plot.title.size

Title size in pts. Can be "none".
plot.title.face

Title font face ("plain", "italic", "bold", "bold.italic").
plot.title.space

Title spacing.
plot.title.position

Alignment of the plot title/subtitle and caption. The setting for plot.title.position applies to both the title and the subtitle. A value of "panel" (the default) means that titles and/or caption are aligned to the plot panels. A value of "plot" means that titles and/or caption are aligned to the entire plot (minus any space for margins and plot tag).
legend.position

the default position of legends ("none", "left", "right", "bottom", "top", "inside")
axis.title.space

Axis title spacing.
legend.title.size

Legend elements text size in pts.
legend.text.size

Legend elements text size in pts. Can be "none".
axis.title.size

Axis title text size in pts.
axis.title.face

Axis font face ("plain", "italic", "bold", "bold.italic").
axis.text.size

Axis text size in pts.
axis.text.angle

Rotate the x axis labels.
tags.size

Tags text size in pts.
tags.face

Tags font face ("plain", "italic", "bold", "bold.italic").

See Also

coord_radar()

Examples

data <- datawizard::reshape_longer(
  aggregate(iris[-5], list(Species = iris$Species), mean),
  c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
)

ggplot(
  data,
  aes(
    x = name,
    y = value,
    color = Species,
    group = Species,
    fill = Species
  )
) +
  geom_polygon(linewidth = 1, alpha = 0.1) +
  coord_radar() +
  theme_radar()