Package 'ggplot2'

Title: Create Elegant Data Visualisations Using the Grammar of Graphics
Description: A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Authors: Hadley Wickham [aut] , Winston Chang [aut] , Lionel Henry [aut], Thomas Lin Pedersen [aut, cre] , Kohske Takahashi [aut], Claus Wilke [aut] , Kara Woo [aut] , Hiroaki Yutani [aut] , Dewey Dunnington [aut] , Teun van den Brand [aut] , Posit, PBC [cph, fnd]
Maintainer: Thomas Lin Pedersen <thomas.pedersen@posit.co>
License: MIT + file LICENSE
Version: 3.5.0.9000
Built: 2024-02-29 19:20:52 UTC
Source: https://github.com/tidyverse/ggplot2

Help Index


Add components to a plot

Description

+ is the key to constructing sophisticated ggplot2 graphics. It allows you to start simple, then get more and more complex, checking your work at each step.

Usage

## S3 method for class 'gg'
e1 + e2

e1 %+% e2

Arguments

e1

An object of class ggplot() or a theme().

e2

A plot component, as described below.

What can you add?

You can add any of the following types of objects:

To replace the current default data frame, you must use ⁠%+%⁠, due to S3 method precedence issues.

You can also supply a list, in which case each element of the list will be added in turn.

See Also

theme()

Examples

base <-
 ggplot(mpg, aes(displ, hwy)) +
 geom_point()
base + geom_smooth()

# To override the data, you must use %+%
base %+% subset(mpg, fl == "p")

# Alternatively, you can add multiple components with a list.
# This can be useful to return from a function.
base + list(subset(mpg, fl == "p"), geom_smooth())

Construct aesthetic mappings

Description

Aesthetic mappings describe how variables in the data are mapped to visual properties (aesthetics) of geoms. Aesthetic mappings can be set in ggplot() and in individual layers.

Usage

aes(x, y, ...)

Arguments

x, y, ...

<data-masking> List of name-value pairs in the form aesthetic = variable describing which variables in the layer data should be mapped to which aesthetics used by the paired geom/stat. The expression variable is evaluated within the layer data, so there is no need to refer to the original dataset (i.e., use ggplot(df, aes(variable)) instead of ggplot(df, aes(df$variable))). The names for x and y aesthetics are typically omitted because they are so common; all other aesthetics must be named.

Details

This function also standardises aesthetic names by converting color to colour (also in substrings, e.g., point_color to point_colour) and translating old style R names to ggplot names (e.g., pch to shape and cex to size).

Value

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

Quasiquotation

aes() is a quoting function. This means that its inputs are quoted to be evaluated in the context of the data. This makes it easy to work with variables from the data frame because you can name those directly. The flip side is that you have to use quasiquotation to program with aes(). See a tidy evaluation tutorial such as the dplyr programming vignette to learn more about these techniques.

See Also

vars() for another quoting function designed for faceting specifications.

Run vignette("ggplot2-specs") to see an overview of other aesthetics that can be modified.

Delayed evaluation for working with computed variables.

Other aesthetics documentation: aes_colour_fill_alpha, aes_group_order, aes_linetype_size_shape, aes_position

Examples

aes(x = mpg, y = wt)
aes(mpg, wt)

# You can also map aesthetics to functions of variables
aes(x = mpg ^ 2, y = wt / cyl)

# Or to constants
aes(x = 1, colour = "smooth")

# Aesthetic names are automatically standardised
aes(col = x)
aes(fg = x)
aes(color = x)
aes(colour = x)

# aes() is passed to either ggplot() or specific layer. Aesthetics supplied
# to ggplot() are used as defaults for every layer.
ggplot(mpg, aes(displ, hwy)) + geom_point()
ggplot(mpg) + geom_point(aes(displ, hwy))

# Tidy evaluation ----------------------------------------------------
# aes() automatically quotes all its arguments, so you need to use tidy
# evaluation to create wrappers around ggplot2 pipelines. The
# simplest case occurs when your wrapper takes dots:
scatter_by <- function(data, ...) {
  ggplot(data) + geom_point(aes(...))
}
scatter_by(mtcars, disp, drat)

# If your wrapper has a more specific interface with named arguments,
# you need the "embrace operator":
scatter_by <- function(data, x, y) {
  ggplot(data) + geom_point(aes({{ x }}, {{ y }}))
}
scatter_by(mtcars, disp, drat)

# Note that users of your wrapper can use their own functions in the
# quoted expressions and all will resolve as it should!
cut3 <- function(x) cut_number(x, 3)
scatter_by(mtcars, cut3(disp), drat)

Colour related aesthetics: colour, fill, and alpha

Description

These aesthetics parameters change the colour (colour and fill) and the opacity (alpha) of geom elements on a plot. Almost every geom has either colour or fill (or both), as well as can have their alpha modified. Modifying colour on a plot is a useful way to enhance the presentation of data, often especially when a plot graphs more than two variables.

Colour and fill

The colour aesthetic is used to draw lines and strokes, such as in geom_point() and geom_line(), but also the line contours of geom_rect() and geom_polygon(). The fill aesthetic is used to colour the inside areas of geoms, such as geom_rect() and geom_polygon(), but also the insides of shapes 21-25 of geom_point().

Colours and fills can be specified in the following ways:

Alpha

Alpha refers to the opacity of a geom. Values of alpha range from 0 to 1, with lower values corresponding to more transparent colors.

Alpha can additionally be modified through the colour or fill aesthetic if either aesthetic provides color values using an rgb specification ("#RRGGBBAA"), where AA refers to transparency values.

See Also

Other aesthetics documentation: aes(), aes_group_order, aes_linetype_size_shape, aes_position

Examples

# Bar chart example
p <- ggplot(mtcars, aes(factor(cyl)))
# Default plotting
p + geom_bar()
# To change the interior colouring use fill aesthetic
p + geom_bar(fill = "red")
# Compare with the colour aesthetic which changes just the bar outline
p + geom_bar(colour = "red")
# Combining both, you can see the changes more clearly
p + geom_bar(fill = "white", colour = "red")
# Both colour and fill can take an rgb specification.
p + geom_bar(fill = "#00abff")
# Use NA for a completely transparent colour.
p + geom_bar(fill = NA, colour = "#00abff")

# Colouring scales differ depending on whether a discrete or
# continuous variable is being mapped. For example, when mapping
# fill to a factor variable, a discrete colour scale is used.
ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar()

# When mapping fill to continuous variable a continuous colour
# scale is used.
ggplot(faithfuld, aes(waiting, eruptions)) +
  geom_raster(aes(fill = density))

# Some geoms only use the colour aesthetic but not the fill
# aesthetic (e.g. geom_point() or geom_line()).
p <- ggplot(economics, aes(x = date, y = unemploy))
p + geom_line()
p + geom_line(colour = "green")
p + geom_point()
p + geom_point(colour = "red")

# For large datasets with overplotting the alpha
# aesthetic will make the points more transparent.
set.seed(1)
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
p  <- ggplot(df, aes(x,y))
p + geom_point()
p + geom_point(alpha = 0.5)
p + geom_point(alpha = 1/10)

# Alpha can also be used to add shading.
p <- ggplot(economics, aes(x = date, y = unemploy)) + geom_line()
p
yrng <- range(economics$unemploy)
p <- p +
  geom_rect(
    aes(NULL, NULL, xmin = start, xmax = end, fill = party),
    ymin = yrng[1], ymax = yrng[2], data = presidential
  )
p
p + scale_fill_manual(values = alpha(c("blue", "red"), .3))

Control aesthetic evaluation

Description

Most aesthetics are mapped from variables found in the data. Sometimes, however, you want to delay the mapping until later in the rendering process. ggplot2 has three stages of the data that you can map aesthetics from, and three functions to control at which stage aesthetics should be evaluated.

after_stat() replaces the old approaches of using either stat(), e.g. stat(density), or surrounding the variable names with .., e.g. ..density...

Usage

# These functions can be used inside the `aes()` function
# used as the `mapping` argument in layers, for example:
# geom_density(mapping = aes(y = after_stat(scaled)))

after_stat(x)

after_scale(x)

stage(start = NULL, after_stat = NULL, after_scale = NULL)

Arguments

x

<data-masking> An aesthetic expression using variables calculated by the stat (after_stat()) or layer aesthetics (after_scale()).

start

<data-masking> An aesthetic expression using variables from the layer data.

after_stat

<data-masking> An aesthetic expression using variables calculated by the stat.

after_scale

<data-masking> An aesthetic expression using layer aesthetics.

Staging

Below follows an overview of the three stages of evaluation and how aesthetic evaluation can be controlled.

Stage 1: direct input

The default is to map at the beginning, using the layer data provided by the user. If you want to map directly from the layer data you should not do anything special. This is the only stage where the original layer data can be accessed.

# 'x' and 'y' are mapped directly
ggplot(mtcars) + geom_point(aes(x = mpg, y = disp))

Stage 2: after stat transformation

The second stage is after the data has been transformed by the layer stat. The most common example of mapping from stat transformed data is the height of bars in geom_histogram(): the height does not come from a variable in the underlying data, but is instead mapped to the count computed by stat_bin(). In order to map from stat transformed data you should use the after_stat() function to flag that evaluation of the aesthetic mapping should be postponed until after stat transformation. Evaluation after stat transformation will have access to the variables calculated by the stat, not the original mapped values. The 'computed variables' section in each stat lists which variables are available to access.

# The 'y' values for the histogram are computed by the stat
ggplot(faithful, aes(x = waiting)) +
  geom_histogram()

# Choosing a different computed variable to display, matching up the
# histogram with the density plot
ggplot(faithful, aes(x = waiting)) +
  geom_histogram(aes(y = after_stat(density))) +
  geom_density()

Stage 3: after scale transformation

The third and last stage is after the data has been transformed and mapped by the plot scales. An example of mapping from scaled data could be to use a desaturated version of the stroke colour for fill. You should use after_scale() to flag evaluation of mapping for after data has been scaled. Evaluation after scaling will only have access to the final aesthetics of the layer (including non-mapped, default aesthetics).

# The exact colour is known after scale transformation
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_density()

# We re-use colour properties for the fill without a separate fill scale
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_density(aes(fill = after_scale(alpha(colour, 0.3))))

Complex staging

If you want to map the same aesthetic multiple times, e.g. map x to a data column for the stat, but remap it for the geom, you can use the stage() function to collect multiple mappings.

# Use stage to modify the scaled fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))

# Using data for computing summary, but placing label elsewhere.
# Also, we're making our own computed variable to use for the label.
ggplot(mpg, aes(class, displ)) +
  geom_violin() +
  stat_summary(
    aes(
      y = stage(displ, after_stat = 8),
      label = after_stat(paste(mean, "±", sd))
    ),
    geom = "text",
    fun.data = ~ round(data.frame(mean = mean(.x), sd = sd(.x)), 2)
  )

Examples

# Default histogram display
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count)))

# Scale tallest bin to 1
ggplot(mpg, aes(displ)) +
  geom_histogram(aes(y = after_stat(count / max(count))))

# Use a transparent version of colour for fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(colour = class, fill = after_scale(alpha(colour, 0.4))))

# Use stage to modify the scaled fill
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))

# Making a proportional stacked density plot
ggplot(mpg, aes(cty)) +
  geom_density(
    aes(
      colour = factor(cyl),
      fill = after_scale(alpha(colour, 0.3)),
      y = after_stat(count / sum(n[!duplicated(group)]))
    ),
    position = "stack", bw = 1
  ) +
  geom_density(bw = 1)

# Imitating a ridgeline plot
ggplot(mpg, aes(cty, colour = factor(cyl))) +
  geom_ribbon(
    stat = "density", outline.type = "upper",
    aes(
      fill = after_scale(alpha(colour, 0.3)),
      ymin = after_stat(group),
      ymax = after_stat(group + ndensity)
    )
  )

# Labelling a bar plot
ggplot(mpg, aes(class)) +
  geom_bar() +
  geom_text(
    aes(
      y = after_stat(count + 2),
      label = after_stat(count)
    ),
    stat = "count"
  )

# Labelling the upper hinge of a boxplot,
# inspired by June Choe
ggplot(mpg, aes(displ, class)) +
  geom_boxplot(outlier.shape = NA) +
  geom_text(
    aes(
      label = after_stat(xmax),
      x = stage(displ, after_stat = xmax)
    ),
    stat = "boxplot", hjust = -0.5
  )

Aesthetics: grouping

Description

The group aesthetic is by default set to the interaction of all discrete variables in the plot. This choice often partitions the data correctly, but when it does not, or when no discrete variable is used in the plot, you will need to explicitly define the grouping structure by mapping group to a variable that has a different value for each group.

Details

For most applications the grouping is set implicitly by mapping one or more discrete variables to x, y, colour, fill, alpha, shape, size, and/or linetype. This is demonstrated in the examples below.

There are three common cases where the default does not display the data correctly.

  1. geom_line() where there are multiple individuals and the plot tries to connect every observation, even across individuals, with a line.

  2. geom_line() where a discrete x-position implies groups, whereas observations span the discrete x-positions.

  3. When the grouping needs to be different over different layers, for example when computing a statistic on all observations when another layer shows individuals.

The examples below use a longitudinal dataset, Oxboys, from the nlme package to demonstrate these cases. Oxboys records the heights (height) and centered ages (age) of 26 boys (Subject), measured on nine occasions (Occasion).

See Also

Other aesthetics documentation: aes(), aes_colour_fill_alpha, aes_linetype_size_shape, aes_position

Examples

p <- ggplot(mtcars, aes(wt, mpg))
# A basic scatter plot
p + geom_point(size = 4)
# Using the colour aesthetic
p + geom_point(aes(colour = factor(cyl)), size = 4)
# Using the shape aesthetic
p + geom_point(aes(shape = factor(cyl)), size = 4)

# Using fill
p <- ggplot(mtcars, aes(factor(cyl)))
p + geom_bar()
p + geom_bar(aes(fill = factor(cyl)))
p + geom_bar(aes(fill = factor(vs)))

# Using linetypes
ggplot(economics_long, aes(date, value01)) +
  geom_line(aes(linetype = variable))

# Multiple groups with one aesthetic
p <- ggplot(nlme::Oxboys, aes(age, height))
# The default is not sufficient here. A single line tries to connect all
# the observations.
p + geom_line()
# To fix this, use the group aesthetic to map a different line for each
# subject.
p + geom_line(aes(group = Subject))

# Different groups on different layers
p <- p + geom_line(aes(group = Subject))
# Using the group aesthetic with both geom_line() and geom_smooth()
# groups the data the same way for both layers
p + geom_smooth(aes(group = Subject), method = "lm", se = FALSE)
# Changing the group aesthetic for the smoother layer
# fits a single line of best fit across all boys
p + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE)

# Overriding the default grouping
# Sometimes the plot has a discrete scale but you want to draw lines
# that connect across groups. This is the strategy used in interaction
# plots, profile plots, and parallel coordinate plots, among others.
# For example, we draw boxplots of height at each measurement occasion.
p <- ggplot(nlme::Oxboys, aes(Occasion, height)) + geom_boxplot()
p
# There is no need to specify the group aesthetic here; the default grouping
# works because occasion is a discrete variable. To overlay individual
# trajectories, we again need to override the default grouping for that layer
# with aes(group = Subject)
p + geom_line(aes(group = Subject), colour = "blue")

Differentiation related aesthetics: linetype, size, shape

Description

The linetype, linewidth, size, and shape aesthetics modify the appearance of lines and/or points. They also apply to the outlines of polygons (linetype and linewidth) or to text (size).

Linetype

The linetype aesthetic can be specified with either an integer (0-6), a name (0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash), a mapping to a discrete variable, or a string of an even number (up to eight) of hexadecimal digits which give the lengths in consecutive positions in the string. See examples for a hex string demonstration.

Linewidth and stroke

The linewidth aesthetic sets the widths of lines, and can be specified with a numeric value (for historical reasons, these units are about 0.75 millimetres). Alternatively, they can also be set via mapping to a continuous variable. The stroke aesthetic serves the same role for points, but is distinct for discriminating points from lines in geoms such as geom_pointrange().

Size

The size aesthetic control the size of points and text, and can be specified with a numerical value (in millimetres) or via a mapping to a continuous variable.

Shape

The shape aesthetic controls the symbols of points, and can be specified with an integer (between 0 and 25), a single character (which uses that character as the plotting symbol), a . to draw the smallest rectangle that is visible (i.e., about one pixel), an NA to draw nothing, or a mapping to a discrete variable. Symbols and filled shapes are described in the examples below.

See Also

Other aesthetics documentation: aes(), aes_colour_fill_alpha, aes_group_order, aes_position

Examples

df <- data.frame(x = 1:10 , y = 1:10)
p <- ggplot(df, aes(x, y))
p + geom_line(linetype = 2)
p + geom_line(linetype = "dotdash")

# An example with hex strings; the string "33" specifies three units on followed
# by three off and "3313" specifies three units on followed by three off followed
# by one on and finally three off.
p + geom_line(linetype = "3313")

# Mapping line type from a grouping variable
ggplot(economics_long, aes(date, value01)) +
  geom_line(aes(linetype = variable))

# Linewidth examples
ggplot(economics, aes(date, unemploy)) +
  geom_line(linewidth = 2, lineend = "round")
ggplot(economics, aes(date, unemploy)) +
  geom_line(aes(linewidth = uempmed), lineend = "round")

# Size examples
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point(size = 4)
p + geom_point(aes(size = qsec))
p + geom_point(size = 2.5) +
  geom_hline(yintercept = 25, size = 3.5)

# Shape examples
p + geom_point()
p + geom_point(shape = 5)
p + geom_point(shape = "k", size = 3)
p + geom_point(shape = ".")
p + geom_point(shape = NA)
p + geom_point(aes(shape = factor(cyl)))

# A look at all 25 symbols
df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
p <- ggplot(df2, aes(x, y))
p + geom_point(aes(shape = z), size = 4) +
  scale_shape_identity()
# While all symbols have a foreground colour, symbols 19-25 also take a
# background colour (fill)
p + geom_point(aes(shape = z), size = 4, colour = "Red") +
  scale_shape_identity()
p + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
  scale_shape_identity()

Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend

Description

The following aesthetics can be used to specify the position of elements: x, y, xmin, xmax, ymin, ymax, xend, yend.

Details

x and y define the locations of points or of positions along a line or path.

x, y and xend, yend define the starting and ending points of segment and curve geometries.

xmin, xmax, ymin and ymax can be used to specify the position of annotations and to represent rectangular areas.

In addition, there are position aesthetics that are contextual to the geometry that they're used in. These are xintercept, yintercept, xmin_final, ymin_final, xmax_final, ymax_final, xlower, lower, xmiddle, middle, xupper, upper, x0 and y0. Many of these are used and automatically computed in geom_boxplot().

See Also

Other aesthetics documentation: aes(), aes_colour_fill_alpha, aes_group_order, aes_linetype_size_shape

Examples

# Generate data: means and standard errors of means for prices
# for each type of cut
dmod <- lm(price ~ cut, data = diamonds)
cut <- unique(diamonds$cut)
cuts_df <- data.frame(
  cut,
  predict(dmod, data.frame(cut), se = TRUE)[c("fit", "se.fit")]
)
ggplot(cuts_df) +
  aes(
   x = cut,
   y = fit,
   ymin = fit - se.fit,
   ymax = fit + se.fit,
   colour = cut
  ) +
  geom_pointrange()

# Using annotate
p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p
p + annotate(
  "rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25,
  fill = "dark grey", alpha = .5
)

# Geom_segment examples
p + geom_segment(
  aes(x = 2, y = 15, xend = 2, yend = 25),
  arrow = arrow(length = unit(0.5, "cm"))
)
p + geom_segment(
  aes(x = 2, y = 15, xend = 3, yend = 15),
  arrow = arrow(length = unit(0.5, "cm"))
)
p + geom_segment(
  aes(x = 5, y = 30, xend = 3.5, yend = 25),
  arrow = arrow(length = unit(0.5, "cm"))
)

# You can also use geom_segment() to recreate plot(type = "h")
# from base R:
set.seed(1)
counts <- as.data.frame(table(x = rpois(100, 5)))
counts$x <- as.numeric(as.character(counts$x))
with(counts, plot(x, Freq, type = "h", lwd = 10))

ggplot(counts, aes(x = x, y = Freq)) +
  geom_segment(aes(yend = 0, xend = x), size = 10)

Create an annotation layer

Description

This function adds geoms to a plot, but unlike a typical geom function, the properties of the geoms are not mapped from variables of a data frame, but are instead passed in as vectors. This is useful for adding small annotations (such as text labels) or if you have your data in vectors, and for some reason don't want to put them in a data frame.

Usage

annotate(
  geom,
  x = NULL,
  y = NULL,
  xmin = NULL,
  xmax = NULL,
  ymin = NULL,
  ymax = NULL,
  xend = NULL,
  yend = NULL,
  ...,
  na.rm = FALSE
)

Arguments

geom

name of geom to use for annotation

x, y, xmin, ymin, xmax, ymax, xend, yend

positioning aesthetics - you must specify at least one of these.

...

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.

  • 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.

na.rm

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

Details

Note that all position aesthetics are scaled (i.e. they will expand the limits of the plot so they are visible), but all other aesthetics are set. This means that layers created with this function will never affect the legend.

Unsupported geoms

Due to their special nature, reference line geoms geom_abline(), geom_hline(), and geom_vline() can't be used with annotate(). You can use these geoms directly for annotations.

See Also

The custom annotations section of the online ggplot2 book.

Examples

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p + annotate("text", x = 4, y = 25, label = "Some text")
p + annotate("text", x = 2:5, y = 25, label = "Some text")
p + annotate("rect", xmin = 3, xmax = 4.2, ymin = 12, ymax = 21,
  alpha = .2)
p + annotate("segment", x = 2.5, xend = 4, y = 15, yend = 25,
  colour = "blue")
p + annotate("pointrange", x = 3.5, y = 20, ymin = 12, ymax = 28,
  colour = "red", size = 2.5, linewidth = 1.5)

p + annotate("text", x = 2:3, y = 20:21, label = c("my label", "label 2"))

p + annotate("text", x = 4, y = 25, label = "italic(R) ^ 2 == 0.75",
  parse = TRUE)
p + annotate("text", x = 4, y = 25,
  label = "paste(italic(R) ^ 2, \" = .75\")", parse = TRUE)

Annotation: Custom grob

Description

This is a special geom intended for use as static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the grob, and the grob will not be modified by any ggplot settings or mappings).

Usage

annotation_custom(grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf)

Arguments

grob

grob to display

xmin, xmax

x location (in data coordinates) giving horizontal location of raster

ymin, ymax

y location (in data coordinates) giving vertical location of raster

Details

Most useful for adding tables, inset plots, and other grid-based decorations.

Note

annotation_custom() expects the grob to fill the entire viewport defined by xmin, xmax, ymin, ymax. Grobs with a different (absolute) size will be center-justified in that region. Inf values can be used to fill the full plot panel (see examples).

Examples

# Dummy plot
df <- data.frame(x = 1:10, y = 1:10)
base <- ggplot(df, aes(x, y)) +
  geom_blank() +
  theme_bw()

# Full panel annotation
base + annotation_custom(
  grob = grid::roundrectGrob(),
  xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf
)

# Inset plot
df2 <- data.frame(x = 1 , y = 1)
g <- ggplotGrob(ggplot(df2, aes(x, y)) +
  geom_point() +
  theme(plot.background = element_rect(colour = "black")))
base +
  annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)

Annotation: log tick marks

Description

[Superseded]

This function is superseded by using guide_axis_logticks().

This annotation adds log tick marks with diminishing spacing. These tick marks probably make sense only for base 10.

Usage

annotation_logticks(
  base = 10,
  sides = "bl",
  outside = FALSE,
  scaled = TRUE,
  short = unit(0.1, "cm"),
  mid = unit(0.2, "cm"),
  long = unit(0.3, "cm"),
  colour = "black",
  linewidth = 0.5,
  linetype = 1,
  alpha = 1,
  color = NULL,
  ...,
  size = deprecated()
)

Arguments

base

the base of the log (default 10)

sides

a string that controls which sides of the plot the log ticks appear on. It can be set to a string containing any of "trbl", for top, right, bottom, and left.

outside

logical that controls whether to move the log ticks outside of the plot area. Default is off (FALSE). You will also need to use coord_cartesian(clip = "off"). See examples.

scaled

is the data already log-scaled? This should be TRUE (default) when the data is already transformed with log10() or when using scale_y_log10(). It should be FALSE when using coord_trans(y = "log10").

short

a grid::unit() object specifying the length of the short tick marks

mid

a grid::unit() object specifying the length of the middle tick marks. In base 10, these are the "5" ticks.

long

a grid::unit() object specifying the length of the long tick marks. In base 10, these are the "1" (or "10") ticks.

colour

Colour of the tick marks.

linewidth

Thickness of tick marks, in mm.

linetype

Linetype of tick marks (solid, dashed, etc.)

alpha

The transparency of the tick marks.

color

An alias for colour.

...

Other parameters passed on to the layer

size

[Deprecated]

See Also

scale_y_continuous(), scale_y_log10() for log scale transformations.

coord_trans() for log coordinate transformations.

Examples

# Make a log-log plot (without log ticks)
a <- ggplot(msleep, aes(bodywt, brainwt)) +
 geom_point(na.rm = TRUE) +
 scale_x_log10(
   breaks = scales::trans_breaks("log10", function(x) 10^x),
   labels = scales::trans_format("log10", scales::math_format(10^.x))
 ) +
 scale_y_log10(
   breaks = scales::trans_breaks("log10", function(x) 10^x),
   labels = scales::trans_format("log10", scales::math_format(10^.x))
 ) +
 theme_bw()

a + annotation_logticks()                # Default: log ticks on bottom and left
a + annotation_logticks(sides = "lr")    # Log ticks for y, on left and right
a + annotation_logticks(sides = "trbl")  # All four sides

a + annotation_logticks(sides = "lr", outside = TRUE) +
 coord_cartesian(clip = "off")  # Ticks outside plot

# Hide the minor grid lines because they don't align with the ticks
a + annotation_logticks(sides = "trbl") + theme(panel.grid.minor = element_blank())

# Another way to get the same results as 'a' above: log-transform the data before
# plotting it. Also hide the minor grid lines.
b <- ggplot(msleep, aes(log10(bodywt), log10(brainwt))) +
 geom_point(na.rm = TRUE) +
 scale_x_continuous(name = "body", labels = scales::label_math(10^.x)) +
 scale_y_continuous(name = "brain", labels = scales::label_math(10^.x)) +
 theme_bw() + theme(panel.grid.minor = element_blank())

b + annotation_logticks()

# Using a coordinate transform requires scaled = FALSE
t <- ggplot(msleep, aes(bodywt, brainwt)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10") +
  theme_bw()
t + annotation_logticks(scaled = FALSE)

# Change the length of the ticks
a + annotation_logticks(
  short = unit(.5,"mm"),
  mid = unit(3,"mm"),
  long = unit(4,"mm")
)

Annotation: a map

Description

Display a fixed map on a plot. This function predates the geom_sf() framework and does not work with sf geometry columns as input. However, it can be used in conjunction with geom_sf() layers and/or coord_sf() (see examples).

Usage

annotation_map(map, ...)

Arguments

map

Data frame representing a map. See geom_map() for details.

...

Other arguments used to modify visual parameters, such as colour or fill.

Examples

## Not run: 
if (requireNamespace("maps", quietly = TRUE)) {
# location of cities in North Carolina
df <- data.frame(
  name = c("Charlotte", "Raleigh", "Greensboro"),
  lat = c(35.227, 35.772, 36.073),
  long = c(-80.843, -78.639, -79.792)
)

p <- ggplot(df, aes(x = long, y = lat)) +
  annotation_map(
    map_data("state"),
    fill = "antiquewhite", colour = "darkgrey"
  ) +
  geom_point(color = "blue") +
  geom_text(
    aes(label = name),
    hjust = 1.105, vjust = 1.05, color = "blue"
  )

# use without coord_sf() is possible but not recommended
p + xlim(-84, -76) + ylim(34, 37.2)

if (requireNamespace("sf", quietly = TRUE)) {
# use with coord_sf() for appropriate projection
p +
  coord_sf(
    crs = sf::st_crs(3347),
    default_crs = sf::st_crs(4326),  # data is provided as long-lat
    xlim = c(-84, -76),
    ylim = c(34, 37.2)
  )

# you can mix annotation_map() and geom_sf()
nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
p +
  geom_sf(
    data = nc, inherit.aes = FALSE,
    fill = NA, color = "black", linewidth = 0.1
  ) +
  coord_sf(crs = sf::st_crs(3347), default_crs = sf::st_crs(4326))
}}
## End(Not run)

Annotation: high-performance rectangular tiling

Description

This is a special version of geom_raster() optimised for static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the raster, and the raster must already have its own colours). This is useful for adding bitmap images.

Usage

annotation_raster(raster, xmin, xmax, ymin, ymax, interpolate = FALSE)

Arguments

raster

raster object to display, may be an array or a nativeRaster

xmin, xmax

x location (in data coordinates) giving horizontal location of raster

ymin, ymax

y location (in data coordinates) giving vertical location of raster

interpolate

If TRUE interpolate linearly, if FALSE (the default) don't interpolate.

Examples

# Generate data
rainbow <- matrix(hcl(seq(0, 360, length.out = 50 * 50), 80, 70), nrow = 50)
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  annotation_raster(rainbow, 15, 20, 3, 4)
# To fill up whole plot
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow, -Inf, Inf, -Inf, Inf) +
  geom_point()

rainbow2 <- matrix(hcl(seq(0, 360, length.out = 10), 80, 70), nrow = 1)
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow2, -Inf, Inf, -Inf, Inf) +
  geom_point()
rainbow2 <- matrix(hcl(seq(0, 360, length.out = 10), 80, 70), nrow = 1)
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow2, -Inf, Inf, -Inf, Inf, interpolate = TRUE) +
  geom_point()

Create a ggplot layer appropriate to a particular data type

Description

autolayer() uses ggplot2 to draw a particular layer for an object of a particular class in a single command. This defines the S3 generic that other classes and packages can extend.

Usage

autolayer(object, ...)

Arguments

object

an object, whose class will determine the behaviour of autolayer

...

other arguments passed to specific methods

Value

a ggplot layer

See Also

autoplot(), ggplot() and fortify()


Create a complete ggplot appropriate to a particular data type

Description

autoplot() uses ggplot2 to draw a particular plot for an object of a particular class in a single command. This defines the S3 generic that other classes and packages can extend.

Usage

autoplot(object, ...)

Arguments

object

an object, whose class will determine the behaviour of autoplot

...

other arguments passed to specific methods

Value

a ggplot object

See Also

autolayer(), ggplot() and fortify()


Create a layer of map borders

Description

This is a quick and dirty way to get map data (from the maps package) onto your plot. This is a good place to start if you need some crude reference lines, but you'll typically want something more sophisticated for communication graphics.

Usage

borders(
  database = "world",
  regions = ".",
  fill = NA,
  colour = "grey50",
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

database

map data, see maps::map() for details

regions

map region

fill

fill colour

colour

border colour

xlim, ylim

latitudinal and longitudinal ranges for extracting map polygons, see maps::map() for details.

...

Arguments passed on to geom_polygon

rule

Either "evenodd" or "winding". If polygons with holes are being drawn (using the subgroup aesthetic) this argument defines how the hole coordinates are interpreted. See the examples in grid::pathGrob() for an explanation.

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.

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().

na.rm

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

Examples

if (require("maps")) {

ia <- map_data("county", "iowa")
mid_range <- function(x) mean(range(x))
seats <- do.call(rbind, lapply(split(ia, ia$subregion), function(d) {
  data.frame(lat = mid_range(d$lat), long = mid_range(d$long), subregion = unique(d$subregion))
}))

ggplot(ia, aes(long, lat)) +
  geom_polygon(aes(group = group), fill = NA, colour = "grey60") +
  geom_text(aes(label = subregion), data = seats, size = 2, angle = 45)
}

if (require("maps")) {
data(us.cities)
capitals <- subset(us.cities, capital == 2)
ggplot(capitals, aes(long, lat)) +
  borders("state") +
  geom_point(aes(size = pop)) +
  scale_size_area() +
  coord_quickmap()
}

if (require("maps")) {
# Same map, with some world context
ggplot(capitals, aes(long, lat)) +
  borders("world", xlim = c(-130, -60), ylim = c(20, 50)) +
  geom_point(aes(size = pop)) +
  scale_size_area() +
  coord_quickmap()
}

Cartesian coordinates

Description

The Cartesian coordinate system is the most familiar, and common, type of coordinate system. Setting limits on the coordinate system will zoom the plot (like you're looking at it with a magnifying glass), and will not change the underlying data like setting limits on a scale will.

Usage

coord_cartesian(
  xlim = NULL,
  ylim = NULL,
  expand = TRUE,
  default = FALSE,
  clip = "on"
)

Arguments

xlim, ylim

Limits for the x and y axes.

expand

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

default

Is this the default coordinate system? If FALSE (the default), then replacing this coordinate system with another one creates a message alerting the user that the coordinate system is being replaced. If TRUE, that warning is suppressed.

clip

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

Examples

# There are two ways of zooming the plot display: with scales or
# with coordinate systems.  They work in two rather different ways.

p <- ggplot(mtcars, aes(disp, wt)) +
  geom_point() +
  geom_smooth()
p

# Setting the limits on a scale converts all values outside the range to NA.
p + scale_x_continuous(limits = c(325, 500))

# Setting the limits on the coordinate system performs a visual zoom.
# The data is unchanged, and we just view a small portion of the original
# plot. Note how smooth continues past the points visible on this plot.
p + coord_cartesian(xlim = c(325, 500))

# By default, the same expansion factor is applied as when setting scale
# limits. You can set the limits precisely by setting expand = FALSE
p + coord_cartesian(xlim = c(325, 500), expand = FALSE)

# Similarly, we can use expand = FALSE to turn off expansion with the
# default limits
p + coord_cartesian(expand = FALSE)

# You can see the same thing with this 2d histogram
d <- ggplot(diamonds, aes(carat, price)) +
  stat_bin_2d(bins = 25, colour = "white")
d

# When zooming the scale, the we get 25 new bins that are the same
# size on the plot, but represent smaller regions of the data space
d + scale_x_continuous(limits = c(0, 1))

# When zooming the coordinate system, we see a subset of original 50 bins,
# displayed bigger
d + coord_cartesian(xlim = c(0, 1))

Cartesian coordinates with fixed "aspect ratio"

Description

A fixed scale coordinate system forces a specified ratio between the physical representation of data units on the axes. The ratio represents the number of units on the y-axis equivalent to one unit on the x-axis. The default, ratio = 1, ensures that one unit on the x-axis is the same length as one unit on the y-axis. Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa. This is similar to MASS::eqscplot(), but it works for all types of graphics.

Usage

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

Arguments

ratio

aspect ratio, expressed as y / x

xlim, ylim

Limits for the x and y axes.

expand

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

clip

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

Examples

# ensures that the ranges of axes are equal to the specified ratio by
# adjusting the plot aspect ratio

p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + coord_fixed(ratio = 1)
p + coord_fixed(ratio = 5)
p + coord_fixed(ratio = 1/5)
p + coord_fixed(xlim = c(15, 30))

# Resize the plot to see that the specified aspect ratio is maintained

Cartesian coordinates with x and y flipped

Description

[Superseded]

This function is superseded because in many cases, coord_flip() can easily be replaced by swapping the x and y aesthetics, or optionally setting the orientation argument in geom and stat layers.

coord_flip() is useful for geoms and statistics that do not support the orientation setting, and converting the display of y conditional on x, to x conditional on y.

Usage

coord_flip(xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")

Arguments

xlim, ylim

Limits for the x and y axes.

expand

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

clip

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

Examples

# The preferred method of creating horizontal instead of vertical boxplots
ggplot(diamonds, aes(price, cut)) +
  geom_boxplot()

# Using `coord_flip()` to make the same plot
ggplot(diamonds, aes(cut, price)) +
  geom_boxplot() +
  coord_flip()

# With swapped aesthetics, the y-scale controls the left axis
ggplot(diamonds, aes(y = carat)) +
  geom_histogram() +
  scale_y_reverse()

# In `coord_flip()`, the x-scale controls the left axis
ggplot(diamonds, aes(carat)) +
  geom_histogram() +
  coord_flip() +
  scale_x_reverse()

# In line and area plots, swapped aesthetics require an explicit orientation
df <- data.frame(a = 1:5, b = (1:5) ^ 2)
ggplot(df, aes(b, a)) +
  geom_area(orientation = "y")

# The same plot with `coord_flip()`
ggplot(df, aes(a, b)) +
  geom_area() +
  coord_flip()

Map projections

Description

[Superseded]

coord_map() projects a portion of the earth, which is approximately spherical, onto a flat 2D plane using any projection defined by the mapproj package. Map projections do not, in general, preserve straight lines, so this requires considerable computation. coord_quickmap() is a quick approximation that does preserve straight lines. It works best for smaller areas closer to the equator.

Both coord_map() and coord_quickmap() are superseded by coord_sf(), and should no longer be used in new code. All regular (non-sf) geoms can be used with coord_sf() by setting the default coordinate system via the default_crs argument. See also the examples for annotation_map() and geom_map().

Usage

coord_map(
  projection = "mercator",
  ...,
  parameters = NULL,
  orientation = NULL,
  xlim = NULL,
  ylim = NULL,
  clip = "on"
)

coord_quickmap(xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")

Arguments

projection

projection to use, see mapproj::mapproject() for list

..., parameters

Other arguments passed on to mapproj::mapproject(). Use ... for named parameters to the projection, and parameters for unnamed parameters. ... is ignored if the parameters argument is present.

orientation

projection orientation, which defaults to c(90, 0, mean(range(x))). This is not optimal for many projections, so you will have to supply your own. See mapproj::mapproject() for more information.

xlim, ylim

Manually specific x/y limits (in degrees of longitude/latitude)

clip

Should drawing be clipped to the extent of the plot panel? A setting of "on" (the default) means yes, and a setting of "off" means no. For details, please see coord_cartesian().

expand

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

Details

Map projections must account for the fact that the actual length (in km) of one degree of longitude varies between the equator and the pole. Near the equator, the ratio between the lengths of one degree of latitude and one degree of longitude is approximately 1. Near the pole, it tends towards infinity because the length of one degree of longitude tends towards 0. For regions that span only a few degrees and are not too close to the poles, setting the aspect ratio of the plot to the appropriate lat/lon ratio approximates the usual mercator projection. This is what coord_quickmap() does, and is much faster (particularly for complex plots like geom_tile()) at the expense of correctness.

See Also

The polygon maps section of the online ggplot2 book.

Examples

if (require("maps")) {
nz <- map_data("nz")
# Prepare a map of NZ
nzmap <- ggplot(nz, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

# Plot it in cartesian coordinates
nzmap
}

if (require("maps")) {
# With correct mercator projection
nzmap + coord_map()
}

if (require("maps")) {
# With the aspect ratio approximation
nzmap + coord_quickmap()
}

if (require("maps")) {
# Other projections
nzmap + coord_map("azequalarea", orientation = c(-36.92, 174.6, 0))
}

if (require("maps")) {
states <- map_data("state")
usamap <- ggplot(states, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

# Use cartesian coordinates
usamap
}

if (require("maps")) {
# With mercator projection
usamap + coord_map()
}

if (require("maps")) {
# See ?mapproject for coordinate systems and their parameters
usamap + coord_map("gilbert")
}

if (require("maps")) {
# For most projections, you'll need to set the orientation yourself
# as the automatic selection done by mapproject is not available to
# ggplot
usamap + coord_map("orthographic")
}

if (require("maps")) {
usamap + coord_map("conic", lat0 = 30)
}

if (require("maps")) {
usamap + coord_map("bonne", lat0 = 50)
}

## Not run: 
if (require("maps")) {
# World map, using geom_path instead of geom_polygon
world <- map_data("world")
worldmap <- ggplot(world, aes(x = long, y = lat, group = group)) +
  geom_path() +
  scale_y_continuous(breaks = (-2:2) * 30) +
  scale_x_continuous(breaks = (-4:4) * 45)

# Orthographic projection with default orientation (looking down at North pole)
worldmap + coord_map("ortho")
}

if (require("maps")) {
# Looking up up at South Pole
worldmap + coord_map("ortho", orientation = c(-90, 0, 0))
}

if (require("maps")) {
# Centered on New York (currently has issues with closing polygons)
worldmap + coord_map("ortho", orientation = c(41, -74, 0))
}

## End(Not run)

Polar coordinates

Description

The polar coordinate system is most commonly used for pie charts, which are a stacked bar chart in polar coordinates. coord_radial() has extended options.

Usage

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

coord_radial(
  theta = "x",
  start = 0,
  end = NULL,
  expand = TRUE,
  direction = 1,
  clip = "off",
  r_axis_inside = NULL,
  rotate_angle = FALSE,
  inner.radius = 0
)

Arguments

theta

variable to map angle to (x or y)

start

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

direction

1, clockwise; -1, anticlockwise

clip

Should drawing be clipped to the extent of the plot panel? A setting of "on" (the default) means yes, and a setting of "off" means no. For details, please see coord_cartesian().

end

Position from 12 o'clock in radians where plot ends, to allow for partial polar coordinates. The default, NULL, is set to start + 2 * pi.

expand

If TRUE, the default, adds a small expansion factor the the limits to prevent overlap between data and axes. If FALSE, limits are taken directly from the scale.

r_axis_inside

If TRUE, places the radius axis inside the panel. If FALSE, places the radius axis next to the panel. The default, NULL, places the radius axis outside if the start and end arguments form a full circle.

rotate_angle

If TRUE, transforms the angle aesthetic in data in accordance with the computed theta position. If FALSE (default), no such transformation is performed. Can be useful to rotate text geoms in alignment with the coordinates.

inner.radius

A numeric between 0 and 1 setting the size of a inner.radius hole.

Note

In coord_radial(), position guides are can be defined by using guides(r = ..., theta = ..., r.sec = ..., theta.sec = ...). Note that these guides require r and theta as available aesthetics. The classic guide_axis() can be used for the r positions and guide_axis_theta() can be used for the theta positions. Using the theta.sec position is only sensible when inner.radius > 0.

See Also

The polar coordinates section of the online ggplot2 book.

Examples

# NOTE: Use these plots with caution - polar coordinates has
# major perceptual problems.  The main point of these examples is
# to demonstrate how these common plots can be described in the
# grammar.  Use with EXTREME caution.

#' # A pie chart = stacked bar chart + polar coordinates
pie <- ggplot(mtcars, aes(x = factor(1), fill = factor(cyl))) +
 geom_bar(width = 1)
pie + coord_polar(theta = "y")



# A coxcomb plot = bar chart + polar coordinates
cxc <- ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar(width = 1, colour = "black")
cxc + coord_polar()
# A new type of plot?
cxc + coord_polar(theta = "y")

# The bullseye chart
pie + coord_polar()

# Hadley's favourite pie chart
df <- data.frame(
  variable = c("does not resemble", "resembles"),
  value = c(20, 80)
)
ggplot(df, aes(x = "", y = value, fill = variable)) +
  geom_col(width = 1) +
  scale_fill_manual(values = c("red", "yellow")) +
  coord_polar("y", start = pi / 3) +
  labs(title = "Pac man")

# Windrose + doughnut plot
if (require("ggplot2movies")) {
movies$rrating <- cut_interval(movies$rating, length = 1)
movies$budgetq <- cut_number(movies$budget, 4)

doh <- ggplot(movies, aes(x = rrating, fill = budgetq))

# Wind rose
doh + geom_bar(width = 1) + coord_polar()
# Race track plot
doh + geom_bar(width = 0.9, position = "fill") + coord_polar(theta = "y")
}

# A partial polar plot
ggplot(mtcars, aes(disp, mpg)) +
  geom_point() +
  coord_radial(start = -0.4 * pi, end = 0.4 * pi, inner.radius = 0.3)

Transformed Cartesian coordinate system

Description

coord_trans() is different to scale transformations in that it occurs after statistical transformation and will affect the visual appearance of geoms - there is no guarantee that straight lines will continue to be straight.

Usage

coord_trans(
  x = "identity",
  y = "identity",
  xlim = NULL,
  ylim = NULL,
  limx = deprecated(),
  limy = deprecated(),
  clip = "on",
  expand = TRUE
)

Arguments

x, y

Transformers for x and y axes or their names.

xlim, ylim

Limits for the x and y axes.

limx, limy

[Deprecated] use xlim and ylim instead.

clip

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

expand

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

Details

Transformations only work with continuous values: see scales::new_transform() for list of transformations, and instructions on how to create your own.

See Also

The coord transformations section of the online ggplot2 book.

Examples

# See ?geom_boxplot for other examples

# Three ways of doing transformation in ggplot:
#  * by transforming the data
ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_point()
#  * by transforming the scales
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10()
#  * by transforming the coordinate system:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10")

# The difference between transforming the scales and
# transforming the coordinate system is that scale
# transformation occurs BEFORE statistics, and coordinate
# transformation afterwards.  Coordinate transformation also
# changes the shape of geoms:

d <- subset(diamonds, carat > 0.5)

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10()

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  coord_trans(x = "log10", y = "log10")

# Here I used a subset of diamonds so that the smoothed line didn't
# drop below zero, which obviously causes problems on the log-transformed
# scale

# With a combination of scale and coordinate transformation, it's
# possible to do back-transformations:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10() +
  coord_trans(x = scales::transform_exp(10), y = scales::transform_exp(10))

# cf.
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm")

# Also works with discrete scales
set.seed(1)
df <- data.frame(a = abs(rnorm(26)),letters)
plot <- ggplot(df,aes(a,letters)) + geom_point()

plot + coord_trans(x = "log10")
plot + coord_trans(x = "sqrt")

Visualise sf objects

Description

This set of geom, stat, and coord are used to visualise simple feature (sf) objects. For simple plots, you will only need geom_sf() as it uses stat_sf() and adds coord_sf() for you. geom_sf() is an unusual geom because it will draw different geometric objects depending on what simple features are present in the data: you can get points, lines, or polygons. For text and labels, you can use geom_sf_text() and geom_sf_label().

Usage

coord_sf(
  xlim = NULL,
  ylim = NULL,
  expand = TRUE,
  crs = NULL,
  default_crs = NULL,
  datum = sf::st_crs(4326),
  label_graticule = waiver(),
  label_axes = waiver(),
  lims_method = "cross",
  ndiscr = 100,
  default = FALSE,
  clip = "on"
)

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

geom_sf_label(
  mapping = aes(),
  data = NULL,
  stat = "sf_coordinates",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  label.padding = unit(0.25, "lines"),
  label.r = unit(0.15, "lines"),
  label.size = 0.25,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  fun.geometry = NULL
)

geom_sf_text(
  mapping = aes(),
  data = NULL,
  stat = "sf_coordinates",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  fun.geometry = NULL
)

stat_sf(
  mapping = NULL,
  data = NULL,
  geom = "rect",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

xlim, ylim

Limits for the x and y axes. These limits are specified in the units of the default CRS. By default, this means projected coordinates (default_crs = NULL). How limit specifications translate into the exact region shown on the plot can be confusing when non-linear or rotated coordinate systems are used as the default crs. First, different methods can be preferable under different conditions. See parameter lims_method for details. Second, specifying limits along only one direction can affect the automatically generated limits along the other direction. Therefore, it is best to always specify limits for both x and y. Third, specifying limits via position scales or xlim()/ylim() is strongly discouraged, as it can result in data points being dropped from the plot even though they would be visible in the final plot region.

expand

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

crs

The coordinate reference system (CRS) into which all data should be projected before plotting. If not specified, will use the CRS defined in the first sf layer of the plot.

default_crs

The default CRS to be used for non-sf layers (which don't carry any CRS information) and scale limits. The default value of NULL means that the setting for crs is used. This implies that all non-sf layers and scale limits are assumed to be specified in projected coordinates. A useful alternative setting is default_crs = sf::st_crs(4326), which means x and y positions are interpreted as longitude and latitude, respectively, in the World Geodetic System 1984 (WGS84).

datum

CRS that provides datum to use when generating graticules.

label_graticule

Character vector indicating which graticule lines should be labeled where. Meridians run north-south, and the letters "N" and "S" indicate that they should be labeled on their north or south end points, respectively. Parallels run east-west, and the letters "E" and "W" indicate that they should be labeled on their east or west end points, respectively. Thus, label_graticule = "SW" would label meridians at their south end and parallels at their west end, whereas label_graticule = "EW" would label parallels at both ends and meridians not at all. Because meridians and parallels can in general intersect with any side of the plot panel, for any choice of label_graticule labels are not guaranteed to reside on only one particular side of the plot panel. Also, label_graticule can cause labeling artifacts, in particular if a graticule line coincides with the edge of the plot panel. In such circumstances, label_axes will generally yield better results and should be used instead.

This parameter can be used alone or in combination with label_axes.

label_axes

Character vector or named list of character values specifying which graticule lines (meridians or parallels) should be labeled on which side of the plot. Meridians are indicated by "E" (for East) and parallels by "N" (for North). Default is "--EN", which specifies (clockwise from the top) no labels on the top, none on the right, meridians on the bottom, and parallels on the left. Alternatively, this setting could have been specified with list(bottom = "E", left = "N").

This parameter can be used alone or in combination with label_graticule.

lims_method

Method specifying how scale limits are converted into limits on the plot region. Has no effect when default_crs = NULL. For a very non-linear CRS (e.g., a perspective centered around the North pole), the available methods yield widely differing results, and you may want to try various options. Methods currently implemented include "cross" (the default), "box", "orthogonal", and "geometry_bbox". For method "cross", limits along one direction (e.g., longitude) are applied at the midpoint of the other direction (e.g., latitude). This method avoids excessively large limits for rotated coordinate systems but means that sometimes limits need to be expanded a little further if extreme data points are to be included in the final plot region. By contrast, for method "box", a box is generated out of the limits along both directions, and then limits in projected coordinates are chosen such that the entire box is visible. This method can yield plot regions that are too large. Finally, method "orthogonal" applies limits separately along each axis, and method "geometry_bbox" ignores all limit information except the bounding boxes of any objects in the geometry aesthetic.

ndiscr

Number of segments to use for discretising graticule lines; try increasing this number when graticules look incorrect.

default

Is this the default coordinate system? If FALSE (the default), then replacing this coordinate system with another one creates a message alerting the user that the coordinate system is being replaced. If TRUE, that warning is suppressed.

clip

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

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.

na.rm

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

show.legend

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

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

inherit.aes

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.

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

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

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

parse

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

nudge_x, nudge_y

Horizontal and vertical adjustment to nudge labels by. Useful for offsetting text from points, particularly on discrete scales. Cannot be jointly specified with position.

label.padding

Amount of padding around label. Defaults to 0.25 lines.

label.r

Radius of rounded corners. Defaults to 0.15 lines.

label.size

Size of label border, in mm.

fun.geometry

A function that takes a sfc object and returns a sfc_POINT with the same length as the input. If NULL, function(x) sf::st_point_on_surface(sf::st_zm(x)) will be used. Note that the function may warn about the incorrectness of the result if the data is not projected, but you can ignore this except when you really care about the exact locations.

check_overlap

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

geom

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

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

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

Geometry aesthetic

geom_sf() uses a unique aesthetic: geometry, giving an column of class sfc containing simple features data. There are three ways to supply the geometry aesthetic:

Unlike other aesthetics, geometry will never be inherited from the plot.

CRS

coord_sf() ensures that all layers use a common CRS. You can either specify it using the crs param, or coord_sf() will take it from the first layer that defines a CRS.

Combining sf layers and regular geoms

Most regular geoms, such as geom_point(), geom_path(), geom_text(), geom_polygon() etc. will work fine with coord_sf(). However when using these geoms, two problems arise. First, what CRS should be used for the x and y coordinates used by these non-sf geoms? The CRS applied to non-sf geoms is set by the default_crs parameter, and it defaults to NULL, which means positions for non-sf geoms are interpreted as projected coordinates in the coordinate system set by the crs parameter. This setting allows you complete control over where exactly items are placed on the plot canvas, but it may require some understanding of how projections work and how to generate data in projected coordinates. As an alternative, you can set default_crs = sf::st_crs(4326), the World Geodetic System 1984 (WGS84). This means that x and y positions are interpreted as longitude and latitude, respectively. You can also specify any other valid CRS as the default CRS for non-sf geoms.

The second problem that arises for non-sf geoms is how straight lines should be interpreted in projected space when default_crs is not set to NULL. The approach coord_sf() takes is to break straight lines into small pieces (i.e., segmentize them) and then transform the pieces into projected coordinates. For the default setting where x and y are interpreted as longitude and latitude, this approach means that horizontal lines follow the parallels and vertical lines follow the meridians. If you need a different approach to handling straight lines, then you should manually segmentize and project coordinates and generate the plot in projected coordinates.

See Also

The simple feature maps section of the online ggplot2 book.

stat_sf_coordinates()

Examples

if (requireNamespace("sf", quietly = TRUE)) {
nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
ggplot(nc) +
  geom_sf(aes(fill = AREA))

# If not supplied, coord_sf() will take the CRS from the first layer
# and automatically transform all other layers to use that CRS. This
# ensures that all data will correctly line up
nc_3857 <- sf::st_transform(nc, 3857)
ggplot() +
  geom_sf(data = nc) +
  geom_sf(data = nc_3857, colour = "red", fill = NA)

# Unfortunately if you plot other types of feature you'll need to use
# show.legend to tell ggplot2 what type of legend to use
nc_3857$mid <- sf::st_centroid(nc_3857$geometry)
ggplot(nc_3857) +
  geom_sf(colour = "white") +
  geom_sf(aes(geometry = mid, size = AREA), show.legend = "point")

# You can also use layers with x and y aesthetics. To have these interpreted
# as longitude/latitude you need to set the default CRS in coord_sf()
ggplot(nc_3857) +
  geom_sf() +
  annotate("point", x = -80, y = 35, colour = "red", size = 4) +
  coord_sf(default_crs = sf::st_crs(4326))

# To add labels, use geom_sf_label().
ggplot(nc_3857[1:3, ]) +
   geom_sf(aes(fill = AREA)) +
   geom_sf_label(aes(label = NAME))
}

# Thanks to the power of sf, a geom_sf nicely handles varying projections
# setting the aspect ratio correctly.
if (requireNamespace('maps', quietly = TRUE)) {
library(maps)
world1 <- sf::st_as_sf(map('world', plot = FALSE, fill = TRUE))
ggplot() + geom_sf(data = world1)

world2 <- sf::st_transform(
  world1,
  "+proj=laea +y_0=0 +lon_0=155 +lat_0=-90 +ellps=WGS84 +no_defs"
)
ggplot() + geom_sf(data = world2)
}

Discretise numeric data into categorical

Description

cut_interval() makes n groups with equal range, cut_number() makes n groups with (approximately) equal numbers of observations; cut_width() makes groups of width width.

Usage

cut_interval(x, n = NULL, length = NULL, ...)

cut_number(x, n = NULL, ...)

cut_width(x, width, center = NULL, boundary = NULL, closed = "right", ...)

Arguments

x

numeric vector

n

number of intervals to create, OR

length

length of each interval

...

Arguments passed on to base::cut.default

breaks

either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut.

labels

labels for the levels of the resulting category. By default, labels are constructed using "(a,b]" interval notation. If labels = FALSE, simple integer codes are returned instead of a factor.

right

logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa.

dig.lab

integer which is used when labels are not given. It determines the number of digits used in formatting the break numbers.

ordered_result

logical: should the result be an ordered factor?

width

The bin width.

center, boundary

Specify either the position of edge or the center of a bin. Since all bins are aligned, specifying the position of a single bin (which doesn't need to be in the range of the data) affects the location of all bins. If not specified, uses the "tile layers algorithm", and sets the boundary to half of the binwidth.

To center on integers, width = 1 and center = 0. boundary = 0.5.

closed

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

Author(s)

Randall Prium contributed most of the implementation of cut_width().

Examples

table(cut_interval(1:100, 10))
table(cut_interval(1:100, 11))

set.seed(1)

table(cut_number(runif(1000), 10))

table(cut_width(runif(1000), 0.1))
table(cut_width(runif(1000), 0.1, boundary = 0))
table(cut_width(runif(1000), 0.1, center = 0))
table(cut_width(runif(1000), 0.1, labels = FALSE))

Prices of over 50,000 round cut diamonds

Description

A dataset containing the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

Usage

diamonds

Format

A data frame with 53940 rows and 10 variables:

price

price in US dollars ($326–$18,823)

carat

weight of the diamond (0.2–5.01)

cut

quality of the cut (Fair, Good, Very Good, Premium, Ideal)

color

diamond colour, from D (best) to J (worst)

clarity

a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

x

length in mm (0–10.74)

y

width in mm (0–58.9)

z

depth in mm (0–31.8)

depth

total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)

table

width of top of diamond relative to widest point (43–95)


Key glyphs for legends

Description

Each geom has an associated function that draws the key when the geom needs to be displayed in a legend. These functions are called ⁠draw_key_*()⁠, where * stands for the name of the respective key glyph. The key glyphs can be customized for individual geoms by providing a geom with the key_glyph argument (see layer() or examples below.)

Usage

draw_key_point(data, params, size)

draw_key_abline(data, params, size)

draw_key_rect(data, params, size)

draw_key_polygon(data, params, size)

draw_key_blank(data, params, size)

draw_key_boxplot(data, params, size)

draw_key_crossbar(data, params, size)

draw_key_path(data, params, size)

draw_key_vpath(data, params, size)

draw_key_dotplot(data, params, size)

draw_key_linerange(data, params, size)

draw_key_pointrange(data, params, size)

draw_key_smooth(data, params, size)

draw_key_text(data, params, size)

draw_key_label(data, params, size)

draw_key_vline(data, params, size)

draw_key_timeseries(data, params, size)

Arguments

data

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

params

A list of additional parameters supplied to the geom.

size

Width and height of key in mm.

Value

A grid grob.

Examples

p <- ggplot(economics, aes(date, psavert, color = "savings rate"))
# key glyphs can be specified by their name
p + geom_line(key_glyph = "timeseries")

# key glyphs can be specified via their drawing function
p + geom_line(key_glyph = draw_key_rect)

US economic time series

Description

This dataset was produced from US economic time series data available from https://fred.stlouisfed.org/. economics is in "wide" format, economics_long is in "long" format.

Usage

economics

economics_long

Format

A data frame with 574 rows and 6 variables:

date

Month of data collection

pce

personal consumption expenditures, in billions of dollars, https://fred.stlouisfed.org/series/PCE

pop

total population, in thousands, https://fred.stlouisfed.org/series/POP

psavert

personal savings rate, https://fred.stlouisfed.org/series/PSAVERT/

uempmed

median duration of unemployment, in weeks, https://fred.stlouisfed.org/series/UEMPMED

unemploy

number of unemployed in thousands, https://fred.stlouisfed.org/series/UNEMPLOY

An object of class tbl_df (inherits from tbl, data.frame) with 2870 rows and 4 columns.


Theme elements

Description

In conjunction with the theme system, the element_ functions specify the display of how non-data components of the plot are drawn.

rel() is used to specify sizes relative to the parent, margin() is used to specify the margins of elements.

Usage

element_blank()

element_rect(
  fill = NULL,
  colour = NULL,
  linewidth = NULL,
  linetype = NULL,
  color = NULL,
  inherit.blank = FALSE,
  size = deprecated()
)

element_line(
  colour = NULL,
  linewidth = NULL,
  linetype = NULL,
  lineend = NULL,
  color = NULL,
  arrow = NULL,
  inherit.blank = FALSE,
  size = deprecated()
)

element_text(
  family = NULL,
  face = NULL,
  colour = NULL,
  size = NULL,
  hjust = NULL,
  vjust = NULL,
  angle = NULL,
  lineheight = NULL,
  color = NULL,
  margin = NULL,
  debug = NULL,
  inherit.blank = FALSE
)

rel(x)

margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")

Arguments

fill

Fill colour.

colour, color

Line/border colour. Color is an alias for colour.

linewidth

Line/border size in mm.

linetype

Line type. An integer (0:8), a name (blank, solid, dashed, dotted, dotdash, longdash, twodash), or a string with an even number (up to eight) of hexadecimal digits which give the lengths in consecutive positions in the string.

inherit.blank

Should this element inherit the existence of an element_blank among its parents? If TRUE the existence of a blank element among its parents will cause this element to be blank as well. If FALSE any blank parent element will be ignored when calculating final element state.

size

text size in pts.

lineend

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

arrow

Arrow specification, as created by grid::arrow()

family

Font family

face

Font face ("plain", "italic", "bold", "bold.italic")

hjust

Horizontal justification (in [0,1][0, 1])

vjust

Vertical justification (in [0,1][0, 1])

angle

Angle (in [0,360][0, 360])

lineheight

Line height

margin

Margins around the text. See margin() for more details. When creating a theme, the margins should be placed on the side of the text facing towards the center of the plot.

debug

If TRUE, aids visual debugging by drawing a solid rectangle behind the complete text area, and a point where each label is anchored.

x

A single number specifying size relative to parent element.

t, r, b, l

Dimensions of each margin. (To remember order, think trouble).

unit

Default units of dimensions. Defaults to "pt" so it can be most easily scaled with the text.

Value

An S3 object of class element, rel, or margin.

Examples

plot <- ggplot(mpg, aes(displ, hwy)) + geom_point()

plot + theme(
  panel.background = element_blank(),
  axis.text = element_blank()
)

plot + theme(
  axis.text = element_text(colour = "red", size = rel(1.5))
)

plot + theme(
  axis.line = element_line(arrow = arrow())
)

plot + theme(
  panel.background = element_rect(fill = "white"),
  plot.margin = margin(2, 2, 2, 2, "cm"),
  plot.background = element_rect(
    fill = "grey90",
    colour = "black",
    linewidth = 1
  )
)

Expand the plot limits, using data

Description

Sometimes you may want to ensure limits include a single value, for all panels or all plots. This function is a thin wrapper around geom_blank() that makes it easy to add such values.

Usage

expand_limits(...)

Arguments

...

named list of aesthetics specifying the value (or values) that should be included in each scale.

Examples

p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + expand_limits(x = 0)
p + expand_limits(y = c(1, 9))
p + expand_limits(x = 0, y = 0)

ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(colour = cyl)) +
  expand_limits(colour = seq(2, 10, by = 2))
ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(colour = factor(cyl))) +
  expand_limits(colour = factor(seq(2, 10, by = 2)))

Generate expansion vector for scales

Description

This is a convenience function for generating scale expansion vectors for the expand argument of scale_(x|y)_continuous and scale_(x|y)_discrete. The expansion vectors are used to add some space between the data and the axes.

Usage

expansion(mult = 0, add = 0)

expand_scale(mult = 0, add = 0)

Arguments

mult

vector of multiplicative range expansion factors. If length 1, both the lower and upper limits of the scale are expanded outwards by mult. If length 2, the lower limit is expanded by mult[1] and the upper limit by mult[2].

add

vector of additive range expansion constants. If length 1, both the lower and upper limits of the scale are expanded outwards by add units. If length 2, the lower limit is expanded by add[1] and the upper limit by add[2].

Examples

# No space below the bars but 10% above them
ggplot(mtcars) +
  geom_bar(aes(x = factor(cyl))) +
  scale_y_continuous(expand = expansion(mult = c(0, .1)))

# Add 2 units of space on the left and right of the data
ggplot(subset(diamonds, carat > 2), aes(cut, clarity)) +
  geom_jitter() +
  scale_x_discrete(expand = expansion(add = 2))

# Reproduce the default range expansion used
# when the 'expand' argument is not specified
ggplot(subset(diamonds, carat > 2), aes(cut, price)) +
  geom_jitter() +
  scale_x_discrete(expand = expansion(add = .6)) +
  scale_y_continuous(expand = expansion(mult = .05))

Lay out panels in a grid

Description

facet_grid() forms a matrix of panels defined by row and column faceting variables. It is most useful when you have two discrete variables, and all combinations of the variables exist in the data. If you have only one variable with many levels, try facet_wrap().

Usage

facet_grid(
  rows = NULL,
  cols = NULL,
  scales = "fixed",
  space = "fixed",
  shrink = TRUE,
  labeller = "label_value",
  as.table = TRUE,
  switch = NULL,
  drop = TRUE,
  margins = FALSE,
  axes = "margins",
  axis.labels = "all",
  facets = deprecated()
)

Arguments

rows, cols

A set of variables or expressions quoted by vars() and defining faceting groups on the rows or columns dimension. The variables can be named (the names are passed to labeller).

For compatibility with the classic interface, rows can also be a formula with the rows (of the tabular display) on the LHS and the columns (of the tabular display) on the RHS; the dot in the formula is used to indicate there should be no faceting on this dimension (either row or column).

scales

Are scales shared across all facets (the default, "fixed"), or do they vary across rows ("free_x"), columns ("free_y"), or both rows and columns ("free")?

space

If "fixed", the default, all panels have the same size. If "free_y" their height will be proportional to the length of the y scale; if "free_x" their width will be proportional to the length of the x scale; or if "free" both height and width will vary. This setting has no effect unless the appropriate scales also vary.

shrink

If TRUE, will shrink scales to fit output of statistics, not raw data. If FALSE, will be range of raw data before statistical summary.

labeller

A function that takes one data frame of labels and returns a list or data frame of character vectors. Each input column corresponds to one factor. Thus there will be more than one with vars(cyl, am). Each output column gets displayed as one separate line in the strip label. This function should inherit from the "labeller" S3 class for compatibility with labeller(). You can use different labeling functions for different kind of labels, for example use label_parsed() for formatting facet labels. label_value() is used by default, check it for more details and pointers to other options.

as.table

If TRUE, the default, the facets are laid out like a table with highest values at the bottom-right. If FALSE, the facets are laid out like a plot with the highest value at the top-right.

switch

By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both".

drop

If TRUE, the default, all factor levels not used in the data will automatically be dropped. If FALSE, all factor levels will be shown, regardless of whether or not they appear in the data.

margins

Either a logical value or a character vector. Margins are additional facets which contain all the data for each of the possible values of the faceting variables. If FALSE, no additional facets are included (the default). If TRUE, margins are included for all faceting variables. If specified as a character vector, it is the names of variables for which margins are to be created.

axes

Determines which axes will be drawn. When "margins" (default), axes will be drawn at the exterior margins. "all_x" and "all_y" will draw the respective axes at the interior panels too, whereas "all" will draw all axes at all panels.

axis.labels

Determines whether to draw labels for interior axes when the axes argument is not "margins". When "all" (default), all interior axes get labels. When "margins", only the exterior axes get labels and the interior axes get none. When "all_x" or "all_y", only draws the labels at the interior axes in the x- or y-direction respectively.

facets

[Deprecated] Please use rows and cols instead.

See Also

The facet grid section of the online ggplot2 book.

Examples

p <- ggplot(mpg, aes(displ, cty)) + geom_point()

# Use vars() to supply variables from the dataset:
p + facet_grid(rows = vars(drv))
p + facet_grid(cols = vars(cyl))
p + facet_grid(vars(drv), vars(cyl))

# To change plot order of facet grid,
# change the order of variable levels with factor()

# If you combine a facetted dataset with a dataset that lacks those
# faceting variables, the data will be repeated across the missing
# combinations:
df <- data.frame(displ = mean(mpg$displ), cty = mean(mpg$cty))
p +
  facet_grid(cols = vars(cyl)) +
  geom_point(data = df, colour = "red", size = 2)

# When scales are constant, duplicated axes can be shown with
# or without labels
ggplot(mpg, aes(cty, hwy)) +
  geom_point() +
  facet_grid(year ~ drv, axes = "all", axis.labels = "all_x")

# Free scales -------------------------------------------------------
# You can also choose whether the scales should be constant
# across all panels (the default), or whether they should be allowed
# to vary
mt <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
  geom_point()

mt + facet_grid(vars(cyl), scales = "free")

# If scales and space are free, then the mapping between position
# and values in the data will be the same across all panels. This
# is particularly useful for categorical axes
ggplot(mpg, aes(drv, model)) +
  geom_point() +
  facet_grid(manufacturer ~ ., scales = "free", space = "free") +
  theme(strip.text.y = element_text(angle = 0))

# Margins ----------------------------------------------------------

# Margins can be specified logically (all yes or all no) or for specific
# variables as (character) variable names
mg <- ggplot(mtcars, aes(x = mpg, y = wt)) + geom_point()
mg + facet_grid(vs + am ~ gear, margins = TRUE)
mg + facet_grid(vs + am ~ gear, margins = "am")
# when margins are made over "vs", since the facets for "am" vary
# within the values of "vs", the marginal facet for "vs" is also
# a margin over "am".
mg + facet_grid(vs + am ~ gear, margins = "vs")

Wrap a 1d ribbon of panels into 2d

Description

facet_wrap() wraps a 1d sequence of panels into 2d. This is generally a better use of screen space than facet_grid() because most displays are roughly rectangular.

Usage

facet_wrap(
  facets,
  nrow = NULL,
  ncol = NULL,
  scales = "fixed",
  shrink = TRUE,
  labeller = "label_value",
  as.table = TRUE,
  switch = deprecated(),
  drop = TRUE,
  dir = "h",
  strip.position = "top",
  axes = "margins",
  axis.labels = "all"
)

Arguments

facets

A set of variables or expressions quoted by vars() and defining faceting groups on the rows or columns dimension. The variables can be named (the names are passed to labeller).

For compatibility with the classic interface, can also be a formula or character vector. Use either a one sided formula, ~a + b, or a character vector, c("a", "b").

nrow, ncol

Number of rows and columns.

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")?

shrink

If TRUE, will shrink scales to fit output of statistics, not raw data. If FALSE, will be range of raw data before statistical summary.

labeller

A function that takes one data frame of labels and returns a list or data frame of character vectors. Each input column corresponds to one factor. Thus there will be more than one with vars(cyl, am). Each output column gets displayed as one separate line in the strip label. This function should inherit from the "labeller" S3 class for compatibility with labeller(). You can use different labeling functions for different kind of labels, for example use label_parsed() for formatting facet labels. label_value() is used by default, check it for more details and pointers to other options.

as.table

If TRUE, the default, the facets are laid out like a table with highest values at the bottom-right. If FALSE, the facets are laid out like a plot with the highest value at the top-right.

switch

By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both".

drop

If TRUE, the default, all factor levels not used in the data will automatically be dropped. If FALSE, all factor levels will be shown, regardless of whether or not they appear in the data.

dir

Direction: either "h" for horizontal, the default, or "v", for vertical.

strip.position

By default, the labels are displayed on the top of the plot. Using strip.position it is possible to place the labels on either of the four sides by setting strip.position = c("top", "bottom", "left", "right")

axes

Determines which axes will be drawn in case of fixed scales. When "margins" (default), axes will be drawn at the exterior margins. "all_x" and "all_y" will draw the respective axes at the interior panels too, whereas "all" will draw all axes at all panels.

axis.labels

Determines whether to draw labels for interior axes when the scale is fixed and the axis argument is not "margins". When "all" (default), all interior axes get labels. When "margins", only the exterior axes get labels, and the interior axes get none. When "all_x" or "all_y", only draws the labels at the interior axes in the x- or y-direction respectively.

See Also

The facet wrap section of the online ggplot2 book.

Examples

p <- ggplot(mpg, aes(displ, hwy)) + geom_point()

# Use vars() to supply faceting variables:
p + facet_wrap(vars(class))

# Control the number of rows and columns with nrow and ncol
p + facet_wrap(vars(class), nrow = 4)


# You can facet by multiple variables
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(cyl, drv))

# Use the `labeller` option to control how labels are printed:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(cyl, drv), labeller = "label_both")

# To change the order in which the panels appear, change the levels
# of the underlying factor.
mpg$class2 <- reorder(mpg$class, mpg$displ)
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(class2))

# By default, the same scales are used for all panels. You can allow
# scales to vary across the panels with the `scales` argument.
# Free scales make it easier to see patterns within each panel, but
# harder to compare across panels.
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(class), scales = "free")

# When scales are constant, duplicated axes can be shown with
# or without labels
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(class), axes = "all", axis.labels = "all_y")

# To repeat the same data in every panel, simply construct a data frame
# that does not contain the faceting variable.
ggplot(mpg, aes(displ, hwy)) +
  geom_point(data = transform(mpg, class = NULL), colour = "grey85") +
  geom_point() +
  facet_wrap(vars(class))

# Use `strip.position` to display the facet labels at the side of your
# choice. Setting it to `bottom` makes it act as a subtitle for the axis.
# This is typically used with free scales and a theme without boxes around
# strip labels.
ggplot(economics_long, aes(date, value)) +
  geom_line() +
  facet_wrap(vars(variable), scales = "free_y", nrow = 2, strip.position = "top") +
  theme(strip.background = element_blank(), strip.placement = "outside")

2d density estimate of Old Faithful data

Description

A 2d density estimate of the waiting and eruptions variables data faithful.

Usage

faithfuld

Format

A data frame with 5,625 observations and 3 variables:

eruptions

Eruption time in mins

waiting

Waiting time to next eruption in mins

density

2d density estimate


Fortify a model with data.

Description

Rather than using this function, I now recommend using the broom package, which implements a much wider range of methods. fortify() may be deprecated in the future.

Usage

fortify(model, data, ...)

Arguments

model

model or other R object to convert to data frame

data

original dataset, if needed

...

other arguments passed to methods

See Also

fortify.lm()


Reference lines: horizontal, vertical, and diagonal

Description

These geoms add reference lines (sometimes called rules) to a plot, either horizontal, vertical, or diagonal (specified by slope and intercept). These are useful for annotating plots.

Usage

geom_abline(
  mapping = NULL,
  data = NULL,
  ...,
  slope,
  intercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_hline(
  mapping = NULL,
  data = NULL,
  ...,
  yintercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_vline(
  mapping = NULL,
  data = NULL,
  ...,
  xintercept,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

mapping

Set of aesthetic mappings created by aes().

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)).

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

xintercept, yintercept, slope, intercept

Parameters that control the position of the line. If these are set, data, mapping and show.legend are overridden.

Details

These geoms act slightly differently from other geoms. You can supply the parameters in two ways: either as arguments to the layer function, or via aesthetics. If you use arguments, e.g. geom_abline(intercept = 0, slope = 1), then behind the scenes the geom makes a new data frame containing just the data you've supplied. That means that the lines will be the same in all facets; if you want them to vary across facets, construct the data frame yourself and use aesthetics.

Unlike most other geoms, these geoms do not inherit aesthetics from the plot default, because they do not understand x and y aesthetics which are commonly set in the plot. They also do not affect the x and y scales.

Aesthetics

These geoms are drawn using geom_line() so they support the same aesthetics: alpha, colour, linetype and linewidth. They also each have aesthetics that control the position of the line:

See Also

See geom_segment() for a more general approach to adding straight line segments to a plot.

Examples

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()

# Fixed values
p + geom_vline(xintercept = 5)
p + geom_vline(xintercept = 1:5)
p + geom_hline(yintercept = 20)

p + geom_abline() # Can't see it - outside the range of the data
p + geom_abline(intercept = 20)

# Calculate slope and intercept of line of best fit
coef(lm(mpg ~ wt, data = mtcars))
p + geom_abline(intercept = 37, slope = -5)
# But this is easier to do with geom_smooth:
p + geom_smooth(method = "lm", se = FALSE)

# To show different lines in different facets, use aesthetics
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  facet_wrap(~ cyl)

mean_wt <- data.frame(cyl = c(4, 6, 8), wt = c(2.28, 3.11, 4.00))
p + geom_hline(aes(yintercept = wt), mean_wt)

# You can also control other aesthetics
ggplot(mtcars, aes(mpg, wt, colour = wt)) +
  geom_point() +
  geom_hline(aes(yintercept = wt, colour = wt), mean_wt) +
  facet_wrap(~ cyl)

Bar charts

Description

There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x position. geom_col() uses stat_identity(): it leaves the data as is.

Usage

geom_bar(
  mapping = NULL,
  data = NULL,
  stat = "count",
  position = "stack",
  ...,
  just = 0.5,
  width = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_col(
  mapping = NULL,
  data = NULL,
  position = "stack",
  ...,
  just = 0.5,
  width = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_count(
  mapping = NULL,
  data = NULL,
  geom = "bar",
  position = "stack",
  ...,
  width = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

just

Adjustment for column placement. Set to 0.5 by default, meaning that columns will be centered about axis breaks. Set to 0 or 1 to place columns to the left/right of axis breaks. Note that this argument may have unintended behaviour when used with alternative positions, e.g. position_dodge().

width

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

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Override the default connection between geom_bar() and stat_count(). For more information about overriding these connections, see how the stat and geom arguments work.

Details

A bar chart uses height to represent a value, and so the base of the bar must always be shown to produce a valid visual comparison. Proceed with caution when using transformed scales with a bar chart. It's important to always use a meaningful reference point for the base of the bar. For example, for log transformations the reference point is 1. In fact, when using a log scale, geom_bar() automatically places the base of the bar at 1. Furthermore, never use stacked bars with a transformed scale, because scaling happens before stacking. As a consequence, the height of bars will be wrong when stacking occurs with a transformed scale.

By default, multiple bars occupying the same x position will be stacked atop one another by position_stack(). If you want them to be dodged side-to-side, use position_dodge() or position_dodge2(). Finally, position_fill() shows relative proportions at each x by stacking the bars and then standardising each bar to have the same height.

Orientation

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

Aesthetics

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

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

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

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

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

geom_histogram() for continuous data, position_dodge() and position_dodge2() for creating side-by-side bar charts.

stat_bin(), which bins data in ranges and counts the cases in each range. It differs from stat_count(), which counts the number of cases at each x position (without binning into ranges). stat_bin() requires continuous x data, whereas stat_count() can be used for both discrete and continuous x data.

Examples

# geom_bar is designed to make it easy to create bar charts that show
# counts (or sums of weights)
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()
# Total engine displacement of each class
g + geom_bar(aes(weight = displ))
# Map class to y instead to flip the orientation
ggplot(mpg) + geom_bar(aes(y = class))

# Bar charts are automatically stacked when multiple bars are placed
# at the same location. The order of the fill is designed to match
# the legend
g + geom_bar(aes(fill = drv))

# If you need to flip the order (because you've flipped the orientation)
# call position_stack() explicitly:
ggplot(mpg, aes(y = class)) +
 geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) +
 theme(legend.position = "top")

# To show (e.g.) means, you need geom_col()
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(trt, outcome)) +
  geom_col()
# But geom_point() displays exactly the same information and doesn't
# require the y-axis to touch zero.
ggplot(df, aes(trt, outcome)) +
  geom_point()

# You can also use geom_bar() with continuous data, in which case
# it will show counts at unique locations
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))
ggplot(df, aes(x)) + geom_bar()
# cf. a histogram of the same data
ggplot(df, aes(x)) + geom_histogram(binwidth = 0.5)

# Use `just` to control how columns are aligned with axis breaks:
df <- data.frame(x = as.Date(c("2020-01-01", "2020-02-01")), y = 1:2)
# Columns centered on the first day of the month
ggplot(df, aes(x, y)) + geom_col(just = 0.5)
# Columns begin on the first day of the month
ggplot(df, aes(x, y)) + geom_col(just = 1)

Heatmap of 2d bin counts

Description

Divides the plane into rectangles, counts the number of cases in each rectangle, and then (by default) maps the number of cases to the rectangle's fill. This is a useful alternative to geom_point() in the presence of overplotting.

Usage

geom_bin_2d(
  mapping = NULL,
  data = NULL,
  stat = "bin2d",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_bin_2d(
  mapping = NULL,
  data = NULL,
  geom = "tile",
  position = "identity",
  ...,
  bins = 30,
  binwidth = NULL,
  drop = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_bin_2d() and stat_bin_2d(). For more information about overriding these connections, see how the stat and geom arguments work.

bins

numeric vector giving number of bins in both vertical and horizontal directions. Set to 30 by default.

binwidth

Numeric vector giving bin width in both vertical and horizontal directions. Overrides bins if both set.

drop

if TRUE removes all cells with 0 counts.

Aesthetics

stat_bin_2d() understands the following aesthetics (required aesthetics are in bold):

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

stat_bin_hex() for hexagonal binning

Examples

d <- ggplot(diamonds, aes(x, y)) + xlim(4, 10) + ylim(4, 10)
d + geom_bin_2d()

# You can control the size of the bins by specifying the number of
# bins in each direction:
d + geom_bin_2d(bins = 10)
d + geom_bin_2d(bins = 30)

# Or by specifying the width of the bins
d + geom_bin_2d(binwidth = c(0.1, 0.1))

Draw nothing

Description

The blank geom draws nothing, but can be a useful way of ensuring common scales between different plots. See expand_limits() for more details.

Usage

geom_blank(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  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.

...

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.

  • 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.

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().

Examples

ggplot(mtcars, aes(wt, mpg))
# Nothing to see here!

A box and whiskers plot (in the style of Tukey)

Description

The boxplot compactly displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all "outlying" points individually.

Usage

geom_boxplot(
  mapping = NULL,
  data = NULL,
  stat = "boxplot",
  position = "dodge2",
  ...,
  outliers = TRUE,
  outlier.colour = NULL,
  outlier.color = NULL,
  outlier.fill = NULL,
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  outlier.alpha = NULL,
  notch = FALSE,
  notchwidth = 0.5,
  staplewidth = 0,
  varwidth = FALSE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_boxplot(
  mapping = NULL,
  data = NULL,
  geom = "boxplot",
  position = "dodge2",
  ...,
  coef = 1.5,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

outliers

Whether to display (TRUE) or discard (FALSE) outliers from the plot. Hiding or discarding outliers can be useful when, for example, raw data points need to be displayed on top of the boxplot. By discarding outliers, the axis limits will adapt to the box and whiskers only, not the full data range. If outliers need to be hidden and the axes needs to show the full data range, please use outlier.shape = NA instead.

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

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

notch

If FALSE (default) make a standard box plot. If TRUE, make a notched box plot. Notches are used to compare groups; if the notches of two boxes do not overlap, this suggests that the medians are significantly different.

notchwidth

For a notched box plot, width of the notch relative to the body (defaults to notchwidth = 0.5).

staplewidth

The relative width of staples to the width of the box. Staples mark the ends of the whiskers with a line.

varwidth

If FALSE (default) make a standard box plot. If TRUE, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups (possibly weighted, using the weight aesthetic).

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_boxplot() and stat_boxplot(). For more information about overriding these connections, see how the stat and geom arguments work.

coef

Length of the whiskers as multiple of IQR. Defaults to 1.5.

Orientation

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

Summary statistics

The lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). This differs slightly from the method used by the boxplot() function, and may be apparent with small samples. See boxplot.stats() for more information on how hinge positions are calculated for boxplot().

The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at most 1.5 * IQR of the hinge. Data beyond the end of the whiskers are called "outlying" points and are plotted individually.

In a notched box plot, the notches extend 1.58 * IQR / sqrt(n). This gives a roughly 95% confidence interval for comparing medians. See McGill et al. (1978) for more details.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation. stat_boxplot() provides the following variables, some of which depend on the orientation:

References

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.

See Also

geom_quantile() for continuous x, geom_violin() for a richer display of the distribution, and geom_jitter() for a useful technique for small data.

Examples

p <- ggplot(mpg, aes(class, hwy))
p + geom_boxplot()
# Orientation follows the discrete axis
ggplot(mpg, aes(hwy, class)) + geom_boxplot()

p + geom_boxplot(notch = TRUE)
p + geom_boxplot(varwidth = TRUE)
p + geom_boxplot(fill = "white", colour = "#3366FF")
# By default, outlier points match the colour of the box. Use
# outlier.colour to override
p + geom_boxplot(outlier.colour = "red", outlier.shape = 1)
# Remove outliers when overlaying boxplot with original data points
p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2)

# Boxplots are automatically dodged when any aesthetic is a factor
p + geom_boxplot(aes(colour = drv))

# You can also use boxplots with continuous x, as long as you supply
# a grouping variable. cut_width is particularly useful
ggplot(diamonds, aes(carat, price)) +
  geom_boxplot()
ggplot(diamonds, aes(carat, price)) +
  geom_boxplot(aes(group = cut_width(carat, 0.25)))
# Adjust the transparency of outliers using outlier.alpha
ggplot(diamonds, aes(carat, price)) +
  geom_boxplot(aes(group = cut_width(carat, 0.25)), outlier.alpha = 0.1)


# It's possible to draw a boxplot with your own computations if you
# use stat = "identity":
set.seed(1)
y <- rnorm(100)
df <- data.frame(
  x = 1,
  y0 = min(y),
  y25 = quantile(y, 0.25),
  y50 = median(y),
  y75 = quantile(y, 0.75),
  y100 = max(y)
)
ggplot(df, aes(x)) +
  geom_boxplot(
   aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100),
   stat = "identity"
 )

2D contours of a 3D surface

Description

ggplot2 can not draw true 3D surfaces, but you can use geom_contour(), geom_contour_filled(), and geom_tile() to visualise 3D surfaces in 2D.

These functions require regular data, where the x and y coordinates form an equally spaced grid, and each combination of x and y appears once. Missing values of z are allowed, but contouring will only work for grid points where all four corners are non-missing. If you have irregular data, you'll need to first interpolate on to a grid before visualising, using interp::interp(), akima::bilinear(), or similar.

Usage

geom_contour(
  mapping = NULL,
  data = NULL,
  stat = "contour",
  position = "identity",
  ...,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 10,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_contour_filled(
  mapping = NULL,
  data = NULL,
  stat = "contour_filled",
  position = "identity",
  ...,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_contour(
  mapping = NULL,
  data = NULL,
  geom = "contour",
  position = "identity",
  ...,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_contour_filled(
  mapping = NULL,
  data = NULL,
  geom = "contour_filled",
  position = "identity",
  ...,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

bins

Number of contour bins. Overridden by breaks.

binwidth

The width of the contour bins. Overridden by bins.

breaks

One of:

  • Numeric vector to set the contour breaks

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

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

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

linemitre

Line mitre limit (number greater than 1).

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom

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

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

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

Aesthetics

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

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

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

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

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

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

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation. The computed variables differ somewhat for contour lines (computed by stat_contour()) and contour bands (filled contours, computed by stat_contour_filled()). The variables nlevel and piece are available for both, whereas level_low, level_high, and level_mid are only available for bands. The variable level is a numeric or a factor depending on whether lines or bands are calculated.

Dropped variables

z

After contouring, the z values of individual data points are no longer available.

See Also

geom_density_2d(): 2d density contours

Examples

# Basic plot
v <- ggplot(faithfuld, aes(waiting, eruptions, z = density))
v + geom_contour()

# Or compute from raw data
ggplot(faithful, aes(waiting, eruptions)) +
  geom_density_2d()


# use geom_contour_filled() for filled contours
v + geom_contour_filled()

# Setting bins creates evenly spaced contours in the range of the data
v + geom_contour(bins = 3)
v + geom_contour(bins = 5)

# Setting binwidth does the same thing, parameterised by the distance
# between contours
v + geom_contour(binwidth = 0.01)
v + geom_contour(binwidth = 0.001)

# Other parameters
v + geom_contour(aes(colour = after_stat(level)))
v + geom_contour(colour = "red")
v + geom_raster(aes(fill = density)) +
  geom_contour(colour = "white")

Count overlapping points

Description

This is a variant geom_point() that counts the number of observations at each location, then maps the count to point area. It useful when you have discrete data and overplotting.

Usage

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

stat_sum(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_count() and stat_sum(). For more information about overriding these connections, see how the stat and geom arguments work.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

For continuous x and y, use geom_bin_2d().

Examples

ggplot(mpg, aes(cty, hwy)) +
 geom_point()

ggplot(mpg, aes(cty, hwy)) +
 geom_count()

# Best used in conjunction with scale_size_area which ensures that
# counts of zero would be given size 0. Doesn't make much different
# here because the smallest count is already close to 0.
ggplot(mpg, aes(cty, hwy)) +
 geom_count() +
 scale_size_area()

# Display proportions instead of counts -------------------------------------
# By default, all categorical variables in the plot form the groups.
# Specifying geom_count without a group identifier leads to a plot which is
# not useful:
d <- ggplot(diamonds, aes(x = cut, y = clarity))
d + geom_count(aes(size = after_stat(prop)))
# To correct this problem and achieve a more desirable plot, we need
# to specify which group the proportion is to be calculated over.
d + geom_count(aes(size = after_stat(prop), group = 1)) +
  scale_size_area(max_size = 10)

# Or group by x/y variables to have rows/columns sum to 1.
d + geom_count(aes(size = after_stat(prop), group = cut)) +
  scale_size_area(max_size = 10)
d + geom_count(aes(size = after_stat(prop), group = clarity)) +
  scale_size_area(max_size = 10)

Vertical intervals: lines, crossbars & errorbars

Description

Various ways of representing a vertical interval defined by x, ymin and ymax. Each case draws a single graphical object.

Usage

geom_crossbar(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  fatten = 2.5,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

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

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

geom_pointrange(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  fatten = 4,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

The statistical transformation to use on the data for this layer. 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.

...

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.

  • 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.

fatten

A multiplicative factor used to increase the size of the middle bar in geom_crossbar() and the middle point in geom_pointrange().

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Orientation

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

Aesthetics

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

Note that geom_pointrange() also understands size for the size of the points.

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

See Also

stat_summary() for examples of these guys in use, geom_smooth() for continuous analogue, geom_errorbarh() for a horizontal error bar.

Examples

# Create a simple example dataset
df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),
  resp = c(1, 5, 3, 4),
  group = factor(c(1, 2, 1, 2)),
  upper = c(1.1, 5.3, 3.3, 4.2),
  lower = c(0.8, 4.6, 2.4, 3.6)
)

p <- ggplot(df, aes(trt, resp, colour = group))
p + geom_linerange(aes(ymin = lower, ymax = upper))
p + geom_pointrange(aes(ymin = lower, ymax = upper))
p + geom_crossbar(aes(ymin = lower, ymax = upper), width = 0.2)
p + geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2)

# Flip the orientation by changing mapping
ggplot(df, aes(resp, trt, colour = group)) +
  geom_linerange(aes(xmin = lower, xmax = upper))

# Draw lines connecting group means
p +
  geom_line(aes(group = group)) +
  geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2)

# If you want to dodge bars and errorbars, you need to manually
# specify the dodge width
p <- ggplot(df, aes(trt, resp, fill = group))
p +
 geom_col(position = "dodge") +
 geom_errorbar(aes(ymin = lower, ymax = upper), position = "dodge", width = 0.25)

# Because the bars and errorbars have different widths
# we need to specify how wide the objects we are dodging are
dodge <- position_dodge(width=0.9)
p +
  geom_col(position = dodge) +
  geom_errorbar(aes(ymin = lower, ymax = upper), position = dodge, width = 0.25)

# When using geom_errorbar() with position_dodge2(), extra padding will be
# needed between the error bars to keep them aligned with the bars.
p +
geom_col(position = "dodge2") +
geom_errorbar(
  aes(ymin = lower, ymax = upper),
  position = position_dodge2(width = 0.5, padding = 0.5)
)

Smoothed density estimates

Description

Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution.

Usage

geom_density(
  mapping = NULL,
  data = NULL,
  stat = "density",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "upper"
)

stat_density(
  mapping = NULL,
  data = NULL,
  geom = "area",
  position = "stack",
  ...,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  na.rm = FALSE,
  bounds = c(-Inf, Inf),
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

outline.type

Type of the outline of the area; "both" draws both the upper and lower lines, "upper"/"lower" draws the respective lines only. "full" draws a closed polygon around the area.

geom, stat

Use to override the default connection between geom_density() and stat_density(). For more information about overriding these connections, see how the stat and geom arguments work.

bw

The smoothing bandwidth to be used. If numeric, the standard deviation of the smoothing kernel. If character, a rule to choose the bandwidth, as listed in stats::bw.nrd(). Note that automatic calculation of the bandwidth does not take weights into account.

adjust

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

kernel

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

n

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

trim

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

bounds

Known lower and upper bounds for estimated data. Default c(-Inf, Inf) means that there are no (finite) bounds. If any bound is finite, boundary effect of default density estimation will be corrected by reflecting tails outside bounds around their closest edge. Data points outside of bounds are removed with a warning.

Orientation

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

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

See geom_histogram(), geom_freqpoly() for other methods of displaying continuous distribution. See geom_violin() for a compact density display.

Examples

ggplot(diamonds, aes(carat)) +
  geom_density()
# Map the values to y to flip the orientation
ggplot(diamonds, aes(y = carat)) +
  geom_density()

ggplot(diamonds, aes(carat)) +
  geom_density(adjust = 1/5)
ggplot(diamonds, aes(carat)) +
  geom_density(adjust = 5)

ggplot(diamonds, aes(depth, colour = cut)) +
  geom_density() +
  xlim(55, 70)
ggplot(diamonds, aes(depth, fill = cut, colour = cut)) +
  geom_density(alpha = 0.1) +
  xlim(55, 70)

# Use `bounds` to adjust computation for known data limits
big_diamonds <- diamonds[diamonds$carat >= 1, ]
ggplot(big_diamonds, aes(carat)) +
  geom_density(color = 'red') +
  geom_density(bounds = c(1, Inf), color = 'blue')


# Stacked density plots: if you want to create a stacked density plot, you
# probably want to 'count' (density * n) variable instead of the default
# density

# Loses marginal densities
ggplot(diamonds, aes(carat, fill = cut)) +
  geom_density(position = "stack")
# Preserves marginal densities
ggplot(diamonds, aes(carat, after_stat(count), fill = cut)) +
  geom_density(position = "stack")

# You can use position="fill" to produce a conditional density estimate
ggplot(diamonds, aes(carat, after_stat(count), fill = cut)) +
  geom_density(position = "fill")

Contours of a 2D density estimate

Description

Perform a 2D kernel density estimation using MASS::kde2d() and display the results with contours. This can be useful for dealing with overplotting. This is a 2D version of geom_density(). geom_density_2d() draws contour lines, and geom_density_2d_filled() draws filled contour bands.

Usage

geom_density_2d(
  mapping = NULL,
  data = NULL,
  stat = "density_2d",
  position = "identity",
  ...,
  contour_var = "density",
  lineend = "butt",
  linejoin = "round",
  linemitre = 10,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_density_2d_filled(
  mapping = NULL,
  data = NULL,
  stat = "density_2d_filled",
  position = "identity",
  ...,
  contour_var = "density",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_density_2d(
  mapping = NULL,
  data = NULL,
  geom = "density_2d",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_density_2d_filled(
  mapping = NULL,
  data = NULL,
  geom = "density_2d_filled",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

Arguments passed on to geom_contour

binwidth

The width of the contour bins. Overridden by bins.

bins

Number of contour bins. Overridden by breaks.

breaks

One of:

  • Numeric vector to set the contour breaks

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

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

contour_var

Character string identifying the variable to contour by. Can be one of "density", "ndensity", or "count". See the section on computed variables for details.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

linemitre

Line mitre limit (number greater than 1).

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_density_2d() and stat_density_2d(). For more information at overriding these connections, see how the stat and geom arguments work.

contour

If TRUE, contour the results of the 2d density estimation.

n

Number of grid points in each direction.

h

Bandwidth (vector of length two). If NULL, estimated using MASS::bandwidth.nrd().

adjust

A multiplicative bandwidth adjustment to be used if 'h' is 'NULL'. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.

Aesthetics

geom_density_2d() understands the following aesthetics (required aesthetics are in bold):

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

geom_density_2d_filled() understands the following aesthetics (required aesthetics are in bold):

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation. stat_density_2d() and stat_density_2d_filled() compute different variables depending on whether contouring is turned on or off. With contouring off (contour = FALSE), both stats behave the same, and the following variables are provided:

With contouring on (contour = TRUE), either stat_contour() or stat_contour_filled() (for contour lines or contour bands, respectively) is run after the density estimate has been obtained, and the computed variables are determined by these stats. Contours are calculated for one of the three types of density estimates obtained before contouring, density, ndensity, and count. Which of those should be used is determined by the contour_var parameter.

Dropped variables

z

After density estimation, the z values of individual data points are no longer available.

If contouring is enabled, then similarly density, ndensity, and count are no longer available after the contouring pass.

See Also

geom_contour(), geom_contour_filled() for information about how contours are drawn; geom_bin_2d() for another way of dealing with overplotting.

Examples

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
 geom_point() +
 xlim(0.5, 6) +
 ylim(40, 110)

# contour lines
m + geom_density_2d()


# contour bands
m + geom_density_2d_filled(alpha = 0.5)

# contour bands and contour lines
m + geom_density_2d_filled(alpha = 0.5) +
  geom_density_2d(linewidth = 0.25, colour = "black")

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y))
# If you map an aesthetic to a categorical variable, you will get a
# set of contours for each value of that variable
d + geom_density_2d(aes(colour = cut))

# If you draw filled contours across multiple facets, the same bins are
# used across all facets
d + geom_density_2d_filled() + facet_wrap(vars(cut))
# If you want to make sure the peak intensity is the same in each facet,
# use `contour_var = "ndensity"`.
d + geom_density_2d_filled(contour_var = "ndensity") + facet_wrap(vars(cut))
# If you want to scale intensity by the number of observations in each group,
# use `contour_var = "count"`.
d + geom_density_2d_filled(contour_var = "count") + facet_wrap(vars(cut))

# If we turn contouring off, we can use other geoms, such as tiles:
d + stat_density_2d(
  geom = "raster",
  aes(fill = after_stat(density)),
  contour = FALSE
) + scale_fill_viridis_c()
# Or points:
d + stat_density_2d(geom = "point", aes(size = after_stat(density)), n = 20, contour = FALSE)

Dot plot

Description

In a dot plot, the width of a dot corresponds to the bin width (or maximum width, depending on the binning algorithm), and dots are stacked, with each dot representing one observation.

Usage

geom_dotplot(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  binwidth = NULL,
  binaxis = "x",
  method = "dotdensity",
  binpositions = "bygroup",
  stackdir = "up",
  stackratio = 1,
  dotsize = 1,
  stackgroups = FALSE,
  origin = NULL,
  right = TRUE,
  width = 0.9,
  drop = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

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.

  • 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.

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

binaxis

The axis to bin along, "x" (default) or "y"

method

"dotdensity" (default) for dot-density binning, or "histodot" for fixed bin widths (like stat_bin)

binpositions

When method is "dotdensity", "bygroup" (default) determines positions of the bins for each group separately. "all" determines positions of the bins with all the data taken together; this is used for aligning dot stacks across multiple groups.

stackdir

which direction to stack the dots. "up" (default), "down", "center", "centerwhole" (centered, but with dots aligned)

stackratio

how close to stack the dots. Default is 1, where dots just touch. Use smaller values for closer, overlapping dots.

dotsize

The diameter of the dots relative to binwidth, default 1.

stackgroups

should dots be stacked across groups? This has the effect that position = "stack" should have, but can't (because this geom has some odd properties).

origin

When method is "histodot", origin of first bin

right

When method is "histodot", should intervals be closed on the right (a, b], or not [a, b)

width

When binaxis is "y", the spacing of the dot stacks for dodging.

drop

If TRUE, remove all bins with zero counts

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Details

There are two basic approaches: dot-density and histodot. With dot-density binning, the bin positions are determined by the data and binwidth, which is the maximum width of each bin. See Wilkinson (1999) for details on the dot-density binning algorithm. With histodot binning, the bins have fixed positions and fixed widths, much like a histogram.

When binning along the x axis and stacking along the y axis, the numbers on y axis are not meaningful, due to technical limitations of ggplot2. You can hide the y axis, as in one of the examples, or manually scale it to match the number of dots.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

References

Wilkinson, L. (1999) Dot plots. The American Statistician, 53(3), 276-281.

Examples

ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot()

ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5)

# Use fixed-width bins
ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(method="histodot", binwidth = 1.5)

# Some other stacking methods
ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5, stackdir = "center")

ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5, stackdir = "centerwhole")

# y axis isn't really meaningful, so hide it
ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5) +
  scale_y_continuous(NULL, breaks = NULL)

# Overlap dots vertically
ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5, stackratio = .7)

# Expand dot diameter
ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5, dotsize = 1.25)

# Change dot fill colour, stroke width
ggplot(mtcars, aes(x = mpg)) +
  geom_dotplot(binwidth = 1.5, fill = "white", stroke = 2)


# Examples with stacking along y axis instead of x
ggplot(mtcars, aes(x = 1, y = mpg)) +
  geom_dotplot(binaxis = "y", stackdir = "center")

ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_dotplot(binaxis = "y", stackdir = "center")

ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_dotplot(binaxis = "y", stackdir = "centerwhole")

ggplot(mtcars, aes(x = factor(vs), fill = factor(cyl), y = mpg)) +
  geom_dotplot(binaxis = "y", stackdir = "center", position = "dodge")

# binpositions="all" ensures that the bins are aligned between groups
ggplot(mtcars, aes(x = factor(am), y = mpg)) +
  geom_dotplot(binaxis = "y", stackdir = "center", binpositions="all")

# Stacking multiple groups, with different fill
ggplot(mtcars, aes(x = mpg, fill = factor(cyl))) +
  geom_dotplot(stackgroups = TRUE, binwidth = 1, binpositions = "all")

ggplot(mtcars, aes(x = mpg, fill = factor(cyl))) +
  geom_dotplot(stackgroups = TRUE, binwidth = 1, method = "histodot")

ggplot(mtcars, aes(x = 1, y = mpg, fill = factor(cyl))) +
  geom_dotplot(binaxis = "y", stackgroups = TRUE, binwidth = 1, method = "histodot")

Horizontal error bars

Description

A rotated version of geom_errorbar().

Usage

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

Examples

df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),
  resp = c(1, 5, 3, 4),
  group = factor(c(1, 2, 1, 2)),
  se = c(0.1, 0.3, 0.3, 0.2)
)

# Define the top and bottom of the errorbars

p <- ggplot(df, aes(resp, trt, colour = group))
p +
  geom_point() +
  geom_errorbarh(aes(xmax = resp + se, xmin = resp - se))

p +
  geom_point() +
  geom_errorbarh(aes(xmax = resp + se, xmin = resp - se, height = .2))

Histograms and frequency polygons

Description

Visualise the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. Histograms (geom_histogram()) display the counts with bars; frequency polygons (geom_freqpoly()) display the counts with lines. Frequency polygons are more suitable when you want to compare the distribution across the levels of a categorical variable.

Usage

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

geom_histogram(
  mapping = NULL,
  data = NULL,
  stat = "bin",
  position = "stack",
  ...,
  binwidth = NULL,
  bins = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

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

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

binwidth

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

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

bins

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

orientation

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

geom, stat

Use to override the default connection between geom_histogram()/geom_freqpoly() and stat_bin(). For more information at overriding these connections, see how the stat and geom arguments work.

center, boundary

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

breaks

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

closed

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

pad

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

Details

stat_bin() is suitable only for continuous x data. If your x data is discrete, you probably want to use stat_count().

By default, the underlying computation (stat_bin()) uses 30 bins; this is not a good default, but the idea is to get you experimenting with different number of bins. You can also experiment modifying the binwidth with center or boundary arguments. binwidth overrides bins so you should do one change at a time. You may need to look at a few options to uncover the full story behind your data.

In addition to geom_histogram(), you can create a histogram plot by using scale_x_binned() with geom_bar(). This method by default plots tick marks in between each bar.

Orientation

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

Aesthetics

geom_histogram() uses the same aesthetics as geom_bar(); geom_freqpoly() uses the same aesthetics as geom_line().

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

Dropped variables

weight

After binning, weights of individual data points (if supplied) are no longer available.

See Also

stat_count(), which counts the number of cases at each x position, without binning. It is suitable for both discrete and continuous x data, whereas stat_bin() is suitable only for continuous x data.

Examples

ggplot(diamonds, aes(carat)) +
  geom_histogram()
ggplot(diamonds, aes(carat)) +
  geom_histogram(binwidth = 0.01)
ggplot(diamonds, aes(carat)) +
  geom_histogram(bins = 200)
# Map values to y to flip the orientation
ggplot(diamonds, aes(y = carat)) +
  geom_histogram()

# For histograms with tick marks between each bin, use `geom_bar()` with
# `scale_x_binned()`.
ggplot(diamonds, aes(carat)) +
  geom_bar() +
  scale_x_binned()

# Rather than stacking histograms, it's easier to compare frequency
# polygons
ggplot(diamonds, aes(price, fill = cut)) +
  geom_histogram(binwidth = 500)
ggplot(diamonds, aes(price, colour = cut)) +
  geom_freqpoly(binwidth = 500)

# To make it easier to compare distributions with very different counts,
# put density on the y axis instead of the default count
ggplot(diamonds, aes(price, after_stat(density), colour = cut)) +
  geom_freqpoly(binwidth = 500)

if (require("ggplot2movies")) {
# Often we don't want the height of the bar to represent the
# count of observations, but the sum of some other variable.
# For example, the following plot shows the number of movies
# in each rating.
m <- ggplot(movies, aes(rating))
m + geom_histogram(binwidth = 0.1)

# If, however, we want to see the number of votes cast in each
# category, we need to weight by the votes variable
m +
  geom_histogram(aes(weight = votes), binwidth = 0.1) +
  ylab("votes")

# For transformed scales, binwidth applies to the transformed data.
# The bins have constant width on the transformed scale.
m +
 geom_histogram() +
 scale_x_log10()
m +
  geom_histogram(binwidth = 0.05) +
  scale_x_log10()

# For transformed coordinate systems, the binwidth applies to the
# raw data. The bins have constant width on the original scale.

# Using log scales does not work here, because the first
# bar is anchored at zero, and so when transformed becomes negative
# infinity. This is not a problem when transforming the scales, because
# no observations have 0 ratings.
m +
  geom_histogram(boundary = 0) +
  coord_trans(x = "log10")
# Use boundary = 0, to make sure we don't take sqrt of negative values
m +
  geom_histogram(boundary = 0) +
  coord_trans(x = "sqrt")

# You can also transform the y axis.  Remember that the base of the bars
# has value 0, so log transformations are not appropriate
m <- ggplot(movies, aes(x = rating))
m +
  geom_histogram(binwidth = 0.5) +
  scale_y_sqrt()
}

# You can specify a function for calculating binwidth, which is
# particularly useful when faceting along variables with
# different ranges because the function will be called once per facet
ggplot(economics_long, aes(value)) +
  facet_wrap(~variable, scales = 'free_x') +
  geom_histogram(binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3)))

Draw a function as a continuous curve

Description

Computes and draws a function as a continuous curve. This makes it easy to superimpose a function on top of an existing plot. The function is called with a grid of evenly spaced values along the x axis, and the results are drawn (by default) with a line.

Usage

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

stat_function(
  mapping = NULL,
  data = NULL,
  geom = "function",
  position = "identity",
  ...,
  fun,
  xlim = NULL,
  n = 101,
  args = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

Ignored by stat_function(), do not use.

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.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom

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

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

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

fun

Function to use. Either 1) an anonymous function in the base or rlang formula syntax (see rlang::as_function()) or 2) a quoted or character name referencing a function; see examples. Must be vectorised.

xlim

Optionally, specify the range of the function.

n

Number of points to interpolate along the x axis.

args

List of additional arguments passed on to the function defined by fun.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

rlang::as_function()

Examples

# geom_function() is useful for overlaying functions
set.seed(1492)
ggplot(data.frame(x = rnorm(100)), aes(x)) +
  geom_density() +
  geom_function(fun = dnorm, colour = "red")

# To plot functions without data, specify range of x-axis
base <-
  ggplot() +
  xlim(-5, 5)

base + geom_function(fun = dnorm)

base + geom_function(fun = dnorm, args = list(mean = 2, sd = .5))

# The underlying mechanics evaluate the function at discrete points
# and connect the points with lines
base + stat_function(fun = dnorm, geom = "point")

base + stat_function(fun = dnorm, geom = "point", n = 20)

base + stat_function(fun = dnorm, geom = "polygon", color = "blue", fill = "blue", alpha = 0.5)

base + geom_function(fun = dnorm, n = 20)

# Two functions on the same plot
base +
  geom_function(aes(colour = "normal"), fun = dnorm) +
  geom_function(aes(colour = "t, df = 1"), fun = dt, args = list(df = 1))

# Using a custom anonymous function
base + geom_function(fun = function(x) 0.5 * exp(-abs(x)))
# or using lambda syntax:
# base + geom_function(fun = ~ 0.5 * exp(-abs(.x)))
# or in R4.1.0 and above:
# base + geom_function(fun = \(x) 0.5 * exp(-abs(x)))
# or using a custom named function:
# f <- function(x) 0.5 * exp(-abs(x))
# base + geom_function(fun = f)

# Using xlim to restrict the range of function
ggplot(data.frame(x = rnorm(100)), aes(x)) +
geom_density() +
geom_function(fun = dnorm, colour = "red", xlim=c(-1, 1))

# Using xlim to widen the range of function
ggplot(data.frame(x = rnorm(100)), aes(x)) +
geom_density() +
geom_function(fun = dnorm, colour = "red", xlim=c(-7, 7))

Hexagonal heatmap of 2d bin counts

Description

Divides the plane into regular hexagons, counts the number of cases in each hexagon, and then (by default) maps the number of cases to the hexagon fill. Hexagon bins avoid the visual artefacts sometimes generated by the very regular alignment of geom_bin_2d().

Usage

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

stat_bin_hex(
  mapping = NULL,
  data = NULL,
  geom = "hex",
  position = "identity",
  ...,
  bins = 30,
  binwidth = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Override the default connection between geom_hex() and stat_bin_hex(). For more information about overriding these connections, see how the stat and geom arguments work.

bins

numeric vector giving number of bins in both vertical and horizontal directions. Set to 30 by default.

binwidth

Numeric vector giving bin width in both vertical and horizontal directions. Overrides bins if both set.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

See Also

stat_bin_2d() for rectangular binning

Examples

d <- ggplot(diamonds, aes(carat, price))
d + geom_hex()


# You can control the size of the bins by specifying the number of
# bins in each direction:
d + geom_hex(bins = 10)
d + geom_hex(bins = 30)

# Or by specifying the width of the bins
d + geom_hex(binwidth = c(1, 1000))
d + geom_hex(binwidth = c(.1, 500))

Jittered points

Description

The jitter geom is a convenient shortcut for geom_point(position = "jitter"). It adds a small amount of random variation to the location of each point, and is a useful way of handling overplotting caused by discreteness in smaller datasets.

Usage

geom_jitter(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "jitter",
  ...,
  width = NULL,
  height = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

width, height

Amount of vertical and horizontal jitter. The jitter is added in both positive and negative directions, so the total spread is twice the value specified here.

If omitted, defaults to 40% of the resolution of the data: this means the jitter values will occupy 80% of the implied bins. Categorical data is aligned on the integers, so a width or height of 0.5 will spread the data so it's not possible to see the distinction between the categories.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

See Also

geom_point() for regular, unjittered points, geom_boxplot() for another way of looking at the conditional distribution of a variable

Examples

p <- ggplot(mpg, aes(cyl, hwy))
p + geom_point()
p + geom_jitter()

# Add aesthetic mappings
p + geom_jitter(aes(colour = class))

# Use smaller width/height to emphasise categories
ggplot(mpg, aes(cyl, hwy)) +
  geom_jitter()
ggplot(mpg, aes(cyl, hwy)) +
  geom_jitter(width = 0.25)

# Use larger width/height to completely smooth away discreteness
ggplot(mpg, aes(cty, hwy)) +
  geom_jitter()
ggplot(mpg, aes(cty, hwy)) +
  geom_jitter(width = 0.5, height = 0.5)

Text

Description

Text geoms are useful for labeling plots. They can be used by themselves as scatterplots or in combination with other geoms, for example, for labeling points or for annotating the height of bars. geom_text() adds only text to the plot. geom_label() draws a rectangle behind the text, making it easier to read.

Usage

geom_label(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  label.padding = unit(0.25, "lines"),
  label.r = unit(0.15, "lines"),
  label.size = 0.25,
  size.unit = "mm",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_text(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  check_overlap = FALSE,
  size.unit = "mm",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

A position adjustment to use on the data for this layer. Cannot be jointy specified with nudge_x or nudge_y. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter().

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

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

...

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

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params.

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

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

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

parse

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

nudge_x, nudge_y

Horizontal and vertical adjustment to nudge labels by. Useful for offsetting text from points, particularly on discrete scales. Cannot be jointly specified with position.

label.padding

Amount of padding around label. Defaults to 0.25 lines.

label.r

Radius of rounded corners. Defaults to 0.15 lines.

label.size

Size of label border, in mm.

size.unit

How the size aesthetic is interpreted: as millimetres ("mm", default), points ("pt"), centimetres ("cm"), inches ("in"), or picas ("pc").

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

check_overlap

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

Details

Note that when you resize a plot, text labels stay the same size, even though the size of the plot area changes. This happens because the "width" and "height" of a text element are 0. Obviously, text labels do have height and width, but they are physical units, not data units. For the same reason, stacking and dodging text will not work by default, and axis limits are not automatically expanded to include all text.

geom_text() and geom_label() add labels for each row in the data, even if coordinates x, y are set to single values in the call to geom_label() or geom_text(). To add labels at specified points use annotate() with annotate(geom = "text", ...) or annotate(geom = "label", ...).

To automatically position non-overlapping text labels see the ggrepel package.

Aesthetics

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

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

geom_label()

Currently geom_label() does not support the check_overlap argument. Also, it is considerably slower than geom_text(). The fill aesthetic controls the background colour of the label.

Alignment

You can modify text alignment with the vjust and hjust aesthetics. These can either be a number between 0 (right/bottom) and 1 (top/left) or a character ("left", "middle", "right", "bottom", "center", "top"). There are two special alignments: "inward" and "outward". Inward always aligns text towards the center, and outward aligns it away from the center.

See Also

The text labels section of the online ggplot2 book.

Examples

p <- ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars)))

p + geom_text()
# Avoid overlaps
p + geom_text(check_overlap = TRUE)
# Labels with background
p + geom_label()
# Change size of the label
p + geom_text(size = 10)

# Set aesthetics to fixed value
p +
  geom_point() +
  geom_text(hjust = 0, nudge_x = 0.05)
p +
  geom_point() +
  geom_text(vjust = 0, nudge_y = 0.5)
p +
  geom_point() +
  geom_text(angle = 45)
## Not run: 
# Doesn't work on all systems
p +
  geom_text(family = "Times New Roman")

## End(Not run)

# Add aesthetic mappings
p + geom_text(aes(colour = factor(cyl)))
p + geom_text(aes(colour = factor(cyl))) +
  scale_colour_discrete(l = 40)
p + geom_label(aes(fill = factor(cyl)), colour = "white", fontface = "bold")

p + geom_text(aes(size = wt))
# Scale height of text, rather than sqrt(height)
p +
  geom_text(aes(size = wt)) +
  scale_radius(range = c(3,6))

# You can display expressions by setting parse = TRUE.  The
# details of the display are described in ?plotmath, but note that
# geom_text uses strings, not expressions.
p +
  geom_text(
    aes(label = paste(wt, "^(", cyl, ")", sep = "")),
    parse = TRUE
  )

# Add a text annotation
p +
  geom_text() +
  annotate(
    "text", label = "plot mpg vs. wt",
    x = 2, y = 15, size = 8, colour = "red"
  )


# Aligning labels and bars --------------------------------------------------
df <- data.frame(
  x = factor(c(1, 1, 2, 2)),
  y = c(1, 3, 2, 1),
  grp = c("a", "b", "a", "b")
)

# ggplot2 doesn't know you want to give the labels the same virtual width
# as the bars:
ggplot(data = df, aes(x, y, group = grp)) +
  geom_col(aes(fill = grp), position = "dodge") +
  geom_text(aes(label = y), position = "dodge")
# So tell it:
ggplot(data = df, aes(x, y, group = grp)) +
  geom_col(aes(fill = grp), position = "dodge") +
  geom_text(aes(label = y), position = position_dodge(0.9))
# You can't nudge and dodge text, so instead adjust the y position
ggplot(data = df, aes(x, y, group = grp)) +
  geom_col(aes(fill = grp), position = "dodge") +
  geom_text(
    aes(label = y, y = y + 0.05),
    position = position_dodge(0.9),
    vjust = 0
  )

# To place text in the middle of each bar in a stacked barplot, you
# need to set the vjust parameter of position_stack()
ggplot(data = df, aes(x, y, group = grp)) +
 geom_col(aes(fill = grp)) +
 geom_text(aes(label = y), position = position_stack(vjust = 0.5))

# Justification -------------------------------------------------------------
df <- data.frame(
  x = c(1, 1, 2, 2, 1.5),
  y = c(1, 2, 1, 2, 1.5),
  text = c("bottom-left", "top-left", "bottom-right", "top-right", "center")
)
ggplot(df, aes(x, y)) +
  geom_text(aes(label = text))
ggplot(df, aes(x, y)) +
  geom_text(aes(label = text), vjust = "inward", hjust = "inward")

Polygons from a reference map

Description

Display polygons as a map. This is meant as annotation, so it does not affect position scales. Note that this function predates the geom_sf() framework and does not work with sf geometry columns as input. However, it can be used in conjunction with geom_sf() layers and/or coord_sf() (see examples).

Usage

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

...

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.

  • 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.

map

Data frame that contains the map coordinates. This will typically be created using fortify() on a spatial object. It must contain columns x or long, y or lat, and region or id.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

Examples

# First, a made-up example containing a few polygons, to explain
# how `geom_map()` works. It requires two data frames:
# One contains the coordinates of each polygon (`positions`), and is
# provided via the `map` argument. The other contains the
# other the values associated with each polygon (`values`).  An id
# variable links the two together.

ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))

values <- data.frame(
  id = ids,
  value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)

positions <- data.frame(
  id = rep(ids, each = 4),
  x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
  0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
  y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
  2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)

ggplot(values) +
  geom_map(aes(map_id = id), map = positions) +
  expand_limits(positions)
ggplot(values, aes(fill = value)) +
  geom_map(aes(map_id = id), map = positions) +
  expand_limits(positions)
ggplot(values, aes(fill = value)) +
  geom_map(aes(map_id = id), map = positions) +
  expand_limits(positions) + ylim(0, 3)

# Now some examples with real maps
if (require(maps)) {

  crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)

  # Equivalent to crimes %>% tidyr::pivot_longer(Murder:Rape)
  vars <- lapply(names(crimes)[-1], function(j) {
    data.frame(state = crimes$state, variable = j, value = crimes[[j]])
  })
  crimes_long <- do.call("rbind", vars)

  states_map <- map_data("state")

  # without geospatial coordinate system, the resulting plot
  # looks weird
  ggplot(crimes, aes(map_id = state)) +
    geom_map(aes(fill = Murder), map = states_map) +
    expand_limits(x = states_map$long, y = states_map$lat)

  # in combination with `coord_sf()` we get an appropriate result
  ggplot(crimes, aes(map_id = state)) +
    geom_map(aes(fill = Murder), map = states_map) +
    # crs = 5070 is a Conus Albers projection for North America,
    #   see: https://epsg.io/5070
    # default_crs = 4326 tells coord_sf() that the input map data
    #   are in longitude-latitude format
    coord_sf(
      crs = 5070, default_crs = 4326,
      xlim = c(-125, -70), ylim = c(25, 52)
    )

 ggplot(crimes_long, aes(map_id = state)) +
   geom_map(aes(fill = value), map = states_map) +
   coord_sf(
     crs = 5070, default_crs = 4326,
     xlim = c(-125, -70), ylim = c(25, 52)
   ) +
   facet_wrap(~variable)
}

Connect observations

Description

geom_path() connects the observations in the order in which they appear in the data. geom_line() connects them in order of the variable on the x axis. geom_step() creates a stairstep plot, highlighting exactly when changes occur. The group aesthetic determines which cases are connected together.

Usage

geom_path(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 10,
  arrow = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

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

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

linemitre

Line mitre limit (number greater than 1).

arrow

Arrow specification, as created by grid::arrow().

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

orientation

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

direction

direction of stairs: 'vh' for vertical then horizontal, 'hv' for horizontal then vertical, or 'mid' for step half-way between adjacent x-values.

Details

An alternative parameterisation is geom_segment(), where each line corresponds to a single case which provides the start and end coordinates.

Orientation

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

Aesthetics

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

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

Missing value handling

geom_path(), geom_line(), and geom_step() handle NA as follows:

See Also

geom_polygon(): Filled paths (polygons); geom_segment(): Line segments

Examples

# geom_line() is suitable for time series
ggplot(economics, aes(date, unemploy)) + geom_line()
ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line()

# You can get a timeseries that run vertically by setting the orientation
ggplot(economics, aes(unemploy, date)) + geom_line(orientation = "y")

# geom_step() is useful when you want to highlight exactly when
# the y value changes
recent <- economics[economics$date > as.Date("2013-01-01"), ]
ggplot(recent, aes(date, unemploy)) + geom_line()
ggplot(recent, aes(date, unemploy)) + geom_step()

# geom_path lets you explore how two variables are related over time,
# e.g. unemployment and personal savings rate
m <- ggplot(economics, aes(unemploy/pop, psavert))
m + geom_path()
m + geom_path(aes(colour = as.numeric(date)))

# Changing parameters ----------------------------------------------
ggplot(economics, aes(date, unemploy)) +
  geom_line(colour = "red")

# Use the arrow parameter to add an arrow to the line
# See ?arrow for more details
c <- ggplot(economics, aes(x = date, y = pop))
c + geom_line(arrow = arrow())
c + geom_line(
  arrow = arrow(angle = 15, ends = "both", type = "closed")
)

# Control line join parameters
df <- data.frame(x = 1:3, y = c(4, 1, 9))
base <- ggplot(df, aes(x, y))
base + geom_path(linewidth = 10)
base + geom_path(linewidth = 10, lineend = "round")
base + geom_path(linewidth = 10, linejoin = "mitre", lineend = "butt")

# You can use NAs to break the line.
df <- data.frame(x = 1:5, y = c(1, 2, NA, 4, 5))
ggplot(df, aes(x, y)) + geom_point() + geom_line()


# Setting line type vs colour/size
# Line type needs to be applied to a line as a whole, so it can
# not be used with colour or size that vary across a line
x <- seq(0.01, .99, length.out = 100)
df <- data.frame(
  x = rep(x, 2),
  y = c(qlogis(x), 2 * qlogis(x)),
  group = rep(c("a","b"),
  each = 100)
)
p <- ggplot(df, aes(x=x, y=y, group=group))
# These work
p + geom_line(linetype = 2)
p + geom_line(aes(colour = group), linetype = 2)
p + geom_line(aes(colour = x))
# But this doesn't
should_stop(p + geom_line(aes(colour = x), linetype=2))

Points

Description

The point geom is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but a variation like geom_jitter(), geom_count(), or geom_bin_2d() is usually more appropriate. A bubblechart is a scatterplot with a third variable mapped to the size of points.

Usage

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Overplotting

The biggest potential problem with a scatterplot is overplotting: whenever you have more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot. There is no one solution to this problem, but there are some techniques that can help. You can add additional information with geom_smooth(), geom_quantile() or geom_density_2d(). If you have few unique x values, geom_boxplot() may also be useful.

Alternatively, you can summarise the number of points at each location and display that in some way, using geom_count(), geom_hex(), or geom_density2d().

Another technique is to make the points transparent (e.g. geom_point(alpha = 0.05)) or very small (e.g. geom_point(shape = ".")).

Aesthetics

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

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

Examples

p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point()

# Add aesthetic mappings
p + geom_point(aes(colour = factor(cyl)))
p + geom_point(aes(shape = factor(cyl)))
# A "bubblechart":
p + geom_point(aes(size = qsec))

# Set aesthetics to fixed value
ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)


# Varying alpha is useful for large datasets
d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 1/10)
d + geom_point(alpha = 1/20)
d + geom_point(alpha = 1/100)


# For shapes that have a border (like 21), you can colour the inside and
# outside separately. Use the stroke aesthetic to modify the width of the
# border
ggplot(mtcars, aes(wt, mpg)) +
  geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)


# You can create interesting shapes by layering multiple points of
# different sizes
p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
p +
  geom_point(aes(colour = factor(cyl)), size = 4) +
  geom_point(colour = "grey90", size = 1.5)
p +
  geom_point(colour = "black", size = 4.5) +
  geom_point(colour = "pink", size = 4) +
  geom_point(aes(shape = factor(cyl)))

# geom_point warns when missing values have been dropped from the data set
# and not plotted, you can turn this off by setting na.rm = TRUE
set.seed(1)
mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
ggplot(mtcars2, aes(wt, mpg)) +
  geom_point()
ggplot(mtcars2, aes(wt, mpg)) +
  geom_point(na.rm = TRUE)

Polygons

Description

Polygons are very similar to paths (as drawn by geom_path()) except that the start and end points are connected and the inside is coloured by fill. The group aesthetic determines which cases are connected together into a polygon. From R 3.6 and onwards it is possible to draw polygons with holes by providing a subgroup aesthetic that differentiates the outer ring points from those describing holes in the polygon.

Usage

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

rule

Either "evenodd" or "winding". If polygons with holes are being drawn (using the subgroup aesthetic) this argument defines how the hole coordinates are interpreted. See the examples in grid::pathGrob() for an explanation.

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

See Also

geom_path() for an unfilled polygon, geom_ribbon() for a polygon anchored on the x-axis

Examples

# When using geom_polygon, you will typically need two data frames:
# one contains the coordinates of each polygon (positions),  and the
# other the values associated with each polygon (values).  An id
# variable links the two together

ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))

values <- data.frame(
  id = ids,
  value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)

positions <- data.frame(
  id = rep(ids, each = 4),
  x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
  0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
  y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
  2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)

# Currently we need to manually merge the two together
datapoly <- merge(values, positions, by = c("id"))

p <- ggplot(datapoly, aes(x = x, y = y)) +
  geom_polygon(aes(fill = value, group = id))
p

# Which seems like a lot of work, but then it's easy to add on
# other features in this coordinate system, e.g.:

set.seed(1)
stream <- data.frame(
  x = cumsum(runif(50, max = 0.1)),
  y = cumsum(runif(50,max = 0.1))
)

p + geom_line(data = stream, colour = "grey30", linewidth = 5)

# And if the positions are in longitude and latitude, you can use
# coord_map to produce different map projections.

if (packageVersion("grid") >= "3.6") {
  # As of R version 3.6 geom_polygon() supports polygons with holes
  # Use the subgroup aesthetic to differentiate holes from the main polygon

  holes <- do.call(rbind, lapply(split(datapoly, datapoly$id), function(df) {
    df$x <- df$x + 0.5 * (mean(df$x) - df$x)
    df$y <- df$y + 0.5 * (mean(df$y) - df$y)
    df
  }))
  datapoly$subid <- 1L
  holes$subid <- 2L
  datapoly <- rbind(datapoly, holes)

  p <- ggplot(datapoly, aes(x = x, y = y)) +
    geom_polygon(aes(fill = value, group = id, subgroup = subid))
  p
}

A quantile-quantile plot

Description

geom_qq() and stat_qq() produce quantile-quantile plots. geom_qq_line() and stat_qq_line() compute the slope and intercept of the line connecting the points at specified quartiles of the theoretical and sample distributions.

Usage

geom_qq_line(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  ...,
  distribution = stats::qnorm,
  dparams = list(),
  line.p = c(0.25, 0.75),
  fullrange = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_qq_line(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  ...,
  distribution = stats::qnorm,
  dparams = list(),
  line.p = c(0.25, 0.75),
  fullrange = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_qq(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  distribution = stats::qnorm,
  dparams = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_qq(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  distribution = stats::qnorm,
  dparams = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

geom

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

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

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

position

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

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

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

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

...

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.

  • 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.

distribution

Distribution function to use, if x not specified

dparams

Additional parameters passed on to distribution function.

line.p

Vector of quantiles to use when fitting the Q-Q line, defaults defaults to c(.25, .75).

fullrange

Should the q-q line span the full range of the plot, or just the data

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.
Variables computed by stat_qq():

Variables computed by stat_qq_line():

Examples

df <- data.frame(y = rt(200, df = 5))
p <- ggplot(df, aes(sample = y))
p + stat_qq() + stat_qq_line()

# Use fitdistr from MASS to estimate distribution params
params <- as.list(MASS::fitdistr(df$y, "t")$estimate)
ggplot(df, aes(sample = y)) +
  stat_qq(distribution = qt, dparams = params["df"]) +
  stat_qq_line(distribution = qt, dparams = params["df"])

# Using to explore the distribution of a variable
ggplot(mtcars, aes(sample = mpg)) +
  stat_qq() +
  stat_qq_line()
ggplot(mtcars, aes(sample = mpg, colour = factor(cyl))) +
  stat_qq() +
  stat_qq_line()

Quantile regression

Description

This fits a quantile regression to the data and draws the fitted quantiles with lines. This is as a continuous analogue to geom_boxplot().

Usage

geom_quantile(
  mapping = NULL,
  data = NULL,
  stat = "quantile",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 10,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_quantile(
  mapping = NULL,
  data = NULL,
  geom = "quantile",
  position = "identity",
  ...,
  quantiles = c(0.25, 0.5, 0.75),
  formula = NULL,
  method = "rq",
  method.args = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

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.

...

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.

  • 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.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

linemitre

Line mitre limit (number greater than 1).

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_quantile() and stat_quantile(). For more information about overriding these connections, see how the stat and geom arguments work.

quantiles

conditional quantiles of y to calculate and display

formula

formula relating y variables to x variables

method

Quantile regression method to use. Available options are "rq" (for quantreg::rq()) and "rqss" (for quantreg::rqss()).

method.args

List of additional arguments passed on to the modelling function defined by method.

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

Examples

m <-
  ggplot(mpg, aes(displ, 1 / hwy)) +
  geom_point()
m + geom_quantile()
m + geom_quantile(quantiles = 0.5)
q10 <- seq(0.05, 0.95, by = 0.05)
m + geom_quantile(quantiles = q10)

# You can also use rqss to fit smooth quantiles
m + geom_quantile(method = "rqss")
# Note that rqss doesn't pick a smoothing constant automatically, so
# you'll need to tweak lambda yourself
m + geom_quantile(method = "rqss", lambda = 0.1)

# Set aesthetics to fixed value
m + geom_quantile(colour = "red", linewidth = 2, alpha = 0.5)

Rectangles

Description

geom_rect() and geom_tile() do the same thing, but are parameterised differently: geom_rect() uses the locations of the four corners (xmin, xmax, ymin and ymax), while geom_tile() uses the center of the tile and its size (x, y, width, height). geom_raster() is a high performance special case for when all the tiles are the same size, and no pattern fills are applied.

Usage

geom_raster(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  hjust = 0.5,
  vjust = 0.5,
  interpolate = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

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

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

hjust, vjust

horizontal and vertical justification of the grob. Each justification value should be a number between 0 and 1. Defaults to 0.5 for both, centering each pixel over its data location.

interpolate

If TRUE interpolate linearly, if FALSE (the default) don't interpolate.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

linejoin

Line join style (round, mitre, bevel).

Details

geom_rect() and geom_tile()'s respond differently to scale transformations due to their parameterisation. In geom_rect(), the scale transformation is applied to the corners of the rectangles. In geom_tile(), the transformation is applied only to the centres and its size is determined after transformation.

Aesthetics

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

Note that geom_raster() ignores colour.

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

Examples

# The most common use for rectangles is to draw a surface. You always want
# to use geom_raster here because it's so much faster, and produces
# smaller output when saving to PDF
ggplot(faithfuld, aes(waiting, eruptions)) +
 geom_raster(aes(fill = density))

# Interpolation smooths the surface & is most helpful when rendering images.
ggplot(faithfuld, aes(waiting, eruptions)) +
 geom_raster(aes(fill = density), interpolate = TRUE)

# If you want to draw arbitrary rectangles, use geom_tile() or geom_rect()
df <- data.frame(
  x = rep(c(2, 5, 7, 9, 12), 2),
  y = rep(c(1, 2), each = 5),
  z = factor(rep(1:5, each = 2)),
  w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2)
)
ggplot(df, aes(x, y)) +
  geom_tile(aes(fill = z), colour = "grey50")
ggplot(df, aes(x, y, width = w)) +
  geom_tile(aes(fill = z), colour = "grey50")
ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) +
  geom_rect(aes(fill = z), colour = "grey50")


# Justification controls where the cells are anchored
df <- expand.grid(x = 0:5, y = 0:5)
set.seed(1)
df$z <- runif(nrow(df))
# default is compatible with geom_tile()
ggplot(df, aes(x, y, fill = z)) +
  geom_raster()
# zero padding
ggplot(df, aes(x, y, fill = z)) +
  geom_raster(hjust = 0, vjust = 0)

# Inspired by the image-density plots of Ken Knoblauch
cars <- ggplot(mtcars, aes(mpg, factor(cyl)))
cars + geom_point()
cars + stat_bin_2d(aes(fill = after_stat(count)), binwidth = c(3,1))
cars + stat_bin_2d(aes(fill = after_stat(density)), binwidth = c(3,1))

cars +
  stat_density(
    aes(fill = after_stat(density)),
    geom = "raster",
    position = "identity"
   )
cars +
  stat_density(
    aes(fill = after_stat(count)),
    geom = "raster",
    position = "identity"
  )

Ribbons and area plots

Description

For each x value, geom_ribbon() displays a y interval defined by ymin and ymax. geom_area() is a special case of geom_ribbon(), where the ymin is fixed to 0 and y is used instead of ymax.

Usage

geom_ribbon(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "both"
)

geom_area(
  mapping = NULL,
  data = NULL,
  stat = "align",
  position = "stack",
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  outline.type = "upper"
)

stat_align(
  mapping = NULL,
  data = NULL,
  geom = "area",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

outline.type

Type of the outline of the area; "both" draws both the upper and lower lines, "upper"/"lower" draws the respective lines only. "full" draws a closed polygon around the area.

geom

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

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

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

Details

An area plot is the continuous analogue of a stacked bar chart (see geom_bar()), and can be used to show how composition of the whole varies over the range of x. Choosing the order in which different components is stacked is very important, as it becomes increasing hard to see the individual pattern as you move up the stack. See position_stack() for the details of stacking algorithm. To facilitate stacking, the default stat = "align" interpolates groups to a common set of x-coordinates. To turn off this interpolation, stat = "identity" can be used instead.

Orientation

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

Aesthetics

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

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

See Also

geom_bar() for discrete intervals (bars), geom_linerange() for discrete intervals (lines), geom_polygon() for general polygons

Examples

# Generate data
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))
h <- ggplot(huron, aes(year))

h + geom_ribbon(aes(ymin=0, ymax=level))
h + geom_area(aes(y = level))

# Orientation cannot be deduced by mapping, so must be given explicitly for
# flipped orientation
h + geom_area(aes(x = level, y = year), orientation = "y")

# Add aesthetic mappings
h +
  geom_ribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") +
  geom_line(aes(y = level))

# The underlying stat_align() takes care of unaligned data points
df <- data.frame(
  g = c("a", "a", "a", "b", "b", "b"),
  x = c(1, 3, 5, 2, 4, 6),
  y = c(2, 5, 1, 3, 6, 7)
)
a <- ggplot(df, aes(x, y, fill = g)) +
  geom_area()

# Two groups have points on different X values.
a + geom_point(size = 8) + facet_grid(g ~ .)

# stat_align() interpolates and aligns the value so that the areas can stack
# properly.
a + geom_point(stat = "align", position = "stack", size = 8)

# To turn off the alignment, the stat can be set to "identity"
ggplot(df, aes(x, y, fill = g)) +
  geom_area(stat = "identity")

Rug plots in the margins

Description

A rug plot is a compact visualisation designed to supplement a 2d display with the two 1d marginal distributions. Rug plots display individual cases so are best used with smaller datasets.

Usage

geom_rug(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  outside = FALSE,
  sides = "bl",
  length = unit(0.03, "npc"),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

outside

logical that controls whether to move the rug tassels outside of the plot area. Default is off (FALSE). You will also need to use coord_cartesian(clip = "off"). When set to TRUE, also consider changing the sides argument to "tr". See examples.

sides

A string that controls which sides of the plot the rugs appear on. It can be set to a string containing any of "trbl", for top, right, bottom, and left.

length

A grid::unit() object that sets the length of the rug lines. Use scale expansion to avoid overplotting of data.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Details

By default, the rug lines are drawn with a length that corresponds to 3% of the total plot size. Since the default scale expansion of for continuous variables is 5% at both ends of the scale, the rug will not overlap with any data points under the default settings.

Aesthetics

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

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

Examples

p <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()
p
p + geom_rug()
p + geom_rug(sides="b")    # Rug on bottom only
p + geom_rug(sides="trbl") # All four sides

# Use jittering to avoid overplotting for smaller datasets
ggplot(mpg, aes(displ, cty)) +
  geom_point() +
  geom_rug()

ggplot(mpg, aes(displ, cty)) +
  geom_jitter() +
  geom_rug(alpha = 1/2, position = "jitter")

# move the rug tassels to outside the plot
# remember to set clip = "off".
p +
  geom_rug(outside = TRUE) +
  coord_cartesian(clip = "off")

# set sides to top right, and then move the margins
p +
  geom_rug(outside = TRUE, sides = "tr") +
  coord_cartesian(clip = "off") +
  theme(plot.margin = margin(1, 1, 1, 1, "cm"))

# increase the line length and
# expand axis to avoid overplotting
p +
  geom_rug(length = unit(0.05, "npc")) +
  scale_y_continuous(expand = c(0.1, 0.1))

Line segments and curves

Description

geom_segment() draws a straight line between points (x, y) and (xend, yend). geom_curve() draws a curved line. See the underlying drawing function grid::curveGrob() for the parameters that control the curve.

Usage

geom_segment(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  arrow = NULL,
  arrow.fill = NULL,
  lineend = "butt",
  linejoin = "round",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_curve(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  curvature = 0.5,
  angle = 90,
  ncp = 5,
  arrow = NULL,
  arrow.fill = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

arrow

specification for arrow heads, as created by grid::arrow().

arrow.fill

fill colour to use for the arrow head (if closed). NULL means use colour aesthetic.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

curvature

A numeric value giving the amount of curvature. Negative values produce left-hand curves, positive values produce right-hand curves, and zero produces a straight line.

angle

A numeric value between 0 and 180, giving an amount to skew the control points of the curve. Values less than 90 skew the curve towards the start point and values greater than 90 skew the curve towards the end point.

ncp

The number of control points used to draw the curve. More control points creates a smoother curve.

Details

Both geoms draw a single segment/curve per case. See geom_path() if you need to connect points across multiple cases.

Aesthetics

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

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

See Also

geom_path() and geom_line() for multi- segment lines and paths.

geom_spoke() for a segment parameterised by a location (x, y), and an angle and radius.

Examples

b <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)
b +
 geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "curve"), data = df) +
 geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "segment"), data = df)

b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = -0.2)
b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = 1)
b + geom_curve(
  aes(x = x1, y = y1, xend = x2, yend = y2),
  data = df,
  arrow = arrow(length = unit(0.03, "npc"))
)

if (requireNamespace('maps', quietly = TRUE)) {
ggplot(seals, aes(long, lat)) +
  geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat),
    arrow = arrow(length = unit(0.1,"cm"))) +
  borders("state")
}

# Use lineend and linejoin to change the style of the segments
df2 <- expand.grid(
  lineend = c('round', 'butt', 'square'),
  linejoin = c('round', 'mitre', 'bevel'),
  stringsAsFactors = FALSE
)
df2 <- data.frame(df2, y = 1:9)
ggplot(df2, aes(x = 1, y = y, xend = 2, yend = y, label = paste(lineend, linejoin))) +
  geom_segment(
     lineend = df2$lineend, linejoin = df2$linejoin,
     size = 3, arrow = arrow(length = unit(0.3, "inches"))
  ) +
  geom_text(hjust = 'outside', nudge_x = -0.2) +
  xlim(0.5, 2)

# You can also use geom_segment to recreate plot(type = "h") :
set.seed(1)
counts <- as.data.frame(table(x = rpois(100,5)))
counts$x <- as.numeric(as.character(counts$x))
with(counts, plot(x, Freq, type = "h", lwd = 10))

ggplot(counts, aes(x, Freq)) +
  geom_segment(aes(xend = x, yend = 0), linewidth = 10, lineend = "butt")

Smoothed conditional means

Description

Aids the eye in seeing patterns in the presence of overplotting. geom_smooth() and stat_smooth() are effectively aliases: they both use the same arguments. Use stat_smooth() if you want to display the results with a non-standard geom.

Usage

geom_smooth(
  mapping = NULL,
  data = NULL,
  stat = "smooth",
  position = "identity",
  ...,
  method = NULL,
  formula = NULL,
  se = TRUE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_smooth(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  method = NULL,
  formula = NULL,
  se = TRUE,
  n = 80,
  span = 0.75,
  fullrange = FALSE,
  xseq = NULL,
  level = 0.95,
  method.args = list(),
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

method

Smoothing method (function) to use, accepts either NULL or a character vector, e.g. "lm", "glm", "gam", "loess" or a function, e.g. MASS::rlm or mgcv::gam, stats::lm, or stats::loess. "auto" is also accepted for backwards compatibility. It is equivalent to NULL.

For method = NULL the smoothing method is chosen based on the size of the largest group (across all panels). stats::loess() is used for less than 1,000 observations; otherwise mgcv::gam() is used with formula = y ~ s(x, bs = "cs") with method = "REML". Somewhat anecdotally, loess gives a better appearance, but is O(N2)O(N^{2}) in memory, so does not work for larger datasets.

If you have fewer than 1,000 observations but want to use the same gam() model that method = NULL would use, then set ⁠method = "gam", formula = y ~ s(x, bs = "cs")⁠.

formula

Formula to use in smoothing function, eg. y ~ x, y ~ poly(x, 2), y ~ log(x). NULL by default, in which case method = NULL implies formula = y ~ x when there are fewer than 1,000 observations and formula = y ~ s(x, bs = "cs") otherwise.

se

Display confidence interval around smooth? (TRUE by default, see level to control.)

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_smooth() and stat_smooth(). For more information about overriding these connections, see how the stat and geom arguments work.

n

Number of points at which to evaluate smoother.

span

Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines. Only used with loess, i.e. when method = "loess", or when method = NULL (the default) and there are fewer than 1,000 observations.

fullrange

If TRUE, the smoothing line gets expanded to the range of the plot, potentially beyond the data. This does not extend the line into any additional padding created by expansion.

xseq

A numeric vector of values at which the smoother is evaluated. When NULL (default), xseq is internally evaluated as a sequence of n equally spaced points for continuous data.

level

Level of confidence interval to use (0.95 by default).

method.args

List of additional arguments passed on to the modelling function defined by method.

Details

Calculation is performed by the (currently undocumented) predictdf() generic and its methods. For most methods the standard error bounds are computed using the predict() method – the exceptions are loess(), which uses a t-based approximation, and glm(), where the normal confidence interval is constructed on the link scale and then back-transformed to the response scale.

Orientation

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

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation. stat_smooth() provides the following variables, some of which depend on the orientation:

See Also

See individual modelling functions for more details: lm() for linear smooths, glm() for generalised linear smooths, and loess() for local smooths.

Examples

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth()

# If you need the fitting to be done along the y-axis set the orientation
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(orientation = "y")

# Use span to control the "wiggliness" of the default loess smoother.
# The span is the fraction of points used to fit each local regression:
# small numbers make a wigglier curve, larger numbers make a smoother curve.
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(span = 0.3)

# Instead of a loess smooth, you can use any other modelling function:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = lm, formula = y ~ splines::bs(x, 3), se = FALSE)

# Smooths are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet.

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(se = FALSE, method = lm)
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(span = 0.8) +
  facet_wrap(~drv)


binomial_smooth <- function(...) {
  geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
# To fit a logistic regression, you need to coerce the values to
# a numeric vector lying between 0 and 1.
ggplot(rpart::kyphosis, aes(Age, Kyphosis)) +
  geom_jitter(height = 0.05) +
  binomial_smooth()

ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) +
  geom_jitter(height = 0.05) +
  binomial_smooth()

ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) +
  geom_jitter(height = 0.05) +
  binomial_smooth(formula = y ~ splines::ns(x, 2))

# But in this case, it's probably better to fit the model yourself
# so you can exercise more control and see whether or not it's a good model.

Line segments parameterised by location, direction and distance

Description

This is a polar parameterisation of geom_segment(). It is useful when you have variables that describe direction and distance. The angles start from east and increase counterclockwise.

Usage

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

Arguments

mapping

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

data

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

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

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

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

stat

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

  • A Stat ggproto subclass, for example StatCount.

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

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

position

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

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

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

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

...

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.

  • 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.

na.rm

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

Aesthetics

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

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

Examples

df <- expand.grid(x = 1:10, y=1:10)

set.seed(1)
df$angle <- runif(100, 0, 2*pi)
df$speed <- runif(100, 0, sqrt(0.1 * df$x))

ggplot(df, aes(x, y)) +
  geom_point() +
  geom_spoke(aes(angle = angle), radius = 0.5)

ggplot(df, aes(x, y)) +
  geom_point() +
  geom_spoke(aes(angle = angle, radius = speed))

Violin plot

Description

A violin plot is a compact display of a continuous distribution. It is a blend of geom_boxplot() and geom_density(): a violin plot is a mirrored density plot displayed in the same way as a boxplot.

Usage

geom_violin(
  mapping = NULL,
  data = NULL,
  stat = "ydensity",
  position = "dodge",
  ...,
  draw_quantiles = NULL,
  trim = TRUE,
  bounds = c(-Inf, Inf),
  scale = "area",
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_ydensity(
  mapping = NULL,
  data = NULL,
  geom = "violin",
  position = "dodge",
  ...,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  trim = TRUE,
  scale = "area",
  drop = TRUE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  bounds = c(-Inf, Inf)
)

Arguments

mapping

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

data

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

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

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

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

position

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.

...

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.

  • 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.

draw_quantiles

If not(NULL) (default), draw horizontal lines at the given quantiles of the density estimate.

trim

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

bounds

Known lower and upper bounds for estimated data. Default c(-Inf, Inf) means that there are no (finite) bounds. If any bound is finite, boundary effect of default density estimation will be corrected by reflecting tails outside bounds around their closest edge. Data points outside of bounds are removed with a warning.

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.

na.rm

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

orientation

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

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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

geom, stat

Use to override the default connection between geom_violin() and stat_ydensity(). For more information about overriding these connections, see how the stat and geom arguments work.

bw

The smoothing bandwidth to be used. If numeric, the standard deviation of the smoothing kernel. If character, a rule to choose the bandwidth, as listed in stats::bw.nrd(). Note that automatic calculation of the bandwidth does not take weights into account.

adjust

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

kernel

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

drop

Whether to discard groups with less than 2 observations (TRUE, default) or keep such groups for position adjustment purposes (FALSE).

Orientation

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

Aesthetics

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

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

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

References

Hintze, J. L., Nelson, R. D. (1998) Violin Plots: A Box Plot-Density Trace Synergism. The American Statistician 52, 181-184.

See Also

geom_violin() for examples, and stat_density() for examples with data along the x axis.

Examples

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_violin()

# Orientation follows the discrete axis
ggplot(mtcars, aes(mpg, factor(cyl))) +
  geom_violin()


p + geom_violin() + geom_jitter(height = 0, width = 0.1)

# Scale maximum width proportional to sample size:
p + geom_violin(scale = "count")

# Scale maximum width to 1 for all violins:
p + geom_violin(scale = "width")

# Default is to trim violins to the range of the data. To disable:
p + geom_violin(trim = FALSE)

# Use a smaller bandwidth for closer density fit (default is 1).
p + geom_violin(adjust = .5)

# Add aesthetic mappings
# Note that violins are automatically dodged when any aesthetic is
# a factor
p + geom_violin(aes(fill = cyl))
p + geom_violin(aes(fill = factor(cyl)))
p + geom_violin(aes(fill = factor(vs)))
p + geom_violin(aes(fill = factor(am)))

# Set aesthetics to fixed value
p + geom_violin(fill = "grey80", colour = "#3366FF")

# Show quartiles
p + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75))

# Scales vs. coordinate transforms -------
if (require("ggplot2movies")) {
# Scale transformations occur before the density statistics are computed.
# Coordinate transformations occur afterwards.  Observe the effect on the
# number of outliers.
m <- ggplot(movies, aes(y = votes, x = rating, group = cut_width(rating, 0.5)))
m + geom_violin()
m +
  geom_violin() +
  scale_y_log10()
m +
  geom_violin() +
  coord_trans(y = "log10")
m +
  geom_violin() +
  scale_y_log10() + coord_trans(y = "log10")

# Violin plots with continuous x:
# Use the group aesthetic to group observations in violins
ggplot(movies, aes(year, budget)) +
  geom_violin()
ggplot(movies, aes(year, budget)) +
  geom_violin(aes(group = cut_width(year, 10)), scale = "width")
}

Extract alt text from a plot

Description

This function returns a text that can be used as alt-text in webpages etc. Currently it will use the alt label, added with ⁠+ labs(alt = <...>)⁠, or a return an empty string, but in the future it might try to generate an alt text from the information stored in the plot.

Usage

get_alt_text(p, ...)

Arguments

p

a ggplot object

...

Currently ignored

Value

A text string

Examples

p <- ggplot(mpg, aes(displ, hwy)) +
  geom_point()

# Returns an empty string
get_alt_text(p)

# A user provided alt text
p <- p + labs(
  alt = paste("A scatterplot showing the negative correlation between engine",
              "displacement as a function of highway miles per gallon")
)

get_alt_text(p)

Create a new ggplot

Description

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

Usage

ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())

Arguments

data

Default dataset to use for plot. If not already a data.frame, will be converted to one by fortify(). If not specified, must be supplied in each layer added to the plot.

mapping

Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot.

...

Other arguments passed on to methods. Not currently used.

environment

[Deprecated] Used prior to tidy evaluation.

Details

ggplot() is used to construct the initial plot object, and is almost always followed by a plus sign (+) to add components to the plot.

There are three common patterns used to invoke ggplot():

The first pattern is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used when adding a layer using data from another data frame.

The second pattern specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly for the plot, but the aesthetics vary from one layer to another.

The third pattern initializes a skeleton ggplot object, which is fleshed out as layers are added. This is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

The ⁠data =⁠ and ⁠mapping =⁠ specifications in the arguments are optional (and are often omitted in practice), so long as the data and the mapping values are passed into the function in the right order. In the examples below, however, they are left in place for clarity.

See Also

The first steps chapter of the online ggplot2 book.

Examples

# Create a data frame with some sample data, then create a data frame
# containing the mean value for each group in the sample data.
set.seed(1)

sample_df <- data.frame(
  group = factor(rep(letters[1:3], each = 10)),
  value = rnorm(30)
)

group_means_df <- setNames(
  aggregate(value ~ group, sample_df, mean),
  c("group", "group_mean")
)

# The following three code blocks create the same graphic, each using one
# of the three patterns specified above. In each graphic, the sample data
# are plotted in the first layer and the group means data frame is used to
# plot larger red points on top of the sample data in the second layer.

# Pattern 1
# Both the `data` and `mapping` arguments are passed into the `ggplot()`
# call. Those arguments are omitted in the first `geom_point()` layer
# because they get passed along from the `ggplot()` call. Note that the
# second `geom_point()` layer re-uses the `x = group` aesthetic through
# that mechanism but overrides the y-position aesthetic.
ggplot(data = sample_df, mapping = aes(x = group, y = value)) +
  geom_point() +
  geom_point(
    mapping = aes(y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )

# Pattern 2
# Same plot as above, passing only the `data` argument into the `ggplot()`
# call. The `mapping` arguments are now required in each `geom_point()`
# layer because there is no `mapping` argument passed along from the
# `ggplot()` call.
ggplot(data = sample_df) +
  geom_point(mapping = aes(x = group, y = value)) +
  geom_point(
    mapping = aes(x = group, y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )

# Pattern 3
# Same plot as above, passing neither the `data` or `mapping` arguments
# into the `ggplot()` call. Both those arguments are now required in
# each `geom_point()` layer. This pattern can be particularly useful when
# creating more complex graphics with many layers using data from multiple
# data frames.
ggplot() +
  geom_point(mapping = aes(x = group, y = value), data = sample_df) +
  geom_point(
    mapping = aes(x = group, y = group_mean), data = group_means_df,
    colour = 'red', size = 3
  )

Create a new ggproto object

Description

Construct a new object with ggproto(), test with is.ggproto(), and access parent methods/fields with ggproto_parent().

Usage

ggproto(`_class` = NULL, `_inherit` = NULL, ...)

ggproto_parent(parent, self)

is.ggproto(x)

Arguments

_class

Class name to assign to the object. This is stored as the class attribute of the object. This is optional: if NULL (the default), no class name will be added to the object.

_inherit

ggproto object to inherit from. If NULL, don't inherit from any object.

...

A list of members in the ggproto object.

parent, self

Access parent class parent of object self.

x

An object to test.

Details

ggproto implements a protype based OO system which blurs the lines between classes and instances. It is inspired by the proto package, but it has some important differences. Notably, it cleanly supports cross-package inheritance, and has faster performance.

In most cases, creating a new OO system to be used by a single package is not a good idea. However, it was the least-bad solution for ggplot2 because it required the fewest changes to an already complex code base.

Calling methods

ggproto methods can take an optional self argument: if it is present, it is a regular method; if it's absent, it's a "static" method (i.e. it doesn't use any fields).

Imagine you have a ggproto object Adder, which has a method addx = function(self, n) n + self$x. Then, to call this function, you would use Adder$addx(10) – the self is passed in automatically by the wrapper function. self be located anywhere in the function signature, although customarily it comes first.

Calling methods in a parent

To explicitly call a methods in a parent, use ggproto_parent(Parent, self).

See Also

The ggproto introduction section of the online ggplot2 book.

Examples

Adder <- ggproto("Adder",
  x = 0,
  add = function(self, n) {
    self$x <- self$x + n
    self$x
  }
 )
is.ggproto(Adder)

Adder$add(10)
Adder$add(10)

Doubler <- ggproto("Doubler", Adder,
  add = function(self, n) {
    ggproto_parent(Adder, self)$add(n * 2)
  }
)
Doubler$x
Doubler$add(10)

Save a ggplot (or other grid object) with sensible defaults

Description

ggsave() is a convenient function for saving a plot. It defaults to saving the last plot that you displayed, using the size of the current graphics device. It also guesses the type of graphics device from the extension.

Usage

ggsave(
  filename,
  plot = last_plot(),
  device = NULL,
  path = NULL,
  scale = 1,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm", "px"),
  dpi = 300,
  limitsize = TRUE,
  bg = NULL,
  create.dir = FALSE,
  ...
)

Arguments

filename

File name to create on disk.

plot

Plot to save, defaults to last plot displayed.

device

Device to use. Can either be a device function (e.g. png), or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only). If NULL (default), the device is guessed based on the filename extension.

path

Path of the directory to save plot to: path and filename are combined to create the fully qualified file name. Defaults to the working directory.

scale

Multiplicative scaling factor.

width, height

Plot size in units expressed by the units argument. If not supplied, uses the size of the current graphics device.

units

One of the following units in which the width and height arguments are expressed: "in", "cm", "mm" or "px".

dpi

Plot resolution. Also accepts a string input: "retina" (320), "print" (300), or "screen" (72). Applies only to raster output types.

limitsize

When TRUE (the default), ggsave() will not save images larger than 50x50 inches, to prevent the common error of specifying dimensions in pixels.

bg

Background colour. If NULL, uses the plot.background fill value from the plot theme.

create.dir

Whether to create new directories if a non-existing directory is specified in the filename or path (TRUE) or return an error (FALSE, default). If FALSE and run in an interactive session, a prompt will appear asking to create a new directory when necessary.

...

Other arguments passed on to the graphics device function, as specified by device.

Details

Note: Filenames with page numbers can be generated by including a C integer format expression, such as ⁠%03d⁠ (as in the default file name for most R graphics devices, see e.g. png()). Thus, filename = "figure%03d.png" will produce successive filenames figure001.png, figure002.png, figure003.png, etc. To write a filename containing the ⁠%⁠ sign, use %%. For example, filename = "figure-100%%.png" will produce the filename ⁠figure-100%.png⁠.

Saving images without ggsave()

In most cases ggsave() is the simplest way to save your plot, but sometimes you may wish to save the plot by writing directly to a graphics device. To do this, you can open a regular R graphics device such as png() or pdf(), print the plot, and then close the device using dev.off(). This technique is illustrated in the examples section.

See Also

The saving section of the online ggplot2 book.

Examples

## Not run: 
ggplot(mtcars, aes(mpg, wt)) +
  geom_point()

# here, the device is inferred from the filename extension
ggsave("mtcars.pdf")
ggsave("mtcars.png")

# setting dimensions of the plot
ggsave("mtcars.pdf", width = 4, height = 4)
ggsave("mtcars.pdf", width = 20, height = 20, units = "cm")

# passing device-specific arguments to '...'
ggsave("mtcars.pdf", colormodel = "cmyk")

# delete files with base::unlink()
unlink("mtcars.pdf")
unlink("mtcars.png")

# specify device when saving to a file with unknown extension
# (for example a server supplied temporary file)
file <- tempfile()
ggsave(file, device = "pdf")
unlink(file)

# save plot to file without using ggsave
p <-
  ggplot(mtcars, aes(mpg, wt)) +
  geom_point()
png("mtcars.png")
print(p)
dev.off()


## End(Not run)

Complete themes

Description

These are complete themes which control all non-data display. Use theme() if you just need to tweak the display of an existing theme.

Usage

theme_grey(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_gray(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_bw(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_linedraw(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_light(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_dark(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_minimal(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_classic(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_void(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

theme_test(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

base_size

base font size, given in pts.

base_family

base font family

base_line_size

base size for line elements

base_rect_size

base size for rect elements

Details

theme_gray()

The signature ggplot2 theme with a grey background and white gridlines, designed to put the data forward yet make comparisons easy.

theme_bw()

The classic dark-on-light ggplot2 theme. May work better for presentations displayed with a projector.

theme_linedraw()

A theme with only black lines of various widths on white backgrounds, reminiscent of a line drawing. Serves a purpose similar to theme_bw(). Note that this theme has some very thin lines (<< 1 pt) which some journals may refuse.

theme_light()

A theme similar to theme_linedraw() but with light grey lines and axes, to direct more attention towards the data.

theme_dark()

The dark cousin of theme_light(), with similar line sizes but a dark background. Useful to make thin coloured lines pop out.

theme_minimal()

A minimalistic theme with no background annotations.

theme_classic()

A classic-looking theme, with x and y axis lines and no gridlines.

theme_void()

A completely empty theme.

theme_test()

A theme for visual unit tests. It should ideally never change except for new features.

See Also

The complete themes section of the online ggplot2 book.

Examples

mtcars2 <- within(mtcars, {
  vs <- factor(vs, labels = c("V-shaped", "Straight"))
  am <- factor(am, labels = c("Automatic", "Manual"))
  cyl  <- factor(cyl)
  gear <- factor(gear)
})

p1 <- ggplot(mtcars2) +
  geom_point(aes(x = wt, y = mpg, colour = gear)) +
  labs(
    title = "Fuel economy declines as weight increases",
    subtitle = "(1973-74)",
    caption = "Data from the 1974 Motor Trend US magazine.",
    tag = "Figure 1",
    x = "Weight (1000 lbs)",
    y = "Fuel economy (mpg)",
    colour = "Gears"
  )

p1 + theme_gray() # the default
p1 + theme_bw()
p1 + theme_linedraw()
p1 + theme_light()
p1 + theme_dark()
p1 + theme_minimal()
p1 + theme_classic()
p1 + theme_void()

# Theme examples with panels

p2 <- p1 + facet_grid(vs ~ am)

p2 + theme_gray() # the default
p2 + theme_bw()
p2 + theme_linedraw()
p2 + theme_light()
p2 + theme_dark()
p2 + theme_minimal()
p2 + theme_classic()
p2 + theme_void()

Axis guide

Description

Axis guides are the visual representation of position scales like those created with scale_(x|y)_continuous() and scale_(x|y)_discrete().

Usage

guide_axis(
  title = waiver(),
  theme = NULL,
  check.overlap = FALSE,
  angle = waiver(),
  n.dodge = 1,
  minor.ticks = FALSE,
  cap = "none",
  order = 0,
  position = waiver()
)

Arguments

title

A character string or expression indicating a title of guide. If NULL, the title is not shown. By default (waiver()), the name of the scale object or the name specified in labs() is used for the title.

theme

A theme object to style the guide individually or differently from the plot's theme settings. The theme argument in the guide overrides, and is combined with, the plot's theme.

check.overlap

silently remove overlapping labels, (recursively) prioritizing the first, last, and middle labels.

angle

Compared to setting the angle in theme() / element_text(), this also uses some heuristics to automatically pick the hjust and vjust that you probably want. Can be one of the following:

  • NULL to take the angles and hjust/vjust directly from the theme.

  • waiver() to allow reasonable defaults in special cases.

  • A number representing the text angle in degrees.

n.dodge

The number of rows (for vertical axes) or columns (for horizontal axes) that should be used to render the labels. This is useful for displaying labels that would otherwise overlap.

minor.ticks

Whether to draw the minor ticks (TRUE) or not draw minor ticks (FALSE, default).

cap

A character to cut the axis line back to the last breaks. Can be "none" (default) to draw the axis line along the whole panel, or "upper" and "lower" to draw the axis to the upper or lower break, or "both" to only draw the line in between the most extreme breaks. TRUE and FALSE are shorthand for "both" and "none" respectively.

order

A positive integer of length 1 that specifies the order of this guide among multiple guides. This controls in which order guides are merged if there are multiple guides for the same position. If 0 (default), the order is determined by a secret algorithm.

position

Where this guide should be drawn: one of top, bottom, left, or right.

Examples

# plot with overlapping text
p <- ggplot(mpg, aes(cty * 100, hwy * 100)) +
  geom_point() +
  facet_wrap(vars(class))

# axis guides can be customized in the scale_* functions or
# using guides()
p + scale_x_continuous(guide = guide_axis(n.dodge = 2))
p + guides(x = guide_axis(angle = 90))

# can also be used to add a duplicate guide
p + guides(x = guide_axis(n.dodge = 2), y.sec = guide_axis())

Axis with logarithmic tick marks

Description

This axis guide replaces the placement of ticks marks at intervals in log10 space.

Usage

guide_axis_logticks(
  long = 2.25,
  mid = 1.5,
  short = 0.75,
  prescale_base = NULL,
  negative_small = 0.1,
  short_theme = element_line(),
  expanded = TRUE,
  cap = "none",
  theme = NULL,
  ...
)

Arguments

long, mid, short

A grid::unit() object or rel() object setting the (relative) length of the long, middle and short ticks. Numeric values are interpreted as rel() objects. The rel() values are used to multiply values of the axis.ticks.length theme setting.

prescale_base

Base of logarithm used to transform data manually. The default, NULL, will use the scale transformation to calculate positions. Only set prescale_base if the data has already been log-transformed. When using a log-transform in the position scale or in coord_trans(), keep the default NULL argument.

negative_small

When the scale limits include 0 or negative numbers, what should be the smallest absolute value that is marked with a tick?

short_theme

A theme element for customising the display of the shortest ticks. Must be a line or blank element, and it inherits from the axis.minor.ticks setting for the relevant position.

expanded

Whether the ticks should cover the range after scale expansion (TRUE, default), or be restricted to the scale limits (FALSE).

cap

A character to cut the axis line back to the last breaks. Can be "none" (default) to draw the axis line along the whole panel, or "upper" and "lower" to draw the axis to the upper or lower break, or "both" to only draw the line in between the most extreme breaks. TRUE and FALSE are shorthand for "both" and "none" respectively.

theme

A theme object to style the guide individually or differently from the plot's theme settings. The theme argument in the guide overrides, and is combined with, the plot's theme.

...

Arguments passed on to guide_axis

check.overlap

silently remove overlapping labels, (recursively) prioritizing the first, last, and middle labels.

angle

Compared to setting the angle in theme() / element_text(), this also uses some heuristics to automatically pick the hjust and vjust that you probably want. Can be one of the following:

  • NULL to take the angles and hjust/vjust directly from the theme.

  • waiver() to allow reasonable defaults in special cases.

  • A number representing the text angle in degrees.

n.dodge

The number of rows (for vertical axes) or columns (for horizontal axes) that should be used to render the labels. This is useful for displaying labels that would otherwise overlap.

order

A positive integer of length 1 that specifies the order of this guide among multiple guides. This controls in which order guides are merged if there are multiple guides for the same position. If 0 (default), the order is determined by a secret algorithm.

position

Where this guide should be drawn: one of top, bottom, left, or right.

title

A character string or expression indicating a title of guide. If NULL, the title is not shown. By default (waiver()), the name of the scale object or the name specified in labs() is used for the title.

Examples

# A standard plot
p <- ggplot(msleep, aes(bodywt, brainwt)) +
  geom_point(na.rm = TRUE)

# The logticks axis works well with log scales
p + scale_x_log10(guide = "axis_logticks") +
  scale_y_log10(guide = "axis_logticks")

# Or with log-transformed coordinates
p + coord_trans(x = "log10", y = "log10") +
  guides(x = "axis_logticks", y = "axis_logticks")

# When data is transformed manually, one should provide `prescale_base`
# Keep in mind that this axis uses log10 space for placement, not log2
p + aes(x = log2(bodywt), y = log10(brainwt)) +
  guides(
    x = guide_axis_logticks(prescale_base = 2),
    y = guide_axis_logticks(prescale_base = 10)
  )

# A plot with both positive and negative extremes, pseudo-log transformed
set.seed(42)
p2 <- ggplot(data.frame(x = rcauchy(1000)), aes(x = x)) +
  geom_density() +
  scale_x_continuous(
    breaks = c(-10^(4:0), 0, 10^(0:4)),
    transform = "pseudo_log"
  )

# The log ticks are mirrored when 0 is included
p2 + guides(x = "axis_logticks")

# To control the tick density around 0, one can set `negative_small`
p2 + guides(x = guide_axis_logticks(negative_small = 1))

Stacked axis guides

Description

This guide can stack other position guides that represent position scales, like those created with scale_(x|y)_continuous() and scale_(x|y)_discrete().

Usage

guide_axis_stack(
  first = "axis",
  ...,
  title = waiver(),
  theme = NULL,
  spacing = NULL,
  order = 0,
  position = waiver()
)

Arguments

first

A position guide given as one of the following:

  • A string, for example "axis".

  • A call to a guide function, for example guide_axis().

...

Additional guides to stack given in the same manner as first.

title

A character string or expression indicating a title of guide. If NULL, the title is not shown. By default (waiver()), the name of the scale object or the name specified in labs() is used for the title.

theme

A theme object to style the guide individually or differently from the plot's theme settings. The theme argument in the guide overrides, and is combined with, the plot's theme.

spacing

A unit() objects that determines how far separate guides are spaced apart.

order

A positive integer of length 1 that specifies the order of this guide among multiple guides. This controls in which order guides are merged if there are multiple guides for the same position. If 0 (default), the order is determined by a secret algorithm.

position

Where this guide should be drawn: one of top, bottom, left, or right.

Details

The first guide will be placed closest to the panel and any subsequent guides provided through ... will follow in the given order.

Examples

#' # A standard plot
p <- ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  theme(axis.line = element_line())

# A normal axis first, then a capped axis
p + guides(x = guide_axis_stack("axis", guide_axis(cap = "both")))

Angle axis guide

Description

This is a specialised guide used in coord_radial() to represent the theta position scale.

Usage

guide_axis_theta(
  title = waiver(),
  theme = NULL,
  angle = waiver(),
  minor.ticks = FALSE,
  cap = "none",
  order = 0,
  position = waiver()
)

Arguments

title

A character string or expression indicating a title of guide. If NULL, the title is not shown. By default (waiver()), the name of the scale object or the name specified in labs() is used for the title.

theme

A theme object to style the guide individually or differently from the plot's theme settings. The theme argument in the guide overrides, and is combined with, the plot's theme.

angle

Compared to setting the angle in theme() / element_text(), this also uses some heuristics to automatically pick the hjust and vjust that you probably want. Can be one of the following:

  • NULL to take the angles and hjust/vjust directly from the theme.

  • waiver() to allow reasonable defaults in special cases.

  • A number representing the text angle in degrees.

minor.ticks

Whether to draw the minor ticks (TRUE) or not draw minor ticks (FALSE, default).

cap

A character to cut the axis line back to the last breaks. Can be "none" (default) to draw the axis line along the whole panel, or "upper" and "lower" to draw the axis to the upper or lower break, or "both" to only draw the line in between the most extreme breaks. TRUE and FALSE are shorthand for "both" and "none" respectively.

order

A positive integer of length 1 that specifies the order of this guide among multiple guides. This controls in which order guides are merged if there are multiple guides for the same position. If 0 (default), the order is determined by a secret algorithm.

position

Where this guide should be drawn: one of top, bottom, left, or right.

Note

The axis labels in this guide are insensitive to hjust and vjust settings. The distance from the tick marks to the labels is determined by the largest margin size set in the theme.

Examples

# A plot using coord_radial
p <- ggplot(mtcars, aes(disp, mpg)) +
  geom_point() +
  coord_radial()

# The `angle` argument can be used to set relative angles
p + guides(theta = guide_axis_theta(angle = 0))

A binned version of guide_legend

Description

This guide is a version of the guide_legend() guide for binned scales. It differs in that it places ticks correctly between the keys, and sports a small axis to better show the binning. Like guide_legend() it can be used for all non-position aesthetics though colour and fill defaults to guide_coloursteps(), and it will merge aesthetics together into the same guide if they are mapped in the same way.

Usage

guide_bins(
  title = waiver(),
  theme = NULL,
  position = NULL,
  direction = NULL,
  override.aes = list(),
  reverse = FALSE,
  order = 0,
  show.limits = NULL,
  ...
)

Arguments

title

A character string or expression indicating a title of guide. If NULL, the title is not shown. By default (waiver()), the name of the scale object or the name specified in labs() is used for the title.

theme

A theme object to style the guide individually or differently from the plot's theme settings. The theme argument in the guide overrides, and is combined with, the plot's theme.

position

A character string indicating where the legend should be placed relative to the plot panels.

direction

A character string indicating the direction of the guide. One of "horizontal" or "vertical."

override.aes

A list specifying aesthetic parameters of legend key. See details and examples.

reverse

logical. If TRUE the order of legends is reversed.

order

positive integer less than 99 that specifies the order of this guide among multiple guides. This controls the order in which multiple guides are displayed, not the contents of the guide itself. If 0 (default), the order is determined by a secret algorithm.

show.limits

Logical. Should the limits of the scale be shown with labels and ticks. Default is NULL meaning it will take the value from the scale. This argument is ignored if labels is given as a vector of values. If one or both of the limits is also given in breaks it will be shown irrespective of the value of show.limits.

...

ignored.

Value

A guide object

Use with discrete scale

This guide is intended to show binned data and work together with ggplot2's binning scales. However, it is sometimes desirable to perform the binning in a separate step, either as part of a stat (e.g. stat_contour_filled()) or prior to the visualisation. If you want to use this guide for discrete data the levels must follow the naming scheme implemented by base::cut(). This means that a bin must be encoded as "(<lower>, <upper>]" with ⁠<lower>⁠ giving the lower bound of the bin and ⁠<upper>⁠ giving the upper bound ("[<lower>, <upper>)" is also accepted). If you use base::cut() to perform the binning everything should work as expected, if not, some recoding may be needed.

See Also

Other guides: guide_colourbar(), guide_coloursteps(), guide_legend(), guides()

Examples

p <- ggplot(mtcars) +
  geom_point(aes(disp, mpg, size = hp)) +
  scale_size_binned()

# Standard look
p

# Remove the axis or style it
p + guides(size = guide_bins(
  theme = theme(legend.axis.line = element_blank())
))

p + guides(size = guide_bins(show.limits = TRUE))

my_arrow <- arrow(length = unit(1.5, "mm"), ends = "both")
p + guides(size = guide_bins(
  theme = theme(legend.axis.line = element_line(arrow = my_arrow))
))

# Guides are merged together if possible
ggplot(mtcars) +
  geom_point(aes(disp, mpg, size = hp, colour = hp)) +
  scale_size_binned() +
  scale_colour_binned(guide = "bins")

Continuous colour bar guide

Description

Colour bar guide shows continuous colour scales mapped onto values. Colour bar is available with scale_fill and scale_colour. For more information, see the inspiration for this function: Matlab's colorbar function.

Usage

guide_colourbar(
  title = waiver(),
  theme = NULL,
  nbin = NULL,
  display = "raster",
  raster = deprecated(),
  alpha = NA,
  draw.ulim = TRUE,
  draw.llim = TRUE,
  position = NULL,
  direction = NULL,
  reverse = FALSE,
  order = 0,
  available_aes = c("colour", "color", "fill"),
  ...
)

guide_colorbar(
  title = waiver(),
  theme = NULL,
  nbin = NULL,
  display = "raster",
  raster = deprecated(),
  alpha =