Package 'ggplot2'

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

What can you add?

You can add any of the following types of objects:

• An aes() object replaces the default aesthetics.

• A layer created by a geom_ or stat_ function adds a new layer.

• A scale overrides the existing scale.

• A theme() modifies the current theme.

• A coord overrides the current coordinate system.

• A facet specification overrides the current faceting.

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())

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

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)

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:

• A name, e.g., "red". R has 657 built-in named colours, which can be listed with grDevices::colors().

• An rgb specification, with a string of the form "#RRGGBB" where each of the pairs RR, GG, BB consists of two hexadecimal digits giving a value in the range 00 to FF. You can optionally make the colour transparent by using the form "#RRGGBBAA".

• An NA, for a completely transparent colour.

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 options for modifying colour: scale_colour_brewer(), scale_colour_gradient(), scale_colour_grey(), scale_colour_hue(), scale_colour_identity(), scale_colour_manual(), scale_colour_viridis_d()

• Other options for modifying fill: scale_fill_brewer(), scale_fill_gradient(), scale_fill_grey(), scale_fill_hue(), scale_fill_identity(), scale_fill_manual(), scale_fill_viridis_d()

• Other options for modifying alpha: scale_alpha(), scale_alpha_manual(), scale_alpha_identity()

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

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))

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)

from_theme(x)

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

Arguments

Staging

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

Stage 1: direct input at the start

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

Sometimes, you may want to map the same aesthetic multiple times, e.g. map x to a data column at the start for the layer stat, but remap it later to a variable from the stat transformation for the layer geom. The stage() function allows you to control multiple mappings for the same aesthetic across all three stages of evaluation.

# 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 variables 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)
  )

Conceptually, aes(x) is equivalent to aes(stage(start = x)), and aes(after_stat(count)) is equivalent to aes(stage(after_stat = count)), and so on. stage() is most useful when at least two of its arguments are specified.

Theme access

The from_theme() function can be used to acces the element_geom() fields of the theme(geom) argument. Using aes(colour = from_theme(ink)) and aes(colour = from_theme(accent)) allows swapping between foreground and accent colours.

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
  )

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

• Geoms commonly used with groups: geom_bar(), geom_histogram(), geom_line()

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

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")

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

• geom_line() and geom_point() for geoms commonly used with these aesthetics.

• aes_group_order() for using linetype, size, or shape for grouping.

• Scales that can be used to modify these aesthetics: scale_linetype(), scale_linewidth(), scale_size(), and scale_shape().

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

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()

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().

Relation to width and height

The position aesthetics mentioned above like x and y are all location based. The width and height aesthetics are closely related length based aesthetics, but are not position aesthetics. Consequently, x and y aesthetics respond to scale transformations, whereas the length based width and height aesthetics are not transformed by scales. For example, if we have the pair ⁠x = 10, width = 2⁠, that gets translated to the locations ⁠xmin = 9, xmax = 11⁠ when using the default identity scales. However, the same pair becomes ⁠xmin = 1, xmax = 100⁠ when using log10 scales, as width = 2 in log10-space spans a 100-fold change.

See Also

• Geoms that commonly use these aesthetics: geom_crossbar(), geom_curve(), geom_errorbar(), geom_line(), geom_linerange(), geom_path(), geom_point(), geom_pointrange(), geom_rect(), geom_segment()

• Scales that can be used to modify positions: scale_continuous(), scale_discrete(), scale_binned(), scale_date().

• See also annotate() for placing annotations.

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)

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

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)

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

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)

Description

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

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")
)

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

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)

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

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()

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

Value

a ggplot layer

See Also

Other plotting automation topics: automatic_plotting, autoplot(), fortify()

Description

There are three functions to make plotting particular data types easier: autoplot(), autolayer() and fortify(). These are S3 generics for which other packages can write methods to display classes of data. The three functions are complementary and allow different levels of customisation. Below we'll explore implementing this series of methods to automate plotting of some class.

Let's suppose we are writing a packages that has a class called 'my_heatmap', that wraps a matrix and we'd like users to easily plot this heatmap.

my_heatmap <- function(...) {
  m <- matrix(...)
  class(m) <- c("my_heatmap", class(m))
  m
}

my_data <- my_heatmap(volcano)

Automatic data shaping

One of the things we have to do is ensure that the data is shaped in the long format so that it is compatible with ggplot2. This is the job of the fortify() function. Because 'my_heatmap' wraps a matrix, we can let the fortify method 'melt' the matrix to a long format. If your data is already based on a long-format ⁠<data.frame>⁠, you can skip implementing a fortify() method.

fortify.my_heatmap <- function(model, ...) {
  data.frame(
    row = as.vector(row(model)),
    col = as.vector(col(model)),
    value = as.vector(model)
  )
}

fortify(my_data)

When you have implemented the fortify() method, it should be easier to construct a plot with the data than with the matrix.

ggplot(my_data, aes(x = col, y = row, fill = value)) +
  geom_raster()

Automatic layers

A next step in automating plotting of your data type is to write an autolayer() method. These are typically wrappers around geoms or stats that automatically set aesthetics or other parameters. If you haven't implemented a fortify() method for your data type, you might have to reshape the data in autolayer().

If you require multiple layers to display your data type, you can use an autolayer() method that constructs a list of layers, which can be added to a plot.

autolayer.my_heatmap <- function(object, ...) {
  geom_raster(
    mapping = aes(x = col, y = row, fill = value),
    data = object,
    ...,
    inherit.aes = FALSE
  )
}

ggplot() + autolayer(my_data)

As a quick tip: if you define a mapping in autolayer(), you might want to set inherit.aes = FALSE to not have aesthetics set in other layers interfere with your layer.

Automatic plots

The last step in automating plotting is to write an autoplot() method for your data type. The expectation is that these return a complete plot. In the example below, we're exploiting the autolayer() method that we have already written to make a complete plot.

autoplot.my_heatmap <- function(object, ..., option = "magma") {
  ggplot() +
    autolayer(my_data) +
    scale_fill_viridis_c(option = option) +
    theme_void()
}

autoplot(my_data)

If you don't have a wish to implement a base R plotting method, you can set the plot method for your class to the autoplot method.

plot.my_heatmap <- autoplot.my_heatmap
plot(my_data)

See Also

Other plotting automation topics: autolayer(), autoplot(), fortify()

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

Value

a ggplot object

See Also

Other plotting automation topics: autolayer(), automatic_plotting, fortify()

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

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()
}

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

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))

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

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

Description

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

Details

Coordinate systems interact with many parts of the plotting system. You can expect the following for coord_flip():

• It does not change the facet order in facet_grid() or facet_wrap().

• The ⁠scale_x_*()⁠ functions apply to the vertical direction, whereas ⁠scale_y_*()⁠ functions apply to the horizontal direction. The same holds for the xlim and ylim arguments of coord_flip() and the xlim() and ylim() functions.

• The x-axis theme settings, such as axis.line.x apply to the horizontal direction. The y-axis theme settings, such as axis.text.y apply to the vertical direction.

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()

Description

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

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)

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,
  r_axis_inside = deprecated(),
  rotate_angle = deprecated()
)

Arguments

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)

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

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")

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

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:

• Do nothing: by default geom_sf() assumes it is stored in the geometry column.

• Explicitly pass an sf object to the data argument. This will use the primary geometry column, no matter what it's called.

• Supply your own using aes(geometry = my_column)

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)
}

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

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))

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)

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

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)

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.

Description

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

• element_blank(): draws nothing, and assigns no space.

• element_rect(): borders and backgrounds.

• element_line(): lines.

• element_text(): text.

• element_geom(): defaults for drawing layers.

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,
  arrow.fill = 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
)

element_geom(
  ink = NULL,
  paper = NULL,
  accent = NULL,
  linewidth = NULL,
  borderwidth = NULL,
  linetype = NULL,
  bordertype = NULL,
  family = NULL,
  fontsize = NULL,
  pointsize = NULL,
  pointshape = NULL
)

rel(x)

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

Arguments

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
  )
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(formula = y ~ x, method = "lm") +
  theme(geom = element_geom(
    ink = "red", accent = "black",
    pointsize = 1, linewidth = 2
  ))

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

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)))

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

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))

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

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")

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",
  space = "fixed",
  shrink = TRUE,
  labeller = "label_value",
  as.table = TRUE,
  switch = deprecated(),
  drop = TRUE,
  dir = "h",
  strip.position = "top",
  axes = "margins",
  axis.labels = "all"
)

Arguments

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")


# The two letters determine the starting position, so 'tr' starts
# in the top-right.
# The first letter determines direction, so 'tr' fills top-to-bottom.
# `dir = "tr"` is equivalent to `dir = "v", as.table = FALSE`
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(vars(class), dir = "tr")

Description

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