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.
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
)
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.
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)).
The statistical transformation to use on the data for this
layer, either as a ggproto Geom subclass or as a string naming the
stat stripped of the stat_ prefix (e.g. "count" rather than
"stat_count")
Position adjustment, either as a string naming the adjustment
(e.g. "jitter" to use position_jitter), or the result of a call to a
position adjustment function. Use the latter if you need to change the
settings of the adjustment.
Other arguments passed on to layer(). These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red" or size = 3. They may also be parameters
to the paired geom/stat.
Number of contour bins. Overridden by breaks.
The width of the contour bins. Overridden by bins.
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.
Line end style (round, butt, square).
Line join style (round, mitre, bevel).
Line mitre limit (number greater than 1).
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
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.
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().
The geometric object to use to display the data, either as a
ggproto Geom subclass or as a string naming the geom stripped of the
geom_ prefix (e.g. "point" rather than "geom_point")
geom_contour() understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
group
linetype
linewidth
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs").
geom_contour_filled() understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
group
linetype
linewidth
subgroup
Learn more about setting these aesthetics in vignette("ggplot2-specs").
stat_contour() understands the following aesthetics (required aesthetics are in bold):
x
y
z
group
order
Learn more about setting these aesthetics in vignette("ggplot2-specs").
stat_contour_filled() understands the following aesthetics (required aesthetics are in bold):
x
y
z
fill
group
order
Learn more about setting these aesthetics in vignette("ggplot2-specs").
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.
levelHeight of contour. For contour lines, this is numeric vector that represents bin boundaries. For contour bands, this is an ordered factor that represents bin ranges.
level_low, level_high, level_mid(contour bands only) Lower and upper bin boundaries for each band, as well the mid point between the boundaries.
nlevelHeight of contour, scaled to maximum of 1.
pieceContour piece (an integer).
zAfter contouring, the z values of individual data points are no longer available.
geom_density_2d(): 2d density contours
# 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()
# \donttest{
# 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")
# }
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