2D contours of a 3D surface

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. To specify a valid surface, the data must contain x, y, and z coordinates, and each unique combination of x and y can appear exactly once. Contouring tends to work best when x and y form a (roughly) evenly spaced grid. If your data is not evenly spaced, you may want to interpolate to a grid before visualising, see geom_density_2d().

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()` or `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, as a string.
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
...	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.
bins	Number of contour bins. Overridden by `binwidth`.
binwidth	The width of the contour bins. Overridden by `breaks`.
breaks	Numeric vector to set the contour breaks. 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 display the data

Aesthetics

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

x
y
alpha
colour
group
linetype
size
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
size
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").

Computed variables

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.

level

Height 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.

nlevel

Height of contour, scaled to maximum of 1.

piece

Contour piece (an integer).

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()

# \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")
# }

Arguments

Aesthetics

Computed variables

See also

Examples