geom_contour_tanaka: Illuminated contours

In metR: Tools for Easier Analysis of Meteorological Fields

Illuminated contours

Description

Illuminated contours (aka Tanaka contours) use varying brightness and width to create an illusion of relief. This can help distinguishing between concave and convex areas (local minimums and maximums), specially in black and white plots or to make photocopy safe plots with divergent colour palettes, or to render a more aesthetically pleasing representation of topography.

Usage

geom_contour_tanaka(
  mapping = NULL,
  data = NULL,
  stat = "Contour2",
  position = "identity",
  ...,
  breaks = NULL,
  bins = NULL,
  binwidth = NULL,
  sun.angle = 60,
  light = "white",
  dark = "gray20",
  range = c(0.01, 0.5),
  smooth = 0,
  proj = NULL,
  proj.latlon = TRUE,
  clip = NULL,
  kriging = 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)).
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. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept. The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
breaks	One of: A numeric vector of breaks A function that takes the range of the data and binwidth as input and returns breaks as output
bins	Number of evenly spaced breaks.
binwidth	Distance between breaks.
sun.angle	angle of the sun in degrees counterclockwise from 12 o' clock
light, dark	valid colour representing the light and dark shading
range	numeric vector of length 2 with the minimum and maximum size of lines
smooth	numeric indicating the degree of smoothing of illumination and size. Larger
proj	The projection to which to project the contours to. It can be either a projection string or a function to apply to the whole contour dataset.
proj.latlon	Logical indicating if the projection step should project from a cartographic projection to a lon/lat grid or the other way around.
clip	A simple features object to be used as a clip. Contours are only drawn in the interior of this polygon.
kriging	Whether to perform ordinary kriging before contouring. Use this if you want to use contours with irregularly spaced data. If FALSE, no kriging is performed. If TRUE, kriging will be performed with 40 points. If a numeric, kriging will be performed with kriging points.
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_contour_tanaka understands the following aesthetics (required aesthetics are in bold)

• x

• y

• z

• linetype

Examples

library(ggplot2)
library(data.table)
# A fresh look at the boring old volcano dataset
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour_fill(aes(z = value)) +
    geom_contour_tanaka(aes(z = value)) +
    theme_void()

# If the transition between segments feels too abrupt,
# smooth it a bit with smooth
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour_fill(aes(z = value)) +
    geom_contour_tanaka(aes(z = value), smooth = 1) +
    theme_void()

data(geopotential)
geo <- geopotential[date == unique(date)[4]]
geo[, gh.z := Anomaly(gh), by = lat]

# In a monochrome contour map, it's impossible to know which areas are
# local maximums or minimums.
ggplot(geo, aes(lon, lat)) +
    geom_contour2(aes(z = gh.z), color = "black", xwrap = c(0, 360))

# With tanaka contours, they are obvious.
ggplot(geo, aes(lon, lat)) +
    geom_contour_tanaka(aes(z = gh.z), dark = "black",
                        xwrap = c(0, 360)) +
    scale_fill_divergent()

# A good divergent color palette has the same luminosity for positive
# and negative values.But that means that printed in grayscale (Desaturated),
# they are indistinguishable.
(g <- ggplot(geo, aes(lon, lat)) +
    geom_contour_fill(aes(z = gh.z), xwrap = c(0, 360)) +
    scale_fill_gradientn(colours = c("#767676", "white", "#484848"),
                         values = c(0, 0.415, 1)))

# Tanaka contours can solve this issue.
g + geom_contour_tanaka(aes(z = gh.z))

metR documentation built on Oct. 14, 2024, 5:09 p.m.