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.
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, 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
The data to be displayed in this layer. There are three options:
NULL, the default, the data is inherited from the plot
data as specified in the call to
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.
function will be called with a single argument,
the plot data. The return value must be a
will be used as the layer data. A
function can be created
~ head(.x, 10)).
The statistical transformation to use on the data for this
layer, either as a
Geom subclass or as a string naming the
stat stripped of the
stat_ prefix (e.g.
"count" rather than
Position adjustment, either as a string naming the adjustment
"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.
A numeric vector of breaks
A function that takes the range of the data and binwidth as input and returns breaks as output
Number of evenly spaced breaks.
Distance between breaks.
angle of the sun in degrees counterclockwise from 12 o' clock
valid colour representing the light and dark shading
numeric vector of length 2 with the minimum and maximum size of lines
numeric indicating the degree of smoothing of illumination and size. Larger
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
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.
geom_contour_tanaka understands the following aesthetics (required aesthetics are in bold)
data.table::setDTthreads(1) 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)] geo[, gh.z := Anomaly(gh), by = lat] #> lon lat lev gh date gh.z #> 1: 0.0 -22.5 700 3150.467 1990-04-01 -0.5528954 #> 2: 2.5 -22.5 700 3146.000 1990-04-01 -5.0194482 #> 3: 5.0 -22.5 700 3141.833 1990-04-01 -9.1861962 #> 4: 7.5 -22.5 700 3139.467 1990-04-01 -11.5528954 #> 5: 10.0 -22.5 700 3139.967 1990-04-01 -11.0528954 #> --- #> 4028: 347.5 -90.0 700 2696.433 1990-04-01 0.0000000 #> 4029: 350.0 -90.0 700 2696.433 1990-04-01 0.0000000 #> 4030: 352.5 -90.0 700 2696.433 1990-04-01 0.0000000 #> 4031: 355.0 -90.0 700 2696.433 1990-04-01 0.0000000 #> 4032: 357.5 -90.0 700 2696.433 1990-04-01 0.0000000 # 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)) #> Warning: 'xwrap' and 'ywrap' will be deprecated. Use ggperiodic::periodic insead. # 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() #> Warning: 'xwrap' and 'ywrap' will be deprecated. Use ggperiodic::periodic insead. # 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))) #> Warning: 'xwrap' and 'ywrap' will be deprecated. Use ggperiodic::periodic insead. # Tanaka contours can solve this issue. g + geom_contour_tanaka(aes(z = gh.z))