Illuminated contours — geom_contour

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
)

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.
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
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]
#>         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))