Plot an image on an existing map that was created with mapPlot(). (See example 4 for a way to start with a blank map.)

  zclip = FALSE,
  border = NA,
  lwd = par("lwd"),
  lty = par("lty"),
  missingColor = NA,
  filledContour = FALSE,
  gridder = "binMean2D",
  debug = getOption("oceDebug")



numeric vector of longitudes corresponding to z matrix.


numeric vector of latitudes corresponding to z matrix.


numeric matrix to be represented as an image.


limit for z (color).


A logical value, TRUE indicating that out-of-range z values should be painted with missingColor and FALSE indicating that these values should be painted with the nearest in-range color. If zlim is given then its min and max set the range. If zlim is not given but breaks is given, then the min and max of breaks sets the range used for z. If neither zlim nor breaks is given, clipping is not done, i.e. the action is as if zclip were FALSE.


The z values for breaks in the color scheme. If this is of length 1, the value indicates the desired number of breaks, which is supplied to pretty(), in determining clean break points.


Either a vector of colors corresponding to the breaks, of length 1 plus the number of breaks, or a function specifying colors, e.g. oce.colorsViridis() for the Viridis scheme.


optional colormap, as created by colormap(). If a colormap is provided, then its properties takes precedence over breaks, col, missingColor, and zclip specified to mapImage.


Color used for borders of patches (passed to polygon()); the default NA means no border.


line width, used if borders are drawn.


line type, used if borders are drawn.


a color to be used to indicate missing data, or NA to skip the drawing of such regions (which will retain whatever material has already been drawn at the regions).


either a logical value indicating whether to use filled contours to plot the image, or a numerical value indicating the resampling rate to be used in interpolating from lon-lat coordinates to x-y coordinates. See “Details” for how this interacts with gridder.


Name of gridding function used if filledContour is TRUE. This can be either "binMean2D" to select binMean2D() or "interp" for akima::interp(). If not provided, then a selection is made automatically, with binMean2D() being used if there are more than 10,000 data points in the present graphical view. This "binMean2D" method is much faster than "interp".


A flag that turns on debugging. Set to 1 to get a moderate amount of debugging information, or to 2 to get more.


The data are on a regular grid in lon-lat space, but not in the projected x-y space. This means that image() cannot be used. Instead, there are two approaches, depending on the value of filledContour.

If filledContour is FALSE, the image pixels'' are with [polygon()], which can be prohibitively slow for fine grids. However, if `filledContour` is `TRUE` or a numerical value, then the pixels'' are remapped into a regular grid and then displayed with .filled.contour(). The remapping starts by converting the regular lon-lat grid to an irregular x-y grid using lonlat2map(). This irregular grid is then interpolated onto a regular x-y grid with binMean2D() or with akima::interp() from the akima package, as determined by the gridder argument. If filledContour is TRUE, the dimensions of the regular x-y grid is the same as that of the original lon-lat grid; otherwise, the number of rows and columns are multiplied by the numerical value of filledContour, e.g. the value 2 means to make the grid twice as fine.

Filling contours can produce aesthetically-pleasing results, but the method involves interpolation, so the data are not represented exactly and analysts are advised to compare the results from the two methods (and perhaps various grid refinement values) to guard against misinterpretation.

If a png() device is to be used, it is advised to supply arguments type="cairo" and antialias="none"; see reference 1.



See also


Dan Kelley


if (FALSE) { library(oce) data(coastlineWorld) data(topoWorld) ## Northern polar region, with color-coded bathymetry par(mfrow=c(1,1), mar=c(2,2,1,1)) cm <- colormap(zlim=c(-5000, 0), col=oceColorsGebco) drawPalette(colormap=cm) mapPlot(coastlineWorld, projection="+proj=stere +lat_0=90", longitudelim=c(-180,180), latitudelim=c(70,110)) mapImage(topoWorld, colormap=cm) mapGrid(15, 15, polarCircle=1, col=gray(0.2)) mapPolygon(coastlineWorld[['longitude']], coastlineWorld[['latitude']], col="tan") }