R: Draws image plot with a legend strip for the color scale...

image.plot {fields} | R Documentation

## Draws image plot with a legend strip for the color scale based on either a regular grid or a grid of quadrilaterals.

### Description

This function combines the R image function with some automatic placement of a legend. This is done by automatically splitting the plotting region into two parts. Putting the image in one and the legend in the other. After the legend is added the plot region is reset to the image plot. This function also allows for plotting quadrilateral cells in the image format that often arise from regular grids transformed with a map projection.

### Usage

    ## S3 method for class 'plot'
    image(...,
                     add = FALSE,
                     breaks= NULL, nlevel = 64, col = NULL,
        horizontal = FALSE, legend.shrink = 0.9, legend.width = 1.2,
        legend.mar = ifelse(horizontal, 3.1, 5.1), legend.lab = NULL,
        legend.line= 2,
        graphics.reset = FALSE, bigplot = NULL, smallplot = NULL,
        legend.only = FALSE,  lab.breaks = NULL,
        axis.args = NULL, legend.args = NULL, legend.cex=1.0, midpoint = FALSE, border = NA,
        lwd = 1,verbose = FALSE )

### Arguments

`...` | The usual arguments to the `image` function as x,y,or z or as a list with x,y,z as components. One can also include a `breaks` an argument for an unequal spaced color scale with color scale boundaries at the breaks (see example below). If a "quadrilateral grid", arguments must be explicitly x,y and z with x, and y being matrices of dimensions equal to, or one more than, z giving the grid locations. The basic concept is that the coordinates of x and y still define a grid but the image cells are quadrilaterals rather than being restricted to rectangles. See details below as to how one handles whether the quads are specified by their vertices or by their midpoints. NOTE graphical argruments passed here will only have impact on the image plot. To change the graphical defaults for the legend use the `par` function beforehand e.g. par( lab.cex=2.0) to increase colorbar labels.
`add` | If true add image and a legend strip to the existing plot.
`bigplot` | Plot coordinates for image plot. If not passed these will be determined within the function.
`border` | This only works if x and y are matrices – if NA the quadralaterals will have a border color that is the same as the interior color. Otherwise this specifies the color to use.
`breaks` | Break points in sorted order to indicate the intervals for assigning the colors. Note that if there are nlevel colors there should be (nlevel+1) breakpoints. If `breaks` is not specified (nlevel+1) equally spaced breaks are created where the first and last bin have their midpoints at the minimum and maximum values in `z` or at `zlim`.
`col` | Color table to use for image (See help file on image for details.). Default is a pleasing range of 64 divisions suggested by Tim Hoar and is similar to the MATLAB (TM) jet color scheme. Note that if `breaks` is specified there must be one less colors specified than the number of breaks.
`graphics.reset` | If FALSE (default) the plotting region ( plt in par) will not be reset and one can add more information onto the image plot. (e.g. using functions such as points or lines.) If TRUE will reset plot parameters to the values before entering the function.
`horizontal` | If false (default) legend will be a vertical strip on the right side. If true the legend strip will be along the bottom.
`lab.breaks` | If breaks are supplied these are text string labels to put at each break value. This is intended to label axis on a transformed scale such as logs.
`axis.args` | Additional arguments for the axis function used to create the legend axis. (See example below adding a log scaling.)
`legend.only` | If TRUE just add the legend to a the plot in the plot region defined by the coordinates in smallplot. In the absence of other information the range for the legend is determined from the `zlim` argument.
`legend.args` | Arguments for a complete specification of the legend label, e.g. if you need to the rotate text or other details. This is in the form of list and is just passed to the mtext function and you will need to give both the side and line arguments for positioning. This usually will not be needed. (See example below.)
`legend.cex` | Character expansion to change size of the legend label.
`legend.line` | Distance in units of character height (as in `mtext`) of the legend label from the color bar. Make this larger if the label collides with the color axis labels.
`legend.mar` | Width in characters of legend margin that has the axis. Default is 5.1 for a vertical legend and 3.1 for a horizontal legend.
`legend.lab` | Label for the axis of the color legend. Default is no label as this is usual evident from the plot title.
`legend.shrink` | Amount to shrink the size of legend relative to the full height or width of the plot.
`legend.width` | Width in characters of the legend strip. Default is 1.2, a little bigger that the width of a character.
`lwd` | Line width of bordering lines around pixels. This might need to be set less than 1.0 to avoid visible rounding of the pixel corners.
`midpoint` | This option for the case of unequally spaced grids with x and y being matrices of grid point locations. If FALSE (default) the quadralaterals will be extended to surround the z locations as midpoints. If TRUE z values will be averaged to yield a midpoint value and the original grid points be used to define the quadralaterals. (See help on poly.image for details). In most cases midpoint should be FALSE to preserve exact values for z and let the grid polygons be modified.
`nlevel` | Number of color levels used in legend strip
`smallplot` | Plot coordinates for legend strip. If not passed these will be determined within the function. Be sure to leave room for the axis labels. For example, if the legend is on the right side `smallplot= c(.85,.9,0,1)` will leave (.1 in plot coordinates) for the axis labels to the right of the color strip. This argument is useful for drawing a plot with the legend that is the same size as the plots without legends.
`verbose` | If TRUE prints intermediate information about setting up plots (for debugging).

### Details

This is a function using the basic R graphics. The coding was done to make it easier for users to see how this function works and to modify.

How this function works: The strategy for `image.plot` is simple, divide the plotting region into two smaller regions `bigplot` and `smallplot`. The image goes in one and the legend in the other. This way there is always room for the legend. Some adjustments are made to this rule by not shrinking the `bigplot` if there is already room for the legend strip and also sticking the legend strip close to the image plot. One can specify the plot regions explicitly by `bigplot` and `smallplot` if the default choices do not work. There may be problems with small plotting regions in fitting both of these elements in the plot region and one may have to change the default character sizes or margins to make things fit. Sometimes this function will not reset the type of margins correctly and the "null" call `par(mar = par("mar"))` may help to fix this issue.

The text is too small! This always seems to happen as one is rushing to finish a talk and the figures have tiny default axis labels. Try just calling the function `fields.style` before plotting. List out this function to see what is changed, however, all text is increased by 20% in size.

Why “image.plot" and not “image"? The R Base function `image` is very useful but it is awkward to place a legend quickly. However, that said if you are drawing several image plots and want a common legend use the `image` function and just just use `image.plot` to add the legend. See the example in the help file. Note that you can use `image` to draw a bunch of images and then follow with `image.plot` and `legend.only=TRUE` to add a common legend. (See examples below.)

Almost cloropleths too: It should be noted that this image function is slightly different than a cloropleth map because the legend is assuming that a continous scale has been discretized into a series of colors. To make the image.plot function as a cloropleth graphic one would of course use the `breaks` option and for clarity perhaps code the different regions as different integer values. In addition, for publication quality one would want to use the `legend.args` to add more descriptive labels at the midpoints in the color strip.

Relationship of x, y and z: If the z component is a matrix then the user should be aware that this function locates the matrix element z[i,j] at the grid locations (x[i], y[j]) this is very different than simply listing out the matrix in the usual row column tabular form. See the example below for details on the difference in formatting. What does one do if you do not really have the "z" values on a regular grid? See the functions `quilt.plot.Rd` and `as.image` to discretise irregular observations to a grid. If the values makes sense as points on a smooth surface see `Tps` and `fastTps` for surface interpolation.

Grids with unequally spacing – quadrialteral pixels: If x and y are matrices that are a smooth transformation of a regular grid then z[i,j] is rendered at a quadrilateral that is centered at x[i,j] and y[i,j] (`midpoint` TRUE). The details of how this cell is found are buried in `poly.image` but it it essentially found using midpoints between the centers.If `midpoint` is FALSE then x and y are interpreted as the corners of the quadrilateral cells. But what about z? The four values of z are now averaged to represent a value at the midpoint of the cell and this is what is used for plotting. Quadrilateral grids were added to help with plotting the gridded output of geophysical models where the regular grid is defined according to one map projection but the image plotting is required in another projection. Typically the regular grid becomes distorted in a smooth way when this happens. See the regional climate example for a illustration of this application. One can add border colors in this case easily because these choices are jsut passed onto the polygon function.

Adding the pixel grid for rectangular images: For adding the grid of pixel borders to a rectangular image try this example after calling `image.plot`

`dx<- x[2] - x[1]`
`dy <- y[2]-y[1]`
`xtemp<- seq( min( x)- dx/2, max(x)+ dx/2,, length(x) +1)`
`ytemp<- seq( min( y)- dy/2, max(y)+ dy/2,, length(y) +1)`
`xline( xtemp, col="grey50", lwd=2); yline( ytemp, col="grey50", lwd=2)`

Here `x` and `y` here are the x and y grid values from the image list.

Fine tuning color scales: This function gives some flexibility in tuning the color scale to fit the rendering of z values. This can either be specially designed color scale with specific colors ( see help on `designer.colors`), positioning the colors at specific points on the [0,1] scale, or mapping distinct colors to intervals of z. The examples below show how to do each of these. In addition, by supplying `lab.break` strings or axis parameters one can annotate the legend axis in an informative matter.

Adding just the legend strip: Note that to add just the legend strip all the numerical information one needs is the `zlim` argument and the color table!

About color tables: We like `tim.colors` as a default color scale and so if this what you use this can be omitted. Unfortunately this is not the default for the `image` function. The topographic color scale (`topo.colors`) is also a close second showing our geophysical bias. Users may find `larry.colors` useful for coding distinct regions in the style of a cloropleith map. See also `terrain.colors` for a subset of the topo ones and `designer.colors` to "roll your own" color table. One nice option in this last function is to fix color transitions at particular quantiles of the data rather than at equally spaced intervals. For color choices see how the `nlevels` argument figures into the legend and main plot number of colors. Also see the `colors` function for a listing of all the colors that come with the R base environment.

The details of placing the legend and dividing up the plotting real estate: It is surprising how hard it is to automatically add the legend! All "plotting coordinates" mentioned here are in device coordinates. The plot region is assumed to be [0,1]X[0,1] and plotting regions are defined as rectangles within this square. We found these easier to work with than user coordinates.

`legend.width` and `legend.mar` are in units of character spaces. These units are helpful in thinking about axis labels that will be put into these areas. To add more or less space between the legend and the image plot alter the mar parameters. The default mar settings (5.1,5.1,5.1,2.1) leaves 2.1 spaces for vertical legends and 5.1 spaces for horizontal legends.

There are always problems with default solutions to placing information on graphs but the choices made here may be useful for most cases. The most annoying thing is that after using image.plot and adding information the next plot that is made may have the slightly smaller plotting region set by the image plotting. The user should set `reset.graphics=TRUE` to avoid the plotting size from changing. The disadvantage, however, of resetting the graphics is that one can no longer add additional graphics elements to the image plot. Note that filled.contour always resets the graphics but provides another mechanism to pass through plotting commands. Apparently `filled.contour`, while very pretty, does not work for multiple plots. `levelplot` that is part of the lattice package has a very similar function to image.plot and a formula syntax in the call.

By keeping the `zlim` argument the same across images one can generate the same color scale. (See the `image` help file.) One useful technique for a panel of images is to just draw the images with `image` and then use image.plot to add a legend to the last plot. (See example below for messing with the outer margins to make this work.) Usually a square plot (`pty="s"`) done in a rectangular plot region will have room for the legend stuck to the right side without any other adjustments. See the examples below for more complicated arrangements of multiple image plots and a summary legends.

### Side Effects

After exiting, the plotting region may be changed to make it possible to add more features to the plot. To be explicit, `par()\$plt` may be changed to reflect a smaller plotting region that has accommodated room for the legend subplot.

If `xlim` and `ylim` are specified the pixels may overplot the axis lines. Just use the `box` function to redraw them.

### See Also

image, poly.image, filled.contour, quilt.plot, plot.surface, add.image, colorbar.plot, tim.colors, designer.colors

### Examples

      x<- 1:10
      y<- 1:15
      z<- outer( x,y,"+")
      image.plot(x,y,z)

    # or
      obj<- list( x=x,y=y,z=z)
      image.plot(obj, legend.lab="Sverdrups")

    # add some points on diagonal using standard plot function
    #(with some clipping beyond 10 anticipated)
      points( 5:12, 5:12, pch="X", cex=3)

    # adding breaks and distinct colors for intervals of z
    # with and without lab.breaks
      brk<- quantile( c(z))
      image.plot(x,y,z, breaks=brk, col=rainbow(4))
    # annotate legend strip  at break values and add a label
      image.plot(x,y,z, breaks=brk, col=rainbow(4),
                           lab.breaks=names(brk))
    #
    # compare to
      zp <-quantile(c(z), c( .05, .1,.5, .9,.95))
      image.plot(x,y,z,
         axis.args=list( at=zp, labels=names(zp) ) )
    # a log scaling for the colors
      ticks<- c( 1, 2,4,8,16,32)
      image.plot(x,y,log(z), axis.args=list( at=log(ticks), labels=ticks))

    # see help file for designer.colors to generate a color scale that adapts to
    # quantiles of z.
    # Two add some color scales together here is an example of  5 blues to white to 5 reds
    # with white being a specific size.
     colorTable<- designer.colors(11, c( "blue","white", "red") )
    # breaks with a gap of 10 to 17 assigned the white color
     brks<- c(seq( 1, 10,,6), seq( 17, 25,,6))
     image.plot( x,y,z,breaks=brks, col=colorTable)
    #
    #fat (5 characters wide) and short (50% of figure)  color bar on the bottom
       image.plot( x,y,z,legend.width=5, legend.shrink=.5, horizontal=TRUE)

    # adding a label with all kinds of additional arguments.
    # use side=4 for vertical legend and side= 1 for horizontal legend
    # to be parallel to axes. See help(mtext).

    image.plot(x,y,z,
           legend.args=list( text="unknown units",
         col="magenta", cex=1.5, side=4, line=2))

    #### example using a irregular quadrilateral grid
    data( RCMexample)

    image.plot( RCMexample$x, RCMexample$y, RCMexample$z[,,1])
    ind<- 50:75 # make a smaller image to show bordering lines
    image.plot( RCMexample$x[ind,ind], RCMexample$y[ind,ind], RCMexample$z[ind,ind,1],
                                          border="grey50", lwd=2)

    #### multiple images with a common legend

    set.panel()

    # Here is quick but quirky way to add a common legend to several plots.
    # The idea is leave some room in the margin and then over plot in this margin

    par(oma=c( 0,0,0,4)) # margin of 4 spaces width at right hand side
    set.panel( 2,2) # 2X2 matrix of plots

    # now draw all your plots using usual image command
    for (  k in 1:4){
      data<- matrix( rnorm(150), 10,15)
      image( data, zlim=c(-4,4), col=tim.colors())
    # and just for fun add a contour plot
      contour( data, add=TRUE)
    }

    par(oma=c( 0,0,0,1))# reset margin to be much smaller.
    image.plot( legend.only=TRUE, zlim=c(-4,4))

    # image.plot tricked into  plotting in margin of old setting

    set.panel() # reset plotting device

    #
    # Here is a more learned strategy to add a common legend to a panel of
    # plots  consult the split.screen help file for more explanations.
    # For this example we draw two
    # images top and bottom and add a single legend color bar on the right side

    # first divide screen into the figure region (left) and legend region (right)
       split.screen( rbind(c(0, .8,0,1), c(.8,1,0,1)))

    # now subdivide up the figure region into two parts
       split.screen(c(2,1), screen=1)-> ind
       zr<- range( 2,35)
    # first image
       screen( ind[1])
       image( x,y,z, col=tim.colors(), zlim=zr)

    # second image
       screen( ind[2])
       image( x,y,z+10, col=tim.colors(), zlim =zr)

    # move to skinny region on right and draw the legend strip
       screen( 2)
       image.plot( zlim=zr,legend.only=TRUE, smallplot=c(.1,.2, .3,.7),
       col=tim.colors())

       close.screen( all=TRUE)

    # you can always add a legend arbitrarily to any plot;
    # note that here the plot is too big for the vertical strip but the
    # horizontal fits nicely.
    plot( 1:10, 1:10)
    image.plot( zlim=c(0,25), legend.only=TRUE)
    image.plot( zlim=c(0,25), legend.only=TRUE, horizontal =TRUE)

    # combining the  usual image function and adding a legend
    # first change margin for some more room
    ## Not run:
    par( mar=c(10,5,5,5))
    image( x,y,z, col=topo.colors(64))
    image.plot( zlim=c(0,25), nlevel=64,legend.only=TRUE, horizontal=TRUE,
    col=topo.colors(64))

    ## End(Not run)
    #
    #
    # sorting out the difference in formatting between matrix storage
    # and the image plot depiction
    # this really has not much to do with image.plot but I hope it is useful

    A<- matrix( 1:48, ncol=6, nrow=8)
    # first column of A will be 1:8
    #   ...  second  is  9:16

    image.plot(1:8, 1:6, A)
    # add labels to each box
    text( c( row(A)), c( col(A)), A)
    # and the indices ...
    text( c( row(A)), c( col(A))-.25,
       paste( "(", c(row(A)), ",",c(col(A)),")", sep=""), col="grey")

    # "columns" of A are horizontal and rows are ordered from bottom to top!
    #
    # matrix in its usual tabular form where the rows are y  and columns are x
    image.plot( t( A[8:1,]), axes=FALSE)

[Package fields version 9.6.1 Index]