scatterplot.density: Use color to show the density of points in a scatterplot
In aqfig: Functions to help display air quality model output and monitoring data

Description Usage Arguments Details Value Author(s) See Also Examples

The plotting region of the scatterplot is divided into bins. The number of data points falling within each bin is summed and then plotted using the image function. This is particularly useful when there are so many points that each point cannot be distinctly identified.

scatterplot.density(x, y, zlim, xylim, num.bins=64,
   col=kristen.colors(32), xlab, ylab, main, density.in.percent=TRUE,
   col.regression.line=1, col.one.to.one.line=grey(0.4),
   col.bar.legend=TRUE, plt.beyond.zlim=FALSE, ...)

`x`	Vector or matrix of x-coordinates of points to be plotted. Missing values are not permitted.
`y`	Vector or matrix of y-coordinates of points to be plotted. Missing values are not permitted.
`zlim`	Vector defining the minimum and maximum of the data density values, to which to assign the two most extreme colors in the `col` argument. If not specified, the range of the calculated density values to be plotted is used.
`xylim`	Specification of extreme values that the first and last bins are expected to contain in the x- and y-directions. May be a single vector of the limits for the x and y axes; e.g., using xylim=c(0,120) specifies that, in both the x- and y-directions, the first bin should contain 0 and the last contain 120. May also be a list in the form: xylim=list(xlim=c(x1 ,x2), ylim=c(y1, y2)), allowing for the different ranges on the axes. If not specified, xlim is the range of `x` and ylim is the range of `y`. Note that `xylim` and `num.bins` together determine how the bins are defined. For more information, see “Details” below.
`num.bins`	Number of bins to be used when calculating the data density in both the x- and y-directions. May be a single number, e.g. num.bins=50, which produces 50 bins in each direction. May also be a list in the form num.bins=list(num.bins.x=n1, num.bins.y=n2) to specify differing numbering of bins for the x- and y-directions. The default is to use 64 bins for both axes (num.bins=64). Note that `xylim` and `num.bins` together determine how the bins are defined. For more information, see “Details” below.
`col`	Color range to use when drawing bins, with the first color assigned to zlim[1] and last color assigned to zlim[2]. Default is kristen.colors(32).
`xlab`	The label for the x-axis. If not specified by the user, defaults to the expression the user named as parameter `x`. This behavior is similar to that for `image`.
`ylab`	The label for the y-axis. If not specified by the user, defaults to the expression the user named as parameter `y`. This behavior is similar to that for `image`.
`main`	The main title for the density scatterplot. If not specified, the default is “Data Density Plot (%)” when density.in.percent=TRUE, and “Data Frequency Plot (counts)” otherwise.
`density.in.percent`	A logical indicating whether the density values should represent a percentage of the total number of data points, rather than a count value. Default is density.in.percent=TRUE.
`col.regression.line`	A color number or color name for the regression line and estimated regression equation (`y` as a linear function of `x`) to be overlaid on density scatterplot. If NULL, the regression line and equation are not displayed. Defaults to a black line and equation text.
`col.one.to.one.line`	A color number or color name for the regression one-to-one line to be overlaid on density scatterplot. If NULL, the one-to-one line is not displayed. Defaults to a dark grey line. If the one-to-one line is displayed, it will be as a dashed line (lty=3).
`col.bar.legend`	A logical indicating whether a “color legend” of the form given by `vertical.image.legend` should be displayed. The default is col.bar.legend=TRUE.
`plt.beyond.zlim`	IF TRUE, and if `zlim` is specified by the user, density values beyond the limits given in `zlim` are plotted. Values less than zlim[1] are plotted in the same color as zlim[1]; values greater than zlim[2] are plotted in the same color as zlim[2]. If TRUE, and `zlim` is not specified by the user, zlim[1] and zlim[2] will be assigned the minimum and maximum values of `z`. In this case, user is warned and `plt.beyond.zlim` is set to FALSE. Default is plt.beyond.zlim=FALSE.
`...`	Any additional parameters to be passed to the `image` function.

The plotting region of the scatterplot is divided into bins. The number of data points falling within each bin is summed and then plotted using the image function. The default is to plot the percent of the data falling within each bin, rather than a raw count value. The arguments xylim and num.bins can include different settings for the x- and y-axis. This makes it easier to plot different variables on each axis, e.g. temperature vs. ozone. Note that xylim and num.bins together determine how the bins are defined.

Note that xylim and num.bins together determine how the bins are defined. This is done using the cut function. Assigning values to bins is more complicated than might be expected. For example, values that fall at cutoff points between bins are difficult to deal with. This function accepts the default setting for cut, which assigns values which fall on a cutoff point to the bin on the left; that is, the intervals are open on the left and closed on the right. This means that a point with x-value equal to xlim[1] and/or y-value equal to ylim[1] would not be assigned to any interval, which is probably not what the user intends in this circumstance. Therefore, this code determines the number of bins in the x-direction so that xlim[1] and xlim[2] are at the center of the first and last bin in the x-direction (and similarly for the y-direction). This means that the first and last bins actually extend a bit past the limits specified. For most applicatons, which use large numbers of data points and bins, this shouldn't be noticeable, but it may be in smalled examples like the first one given below.

A density scatterplot; that is, a pattern of shaded squares representing the counts/percentages of the points falling in each square.

Original version (plot.density.scatter.plot) by Kristen Foley, adapted for aqfig by Jenise Swall

vertical.image.legend, kristen.colors, image, cut

## As a simple test case, build x and y vectors consisting only of the
## integers 1-3.
x <- c( rep(1, 7), rep(2, 12), rep(3, 6) )
y <- c( rep(1, 5), rep(2, 2), rep(1, 2), rep(2, 8), rep(3, 2),
        rep(2, 2), rep(3, 4) )

## For this test case, I've totaled the counts below.
count.df <- data.frame(x=rep(1:3, each=3), y=rep(1:3, times=3), ct=c(5,
2, 0, 2, 8, 2, 0, 2, 4) )

## Make a density scatterplot with counts and percentages.
par(mfrow=c(1,2))
scatterplot.density(x, y, num.bins=3, col=heat.colors(7),
                    density.in.percent=FALSE,
                    col.one.to.one.line="green")
text(count.df$x, count.df$y, count.df$ct, col="purple")
scatterplot.density(x, y, num.bins=3, col=heat.colors(7), col.one.to.one.line=1)
text(count.df$x, count.df$y, count.df$ct/sum(count.df$ct))


## An example closer to actual usage.
x <- rnorm(100000,50,5)
y <- 3 + (.89*x) + rnorm(100000,0,5)
par(mfrow=c(1,1))
scatterplot.density(x, y)

Loading required package: geoR
--------------------------------------------------------------
 Analysis of Geostatistical Data
 For an Introduction to geoR go to http://www.leg.ufpr.br/geoR
 geoR version 1.7-5.2.1 (built on 2016-05-02) is now loaded
--------------------------------------------------------------

Warning message:
no DISPLAY variable so Tk is not available