colPoints: Points colored relative to third dimension

In berryFunctions: Function Collection Related to Plotting and Hydrology

Points colored relative to third dimension

Description

Draw colored points for 3D-data in a 2D-plane. Color is relative to third dimension, by different classification methods. Can take 3 vectors or, as in image, 2 vectors and a matrix for z.
Adding points after smallPlot is called for the legend may be incorrect if the original function messes with the graph margins, see the note in colPointsLegend.

Usage

colPoints(
  x,
  y,
  z,
  data,
  add = TRUE,
  col = seqPal(100),
  col2 = c(NA, "grey", "black"),
  Range = range(z, finite = TRUE),
  method = "linear",
  breaks = length(col),
  sdlab = 1,
  legend = TRUE,
  legargs = NULL,
  lines = FALSE,
  nint = 30,
  xlab = gsub("\"", "", deparse(substitute(x))),
  ylab = gsub("\"", "", deparse(substitute(y))),
  zlab = gsub("\"", "", deparse(substitute(z))),
  axes = TRUE,
  log = "",
  las = 1,
  bglines = NULL,
  pch = 16,
  x1 = 0.6,
  y1 = ifelse(horizontal, 0.88, 0.3),
  x2 = 0.99,
  y2 = 0.99,
  density = NULL,
  horizontal = TRUE,
  quiet = FALSE,
  ...
)

Arguments

x, y	Vectors with coordinates of the points to be drawn
z	z values belonging to coordinates. Vector or matrix with the color-defining height values
data	Optional: data.frame with the column names as given by x,y and z.
add	Logical. Should the points be added to current (existing!) plot? If FALSE, a new plot is started. DEFAULT: TRUE (It's called colPoints, after all)
col	Vector of colors to be used. DEFAULT: 100 colors from sequential palette seqPal (color-blind safe, black/white-print safe)
col2	Color for points where z is NA, or lower / higher than Range. DEFAULT: c(NA, 1, 8)
Range	Ends of color bar. If NULL, it is again the DEFAULT: range(z, finite=TRUE)
method	Classification method (partial matching is performed), see classify. DEFAULT: "linear"
breaks	Specification for method, see classify. DEFAULT: different defaults for each method
sdlab	Type of label and breakpoints if method="sd", see classify. DEFAULT: 1
legend	Logical. Should a colPointsLegend be drawn? DEFAULT: TRUE
legargs	List. Arguments passed to colPointsLegend. DEFAULT: NULL, with some defaults specified internally
lines	Logical. Should lines be drawn instead of / underneath the points? (color of each segments is taken from starting point, last point is endpoint.) If lines=TRUE and pch is not given, pch is set to NA. DEFAULT: FALSE
nint	Numeric of length 1. Number of interpolation points between each coordinate if lines=TRUE. nint=1 means no interpolation. Values below 10 will smooth coordinates and might miss the original points. DEFAULT: 30
xlab, ylab, zlab	X axis label, y axis label, colPointsLegend title. DEFAULT: gsub("\"", "", deparse(substitute(x/y/z)))
axes, las	Draw axes? Label Axis Style. Only used when add=FALSE. See par. DEFAULT: axes=TRUE, las=1 (all labels horizontal)
log	Logarithmic axes with log="y", "xy" or "x". For logarithmic colorscale, see method="log". DEFAULT: ""
bglines	If not NULL, passed to abline to draw background lines before adding colored points. DEFAULT: NULL
pch	Point CHaracter. See par. DEFAULT: 16
x1, x2, y1, y2	Relative coordinates [0:1] of inset plot, see smallPlot. Passed to colPointsLegend. DEFAULT: x: 0.6-0.99, y: 0.88-0.98
density	Arguments for density line in colPointsLegend, or FALSE to suppress drawing it. DEFAULT: NULL
horizontal	Logical passed to colPointsLegend. DEFAULT: TRUE
quiet	Turn off warnings? DEFAULT: FALSE
...	Further graphical arguments passed to plot, points and segments, eg cex, xlim (when add=F), mgp, main, sub, asp (when add=F), etc. Note: col does not work, as it is already another argument

Value

Invisible list of values that can be passed to colPointsLegend or colPointsHist.

Note

Rstudio scales graphics really badly, so don't expect the right legend width out of the box if you use Rstudio! Exporting via png("myplot.png", 600,400); colPoints(x,y,z); dev.off() usually works much better

Author(s)

Berry Boessenkool, [e-mail], 2011-2014. I'd be interested in hearing what you used the function for.

References

http://uxblog.idvsolutions.com/2011/10/telling-truth.html, https://www.theusrus.de/blog/the-good-the-bad-22012/

See Also

classify, colPointsLegend, colPointsHist

Examples

i <- c( 22,  40,  48,  60,  80,  70,  70,  63,  55,  48,  45,  40,  30,  32)
j <- c(  5,  10,  15,  20,  12,  30,  45,  40,  30,  36,  56,  33,  45,  23)
k <- c(175, 168, 163, 132, 120, 117, 110, 130, 131, 160, 105, 174, 190, 183)

# basic usage:
colPoints(i,j,k, cex=1.5, pch="+", add=FALSE)

# with custom Range:
colPoints(i,j,k, cex=1.5, pch="+", add=FALSE, Range=c(150,190), density=FALSE)
# can be used to allow comparison between several plots
# points outside the range are plotted with col2

# with custom colors:
mycols <- colorRampPalette(c("blue","yellow","red"))(50)
colPoints(i,j,k, cex=1.5, pch="+", add=FALSE, col=mycols)

# With legend title:
colPoints(i,j,k, cex=2, add=FALSE, zlab="Elevation [m above NN.]",
         legargs=list(density=FALSE))
?colPointsLegend # to see which arguments can be set via legargs


# colPoints with matrix:
colPoints(z=volcano, add=FALSE)
# image and contour by default transpose and reverse the matrix!
# colPoints shows what is really in the data.

# add single newly measured points to image (fictional data):
mx <- c( 22,  40,  45,  30,  30,  10)
my <- c(  5,  33,  56,  70,  45,  45)
mz <- c(110, 184, 127, 133, 170, 114)
colPoints(mx,my,mz, cex=5, pch="*", Range=c(94, 195), col=seqPal(), col2=NA, legend=FALSE)
points(mx,my, cex=4)
text(mx,my,mz, adj=-0.5, font=2)


# with logarithmic color scale:
shp <- seq(0.2,3, by=0.1)
scl <- seq(0.2,3, by=0.1)
wsim <- sapply(shp, function(h) sapply(scl, function(c) mean(rweibull(1e3, shape=h, scale=c))))
colPoints(shp, scl, (wsim), add=FALSE, asp=1)
colPoints(shp, scl, (wsim), add=FALSE, asp=1, method="log")


# with lines (nint to change number of linear interpolation points):
colPoints(i,j,k, cex=1.5, add=FALSE, lines=TRUE, nint=10, lwd=2)
# With NAs separating lines:
tfile <- system.file("extdata/rivers.txt", package="berryFunctions")
rivers <- read.table(tfile, header=TRUE, dec=",")
colPoints(x,y,n, data=rivers, add=FALSE, lines=TRUE)
colPoints(x,y,n, data=rivers, add=FALSE, lines=TRUE, pch=3, lwd=3)
colPoints(x,y,n, data=rivers, add=FALSE, lines=TRUE, pch=3, lwd=3, nint=2)
colPoints("x","y","n", data=rivers, add=FALSE)

# different classification methods:
# see ?classify

colPoints(i,j,k, add=FALSE) # use classify separately:
text(i,j+1,k, col=divPal(100,rev=TRUE)[classify(k)$index], cex=1)


# Add histogram:
cp <- colPoints(i,j,k, add=FALSE)
do.call(colPointsHist, cp[c("z","at","labels","bb","nbins")])
do.call(colPointsHist, owa(cp[c("z","at","labels","bb","nbins")],
                           list(bg=5, breaks=5)))
do.call(colPointsHist, owa(cp[c("z","at","labels","bb","nbins")],
                           list(mar=c(0,0,0,0), x1=0.5, x2=1, y1=0.8,
                             y2=0.99, yaxt="n")))
# histogram in lower panel:
layout(matrix(1:2), heights=c(8,4) )
colPoints(i,j,k, add=FALSE, y1=0.8, y2=1)
colPointsHist(z=k, x1=0.05, x2=1, y1=0, y2=0.4, mar=3, outer=TRUE)
layout(1)


# Customizing the legend :
cp <- colPoints(i,j,k, legend=FALSE, add=FALSE)
colPointsLegend(x1=0.2, x2=0.95, y1=0.50, y2=0.40, z=k, labelpos=5, atminmax=TRUE, bg=7)
colPointsLegend(x1=0.5, x2=0.90, y1=0.28, y2=0.18, z=k, Range=c(80, 200), nbins=12, font=3)
colPointsLegend(x1=0.1, x2=0.40, y1=0.15, y2=0.05, z=k, labelpos=5, lines=FALSE, title="")
colPointsLegend(z=k, horizontal=FALSE)
colPointsLegend(x1=0.01, y2=0.80, z=k, horizontal=FALSE, labelpos=4, cex=1.2)
colPointsLegend(x1=0.23, y2=0.95, z=k, horizontal=FALSE, labelpos=5, cex=0.8,
  dens=FALSE, title="", at=c(130,150,170), labels=c("y","rr","Be"), lines=FALSE)
# For method other than colPoints' default, it is easiest to include these
# options as a list in legargs, but you can also use the invisible output
# from colPoints for later calls to colPointsLegend
do.call(colPointsLegend, cp)
do.call(colPointsLegend, owa(cp, list(colors=divPal(100), cex=1.2)))


# santiago.begueria.es/2010/10/generating-spatially-correlated-random-fields-with-r
if(require(gstat)){
xyz <- gstat(formula=z~1, locations=~x+y, dummy=TRUE, beta=1,
             model=vgm(psill=0.025,model="Exp",range=5), nmax=20)
xyz <- predict(xyz, newdata=data.frame(x=runif(200, 20,40),y=runif(200, 50,70)), nsim=1)
head(xyz)
colPoints(x,y,sim1, data=xyz, add=FALSE)
}

berryFunctions documentation built on May 29, 2024, 4:01 a.m.