demo
demo(graphics)
##
##
## demo(graphics)
## ---- ~~~~~~~~
##
## > # Copyright (C) 1997-2009 The R Core Team
## >
## > require(datasets)
##
## > require(grDevices); require(graphics)
##
## > ## Here is some code which illustrates some of the differences between
## > ## R and S graphics capabilities. Note that colors are generally specified
## > ## by a character string name (taken from the X11 rgb.txt file) and that line
## > ## textures are given similarly. The parameter "bg" sets the background
## > ## parameter for the plot and there is also an "fg" parameter which sets
## > ## the foreground color.
## >
## >
## > x <- stats::rnorm(50)
##
## > opar <- par(bg = "white")
##
## > plot(x, ann = FALSE, type = "n")
##
## > abline(h = 0, col = gray(.90))
##
## > lines(x, col = "green4", lty = "dotted")
##
## > points(x, bg = "limegreen", pch = 21)
##
## > title(main = "Simple Use of Color In a Plot",
## + xlab = "Just a Whisper of a Label",
## + col.main = "blue", col.lab = gray(.8),
## + cex.main = 1.2, cex.lab = 1.0, font.main = 4, font.lab = 3)
##
## > ## A little color wheel. This code just plots equally spaced hues in
## > ## a pie chart. If you have a cheap SVGA monitor (like me) you will
## > ## probably find that numerically equispaced does not mean visually
## > ## equispaced. On my display at home, these colors tend to cluster at
## > ## the RGB primaries. On the other hand on the SGI Indy at work the
## > ## effect is near perfect.
## >
## > par(bg = "gray")
##
## > pie(rep(1,24), col = rainbow(24), radius = 0.9)
##
## > title(main = "A Sample Color Wheel", cex.main = 1.4, font.main = 3)
##
## > title(xlab = "(Use this as a test of monitor linearity)",
## + cex.lab = 0.8, font.lab = 3)
##
## > ## We have already confessed to having these. This is just showing off X11
## > ## color names (and the example (from the postscript manual) is pretty "cute".
## >
## > pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
##
## > names(pie.sales) <- c("Blueberry", "Cherry",
## + "Apple", "Boston Cream", "Other", "Vanilla Cream")
##
## > pie(pie.sales,
## + col = c("purple","violetred1","green3","cornsilk","cyan","white"))
##
## > title(main = "January Pie Sales", cex.main = 1.8, font.main = 1)
##
## > title(xlab = "(Don't try this at home kids)", cex.lab = 0.8, font.lab = 3)
##
## > ## Boxplots: I couldn't resist the capability for filling the "box".
## > ## The use of color seems like a useful addition, it focuses attention
## > ## on the central bulk of the data.
## >
## > par(bg="cornsilk")
##
## > n <- 10
##
## > g <- gl(n, 100, n*100)
##
## > x <- rnorm(n*100) + sqrt(as.numeric(g))
##
## > boxplot(split(x,g), col="lavender", notch=TRUE)
##
## > title(main="Notched Boxplots", xlab="Group", font.main=4, font.lab=1)
##
## > ## An example showing how to fill between curves.
## >
## > par(bg="white")
##
## > n <- 100
##
## > x <- c(0,cumsum(rnorm(n)))
##
## > y <- c(0,cumsum(rnorm(n)))
##
## > xx <- c(0:n, n:0)
##
## > yy <- c(x, rev(y))
##
## > plot(xx, yy, type="n", xlab="Time", ylab="Distance")
##
## > polygon(xx, yy, col="gray")
##
## > title("Distance Between Brownian Motions")
##
## > ## Colored plot margins, axis labels and titles. You do need to be
## > ## careful with these kinds of effects. It's easy to go completely
## > ## over the top and you can end up with your lunch all over the keyboard.
## > ## On the other hand, my market research clients love it.
## >
## > x <- c(0.00, 0.40, 0.86, 0.85, 0.69, 0.48, 0.54, 1.09, 1.11, 1.73, 2.05, 2.02)
##
## > par(bg="lightgray")
##
## > plot(x, type="n", axes=FALSE, ann=FALSE)
##
## > usr <- par("usr")
##
## > rect(usr[1], usr[3], usr[2], usr[4], col="cornsilk", border="black")
##
## > lines(x, col="blue")
##
## > points(x, pch=21, bg="lightcyan", cex=1.25)
##
## > axis(2, col.axis="blue", las=1)
##
## > axis(1, at=1:12, lab=month.abb, col.axis="blue")
##
## > box()
##
## > title(main= "The Level of Interest in R", font.main=4, col.main="red")
##
## > title(xlab= "1996", col.lab="red")
##
## > ## A filled histogram, showing how to change the font used for the
## > ## main title without changing the other annotation.
## >
## > par(bg="cornsilk")
##
## > x <- rnorm(1000)
##
## > hist(x, xlim=range(-4, 4, x), col="lavender", main="")
##
## > title(main="1000 Normal Random Variates", font.main=3)
##
## > ## A scatterplot matrix
## > ## The good old Iris data (yet again)
## >
## > pairs(iris[1:4], main="Edgar Anderson's Iris Data", font.main=4, pch=19)
##
## > pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21,
## + bg = c("red", "green3", "blue")[unclass(iris$Species)])
##
## > ## Contour plotting
## > ## This produces a topographic map of one of Auckland's many volcanic "peaks".
## >
## > x <- 10*1:nrow(volcano)
##
## > y <- 10*1:ncol(volcano)
##
## > lev <- pretty(range(volcano), 10)
##
## > par(bg = "lightcyan")
##
## > pin <- par("pin")
##
## > xdelta <- diff(range(x))
##
## > ydelta <- diff(range(y))
##
## > xscale <- pin[1]/xdelta
##
## > yscale <- pin[2]/ydelta
##
## > scale <- min(xscale, yscale)
##
## > xadd <- 0.5*(pin[1]/scale - xdelta)
##
## > yadd <- 0.5*(pin[2]/scale - ydelta)
##
## > plot(numeric(0), numeric(0),
## + xlim = range(x)+c(-1,1)*xadd, ylim = range(y)+c(-1,1)*yadd,
## + type = "n", ann = FALSE)
##
## > usr <- par("usr")
##
## > rect(usr[1], usr[3], usr[2], usr[4], col="green3")
##
## > contour(x, y, volcano, levels = lev, col="yellow", lty="solid", add=TRUE)
##
## > box()
##
## > title("A Topographic Map of Maunga Whau", font= 4)
##
## > title(xlab = "Meters North", ylab = "Meters West", font= 3)
##
## > mtext("10 Meter Contour Spacing", side=3, line=0.35, outer=FALSE,
## + at = mean(par("usr")[1:2]), cex=0.7, font=3)
##
## > ## Conditioning plots
## >
## > par(bg="cornsilk")
##
## > coplot(lat ~ long | depth, data = quakes, pch = 21, bg = "green3")
##
## > par(opar)
demo(persp)
##
##
## demo(persp)
## ---- ~~~~~
##
## > ### Demos for persp() plots -- things not in example(persp)
## > ### -------------------------
## >
## > require(datasets)
##
## > require(grDevices); require(graphics)
##
## > ## (1) The Obligatory Mathematical surface.
## > ## Rotated sinc function.
## >
## > x <- seq(-10, 10, length.out = 50)
##
## > y <- x
##
## > rotsinc <- function(x,y)
## + {
## + sinc <- function(x) { y <- sin(x)/x ; y[is.na(y)] <- 1; y }
## + 10 * sinc( sqrt(x^2+y^2) )
## + }
##
## > sinc.exp <- expression(z == Sinc(sqrt(x^2 + y^2)))
##
## > z <- outer(x, y, rotsinc)
##
## > oldpar <- par(bg = "white")
##
## > persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")
##
## > title(sub=".")## work around persp+plotmath bug
##
## > title(main = sinc.exp)
##
## > persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue",
## + ltheta = 120, shade = 0.75, ticktype = "detailed",
## + xlab = "X", ylab = "Y", zlab = "Z")
##
## > title(sub=".")## work around persp+plotmath bug
##
## > title(main = sinc.exp)
##
## > ## (2) Visualizing a simple DEM model
## >
## > z <- 2 * volcano # Exaggerate the relief
##
## > x <- 10 * (1:nrow(z)) # 10 meter spacing (S to N)
##
## > y <- 10 * (1:ncol(z)) # 10 meter spacing (E to W)
##
## > persp(x, y, z, theta = 120, phi = 15, scale = FALSE, axes = FALSE)
##
## > ## (3) Now something more complex
## > ## We border the surface, to make it more "slice like"
## > ## and color the top and sides of the surface differently.
## >
## > z0 <- min(z) - 20
##
## > z <- rbind(z0, cbind(z0, z, z0), z0)
##
## > x <- c(min(x) - 1e-10, x, max(x) + 1e-10)
##
## > y <- c(min(y) - 1e-10, y, max(y) + 1e-10)
##
## > fill <- matrix("green3", nrow = nrow(z)-1, ncol = ncol(z)-1)
##
## > fill[ , i2 <- c(1,ncol(fill))] <- "gray"
##
## > fill[i1 <- c(1,nrow(fill)) , ] <- "gray"
##
## > par(bg = "lightblue")
##
## > persp(x, y, z, theta = 120, phi = 15, col = fill, scale = FALSE, axes = FALSE)
##
## > title(main = "Maunga Whau\nOne of 50 Volcanoes in the Auckland Region.",
## + font.main = 4)
##
## > par(bg = "slategray")
##
## > persp(x, y, z, theta = 135, phi = 30, col = fill, scale = FALSE,
## + ltheta = -120, lphi = 15, shade = 0.65, axes = FALSE)
##
## > ## Don't draw the grid lines : border = NA
## > persp(x, y, z, theta = 135, phi = 30, col = "green3", scale = FALSE,
## + ltheta = -120, shade = 0.75, border = NA, box = FALSE)
##
## > ## `color gradient in the soil' :
## > fcol <- fill ; fcol[] <- terrain.colors(nrow(fcol))
##
## > persp(x, y, z, theta = 135, phi = 30, col = fcol, scale = FALSE,
## + ltheta = -120, shade = 0.3, border = NA, box = FALSE)
##
## > ## `image like' colors on top :
## > fcol <- fill
##
## > zi <- volcano[ -1,-1] + volcano[ -1,-61] +
## + volcano[-87,-1] + volcano[-87,-61] ## / 4
##
## > fcol[-i1,-i2] <-
## + terrain.colors(20)[cut(zi,
## + stats::quantile(zi, seq(0,1, length.out = 21)),
## + include.lowest = TRUE)]
##
## > persp(x, y, 2*z, theta = 110, phi = 40, col = fcol, scale = FALSE,
## + ltheta = -120, shade = 0.4, border = NA, box = FALSE)
##
## > ## reset par():
## > par(oldpar)
