R基本包中的常用绘图 　　

R基本包中的常用绘图

利用R基本载入的包绘图

17 Nov 2016

Go back the advanced plot function we could use

the advanced plot function we could use

Yu Dalang

2016-11-18

这里绘制一下R语言中常用的一些图。

这个文档可以和百度文库中的R语言的可视化完整版

http://wenku.baidu.com/link?url=S4WFYkdh4lTkP230EkbawOEFYZDZHWcNrC_k_AckAmJqZMzpr7jXuGDmOt9MsHppnpAyLnrbuNcEZFESt8THUJrP-2mPr9MQUMOCN5XPp__

1 plot 散点图系列

1.1 plot函数

我们最熟悉x,y 二元散点图

x <- stats::rnorm(50)
opar <- par(bg = "white")
#ann一些默认的标记是否显示，如坐标轴标题和图标题
#type="n"表示作图的类型是一幅空图
plot(x, ann = FALSE, type = "n")
abline(h = 0, col = gray(.90))#加一条水平横线
lines(x, col = "green4", lty = "dotted")#加线
points(x, bg = "limegreen", pch = 21)#加点
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)

## 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")

1.2 sunflowerplot

相同的坐标点为花朵，花瓣数目等于重复次数

sunflowerplot(x = sort(2*round(rnorm(100))), y = round(rnorm(100), 0),
              main = "Sunflower Plot of Rounded N(0,1)")

1.3 coplot 在|后面条件下的x,y二元图

Index <- seq(length = nrow(warpbreaks)) # to get nicer default labels
coplot(breaks ~ Index | wool * tension, data = warpbreaks,
       col = "red", bg = "pink", pch = 21,
       bar.bg = c(fac = "light blue"))

## Conditioning plots
par(bg="cornsilk")
coplot(lat ~ long | depth, data = quakes, pch = 21, bg = "green3")

1.4 interaction.plot 双因素交互图

with(OrchardSprays, {
  interaction.plot(treatment, rowpos, decrease)
  interaction.plot(rowpos, treatment, decrease, cex.axis = 0.8)
  ## order the rows by their mean effect
  rowpos <- factor(rowpos,
                   levels = sort.list(tapply(decrease, rowpos, mean)))
  interaction.plot(rowpos, treatment, decrease, col = 2:9, lty = 1)
})

1.5 matplot 列对应的二元图,可以在一幅图上画很多图

sines <- outer(1:20, 1:4, function(x, y) sin(x / 20 * pi * y))
#below equal to matplot(1:20,sines)
matplot(sines, type = "b", pch = 21:23, col = 2:5, bg = 2:5,
        main = "matplot(...., pch = 21:23, bg = 2:5)")

##############################################
nam.var <- colnames(iris)[-5]
nam.spec <- as.character(iris[1+50*0:2, "Species"])
iris.S <- array(NA, dim = c(50,4,3),
                dimnames = list(NULL, nam.var, nam.spec))
for(i in 1:3) iris.S[,,i] <- data.matrix(iris[1:50+50*(i-1), -5])

matplot(iris.S[, "Petal.Length",], iris.S[, "Petal.Width",], pch = "SCV",
        col = rainbow(3, start = 0.8, end = 0.1),
        sub = paste(c("S", "C", "V"), dimnames(iris.S)[[3]],
                    sep = "=", collapse= ",  "),
        main = "Fisher's Iris Data")

1.6 类似于Matplot,pairs是各列之间的二元图

pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21,
      bg = c("red", "green3", "blue")[unclass(iris$Species)])

2 饼图

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)

##########################################

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)

3 boxplot 线箱图

par(bg="cornsilk")
n <- 10
g <- gl(n, 100, n*100)
x <- rnorm(n*100) + sqrt(as.numeric(g))
boxplot(split(x,g), col=1:10, notch=TRUE)
title(main="Notched Boxplots", xlab="Group", font.main=4, font.lab=1)

4 hist 频率直方图

## 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)

5 barplot

mp <- barplot(VADeaths) # default
tot <- colMeans(VADeaths)
text(mp, tot + 3, format(tot), xpd = TRUE, col = "blue")

barplot(VADeaths, beside = TRUE,
        col = c("lightblue", "mistyrose", "lightcyan",
                "lavender", "cornsilk"),
        legend = rownames(VADeaths), ylim = c(0, 100))
title(main = "Death Rates in Virginia", font.main = 4)

hh <- t(VADeaths)[, 5:1]
mybarcol <- "gray20"
mp <- barplot(hh, beside = TRUE,
              col = c("lightblue", "mistyrose",
                      "lightcyan", "lavender"),
              legend = colnames(VADeaths), ylim = c(0,100),
              main = "Death Rates in Virginia", font.main = 4,
              sub = "Faked upper 2*sigma error bars", col.sub = mybarcol,
              cex.names = 1.5)
segments(mp, hh, mp, hh + 2*sqrt(1000*hh/100), col = mybarcol, lwd = 1.5)
stopifnot(dim(mp) == dim(hh))  # corresponding matrices
mtext(side = 1, at = colMeans(mp), line = -2,
      text = paste("Mean", formatC(colMeans(hh))), col = "red")

6 stripchart

把x的值画在一条线段上。

x <- stats::rnorm(50)
xr <- round(x, 1)
stripchart(x) ; m <- mean(par("usr")[1:2])
text(m, 1.04, "stripchart(x, \"overplot\")")
stripchart(xr, method = "stack", add = TRUE, at = 1.2)
text(m, 1.35, "stripchart(round(x,1), \"stack\")")
stripchart(xr, method = "jitter", add = TRUE, at = 0.7)
text(m, 0.85, "stripchart(round(x,1), \"jitter\")")

7 fourfoldplot

## Use the Berkeley admission data as in Friendly (1995).
x <- aperm(UCBAdmissions, c(2, 1, 3))
dimnames(x)[[2]] <- c("Yes", "No")
names(dimnames(x)) <- c("Sex", "Admit?", "Department")
stats::ftable(x)
##               Department   A   B   C   D   E   F
## Sex    Admit?                                   
## Male   Yes               512 353 120 138  53  22
##        No                313 207 205 279 138 351
## Female Yes                89  17 202 131  94  24
##        No                 19   8 391 244 299 317
## Fourfold display of data aggregated over departments, with
## frequencies standardized to equate the margins for admission
## and sex.
## Figure 1 in Friendly (1994).
fourfoldplot(margin.table(x, c(1, 2)))

## Fourfold display of x, with frequencies in each table
## standardized to equate the margins for admission and sex.
## Figure 2 in Friendly (1994).
fourfoldplot(x)

## Fourfold display of x, with frequencies in each table
## standardized to equate the margins for admission. but not
## for sex.
## Figure 3 in Friendly (1994).
fourfoldplot(x, margin = 2)

8 3D plot: persp

x <- seq(-10, 10, length= 30)
y <- x
f <- function(x, y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r }
z <- outer(x, y, f)
z[is.na(z)] <- 1
op <- par(bg = "white")
persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")

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 = "Sinc( r )"
) -> res
round(res, 3)
##       [,1]   [,2]   [,3]   [,4]
## [1,] 0.087 -0.025  0.043 -0.043
## [2,] 0.050  0.043 -0.075  0.075
## [3,] 0.000  0.074  0.042 -0.042
## [4,] 0.000 -0.273 -2.890  3.890
# (2) Add to existing persp plot - using trans3d() :

xE <- c(-10,10); xy <- expand.grid(xE, xE)
points(trans3d(xy[,1], xy[,2], 6, pmat = res), col = 2, pch = 16)
lines (trans3d(x, y = 10, z = 6 + sin(x), pmat = res), col = 3)

phi <- seq(0, 2*pi, len = 201)
r1 <- 7.725 # radius of 2nd maximum
xr <- r1 * cos(phi)
yr <- r1 * sin(phi)
lines(trans3d(xr,yr, f(xr,yr), res), col = "pink", lwd = 2)

## (no hidden lines)