R绘图基础（一）布局颜色等

一，布局

Ｒ绘图所占的区域，被分成两大部分，一是外围边距，一是绘图区域。

外围边距可使用par()函数中的oma来进行设置。比如oma=c(4,3,2,1)，就是指外围边距分别为下边距：4行，左边距3行，上边距2行，右边距1行。很明显这个设置顺序是从x轴开始顺时针方向。这里的行是指可以显示1行普通字体。所以当我们使用mtext中的line参数时，设置的大小就应该是[0,行数)的开区间。当我们使用mtext在外围边距上书写内容时，设置mtext中的outer=TRUE即可。

绘图区域可使用par()函数中的mfrow, mfcol来进行布局。mfrow和mfcol可以使用绘图区域被区分为多个区域。默认值为mfrow(1,1)。

比如mfrow(2,3)就是指将绘图区域分成2行3列，并按行的顺序依次绘图填充；
比如mfcol(3,2)就是指将绘图区域分成3行2列，并按列的顺序依次绘图填充；

我们将每一个细分的绘图区域分为两个部分，一是绘图边距，一是主绘图。

绘图边距需要容纳的内容有坐标轴，坐标轴标签，标题。通常来讲，我们都只需要一个x轴，一个y轴，所以在设置时，一般的下边距和左边距都会大一些。如果多个x轴或者y轴，才考虑将上边距或者右边距放大一些。绘图边距可以使用par()函数中mar来设置。比如mar=c(4,3,2,1)，与外围边距的设置类似，是指绘图边距分别为下边距：4行，左边距3行，上边距2行，右边距1行。很明显这个设置顺序是从x轴开始顺时针方向。行的概念与之前的相同。也可以使用mai来设置。mai与mar唯一不同之处在于mai不是以行为单位，而是以inch为单位。

SOUTH<-1; WEST<-2; NORTH<-3; EAST<-4;   GenericFigure <- function(ID, size1, size2) { plot(0:10, 0:10, type="n", xlab="X", ylab="Y") text(5,5, ID, col="red", cex=size1) box("plot", col="red") mtext(paste("cex",size2,sep=""), SOUTH, line=3, adj=1.0, cex=size2, col="blue") title(paste("title",ID,sep="")) }   MultipleFigures <- function() { GenericFigure("1", 3, 0.5) box("figure", lty="dotted", col="blue")   GenericFigure("2", 3, 1) box("figure", lty="dotted", col="blue")   GenericFigure("3", 3, 1.5) box("figure", lty="dotted", col="blue")   GenericFigure("4", 3, 2) box("figure", lty="dotted", col="blue") }   par(mfrow=c(2,2),mar=c(6,4,2,1),oma=c(4,3,2,1))   MultipleFigures()   box("inner", lty="dotted", col="green") box("outer", lty="solid", col="green")   mtext("Outer Margin Area (oma) of South: 6", SOUTH, line=1, cex=1, outer=TRUE)   plotline<-function(n,direc){ for(i in 0:n){ mtext(paste("line",i,sep=""), direc, line=i, cex=1, col="black", adj=1, outer=TRUE) } } plotline(4,SOUTH)

Ｒ绘图布局

但是，使用mfrow,mfcol只能是矩阵似的布局，如果我们需要简单地实际不规则的布局，那该怎么办呢？还有强大的layout()专门用于布局。

layout(mat, widths = rep(1, ncol(mat)), heights = rep(1,nrow(mat)),respect = FALSE)

其中，mat就是一个距阵，假设我们要画N个图，那么，mat就要描述每个图所在的位置，其中1…N-1都必须至少出现过一次。比如有三个图，我们希望的布局是第一排有一个图，第二排有两个图，那么mat<-matrix(c(1,1,2,3), nrow=2, ncol=2, byrow = TRUE)；如果希望第一排有两个图，第二排有一个图，那么就是mat<-matrix(c(1,2,3,3),nrow=2,ncol=2,byrow=TRUE)。很明显，还是将画布分成许多小格矩阵，这里就是2Ｘ2的，如果希望第1个图放入第一排的1，2格，那就在矩阵的第1，2的位置写入1，如果是希望第2个图放在第一排的1，2格，那就在矩阵的第1，2的位置写入2。

> attach(mtcars) > layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE)) > hist(wt) > hist(mpg) > hist(disp)

layout不规则布局0

那么参数中的widths和heights又是做什么用的呢？它们就是用来指定每行或者每列的宽度和高度的。我们可以做以下的比较：

> attach(mtcars) > layout(matrix(c(1,1,1,1,2,2,2,3,2,2,2,3),3,4,byrow=TRUE)) > hist(wt) > hist(mpg) > hist(disp) > layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), + widths=c(3,1), heights=c(1,2)) > hist(wt) > hist(mpg) > hist(disp)

layout不规则布局1

我们发现，这两种方法得到的是相同的结果，在代码的书写上，第2种方法要更清晰一些。

想象一下，似乎每个图的位置必须是连在一起的小矩阵，如果，不构成连在一起小矩阵，会成为怎么样的一个结果呢？

> attach(mtcars) > layout(matrix(c(1,1,2,1,1,1),nrow=2,ncol=3,byrow=T)) > hist(wt) > hist(mpg)

layout内嵌图布局

如果需要大图，小图，内嵌图这layout()虽然很容易实现，但多少有点麻烦，不是那么随心所欲。还是回过头来使用par()吧。使用par()的中fig=在画布任意位置上画图。在使用fig参数时，需要把画布理解成左下角为坐标(0,0)，右上角为(1,1)的一个坐标系。fig=c(x1,x2,y1,y2)来设置该参，x1<x2,y1<y2，x1,y1定位绘图区的左下角，x2,y2定位绘图区的右上角。使用new=TRUE参数来确认是否在原画布上继续画，还重新在一张新画布上开始画。

> par(fig=c(0,0.85,0,0.85), new=F) > plot(mtcars$wt, mtcars$mpg, xlab="Miles Per Gallon", + ylab="Car Weight") > par(fig=c(0,0.8,0.55,1), new=TRUE) > boxplot(mtcars$wt, horizontal=TRUE, axes=FALSE) > par(fig=c(0.65,1,0,0.8),new=TRUE) > boxplot(mtcars$mpg, axes=FALSE) > mtext("Enhanced Scatterplot", side=3, outer=TRUE, line=-3) > par(fig=c(0.4,0.75,0.4,0.7),new=TRUE,mar=c(2,2,0,0),mgp=c(1,.4,0),cex=1,cex.lab=0.7,cex.axis=0.7) > hist(mtcars$mpg, main="")

par实现内嵌图

二，颜色

计算机保存及还原颜色时有多种方案，较为常用的是两个，RGB和HSV。R预设了657种颜色，可以通过colors()函数调用（或者英式拼写colours()）。比如我们常用的红，绿，蓝，

> colors()[c(552,254,26)] [1] "red" "green" "blue"

我们可以使用grep来调取我们感兴趣的颜色，

> grep("red",colors()) [1] 100 372 373 374 375 376 476 503 504 505 506 507 524 525 526 527 528 552 553 [20] 554 555 556 641 642 643 644 645   > colors()[grep("red",colors())] [1] "darkred" "indianred" "indianred1" "indianred2" [5] "indianred3" "indianred4" "mediumvioletred" "orangered" [9] "orangered1" "orangered2" "orangered3" "orangered4" [13] "palevioletred" "palevioletred1" "palevioletred2" "palevioletred3" [17] "palevioletred4" "red" "red1" "red2" [21] "red3" "red4" "violetred" "violetred1" [25] "violetred2" "violetred3" "violetred4"   > colors()[grep("sky",colors())] [1] "deepskyblue" "deepskyblue1" "deepskyblue2" "deepskyblue3" [5] "deepskyblue4" "lightskyblue" "lightskyblue1" "lightskyblue2" [9] "lightskyblue3" "lightskyblue4" "skyblue" "skyblue1" [13] "skyblue2" "skyblue3" "skyblue4"   > SetTextContrastColor <- function(color) + { + ifelse( mean(col2rgb(color)) > 127, "black", "white") + } > # Define this array of text contrast colors that correponds to each > # member of the colors() array. > TextContrastColor <- unlist( lapply(colors(), SetTextContrastColor) )   > # 1a. Plot matrix of R colors, in index order, 25 per row. > # This example plots each row of rectangles one at a time. > colCount <- 25 # number per row > rowCount <- 27 > plot( c(1,colCount), c(0,rowCount), type="n", ylab="", xlab="", + axes=FALSE, ylim=c(rowCount,0)) > title("R colors") > > for (j in 0:(rowCount-1)) + { + base <- j*colCount + remaining <- length(colors()) - base + RowSize <- ifelse(remaining < colCount, remaining, colCount) + rect((1:RowSize)-0.5,j-0.5, (1:RowSize)+0.5,j+0.5, + border="black", + col=colors()[base + (1:RowSize)]) + text((1:RowSize), j, paste(base + (1:RowSize)), cex=0.7, + col=TextContrastColor[base + (1:RowSize)]) + }

Ｒ颜色表

对于大多数理工出身的人来讲，理解颜色并不难，难的是如何选择一种或者多种理想的颜色，让绘图很漂亮。Ｒ当中有一个包RColorBrewer就可以为我们解决这个难题，其中预设了很多种颜色组合以供我们使用。

> library(RColorBrewer) > display.brewer.all()

RColorBrewer中预设的颜色表

我们看到其中Set3和Paired设定了12种颜色，其余的有多有少，以9种居多。在使用其颜色时，使用brewer.pal(n, name)调用即可，其中n最小值为3最大值为每组预设值数组的长度。比如brewer.pal(12,Paired)

在绘图时，有如下颜色参数

参数	描述
col	绘图使用的颜色，许多函数接受一组颜色，并对不同的数据依次使用颜色。
col.axis	坐标轴字符颜色
col.lab	x,y坐标标记颜色
col.main	标题颜色
col.sub	副标题颜色
fg	绘图前景色，包括坐标轴，各类boxes
bg	绘图背景色

> require(graphics) > par(col.axis="green",col.lab="blue",col.main="darkred",fg="white",bg="black") > plot(cars,main="speed vs dist")

颜色设置示例

三，字体

字体参数如下：

参数	描述
font	字体描述，1正常，2加粗，3斜体，4加粗，斜体，5符号
font.axis	坐标轴字符描述
font.lab	坐标轴标记字体描述
font.main	标题字体描述
font.sub	副标题字体描述
ps	字体点阵大小，大约为1/72英寸。在使用时text size=ps*cex
cex	字体放大或者缩小多少倍
cex.axis	坐标轴字符大小
cex.lab	坐标轴标记字体大小
cex.main	标题字体大小
cex.sub	副标题字体大小
family	绘图字体。标准字体是”serif”,”sans”,”mono”,”symbol”。当然可以指定任何自己已有的字体库。但它是设备依赖的。

> fonts<-names(pdfFonts()) > cnt<-length(fonts) > x<-cnt+10 > x<-1:x > y<-x > pdf("font.pdf") > plot(x,y,type="n") > for(i in 1:cnt){text(i,i*1.3,family=fonts[i],paste("font",fonts[i]),adj=0)} > dev.off()

字体

四，符号与线形

画图中的符号由pch参数来控制。其描边色由col控制，填充色由bg控制。

> pchShow <- + function(extras = c("*",".", "o","O","0","+","-","|","%","#"), + cex = 3, ## good for both .Device=="postscript" and "x11" + col = "red3", bg = "gold", coltext = "brown", cextext = 1.2, + main = paste("plot symbols : points (... pch = *, cex =", + cex,")")) + { + nex <- length(extras) + np <- 26 + nex + ipch <- 0:(np-1) + k <- floor(sqrt(np)) + dd <- c(-1,1)/2 + rx <- dd + range(ix <- ipch %/% k) + ry <- dd + range(iy <- 3 + (k-1)- ipch %% k) + pch <- as.list(ipch) # list with integers & strings + if(nex > 0) pch[26+ 1:nex] <- as.list(extras) + plot(rx, ry, type="n", axes = FALSE, xlab = "", ylab = "", + main = main) + abline(v = ix, h = iy, col = "lightgray", lty = "dotted") + for(i in 1:np) { + pc <- pch[[i]] + ## 'col' symbols with a 'bg'-colored interior (where available) : + points(ix[i], iy[i], pch = pc, col = col, bg = bg, cex = cex) + if(cextext > 0) + text(ix[i] - 0.3, iy[i], pc, col = coltext, cex = cextext) + } + } > > pchShow()

符号

线形主要由lty和lwd来控制。lty: line type. lwd: line width.顾名思意，lty控制线的形状，而lwd控制线的粗细，默认值为1。设计成2表示两倍线宽。

线形

五，坐标轴，图例，标记与标题

相关参数来控制，它们有

参数	描述
main	主标题
sub	副标题
xlab	x轴标记
ylab	y轴标记
xlim	x轴上下限（范围）
ylim	y轴上下限
mgp	坐标轴标记，坐标字符，坐标刻度线距离坐标轴的行数，默认值为c(3,1,0)

增加一个新的坐标轴使用axis()函数。

参数	描述
side	坐标轴所在的位置，1:下,2:左,3:上,4:右
at	坐标轴具体位置，通常由自动给出。
labels	坐标字符串
pos	坐标轴线所在的行，默认值为重绘所在位置上的原坐标
lty	线型
col	颜色
las	坐标标记与坐标轴方位关系，＝0为平等，＝2为垂直
lwd.ticks	坐标刻度线宽度
col.ticks	坐标刻度线颜色
(…)	其它par()中可用的参数

> # A Silly Axis Example > > # specify the data > x <- c(1:10); y <- x; z <- 10/x > > # create extra margin room on the right for an axis > par(mar=c(5, 4, 4, 8) + 0.1) > > # plot x vs. y > plot(x, y,type="b", pch=21, col="red", + yaxt="n", lty=3, xlab="", ylab="") > > # add x vs. 1/x > lines(x, z, type="b", pch=22, col="blue", lty=2) > > # draw an axis on the left > axis(2, at=x,labels=x, col.axis="red", las=2) > > # draw an axis on the right, with smaller text and ticks > axis(4, at=z,labels=round(z,digits=2), + col.axis="blue", las=2, cex.axis=0.7, tck=-.01) > > # add a title for the right axis > mtext("y=1/x", side=4, line=3, cex.lab=1,las=2, col="blue") > > # add a main title and bottom and left axis labels > title("An Example of Creative Axes", xlab="X values", + ylab="Y=X")

坐标轴

图例使用legend()函数控制

参数	描述
x,y	图例所在位置，可以使用”bottom”,”bottomleft”,”left”,”topleft”,”top”,”topright”,”right”,”bottomleft”,”center”来指定。
inset	设置在主绘图边距
title	图例的标题
legend	图例的内容
…	其它par()可用的参数

> attach(mtcars) > boxplot(mpg~cyl, main="Milage by Car Weight", + yaxt="n", xlab="Milage", horizontal=TRUE, + col=terrain.colors(3)) > legend("topright", inset=.05, title="Number of Cylinders", + c("4","6","8"), fill=terrain.colors(3), horiz=TRUE)

图例

文本框使用text或者mtext函数。text可以在主绘图区内加文本框，mtext在边距或者外边距上加文本框。

text(location, “text to place”, pos, …)
mtext(“text to place”, side, line=n, …)

参数	描述
location	图例所在位置
pos	所在的相对位置，1:下面,2:左边,3:上面,4:右边
side	所在边距的位置，1:下,2:左,3:上,4:右
…	其它par()可用的参数

> attach(mtcars) > plot(wt, mpg, main="Milage vs. Car Weight", + xlab="Weight", ylab="Mileage", pch=18, col="blue") > text(wt, mpg, row.names(mtcars), cex=0.6, pos=4, col="red")

文本框

六，各类图型

点，线，面

点：points，线：abline,lines,segments，面：box,polygon,polypath,rect，特殊的：arrows,symbols

points不仅仅可以画前文中pch所设定的任意一个符号，还可以以字符为符号。

> ## ------------ test code for various pch specifications ------------- > # Try this in various font families (including Hershey) > # and locales. Use sign=-1 asserts we want Latin-1. > # Standard cases in a MBCS locale will not plot the top half. > TestChars <- function(sign=1, font=1, ...) + { + if(font == 5) { sign <- 1; r <- c(32:126, 160:254) + } else if (l10n_info()$MBCS) r <- 32:126 else r <- 32:255 + if (sign == -1) r <- c(32:126, 160:255) + par(pty="s") + plot(c(-1,16), c(-1,16), type="n", xlab="", ylab="", + xaxs="i", yaxs="i") + grid(17, 17, lty=1) + for(i in r) try(points(i%%16, i%/%16, pch=sign*i, font=font,...)) + } > TestChars() > try(TestChars(sign=-1))

画点

abline可以由斜率和截距来确定一条直线，lines可以连接两个或者多个点，segments可以按起止位置画线。

> require(stats) > sale5 <- c(6, 4, 9, 7, 6, 12, 8, 10, 9, 13) > plot(sale5,new=T) > abline(lsfit(1:10,sale5)) > abline(lsfit(1:10,sale5, intercept = FALSE), col= 4) > abline(h=6, v=8, col = "gray60") > text(8,6, "abline( h = 6 )", col = "gray60", adj = c(0, -.1)) > abline(h = 4:8, v = 6:12, col = "lightgray", lty=3) > abline(a=1, b=2, col = 2) > text(5,11, "abline( 1, 2 )", col=2, adj=c(-.1,-.1)) > segments(6,4,9,5,col="green") > text(6,5,"segments(6,4,9,5)") > lines(sale5,col="pink")

线段

box画出当前盒子的边界,polygon画多边形,polypath画路径,rect画距形。

> x <- c(1:9,8:1) > y <- c(1,2*(5:3),2,-1,17,9,8,2:9) > op <- par(mfcol=c(3,1)) > for(xpd in c(FALSE,TRUE,NA)) { + plot(1:10, main = paste("xpd =", xpd)) + box("figure", col = "pink", lwd=3) + polygon(x,y, xpd=xpd, col="orange", lty=2, lwd=2, border="red") + } > par(op) > plotPath <- function(x, y, col = "grey", rule = "winding") { + plot.new() + plot.window(range(x, na.rm = TRUE), range(y, na.rm = TRUE)) + polypath(x, y, col = col, rule = rule) + if (!is.na(col)) + mtext(paste("Rule:", rule), side = 1, line = 0) + } > > plotRules <- function(x, y, title) { + plotPath(x, y) + plotPath(x, y, rule = "evenodd") + mtext(title, side = 3, line = 0) + plotPath(x, y, col = NA) + } > > op <- par(mfrow = c(5, 3), mar = c(2, 1, 1, 1)) > > plotRules(c(.1, .1, .9, .9, NA, .2, .2, .8, .8), + c(.1, .9, .9, .1, NA, .2, .8, .8, .2), + "Nested rectangles, both clockwise") > plotRules(c(.1, .1, .9, .9, NA, .2, .8, .8, .2), + c(.1, .9, .9, .1, NA, .2, .2, .8, .8), + "Nested rectangles, outer clockwise, inner anti-clockwise") > plotRules(c(.1, .1, .4, .4, NA, .6, .9, .9, .6), + c(.1, .4, .4, .1, NA, .6, .6, .9, .9), + "Disjoint rectangles") > plotRules(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), + c(.1, .6, .6, .1, NA, .4, .9, .9, .4), + "Overlapping rectangles, both clockwise") > plotRules(c(.1, .1, .6, .6, NA, .4, .9, .9, .4), + c(.1, .6, .6, .1, NA, .4, .4, .9, .9), + "Overlapping rectangles, one clockwise, other anti-clockwise") > > par(op) > require(grDevices) > ## set up the plot region: > op <- par(bg = "thistle") > plot(c(100, 250), c(300, 450), type = "n", xlab="", ylab="", + main = "2 x 11 rectangles; 'rect(100+i,300+i, 150+i,380+i)'") > i <- 4*(0:10) > ## draw rectangles with bottom left (100, 300)+i > ## and top right (150, 380)+i > rect(100+i, 300+i, 150+i, 380+i, col=rainbow(11, start=.7,end=.1)) > rect(240-i, 320+i, 250-i, 410+i, col=heat.colors(11), lwd=i/5) > ## Background alternating ( transparent / "bg" ) : > j <- 10*(0:5) > rect(125+j, 360+j, 141+j, 405+j/2, col = c(NA,0), + border = "gold", lwd = 2) > rect(125+j, 296+j/2, 141+j, 331+j/5, col = c(NA,"midnightblue")) > mtext("+ 2 x 6 rect(*, col = c(NA,0)) and col = c(NA,\"m..blue\"))")

多边形

路径

矩形

arrows用于画箭头，symbols用于画符号

> ## Note that example(trees) shows more sensible plots! > N <- nrow(trees) > with(trees, { + ## Girth is diameter in inches + symbols(Height, Volume, circles = Girth/24, inches = FALSE, + main = "Trees' Girth") # xlab and ylab automatically + ## Colours too: + op <- palette(rainbow(N, end = 0.9)) + symbols(Height, Volume, circles = Girth/16, inches = FALSE, bg = 1:N, + fg = "gray30", main = "symbols(*, circles = Girth/16, bg = 1:N)") + palette(op) + })

符号

画圆

> library(plotrix) > plot(1:5,seq(1,10,length=5),type="n",xlab="",ylab="",main="Test draw.circle") > draw.circle(2,4,c(1,0.66,0.33),border="purple", + col=c("#ff00ff","#ff77ff","#ffccff"),lty=1,lwd=1) > draw.circle(2.5,8,0.6,border="red",lty=3,lwd=3) > draw.circle(4,3,0.7,border="green",lty=1,lwd=1) > draw.circle(3.5,7,0.8,border="blue",lty=2,lwd=2)

圆

散点图及趋势线

一维点图使用dotchart函数。

> # Dotplot: Grouped Sorted and Colored > # Sort by mpg, group and color by cylinder > x <- mtcars[order(mtcars$mpg),] # sort by mpg > x$cyl <- factor(x$cyl) # it must be a factor > x$color[x$cyl==4] <- "red" > x$color[x$cyl==6] <- "blue" > x$color[x$cyl==8] <- "darkgreen" > dotchart(x$mpg,labels=row.names(x),cex=.7,groups= x$cyl, + main="Gas Milage for Car Models\ngrouped by cylinder", + xlab="Miles Per Gallon", gcolor="black", color=x$color)

一维点图

二维散点图使用plot函数。直趋势线使用abline函数，拟合曲线在拟合后使用line函数绘制。

> attach(mtcars) > plot(wt, mpg, main="Scatterplot Example", + xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19) > # Add fit lines > abline(lm(mpg~wt), col="red") # regression line (y~x) > lines(lowess(wt,mpg), col="blue") # lowess line (x,y)

点阵图

曲线

曲线使用lines函数。其type参数可以使用”p”,”l”,”o”,”b,c”,”s,S”,”h”,”n”等。

> x <- c(1:5); y <- x # create some data > par(pch=22, col="blue") # plotting symbol and color > par(mfrow=c(2,4)) # all plots on one page > opts = c("p","l","o","b","c","s","S","h") > for(i in 1:length(opts)){ + heading = paste("type=",opts[i]) + plot(x, y, main=heading) + lines(x, y, type=opts[i]) + }

曲线

柱状图

普通的柱状图使用barplot函数。其参数horiz=TRUE表示水平画图，beside=TRUE表示如果是多组数据的话，在并排画图，否则原位堆叠画图。

> par(mfrow=c(1,2)) > counts <- table(mtcars$vs, mtcars$gear) > barplot(counts, main="Car Distribution by Gears and VS", + xlab="Number of Gears", col=c("darkblue","red"), + legend = rownames(counts),horiz=TRUE) > barplot(counts, main="Car Distribution by Gears and VS", + xlab="Number of Gears", col=c("darkblue","red"), + legend = rownames(counts), beside=TRUE)

bar图

柱状统计图使用hist函数。其breaks参数设置每组的范围。使用density函数可以拟合曲线。

> hist(mtcars$mpg, breaks=12) > dens<-density(mtcars$mpg) > lines(dens$x,dens$y*100,col="red")

柱状图

饼图

饼图使用pie函数。

> x<-table(mtcars$gear) > pie(x,label=paste("gear=",rownames(x),sep=""))

饼图

3维饼图使用plotrix库中的pie3D函数。

> x<-table(mtcars$gear) > pie3D(x,labels=paste("gear=",rownames(x),sep=""),explode=0.1)

3Ｄ饼图

箱线图

箱线图使用boxplot函数。boxplot中参数x为公式。R中的公式如何定义呢？最简单的 y ~ x 就是y是x的一次函数。好了，下面就是相关的符号代表的意思：

符号	示例	意义
+	+x	包括该变量
-	-x	不包括该变量
:	x:z	包括两变量的相互关系
*	x*z	包括两变量，以及它们之间的相互关系
/	x/z	nesting: include z nested within x
|	x|z	条件或分组：包括指定z的x
^	(u+v+w)^3	include these variables and all interactions up to three way
poly	poly(x,3)	polynomial regression: orthogonal polynomials
Error	Error(a/b)	specify the error term
I	I(x*z)	as is: include a new variable consisting of these variables multiplied
1	-1	截距：减去该截距

> boxplot(mpg~cyl,data=mtcars, main="Car Milage Data", + xlab="Number of Cylinders", ylab="Miles Per Gallon") > boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, + col=(c("gold","darkgreen")), + main="Tooth Growth", xlab="Suppliment and Dose")

箱线图

如果我想在箱线图上叠加样品点，即所谓的蜂群图，如何做呢？

> source("http://bioconductor.org/biocLite.R") > biocLite(c("beeswarm","ggplot2")) > library(beeswarm) > library(ggplot2) > data(breast) > beeswarm <- beeswarm(time_survival ~ event_survival, + data = breast, method = 'swarm', + pwcol = ER)[, c(1, 2, 4, 6)] > colnames(beeswarm) <- c("x", "y", "ER", "event_survival") > > beeswarm.plot <- ggplot(beeswarm, aes(x, y)) + + xlab("") + + scale_y_continuous(expression("Follow-up time (months)")) > beeswarm.plot2 <- beeswarm.plot + geom_boxplot(aes(x, y, + group = round(x)), outlier.shape = NA) > beeswarm.plot3 <- beeswarm.plot2 + geom_point(aes(colour = ER)) + + scale_colour_manual(values = c("black", "red")) + + scale_x_continuous(breaks = c(1:2), + labels = c("Censored", "Metastasis"), expand = c(0, 0.5)) > print(beeswarm.plot3)

蜂群图

> require(beeswarm) > data(breast) > > beeswarm(time_survival ~ event_survival, data = breast, + method = 'swarm', + pch = 16, pwcol = as.numeric(ER), + xlab = '', ylab = 'Follow-up time (months)', + labels = c('Censored', 'Metastasis')) > > boxplot(time_survival ~ event_survival, + data = breast, add = T, + names = c("",""), col="#0000ff22")

蜂群图

分枝树

> require(graphics) > opar<-par(mfrow=c(2,1),mar=c(4,3,0.5,0.5)) > hc <- hclust(dist(USArrests), "ave") > plot(hc,main="") > plot(hc, hang = -1,main="") > par(opar)

分枝树

文氏图

> library(limma) > Y <- matrix(rnorm(100*6),100,6) > Y[1:10,3:4] <- Y[1:10,3:4]+3 > Y[1:20,5:6] <- Y[1:20,5:6]+3 > design <- cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)) > fit <- eBayes(lmFit(Y,design)) > results <- decideTests(fit) > a <- vennCounts(results) > print(a) x1 x2 x3 Counts [1,] 0 0 0 89 [2,] 0 0 1 11 [3,] 0 1 0 0 [4,] 0 1 1 0 [5,] 1 0 0 0 [6,] 1 0 1 0 [7,] 1 1 0 0 [8,] 1 1 1 0 attr(,"class") [1] "VennCounts" > vennDiagram(a) > vennDiagram(results,include=c("up","down"),counts.col=c("red","green"))

文氏图

七，综合应用

点阵图加两方向柱状图

> def.par <- par(no.readonly = TRUE) # save default, for resetting... > > x <- pmin(3, pmax(-3, rnorm(50))) > y <- pmin(3, pmax(-3, rnorm(50))) > xhist <- hist(x, breaks=seq(-3,3,0.5), plot=FALSE) > yhist <- hist(y, breaks=seq(-3,3,0.5), plot=FALSE) > top <- max(c(xhist$counts, yhist$counts)) > xrange <- c(-3,3) > yrange <- c(-3,3) > nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE) > #layout.show(nf) > > par(mar=c(3,3,1,1)) > plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="") > par(mar=c(0,3,1,1)) > barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0) > par(mar=c(3,0,1,1)) > barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE) > > par(def.par)

点阵＋柱状

带误差的点线图

> # Clustered Error Bar for Groups of Cases. > # Example: Experimental Condition (Stereotype Threat Yes/No) x Gender (Male / Female) > # The following values would be calculated from data and are set fixed now for > # code reproduction > > means.females <- c(0.08306698, -0.83376319) > stderr.females <- c(0.13655378, 0.06973371) > > names(means.females) <- c("No","Yes") > names(stderr.females) <- c("No","Yes") > > means.males <- c(0.4942997, 0.2845608) > stderr.males <- c(0.07493673, 0.18479661) > > names(means.males) <- c("No","Yes") > names(stderr.males) <- c("No","Yes") > > # Error Bar Plot > > library (gplots) > > # Draw the error bar for female experiment participants: > plotCI(x = means.females, uiw = stderr.females, lty = 2, xaxt ="n", xlim = c(0.5,2.5), ylim = c(-1,1), gap = 0, ylab="Microworld Performance (Z Score)", xlab="Stereotype Threat", main = "Microworld performance over experimental conditions") > > # Add the males to the existing plot > plotCI(x = means.males, uiw = stderr.males, lty = 1, xaxt ="n", xlim = c(0.5,2.5), ylim = c(-1,1), gap = 0, add = TRUE) > > # Draw the x-axis (omitted above) > axis(side = 1, at = 1:2, labels = names(stderr.males), cex = 0.7) > > # Add legend for male and female participants > legend(2,1,legend=c("Male","Female"),lty=1:2)

误差图

误差绘图plotCI加强版是plotmeans,具体可使用?plotmeans来试用它。这里就不多讲了。

带误差的柱状图。还是使用gplots包

> library(gplots) > # Example with confidence intervals and grid > hh <- t(VADeaths)[, 5:1] > mybarcol <- "gray20" > ci.l <- hh * 0.85 > ci.u <- hh * 1.15 > mp <- barplot2(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 95 percent error bars", col.sub = mybarcol, + cex.names = 1.5, plot.ci = TRUE, ci.l = ci.l, ci.u = ci.u, + plot.grid = TRUE) > mtext(side = 1, at = colMeans(mp), line = 2, + text = paste("Mean", formatC(colMeans(hh))), col = "red") > box()

带误差的柱状图

漂亮的箱线图

> require(gplots) #for smartlegend > > data(ToothGrowth) > boxplot(len ~ dose, data = ToothGrowth, + boxwex = 0.25, at = 1:3 - 0.2, + subset= supp == "VC", col="yellow", + main="Guinea Pigs' Tooth Growth", + xlab="Vitamin C dose mg", + ylab="tooth length", ylim=c(0,35)) > boxplot(len ~ dose, data = ToothGrowth, add = TRUE, + boxwex = 0.25, at = 1:3 + 0.2, + subset= supp == "OJ", col="orange") > > smartlegend(x="left",y="top", inset = 0, + c("Ascorbic acid", "Orange juice"), + fill = c("yellow", "orange"))

箱线图

基因芯片热图，具体参考http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/r/heatmap/

> library("ALL") > data("ALL") > eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")] > library("limma") > f <- factor(as.character(eset$mol.biol)) > design <- model.matrix(~f) > fit <- eBayes(lmFit(eset,design)) > selected <- p.adjust(fit$p.value[, 2]) <0.005 > esetSel <- eset [selected, ] > color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") "#FF0000" else "#0000FF" } > patientcolors <- unlist(lapply(esetSel$mol.bio, color.map)) > library("gplots") > heatmap.2(exprs(esetSel), col=redgreen(75), scale="row", ColSideColors=patientcolors, + key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)