# ----------------------------------------------------#
# R in Action (2nd ed): Chapter 3 #
# Getting started with graphs #
# requires that the Hmisc and RColorBrewer packages #
# have been installed #
# install.packages(c("Hmisc", "RColorBrewer")) #
#-----------------------------------------------------# par(ask=TRUE)
opar <- par(no.readonly=TRUE) # make a copy of current settings attach(mtcars) # be sure to execute this line plot(wt, mpg)
abline(lm(mpg~wt))
title("Regression of MPG on Weight")
# Input data for drug example
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40) plot(dose, drugA, type="b") opar <- par(no.readonly=TRUE) # make a copy of current settings
par(lty=2, pch=17) # change line type and symbol
plot(dose, drugA, type="b") # generate a plot
par(opar) # restore the original settings plot(dose, drugA, type="b", lty=3, lwd=3, pch=15, cex=2) # choosing colors
library(RColorBrewer)
n <- 7
mycolors <- brewer.pal(n, "Set1")
barplot(rep(1,n), col=mycolors) n <- 10
mycolors <- rainbow(n)
pie(rep(1, n), labels=mycolors, col=mycolors)
mygrays <- gray(0:n/n)
pie(rep(1, n), labels=mygrays, col=mygrays) # Listing 3.1 - Using graphical parameters to control graph appearance
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(pin=c(2, 3))
par(lwd=2, cex=1.5)
par(cex.axis=.75, font.axis=3)
plot(dose, drugA, type="b", pch=19, lty=2, col="red")
plot(dose, drugB, type="b", pch=23, lty=6, col="blue", bg="green")
par(opar) # Adding text, lines, and symbols
plot(dose, drugA, type="b",
col="red", lty=2, pch=2, lwd=2,
main="Clinical Trials for Drug A",
sub="This is hypothetical data",
xlab="Dosage", ylab="Drug Response",
xlim=c(0, 60), ylim=c(0, 70)) # Listing 3.2 - An Example of Custom Axes
x <- c(1:10)
y <- x
z <- 10/x
opar <- par(no.readonly=TRUE)
par(mar=c(5, 4, 4, 8) + 0.1)
plot(x, y, type="b",
pch=21, col="red",
yaxt="n", lty=3, ann=FALSE)
lines(x, z, type="b", pch=22, col="blue", lty=2)
axis(2, at=x, labels=x, col.axis="red", las=2)
axis(4, at=z, labels=round(z, digits=2),
col.axis="blue", las=2, cex.axis=0.7, tck=-.01)
mtext("y=1/x", side=4, line=3, cex.lab=1, las=2, col="blue")
title("An Example of Creative Axes",
xlab="X values",
ylab="Y=X")
par(opar) # Listing 3.3 - Comparing Drug A and Drug B response by dose
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(lwd=2, cex=1.5, font.lab=2)
plot(dose, drugA, type="b",
pch=15, lty=1, col="red", ylim=c(0, 60),
main="Drug A vs. Drug B",
xlab="Drug Dosage", ylab="Drug Response")
lines(dose, drugB, type="b",
pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
library(Hmisc)
minor.tick(nx=3, ny=3, tick.ratio=0.5)
legend("topleft", inset=.05, title="Drug Type", c("A","B"),
lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
par(opar) # Example of labeling points
attach(mtcars)
plot(wt, mpg,
main="Mileage vs. Car Weight",
xlab="Weight", ylab="Mileage",
pch=18, col="blue")
text(wt, mpg,
row.names(mtcars),
cex=0.6, pos=4, col="red")
detach(mtcars) # View font families
opar <- par(no.readonly=TRUE)
par(cex=1.5)
plot(1:7,1:7,type="n")
text(3,3,"Example of default text")
text(4,4,family="mono","Example of mono-spaced text")
text(5,5,family="serif","Example of serif text")
par(opar) # Combining graphs
attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(wt,mpg, main="Scatterplot of wt vs. mpg")
plot(wt,disp, main="Scatterplot of wt vs. disp")
hist(wt, main="Histogram of wt")
boxplot(wt, main="Boxplot of wt")
par(opar)
detach(mtcars) attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(3,1))
hist(wt)
hist(mpg)
hist(disp)
par(opar)
detach(mtcars) attach(mtcars)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
hist(wt)
hist(mpg)
hist(disp)
detach(mtcars) attach(mtcars)
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)
detach(mtcars) # Listing 3.4 - Fine placement of figures in a graph
opar <- par(no.readonly=TRUE)
par(fig=c(0, 0.8, 0, 0.8))
plot(mtcars$mpg, mtcars$wt,
xlab="Miles Per Gallon",
ylab="Car Weight")
par(fig=c(0, 0.8, 0.55, 1), new=TRUE)
boxplot(mtcars$mpg, horizontal=TRUE, axes=FALSE)
par(fig=c(0.65, 1, 0, 0.8), new=TRUE)
boxplot(mtcars$wt, axes=FALSE)
mtext("Enhanced Scatterplot", side=3, outer=TRUE, line=-3)
par(opar)

吴裕雄--天生自然 R语言开发学习:图形初阶(续二)的更多相关文章

  1. 吴裕雄--天生自然 R语言开发学习:时间序列(续二)

    #-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # r ...

  2. 吴裕雄--天生自然 R语言开发学习:方差分析(续二)

    #-------------------------------------------------------------------# # R in Action (2nd ed): Chapte ...

  3. 吴裕雄--天生自然 R语言开发学习:回归(续二)

    #------------------------------------------------------------# # R in Action (2nd ed): Chapter 8 # # ...

  4. 吴裕雄--天生自然 R语言开发学习:分类(续二)

    #-----------------------------------------------------------------------------# # R in Action (2nd e ...

  5. 吴裕雄--天生自然 R语言开发学习:聚类分析(续一)

    #-------------------------------------------------------# # R in Action (2nd ed): Chapter 16 # # Clu ...

  6. 吴裕雄--天生自然 R语言开发学习:时间序列(续三)

    #-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # r ...

  7. 吴裕雄--天生自然 R语言开发学习:时间序列(续一)

    #-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # r ...

  8. 吴裕雄--天生自然 R语言开发学习:方差分析(续一)

    #-------------------------------------------------------------------# # R in Action (2nd ed): Chapte ...

  9. 吴裕雄--天生自然 R语言开发学习:回归(续四)

    #------------------------------------------------------------# # R in Action (2nd ed): Chapter 8 # # ...

  10. 吴裕雄--天生自然 R语言开发学习:回归(续三)

    #------------------------------------------------------------# # R in Action (2nd ed): Chapter 8 # # ...

随机推荐

  1. InnoDB和MyISAM区别总结

    原来是MyISAM类型不支持事务处理等高级处理,而InnoDB类型支持. MyISAM类型的表强调的是性能,其执行数度比InnoDB类型更快,但是不提供事务支持,而InnoDB提供事务支持已经外部键等 ...

  2. mysql安装完之后,登陆后发现只有两个数据库

    mysql安装完之后,登陆后发现只有两个数据库:mysql> show databases;+--------------------+| Database           |+------ ...

  3. psmisc

    https://packages.ubuntu.com/xenial/psmisc Package: psmisc (22.21-2.1build1) Other Packages Related t ...

  4. 编程作业2.2:Regularized Logistic regression

    题目 在本部分的练习中,您将使用正则化的Logistic回归模型来预测一个制造工厂的微芯片是否通过质量保证(QA),在QA过程中,每个芯片都会经过各种测试来保证它可以正常运行.假设你是这个工厂的产品经 ...

  5. sudo输入密码

    网易云音乐, 启动问题. 修改 .desktop 文件 Exec=sh /home/xyq/.music.sh # ~/.music.sh echo 密码 | sudo -S netease-clou ...

  6. 数据分析-Matplotlib:绘图和可视化

    学习路线 简介 简单绘制线形图 plot函数 支持图类型 保存图表 1.简介 Matplotlib是一个强大的Python绘图和数据可视化的工具包.数据可视化也是我们数据分析的最重要的工作之一,可以帮 ...

  7. tensorflow C++接口调用目标检测pb模型代码

    #include <iostream> #include "tensorflow/cc/ops/const_op.h" #include "tensorflo ...

  8. 小白学习之pytorch框架(5)-多层感知机(MLP)-(tensor、variable、计算图、ReLU()、sigmoid()、tanh())

    先记录一下一开始学习torch时未曾记录(也未好好弄懂哈)导致又忘记了的tensor.variable.计算图 计算图 计算图直白的来说,就是数学公式(也叫模型)用图表示,这个图即计算图.借用 htt ...

  9. 测试浏览器是否支持JavaScript脚本

    如果用户不能确定浏览器是否支持JavaScript脚本,那么可以应用HTML提供的注释符号进行验证.HTML注释符号是以 <-- 开始以 --> 结束的.如果在此注释符号内编写 JavaS ...

  10. Python程序中的进程操作--—--开启多进程

    Python程序中的进程操作-----开启多进程 之前我们已经了解了很多进程相关的理论知识,了解进程是什么应该不再困难了,刚刚我们已经了解了,运行中的程序就是一个进程.所有的进程都是通过它的父进程来创 ...