# ----------------------------------------------------#
# 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. 一个支持种子、磁力、迅雷下载和磁力搜索的APP源代码

    磁力搜索网站2020/01/12更新 https://www.cnblogs.com/cilisousuo/p/12099547.html 一个支持种子.磁力.迅雷下载和磁力搜索的APP源代码 Lic ...

  2. uploadifive使用笔记

    官网地址:http://www.uploadify.com/ uploadifive 是基于H5开发,所以支持移动端和PC端 <input type="file" name= ...

  3. UVA 10269 Super Mario,最短路+动态规划

    这个题目我昨晚看到的,没什么思路,因为马里奥有boot加速器,只要中间没有城堡,即可不耗时间和脚力,瞬间移动不超过L距离,遇见城堡就要停下来,当然不能该使用超过K次...我纠结了很久,最终觉得还是只能 ...

  4. 201509-1 数列分段 Java

    思路: 后一个和前一个不相等就算一段 import java.util.Scanner; public class Main { public static void main(String[] ar ...

  5. PAT Advanced 1088 Rational Arithmetic (20) [数学问题-分数的四则运算]

    题目 For two rational numbers, your task is to implement the basic arithmetics, that is, to calculate ...

  6. Java多线程求和

    package test; import java.util.concurrent.*; import java.util.concurrent.locks.Lock; import java.uti ...

  7. C++数组常用操作

    1. 遍历数组 使用基于范围的for循环来遍历整个数组 用_countof()来得到数组中的元素个数 #include <iostream> #include <cstdio> ...

  8. spring 事物面试题

    1.spring 事物管理器中事物传播机制 2.spring中事物的隔离级别 读未提交-事物未提交,另一个事物可以读取到,脏读 读已提交-事物已提交,先前读取的数据与后来读取的数据不同,不可重复读 可 ...

  9. Ubuntu下安装Docker,及Docker的一些常用命令操作

    1.什么是 Docker         Docker 是一个开源项目,Docker 项目的目标是实现轻量级的操作系统虚拟化解决方案.         Docker 的基础是 Linux 容器(LXC ...

  10. 吴裕雄--天生自然 pythonTensorFlow图形数据处理:读取MNIST手写图片数据写入的TFRecord文件

    import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_dat ...