R与数据分析旧笔记（三）不知道取什么题目

连线图

> a=c(2,3,4,5,6)
> b=c(4,7,8,9,12)
> plot(a,b,type="l")

多条曲线效果

plot(rain$Tokyo,type="l",col="red",ylim=c(0,300),
main="Monthly Rainfall in major cities",
xlab="Month of Year",
ylab="Rainfall(mm)",
lwd=2)
lines(rain$NewYork,type="l",col="blue",lwd=2)
lines(rain$London,type="l",col="green",lwd=2)
lines(rain$Berlin,type="l",col="orange",lwd=2)

（数据集没找着_(:з」∠)_）

密度图

> plot(density(rnorm(1000)))

R内置数据集

> data()#列出内置数据
Data sets in package ‘datasets’:

AirPassengers           Monthly Airline Passenger Numbers 1949-1960
BJsales                 Sales Data with Leading Indicator
BJsales.lead (BJsales)
                        Sales Data with Leading Indicator
BOD                     Biochemical Oxygen Demand
CO2                     Carbon Dioxide Uptake in Grass Plants
ChickWeight             Weight versus age of chicks on different diets
DNase                   Elisa assay of DNase
EuStockMarkets          Daily Closing Prices of Major European Stock
                        Indices, 1991-1998
Formaldehyde            Determination of Formaldehyde
HairEyeColor            Hair and Eye Color of Statistics Students
Harman23.cor            Harman Example 2.3
Harman74.cor            Harman Example 7.4
Indometh                Pharmacokinetics of Indomethacin..........................略过略过～

热力图

> mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
>
> heatmap(as.matrix(mtcars),
+ Rowv=NA,
+ Colv=NA,
+ col=heat.colors(256),
+ scale="column",
+ margins=c(2,8),
+ main="Car characteristics by Model")
>

Iris（鸢尾花）数据集参数：
Sepal花萼
Petal花瓣
Species种属

> head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

向日葵散点图

> sunflowerplot(iris[,3:4],col="gold",seg.col="gold")

散点图集

遍历样本中全部的变量配对画出二元图
直观地了解所有变量之间的关系

> pairs(iris[,1:4])

用plot也可以实现同样的效果

> plot(iris[,1:4],
+ main="Relationships between characteristics of iris flowers",
+ pch=19,
+ col="blue",
+ cex=0.9)
>

散点图集

利用par()在同一个device输出多个散点图
par命令博大精深，用于设置绘图参数

> par(mfrow=c(3,1))
> plot(iris[,1],iris[,2]);plot(iris[,2],iris[,3]);plot(iris[,3],iris[,1])

关于绘图颜色

> colors()#颜色查询
  [1] "white"                "aliceblue"            "antiquewhite"
  [4] "antiquewhite1"        "antiquewhite2"        "antiquewhite3"
  [7] "antiquewhite4"        "aquamarine"           "aquamarine1"
 [10] "aquamarine2"          "aquamarine3"          "aquamarine4"
 [13] "azure"                "azure1"               "azure2"
 [16] "azure3"               "azure4"               "beige"
 [19] "bisque"               "bisque1"              "bisque2"
 [22] "bisque3"              "bisque4"              "black"
..................................................哔～......................................
>dev.new()#建立新的图形框
>dev.list()#窗口列表
>dev.cur()#当前窗口
>dev.next()#下个窗口

三维散点图

> library(scatterplot3d)
> scatterplot3d(iris[2:4])

三维作图

> x<-y<-seq(-2*pi,2*pi,pi/15)
> f<-function(x,y)sin(x)*sin(y)
> z<-outer(x,y,f)
> contour(x,y,z,col="blue")
> persp(x,y,z,theta=30,phi=30,expand=0.7,col="lightblue")

地图

> library(maps)
> map("state",interior=FALSE)
> map("state",boundary=FALSE,col="red",add=TRUE)
> map("world",fill=TRUE,col=heat.colors(10))