R语言基础 chapter2

一、回顾综合性例子

模拟产生统计专业同学的名单（学号区分），记录数学分析，线性代数，概率统计三科成绩，然后进行一些统计分析。#案例中随机数据不一定与本例数据相同

#产生学号seq()
num <- seq(10378001,10378100)
num
[1] 10378001 10378002 10378003 10378004 10378005 10378006 10378007 10378008 10378009
[10] 10378010 10378011 10378012 10378013 10378014 10378015 10378016 10378017 10378018
[19] 10378019 10378020 10378021 10378022 10378023 10378024 10378025 10378026 10378027
[28] 10378028 10378029 10378030 10378031 10378032 10378033 10378034 10378035 10378036
[37] 10378037 10378038 10378039 10378040 10378041 10378042 10378043 10378044 10378045
[46] 10378046 10378047 10378048 10378049 10378050 10378051 10378052 10378053 10378054
[55] 10378055 10378056 10378057 10378058 10378059 10378060 10378061 10378062 10378063
[64] 10378064 10378065 10378066 10378067 10378068 10378069 10378070 10378071 10378072
[73] 10378073 10378074 10378075 10378076 10378077 10378078 10378079 10378080 10378081
[82] 10378082 10378083 10378084 10378085 10378086 10378087 10378088 10378089 10378090
[91] 10378091 10378092 10378093 10378094 10378095 10378096 10378097 10378098 10378099
[100] 10378100

#模拟成绩runif和rnorm 其中round()四舍五入
x1 <- round(runif(100,min = 80,max = 100))#均匀分布随机数 #随机数据不一定与本例数据相同
x1
[1] 99 86 88 95 94 98 96 83 97 95 84 95 98 82 95 83 89 84 83 83 99 85
[23] 83 93 86 80 82 92 87 93 88 86 83 85 90 94 89 89 89 98 94 95 81 85
[45] 88 86 86 82 83 95 83 88 96 85 84 93 92 89 97 95 87 99 84 95 82 94
[67] 94 100 88 81 97 87 87 100 84 92 88 81 83 96 100 94 87 95 83 98 93 97
[89] 98 85 94 97 82 98 89 82 93 88 87 88

x2 <- round(rnorm(100,mean = 80,sd = 7))#正态分布随机数 #随机数据不一定与本例数据相同
x2
[1] 86 71 75 80 89 85 92 70 69 87 79 91 84 93 75 79 80 88 76 81 88 81 84 82 89 78 72 79 80
[30] 81 73 71 84 82 73 86 80 73 73 80 80 69 80 84 83 90 84 73 72 74 85 85 86 86 80 67 75 83
[59] 79 82 71 90 85 80 68 77 68 79 85 70 76 83 85 65 81 78 78 86 85 85 95 75 80 77 73 83 77
[88] 70 82 87 75 77 82 82 80 84 73 87 79 83

x3 <- round(rnorm(100,mean = 83,sd = 18))
x3
[1] 86 55 87 84 78 80 85 84 37 80 83 73 102 80 93 85 70 75 115 100 115 100
[23] 80 59 103 84 86 88 81 84 102 67 73 93 95 71 90 69 89 74 101 85 37 74
[45] 95 84 84 82 79 75 109 93 96 79 94 96 87 97 68 77 89 61 90 91 62 40
[67] 89 105 107 86 118 71 80 79 71 88 82 70 97 69 100 123 56 116 94 75 80 80
[89] 109 79 113 93 69 89 63 86 109 78 75 101

x3[which(x3 > 100)] <- 100
x3
[1] 86 55 87 84 78 80 85 84 37 80 83 73 100 80 93 85 70 75 100 100 100 100
[23] 80 59 100 84 86 88 81 84 100 67 73 93 95 71 90 69 89 74 100 85 37 74
[45] 95 84 84 82 79 75 100 93 96 79 94 96 87 97 68 77 89 61 90 91 62 40
[67] 89 100 100 86 100 71 80 79 71 88 82 70 97 69 100 100 56 100 94 75 80 80
[89] 100 79 100 93 69 89 63 86 100 78 75 100

#合成数据框并保存data.frame()和write.table
x <- data.frame(num,x1,x2,x3)
x
num x1 x2 x3
1 10378001 99 86 86
2 10378002 86 71 55
3 10378003 88 75 87
4 10378004 95 80 84
5 10378005 94 89 78
6 10378006 98 85 80
7 10378007 96 92 85
8 10378008 83 70 84
9 10378009 97 69 37
10 10378010 95 87 80
11 10378011 84 79 83
12 10378012 95 91 73
13 10378013 98 84 100
14 10378014 82 93 80
15 10378015 95 75 93
16 10378016 83 79 85
17 10378017 89 80 70
18 10378018 84 88 75
19 10378019 83 76 100
20 10378020 83 81 100
21 10378021 99 88 100
22 10378022 85 81 100
23 10378023 83 84 80
24 10378024 93 82 59
25 10378025 86 89 100
26 10378026 80 78 84
27 10378027 82 72 86
28 10378028 92 79 88
29 10378029 87 80 81
30 10378030 93 81 84
31 10378031 88 73 100
32 10378032 86 71 67
33 10378033 83 84 73
34 10378034 85 82 93
35 10378035 90 73 95
36 10378036 94 86 71
37 10378037 89 80 90
38 10378038 89 73 69
39 10378039 89 73 89
40 10378040 98 80 74
41 10378041 94 80 100
42 10378042 95 69 85
43 10378043 81 80 37
44 10378044 85 84 74
45 10378045 88 83 95
46 10378046 86 90 84
47 10378047 86 84 84
48 10378048 82 73 82
49 10378049 83 72 79
50 10378050 95 74 75
51 10378051 83 85 100
52 10378052 88 85 93
53 10378053 96 86 96
54 10378054 85 86 79
55 10378055 84 80 94
56 10378056 93 67 96
57 10378057 92 75 87
58 10378058 89 83 97
59 10378059 97 79 68
60 10378060 95 82 77
61 10378061 87 71 89
62 10378062 99 90 61
63 10378063 84 85 90
64 10378064 95 80 91
65 10378065 82 68 62
66 10378066 94 77 40
67 10378067 94 68 89
68 10378068 100 79 100
69 10378069 88 85 100
70 10378070 81 70 86
71 10378071 97 76 100
72 10378072 87 83 71
73 10378073 87 85 80
74 10378074 100 65 79
75 10378075 84 81 71
76 10378076 92 78 88
77 10378077 88 78 82
78 10378078 81 86 70
79 10378079 83 85 97
80 10378080 96 85 69
81 10378081 100 95 100
82 10378082 94 75 100
83 10378083 87 80 56
84 10378084 95 77 100
85 10378085 83 73 94
86 10378086 98 83 75
87 10378087 93 77 80
88 10378088 97 70 80
89 10378089 98 82 100
90 10378090 85 87 79
91 10378091 94 75 100
92 10378092 97 77 93
93 10378093 82 82 69
94 10378094 98 82 89
95 10378095 89 80 63
96 10378096 82 84 86
97 10378097 93 73 100
98 10378098 88 87 78
99 10378099 87 79 75
100 10378100 88 83 100

write.table(x,file = “f:\R_note\student_grades.txt”,col.names = F,row.names = F,sep = " ")

#计算各科平均分，函数mean(),colMeans(),apply()
mean(x)
[1] NA
Warning message:
In mean.default(x) : 参数不是数值也不是逻辑值：回覆NA

colMeans(x)
num x1 x2 x3
10378050.50 89.82 79.92 83.12

colMeans(x)[C(“X1”,“X2”,“X3”)]
Error in C(“X1”, “X2”, “X3”) : 不能把对象解释成因子

apply(x,2,mean) #对象是x，2表示列，1表示行
num x1 x2 x3
10378050.50 89.82 79.92 83.12

#求各科最值，函数max(),min(),apply()
apply(x,2,max)
num x1 x2 x3
10378100 100 95 100

apply(x,2,min)
num x1 x2 x3
10378001 80 65 37

#求每个人的总分
apply(x[c(“x1”,“x2”,“x3”)],1,sum)
[1] 271 212 250 259 261 263 273 237 203 262 246 259 282 255 263 247 239 247 259 264 287 266
[23] 247 234 275 242 240 259 248 258 261 224 240 260 258 251 259 231 251 252 274 249 198 243
[45] 266 260 254 237 234 244 268 266 278 250 258 256 254 269 244 254 247 250 259 266 212 211
[67] 251 279 273 237 273 241 252 244 236 258 248 237 265 250 295 269 223 272 250 256 250 247
[89] 280 251 269 267 233 269 232 252 266 253 241 271

which.max(apply(x[c(“x1”,“x2”,“x3”)],1,sum))
[1] 81

x
num x1 x2 x3
1 10378001 99 86 86
2 10378002 86 71 55
3 10378003 88 75 87
4 10378004 95 80 84
5 10378005 94 89 78
6 10378006 98 85 80
7 10378007 96 92 85
8 10378008 83 70 84
9 10378009 97 69 37
10 10378010 95 87 80
11 10378011 84 79 83
12 10378012 95 91 73
13 10378013 98 84 100
14 10378014 82 93 80
15 10378015 95 75 93
16 10378016 83 79 85
17 10378017 89 80 70
18 10378018 84 88 75
19 10378019 83 76 100
20 10378020 83 81 100
21 10378021 99 88 100
22 10378022 85 81 100
23 10378023 83 84 80
24 10378024 93 82 59
25 10378025 86 89 100
26 10378026 80 78 84
27 10378027 82 72 86
28 10378028 92 79 88
29 10378029 87 80 81
30 10378030 93 81 84
31 10378031 88 73 100
32 10378032 86 71 67
33 10378033 83 84 73
34 10378034 85 82 93
35 10378035 90 73 95
36 10378036 94 86 71
37 10378037 89 80 90
38 10378038 89 73 69
39 10378039 89 73 89
40 10378040 98 80 74
41 10378041 94 80 100
42 10378042 95 69 85
43 10378043 81 80 37
44 10378044 85 84 74
45 10378045 88 83 95
46 10378046 86 90 84
47 10378047 86 84 84
48 10378048 82 73 82
49 10378049 83 72 79
50 10378050 95 74 75
51 10378051 83 85 100
52 10378052 88 85 93
53 10378053 96 86 96
54 10378054 85 86 79
55 10378055 84 80 94
56 10378056 93 67 96
57 10378057 92 75 87
58 10378058 89 83 97
59 10378059 97 79 68
60 10378060 95 82 77
61 10378061 87 71 89
62 10378062 99 90 61
63 10378063 84 85 90
64 10378064 95 80 91
65 10378065 82 68 62
66 10378066 94 77 40
67 10378067 94 68 89
68 10378068 100 79 100
69 10378069 88 85 100
70 10378070 81 70 86
71 10378071 97 76 100
72 10378072 87 83 71
73 10378073 87 85 80
74 10378074 100 65 79
75 10378075 84 81 71
76 10378076 92 78 88
77 10378077 88 78 82
78 10378078 81 86 70
79 10378079 83 85 97
80 10378080 96 85 69
81 10378081 100 95 100
82 10378082 94 75 100
83 10378083 87 80 56
84 10378084 95 77 100
85 10378085 83 73 94
86 10378086 98 83 75
87 10378087 93 77 80
88 10378088 97 70 80
89 10378089 98 82 100
90 10378090 85 87 79
91 10378091 94 75 100
92 10378092 97 77 93
93 10378093 82 82 69
94 10378094 98 82 89
95 10378095 89 80 63
96 10378096 82 84 86
97 10378097 93 73 100
98 10378098 88 87 78
99 10378099 87 79 75
100 10378100 88 83 100

x$num[which.max(apply(x[c(“x1”,“x2”,“x3”)],1,sum))]
[1] 10378081

二、数据可视化函数

1.对x1进行直方图分析
绘制直方图函数hist()
2.探索各科成绩的关联关系
散点图绘制函数plot()
3.列联表分析table()
列联表函数table(),柱状图绘制函数barplot()
3.饼图pie()
饼图绘制函数pie()
4.箱尾图boxplot()
箱子的上下横线为样本的25%和75%分位数
中间的横线是中位数
上下延伸的直线为尾线，分别对应最值
异常值为圆点
5.星相图stars()
每个观测单位的数值表示为一个图形；
每个图形的每个角表示一个变量，字符串类型会标注在图的下方
角线的长度表达值的大小
6.脸谱图faces()
安装aplpack包
用五官的宽度和高度来描绘数值
人对脸谱高度敏感和强记忆
适合较少样本
7.茎叶图stem()
8.QQ图
判断是否正态分布
直线的斜率是标准差，截距是均值
点的散布越接近直线，则越接近正态分布

小案例 #本案例继续引用上边的综合案例的数据

hist(x$x1)#直方图

plot(x1,x2) #散点图

table(x1)#列联表分析
x1
80 81 82 83 84 85 86 87 88 89 90 92 93 94 95 96 97 98 99 100
1 3 6 10 5 5 5 6 8 6 1 3 5 7 9 3 5 6 3 3

barplot(table(x1))#柱状图

pie(table(x1)) #饼图

boxplot(x1,x2,x3)#箱尾图
boxplot(x[2:4],col = c(“red”,“green”,“blue”),notch = T)
boxplot(x1,x2,x3,horizontal = T)

stars(x[c(“x1”,“x2”,“x3”)])#星相图
stars(x[c(“x1”,“x2”,“x3”)],full = T,draw.segments = T) #雷达图，full = F画半圆，draw.segment表示扇形通过扇形的面积来判断全面程度

library(aplpack)
载入需要的程辑包：tcltk
Warning message:
程辑包‘aplpack’是用R版本3.5.1 来建造的

faces(x[c(“x1”,“x2”,“x3”)]) #脸谱图（需加载以上包aplpack）
effect of variables:
modified item Var
"height of face " “x1”
"width of face " “x2”
“structure of face” “x3”
"height of mouth " “x1”
"width of mouth " “x2”
"smiling " “x3”
"height of eyes " “x1”
"width of eyes " “x2”
"height of hair " “x3”
"width of hair " “x1”
"style of hair " “x2”
"height of nose " “x3”
"width of nose " “x1”
"width of ear " “x2”
"height of ear " “x3”

stem(x$x1) #茎叶图
The decimal point is at the |
80 | 0000
82 | 0000000000000000
84 | 0000000000
86 | 00000000000
88 | 00000000000000
90 | 0
92 | 00000000
94 | 0000000000000000
96 | 00000000
98 | 000000000
100 | 000

qqnorm(x1)

qqline(x1)

qqnorm(x3)

qqline(x3)

散点图的进一步设置

plot(x1,x2,
main = “数学分析与线性代数成绩的关系”,
xlab = “数学分析”, ylab = “线性代数”,xlim = c(60,100),
ylim = c(60,100),
xaxs = “i”,#set x axis style as internal
yaxs = “i”,#set y axis style as internal
col = “red”,#set the color of plotting symbol to red
pch = 19 #set the plotting symbol to filled dots)

散点图之连线图

a <- c(2,3,4,5,6)
b <- c(4,7,8,9,12)
plot(a,b,type = “l”)

R内置数据集
函数data()列出内置函数,寻找需要的数据集名称调用出来

data()
mtcars #例子摩托车牌子
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

热力图
利用内置的mtcars数据集绘制

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
Sepal花萼
Petal花瓣
Species种属

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
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica

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
用来克服散点图种数据点重叠问题
在有重叠的地方用一朵向日葵的花瓣树木来表示重叠数据的个数

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pairs()
直观地了解所有变量之间的关系
用plot()也可以实现同样的效果
利用par()在同一个device输出多个散点图
par命令用于设置绘图参数，help(par)

遍历样本中全部的变量配对画出二元图pairs(),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 = “Relationship between characteristics of iris flowers”,
pch = 19,
col = “blue”,
cex = 0.9)

#利用par()在同一个device输出多个散点图,par命令用于设置绘图参数，help(par)

par(mfrow = c(3,1)) #3行1列
plot(x1,x2)

plot(x2,x3)

plot(x3,x1)

colors()
[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”
[25] “blanchedalmond” “blue” “blue1”
[28] “blue2” “blue3” “blue4”
[31] “blueviolet” “brown” “brown1”
[34] “brown2” “brown3” “brown4”
[37] “burlywood” “burlywood1” “burlywood2”
[40] “burlywood3” “burlywood4” “cadetblue”
[43] “cadetblue1” “cadetblue2” “cadetblue3”
[46] “cadetblue4” “chartreuse” “chartreuse1”
[49] “chartreuse2” “chartreuse3” “chartreuse4”
[52] “chocolate” “chocolate1” “chocolate2”
[55] “chocolate3” “chocolate4” “coral”
[58] “coral1” “coral2” “coral3”
[61] “coral4” “cornflowerblue” “cornsilk”
[64] “cornsilk1” “cornsilk2” “cornsilk3”
[67] “cornsilk4” “cyan” “cyan1”
[70] “cyan2” “cyan3” “cyan4”
[73] “darkblue” “darkcyan” “darkgoldenrod”
[76] “darkgoldenrod1” “darkgoldenrod2” “darkgoldenrod3”
[79] “darkgoldenrod4” “darkgray” “darkgreen”
[82] “darkgrey” “darkkhaki” “darkmagenta”
[85] “darkolivegreen” “darkolivegreen1” “darkolivegreen2”
[88] “darkolivegreen3” “darkolivegreen4” “darkorange”
[91] “darkorange1” “darkorange2” “darkorange3”
[94] “darkorange4” “darkorchid” “darkorchid1”
[97] “darkorchid2” “darkorchid3” “darkorchid4”
[100] “darkred” “darksalmon” “darkseagreen”
[103] “darkseagreen1” “darkseagreen2” “darkseagreen3”
[106] “darkseagreen4” “darkslateblue” “darkslategray”
[109] “darkslategray1” “darkslategray2” “darkslategray3”
[112] “darkslategray4” “darkslategrey” “darkturquoise”
[115] “darkviolet” “deeppink” “deeppink1”
[118] “deeppink2” “deeppink3” “deeppink4”
[121] “deepskyblue” “deepskyblue1” “deepskyblue2”
[124] “deepskyblue3” “deepskyblue4” “dimgray”
[127] “dimgrey” “dodgerblue” “dodgerblue1”
[130] “dodgerblue2” “dodgerblue3” “dodgerblue4”
[133] “firebrick” “firebrick1” “firebrick2”
[136] “firebrick3” “firebrick4” “floralwhite”
[139] “forestgreen” “gainsboro” “ghostwhite”
[142] “gold” “gold1” “gold2”
[145] “gold3” “gold4” “goldenrod”
[148] “goldenrod1” “goldenrod2” “goldenrod3”
[151] “goldenrod4” “gray” “gray0”
[154] “gray1” “gray2” “gray3”
[157] “gray4” “gray5” “gray6”
[160] “gray7” “gray8” “gray9”
[163] “gray10” “gray11” “gray12”
[166] “gray13” “gray14” “gray15”
[169] “gray16” “gray17” “gray18”
[172] “gray19” “gray20” “gray21”
[175] “gray22” “gray23” “gray24”
[178] “gray25” “gray26” “gray27”
[181] “gray28” “split.screen” “gray30”
[184] “gray31” “gray32” “gray33”
[187] “gray34” “gray35” “gray36”
[190] “gray37” “gray38” “gray39”
[193] “gray40” “gray41” “gray42”
[196] “gray43” “gray44” “gray45”
[199] “gray46” “gray47” “gray48”
[202] “gray49” “gray50” “gray51”
[205] “gray52” “gray53” “gray54”
[208] “gray55” “gray56” “gray57”
[211] “gray58” “gray59” “gray60”
[214] “gray61” “gray62” “gray63”
[217] “gray64” “gray65” “gray66”
[220] “gray67” “gray68” “gray69”
[223] “gray70” “gray71” “gray72”
[226] “gray73” “gray74” “gray75”
[229] “gray76” “gray77” “gray78”
[232] “gray79” “gray80” “gray81”
[235] “gray82” “gray83” “gray84”
[238] “gray85” “gray86” “gray87”
[241] “gray88” “gray89” “gray90”
[244] “gray91” “gray92” “gray93”
[247] “gray94” “gray95” “gray96”
[250] “gray97” “gray98” “gray99”
[253] “gray100” “green” “green1”
[256] “green2” “green3” “green4”
[259] “greenyellow” “grey” “grey0”
[262] “grey1” “grey2” “grey3”
[265] “grey4” “grey5” “grey6”
[268] “grey7” “grey8” “grey9”
[271] “grey10” “grey11” “grey12”
[274] “grey13” “grey14” “grey15”
[277] “grey16” “grey17” “grey18”
[280] “grey19” “grey20” “grey21”
[283] “grey22” “grey23” “grey24”
[286] “grey25” “grey26” “grey27”
[289] “grey28” “grey29” “grey30”
[292] “grey31” “grey32” “grey33”
[295] “grey34” “grey35” “grey36”
[298] “grey37” “grey38” “grey39”
[301] “grey40” “grey41” “grey42”
[304] “grey43” “grey44” “grey45”
[307] “grey46” “grey47” “grey48”
[310] “grey49” “grey50” “grey51”
[313] “grey52” “grey53” “grey54”
[316] “grey55” “grey56” “grey57”
[319] “grey58” “grey59” “grey60”
[322] “grey61” “grey62” “grey63”
[325] “grey64” “grey65” “grey66”
[328] “grey67” “grey68” “grey69”
[331] “grey70” “grey71” “grey72”
[334] “grey73” “grey74” “grey75”
[337] “grey76” “grey77” “grey78”
[340] “grey79” “grey80” “grey81”
[343] “grey82” “grey83” “grey84”
[346] “grey85” “grey86” “grey87”
[349] “grey88” “grey89” “grey90”
[352] “grey91” “grey92” “grey93”
[355] “grey94” “grey95” “grey96”
[358] “grey97” “grey98” “grey99”
[361] “grey100” “honeydew” “honeydew1”
[364] “honeydew2” “honeydew3” “honeydew4”
[367] “hotpink” “hotpink1” “hotpink2”
[370] “hotpink3” “hotpink4” “indianred”
[373] “indianred1” “indianred2” “indianred3”
[376] “indianred4” “ivory” “ivory1”
[379] “ivory2” “ivory3” “ivory4”
[382] “khaki” “khaki1” “khaki2”
[385] “khaki3” “khaki4” “lavender”
[388] “lavenderblush” “lavenderblush1” “lavenderblush2”
[391] “lavenderblush3” “lavenderblush4” “lawngreen”
[394] “lemonchiffon” “lemonchiffon1” “lemonchiffon2”
[397] “lemonchiffon3” “lemonchiffon4” “lightblue”
[400] “lightblue1” “lightblue2” “lightblue3”
[403] “lightblue4” “lightcoral” “lightcyan”
[406] “lightcyan1” “lightcyan2” “lightcyan3”
[409] “lightcyan4” “lightgoldenrod” “lightgoldenrod1”
[412] “lightgoldenrod2” “lightgoldenrod3” “lightgoldenrod4”
[415] “lightgoldenrodyellow” “lightgray” “lightgreen”
[418] “lightgrey” “lightpink” “lightpink1”
[421] “lightpink2” “lightpink3” “lightpink4”
[424] “lightsalmon” “lightsalmon1” “lightsalmon2”
[427] “lightsalmon3” “lightsalmon4” “lightseagreen”
[430] “lightskyblue” “lightskyblue1” “lightskyblue2”
[433] “lightskyblue3” “lightskyblue4” “lightslateblue”
[436] “lightslategray” “lightslategrey” “lightsteelblue”
[439] “lightsteelblue1” “lightsteelblue2” “lightsteelblue3”
[442] “lightsteelblue4” “lightyellow” “lightyellow1”
[445] “lightyellow2” “lightyellow3” “lightyellow4”
[448] “limegreen” “linen” “magenta”
[451] “magenta1” “magenta2” “magenta3”
[454] “magenta4” “maroon” “maroon1”
[457] “maroon2” “maroon3” “maroon4”
[460] “mediumaquamarine” “mediumblue” “mediumorchid”
[463] “mediumorchid1” “mediumorchid2” “mediumorchid3”
[466] “mediumorchid4” “mediumpurple” “mediumpurple1”
[469] “mediumpurple2” “mediumpurple3” “mediumpurple4”
[472] “mediumseagreen” “mediumslateblue” “mediumspringgreen”
[475] “mediumturquoise” “mediumvioletred” “midnightblue”
[478] “mintcream” “mistyrose” “mistyrose1”
[481] “mistyrose2” “mistyrose3” “mistyrose4”
[484] “moccasin” “navajowhite” “navajowhite1”
[487] “navajowhite2” “navajowhite3” “navajowhite4”
[490] “navy” “navyblue” “oldlace”
[493] “olivedrab” “olivedrab1” “olivedrab2”
[496] “olivedrab3” “olivedrab4” “orange”
[499] “orange1” “orange2” “orange3”
[502] “orange4” “orangered” “orangered1”
[505] “orangered2” “orangered3” “orangered4”
[508] “orchid” “orchid1” “orchid2”
[511] “orchid3” “orchid4” “palegoldenrod”
[514] “palegreen” “palegreen1” “palegreen2”
[517] “palegreen3” “palegreen4” “paleturquoise”
[520] “paleturquoise1” “paleturquoise2” “paleturquoise3”
[523] “paleturquoise4” “palevioletred” “palevioletred1”
[526] “palevioletred2” “palevioletred3” “palevioletred4”
[529] “papayawhip” “peachpuff” “peachpuff1”
[532] “peachpuff2” “peachpuff3” “peachpuff4”
[535] “peru” “pink” “pink1”
[538] “pink2” “pink3” “pink4”
[541] “plum” “plum1” “plum2”
[544] “plum3” “plum4” “powderblue”
[547] “purple” “purple1” “purple2”
[550] “purple3” “purple4” “red”
[553] “red1” “red2” “red3”
[556] “red4” “rosybrown” “rosybrown1”
[559] “rosybrown2” “rosybrown3” “rosybrown4”
[562] “royalblue” “royalblue1” “royalblue2”
[565] “royalblue3” “royalblue4” “saddlebrown”
[568] “salmon” “salmon1” “salmon2”
[571] “salmon3” “salmon4” “sandybrown”
[574] “seagreen” “seagreen1” “seagreen2”
[577] “seagreen3” “seagreen4” “seashell”
[580] “seashell1” “seashell2” “seashell3”
[583] “seashell4” “sienna” “sienna1”
[586] “sienna2” “sienna3” “sienna4”
[589] “skyblue” “skyblue1” “skyblue2”
[592] “skyblue3” “skyblue4” “slateblue”
[595] “slateblue1” “slateblue2” “slateblue3”
[598] “slateblue4” “slategray” “slategray1”
[601] “slategray2” “slategray3” “slategray4”
[604] “slategrey” “snow” “snow1”
[607] “snow2” “snow3” “snow4”
[610] “springgreen” “springgreen1” “springgreen2”
[613] “springgreen3” “springgreen4” “steelblue”
[616] “steelblue1” “steelblue2” “steelblue3”
[619] “steelblue4” “tan” “tan1”
[622] “tan2” “tan3” “tan4”
[625] “thistle” “thistle1” “thistle2”
[628] “thistle3” “thistle4” “tomato”
[631] “tomato1” “tomato2” “tomato3”
[634] “tomato4” “turquoise” “turquoise1”
[637] “turquoise2” “turquoise3” “turquoise4”
[640] “violet” “violetred” “violetred1”
[643] “violetred2” “violetred3” “violetred4”
[646] “wheat” “wheat1” “wheat2”
[649] “wheat3” “wheat4” “whitesmoke”
[652] “yellow” “yellow1” “yellow2”
[655] “yellow3” “yellow4” “yellowgreen”

绘图设备
dev.cur() #当前的图形窗
dev.list() #显示图形窗列表
dev.next(which = dev.cur()) #下一个
dev.prev(which = dev.cur()) #前一个
dev.off(which = dev.cur()) #关闭
dev.set(which = dev.next()) #
dev,new(…) #建立新的图形窗
graphics.off()

b1 <- c(1,2,3)
b2 <- c(4,2,6)
plot(b1,b2)

b3 <- c(3,4,2,5)
b4 <- c(5,4,9,7)
plot(b3,b4)

dev.new() #更新画图面板
NULL

plot(b1,b2)
dev.off(which = dev.cur())
RStudioGD
2