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‎第8章 高维数据型图表/crimeRatesByState2005.tsv‎

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state murder Forcible_rate Robbery aggravated_assult burglary larceny_theft motor_vehicle_theft populationAlabama 8.2 34.3 141.4 247.8 953.8 2650 288.3 4627851Alaska 4.8 81.1 80.9 465.1 622.5 2599.1 391 686293Arizona 7.5 33.8 144.4 327.4 948.4 2965.2 924.4 6500180Arkansas 6.7 42.9 91.1 386.8 1084.6 2711.2 262.1 2855390California 6.9 26 176.1 317.3 693.3 1916.5 712.8 36756666Colorado 3.7 43.4 84.6 264.7 744.8 2735.2 559.5 4861515Connecticut 2.9 20 113 138.6 437.1 1824.1 296.8 3501252Delaware 4.4 44.7 154.8 428.2 688.9 2144 278.5 873092Florida 5 37.1 169.4 496.6 926.3 2658.3 423.3 18328340Georgia 6.2 23.6 154.8 264.3 931 2751.1 490.2 9685744Hawaii 1.9 26.9 78.5 147.8 767.9 3308.4 716.4 1288198Idaho 2.4 40.4 18.6 195.4 564.4 1931.7 201.8 1523816Illinois 6 33.7 181.7 330.2 606.9 2164.8 308.6 12901563Indiana 5.7 29.6 108.6 179.9 697.6 2412 346.7 6376792Iowa 1.3 27.9 38.9 223.3 606.4 2042.7 184.6 3002555Kansas 3.7 38.4 65.3 280 689.2 2758.1 339.6 2802134Kentucky 4.6 34 88.4 139.8 634 1685.8 210.8 4269245Louisiana 9.9 31.4 118 435.1 870.6 2494.5 318.1 4410796Maine 1.4 24.7 24.4 61.7 478.5 1832.6 102 1316456Maryland 9.9 22.6 256.7 413.8 641.4 2294.3 608.4 5633597Massachusetts 2.7 27.1 119 308.1 541.1 1527.4 295.1 6497967Michigan 6.1 51.3 131.8 362.9 696.8 1917.8 476.5 10003422Minnesota 2.2 44 92 158.7 578.9 2226.9 278.2 5220393Mississippi 7.3 39.3 82.3 149.4 919.7 2083.9 256.5 2938618Missouri 6.9 28 124.1 366.4 738.3 2746.2 443.1 5911605Montana 1.9 32.2 18.9 228.5 389.2 2543 210.7 967440Nebraska 2.5 32.9 59.1 192.5 532.4 2574.3 316.5 1783432Nevada 8.5 42.1 194.7 361.5 972.4 2153.9 1115.2 2600167New Hampshire 1.4 30.9 27.4 72.3 317 1377.3 102.1 1315809New Jersey 4.8 13.9 151.6 184.4 447.1 1568.4 317.5 8682661New Mexico 7.4 54.1 98.7 541.9 1093.9 2639.9 414.5 1984356New York 4.5 18.9 182.7 239.7 353.3 1569.6 185.6 19490297North Carolina 6.7 26.5 145.5 289.4 1201.1 2546.2 327.8 9222414North Dakota 1.1 24.2 7.4 65.5 311.9 1500.3 166 641481Ohio 5.1 39.8 163.1 143.4 872.8 2429 360.9 11485910Oklahoma 5.3 41.7 91 370.5 1006 2644.2 391.8 3642361Oregon 2.2 34.8 68.1 181.8 758.6 3112.2 529 3790060Pennsylvania 6.1 28.9 154.6 235 451.6 1729.1 236.5 12448279Rhode Island 3.2 29.8 72.1 146.1 494.2 1816 408.7 1050788South Carolina 7.4 42.5 132.1 579 1000.9 2954.1 384.4 4479800South Dakota 2.3 46.7 18.6 108.1 324.4 1343.7 108.4 804194Tennessee 7.2 36.4 167.3 541.9 1026.9 2828.1 420.6 6214888Texas 6.2 37.2 156.6 329.8 961.6 2961.7 408.7 24326974Utah 2.3 37.3 44.3 143.4 606.2 2918.8 343.9 2736424Vermont 1.3 23.3 11.7 83.5 491.8 1686.1 102.9 621270Virginia 6.1 22.7 99.2 154.8 392.1 2035 211.1 7769089Washington 3.3 44.7 92.1 205.8 959.7 3149.5 783.9 6549224West Virginia 4.4 17.7 44.6 206.1 621.2 1794 210 1814468Wisconsin 3.5 20.6 82.2 135.2 440.8 1992.8 226.6 5627967Wyoming 2.7 24 15.3 188.1 476.3 2533.9 145.1 532668

‎第8章 高维数据型图表/五瓣花_单元.csv‎

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‎第8章 高维数据型图表/图8_7_8五瓣花.R‎

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library(ggplot2)
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library(wesanderson)
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Flower <- read.csv("Îå°ê»¨_µ¥Ôª.csv")
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N_Flower<-nrow(Flower)
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crime <- read.csv("crimeRatesByState2005.tsv",
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header = TRUE, sep = "\t", stringsAsFactors = F)
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mydata<-crime[,c(2,4,3,5,6,7,8)]
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N<-nrow(mydata)
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Scale_x<-8
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Scale_y<-4
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xlabel<-seq(2.5,10,2.5)
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xbreak<-(xlabel-min(mydata[,1]))/(max(mydata[,1])-min(mydata[,1]))*Scale_x+0.1
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ylabel<-seq(0,280,100)
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ybreak<-(ylabel-min(mydata[,2]))/(max(mydata[,2])-min(mydata[,2]))*Scale_y+0.1
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mydata[,1]<-(mydata[,1]-min(mydata[,1]))/(max(mydata[,1])-min(mydata[,1]))*Scale_x+0.1
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mydata[,2]<-(mydata[,2]-min(mydata[,2]))/(max(mydata[,2])-min(mydata[,2]))*Scale_y+0.1
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for (j in 3:7){
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mydata[,j]<-(mydata[,j]-min(mydata[,j]))/(max(mydata[,j])-min(mydata[,j]))+0.1
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for (i in 1:N){
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temp<-cbind(Group_Scatter=rep(i,N_Flower,1),
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Group_Flower=rep(colnames(mydata)[j],N_Flower,1),
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x=rep(mydata[i,1],N_Flower,1),
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y=rep(mydata[i,2],N_Flower,1),
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mydata[i,j]*Flower[Flower$Group==(j-2),c(1,2)])
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if (i==1){
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temp_Flower<-temp
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}else
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{
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temp_Flower<-rbind(temp_Flower,temp)
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}
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}
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if (j==3){
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Flower_data<-temp_Flower
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}else{
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Flower_data<-rbind(Flower_data,temp_Flower)
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}
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}
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Flower_data$Group_Flower<-as.factor(Flower_data$Group_Flower)
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ggplot(data=Flower_data)+
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geom_curve(aes(x=x,y=y,xend = x+0.00000001, yend =0),colour="grey50",size=0.1,curvature = 0.2,alpha=0.9)+
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geom_polygon(aes(x=x+Flower_x,y=y+Flower_y,fill=Group_Flower,
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group=interaction(Group_Flower,Group_Scatter)),size=0.1,colour="black",alpha=0.9)+
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scale_fill_manual(values = wes_palette("Darjeeling",5)[c(1,2,3,5,4)])+
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scale_x_continuous(breaks=xbreak, labels=xlabel)+
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scale_y_continuous(breaks=ybreak, labels=ylabel)+
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xlab(colnames(mydata)[1])+
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ylab(colnames(mydata)[2])+
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coord_fixed()

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