# # un_1_3_plot_oc.r -- Does Two-Dim Plot of United Nations # First 3 Sessions # # # library(MASS) library(pcurve) # # T <- matrix(scan("c:/R_Files/un_1_3_oc_2.txt",0),ncol=8,byrow=TRUE) # # plot(T[,7],T[,8],type="n",asp=1,main="Figure 5.8: United Nations\nSessions 1 - 3 (1946-48)", xlab="Communist/Non-Communist", ylab="Pro/Anti Israel", xlim=c(-1.0,1.0),ylim=c(-1.0,1.0)) # USA and Canada points(T[T[,2] <= 20,7],T[T[,2] <= 20,8],pch=16,col="black") # Australia and New Zealand points(T[T[,2] >= 900,7],T[T[,2] >= 900,8],pch=16,col="black") # Israel points(T[T[,2] == 666,7],T[T[,2] == 666,8],pch=16,col="black") # Latin America points(T[T[,2] > 20 & T[,2] <= 165,7],T[T[,2] > 20 & T[,2] <= 165 ,8],pch=22,bg="black",col="black") # Western Europe points(T[T[,2] >= 200 & T[,2] <= 220,7],T[T[,2] >= 200 & T[,2] <= 220 ,8],pch=23,col="black") points(T[T[,2] == 350,7],T[T[,2] == 350,8],pch=23,col="black") points(T[T[,2] >= 380 & T[,2] <= 395,7],T[T[,2] >= 380 & T[,2] <= 395 ,8],pch=23,col="black") # Communist Bloc points(T[T[,2] >= 290 & T[,2] <= 345,7],T[T[,2] >= 290 & T[,2] <= 345 ,8],pch=16,col="black") points(T[T[,2] >= 365 & T[,2] <= 370,7],T[T[,2] >= 365 & T[,2] <= 370 ,8],pch=16,col="black") # Sub-Saharan Africa points(T[T[,2] >= 450 & T[,2] <= 560,7],T[T[,2] >= 450 & T[,2] <= 560 ,8],pch=16,col="black") # Islamic Countries points(T[T[,2] >= 630 & T[,2] <= 660,7],T[T[,2] >= 630 & T[,2] <= 660 ,8],pch=24,bg="black",col="black") points(T[T[,2] >= 670 & T[,2] <= 700,7],T[T[,2] >= 670 & T[,2] <= 700 ,8],pch=24,bg="black",col="black") points(T[T[,2] == 770,7],T[T[,2] == 770,8],pch=24,bg="black",col="black") # Asia points(T[T[,2] >= 713 & T[,2] <= 750,7],T[T[,2] >= 713 & T[,2] <= 750 ,8],pch=25,col="black") points(T[T[,2] >= 775 & T[,2] <= 840,7],T[T[,2] >= 775 & T[,2] <= 840 ,8],pch=25,col="black") # # points(-1.00, 0.7,pch=24,bg="black",col="black") text(-1.00, 0.7," Islamic",col="black",adj=0) points(-1.00, 0.6,pch=22,bg="black",col="black") text(-1.00, 0.6," Latin Am.",col="black",adj=0) points(-1.00, 0.5,pch=23,col="black") text(-1.00, 0.5," W. Eur.",col="black",adj=0) points(-1.00, 0.4,pch=25,col="black") text(-1.00, 0.4," Asia",col="black",adj=0) #text(-1.00, 0.3,"Sub-Sahara",col="black",adj=0) # text(-0.670, -0.141,"USSR ",col="black",adj=1) text( 0.236, -0.224," USA ",col="black",adj=0) text( 0.311, -0.368," UK",col="black",adj=0) text(-0.554, -0.449,"YUG ",col="black",adj=1) text(-0.200, -0.926,"Israel ",col="black",adj=1) #text( 0.284, 0.170," Greece",col="violet",adj=0) text( 0.409,-0.283," S.Africa",col="black",adj=0) text( 0.269,-0.107," Turkey",col="black",adj=0) #text( 0.201, 0.343," El Salvador",col="black",adj=0) #text(-0.187,-0.326,"Guatemala ",col="black",adj=1) #text(-0.257, 0.035,"India ",col="orange",adj=1) #text(-0.169, 0.421," Iraq",col="green",adj=0) # # 1 USA United States 2 37 661 0.944 0.002 0.300 -0.236 # 2 CAN Canada 20 35 642 0.945 0.002 0.293 -0.338 # 3 CUB Cuba 40 37 604 0.939 0.004 0.031 0.157 # 4 HAI Haiti 41 55 508 0.892 0.002 -0.060 0.208 # 5 DOM Dominican Rep 42 41 623 0.934 0.002 0.228 0.082 # 6 MEX Mexico 70 50 589 0.915 0.002 -0.055 0.027 # 7 GUA Guatemala 90 49 514 0.905 0.002 -0.187 -0.326 # 8 HON Honduras 91 44 511 0.914 0.002 0.004 0.166 # 9 SAL El Salvador 92 25 491 0.949 0.002 0.201 0.343 # 10 NIC Nicaragua 93 28 589 0.952 0.008 0.154 0.172 # 11 COS Costa Rica 94 56 453 0.876 0.002 0.108 -0.150 # 12 PAN Panama 95 37 520 0.929 0.002 0.129 -0.093 # 13 COL Colombia 100 81 618 0.869 0.002 0.020 0.185 # 14 VEN Venezuela 101 57 619 0.908 0.003 -0.023 0.152 # 15 ECU Ecuador 130 53 564 0.906 0.003 0.066 0.167 # 16 PER Peru 135 37 600 0.938 0.007 0.222 0.164 # 17 BRA Brazil 140 45 655 0.931 0.002 0.106 0.214 # 18 BOL Bolivia 145 43 597 0.928 0.003 0.146 0.126 # 19 PAR Paraguay 150 18 472 0.962 0.005 0.150 0.168 # 20 CHL Chile 155 61 618 0.901 0.002 0.129 -0.093 # 21 ARG Argentina 160 66 618 0.893 0.002 0.128 0.140 # 22 URU Uruguay 165 62 632 0.902 0.002 0.109 -0.212 # 23 UK United Kingdo 200 45 652 0.931 0.002 0.342 -0.318 # 24 NTH Netherlands 210 45 659 0.932 0.002 0.326 -0.276 # 25 BLM Belgium 211 78 660 0.882 0.002 0.284 -0.068 # 26 LUX Luxembourg 212 57 612 0.907 0.002 0.280 -0.063 # 27 FRN France 220 107 635 0.831 0.002 0.118 -0.263 # 28 POL Poland 290 6 666 0.991 0.002 -0.669 -0.175 # 29 CZR Czechoslovaki 315 9 658 0.986 0.002 -0.648 -0.204 # 30 YUG Yugoslavia 345 15 651 0.977 0.006 -0.659 -0.674 # 31 GRC Greece 350 38 620 0.939 0.002 0.284 0.170 # 32 RUS Russian Fed 365 1 670 0.999 0.005 -0.678 -0.208 # 33 UKR Ukraine 369 1 675 0.999 0.003 -0.680 -0.208 # 34 BYL Belarus 370 2 670 0.997 0.004 -0.678 -0.208 # 35 SWD Sweden 380 42 577 0.927 0.002 0.212 -0.402 # 36 NOR Norway 385 36 649 0.945 0.002 0.177 -0.462 # 37 DEN Denmark 390 52 655 0.921 0.002 0.229 -0.424 # 38 ICE Iceland 395 24 506 0.953 0.009 0.231 -0.278 # 39 LBR Liberia 450 74 577 0.872 0.002 -0.059 0.206 # 40 ETH Ethiopia 530 90 562 0.840 0.002 -0.107 0.105 # 41 SAF South Africa 560 62 591 0.895 0.004 0.429 -0.422 # 42 IRN Iran (Islamic 630 48 590 0.919 0.002 -0.101 0.251 # 43 TUR Turkey 640 60 629 0.905 0.002 0.329 -0.133 # 44 IRQ Iraq 645 21 570 0.963 0.002 -0.169 0.421 # 45 EGY Egypt 651 46 613 0.925 0.004 -0.151 0.381 # 46 SYR Syrian Arab R 652 32 569 0.944 0.002 -0.163 0.348 # 47 LEB Lebanon 660 47 600 0.922 0.002 -0.061 0.375 # 48 ISR Israel 666 14 212 0.934 0.003 -0.172 -0.592 # 49 SAU Saudi Arabia 670 25 580 0.957 0.002 -0.172 0.404 # 50 YAR Yemen Arab Re 678 21 419 0.950 0.002 -0.226 0.417 # 51 AFG Afghanistan 700 40 450 0.911 0.002 -0.238 0.308 # 52 TAW Taiwan 713 78 635 0.877 0.003 -0.006 0.116 # 53 IND India 750 84 595 0.859 0.002 -0.257 0.035 # 54 PAK Pakistan 770 40 476 0.916 0.002 -0.186 0.385 # 55 BUR Myanmar/Burma 775 47 383 0.877 0.002 -0.190 0.315 # 56 THI Thailand 800 69 399 0.827 0.005 0.144 -0.141 # 57 PHI Philippines 840 47 626 0.925 0.002 -0.053 0.222 # 58 AUL Australia 900 96 648 0.852 0.003 0.202 -0.099 # 59 NEW New Zealand 920 60 632 0.905 0.002 0.239 -0.297 #