Control recuperatorio_Santiago Maldonado Vizcarra_20201547
Santiago Maldonado Vizcarra
2022-11-11
EJERCICIO 1:
library(rio)
data1 = import("~/PUCP/PUCP FACU/6TO CICLO/ESTADÍSTICA/GPIFINAL_data.xlsx")
data1## Country 2022_safesec 2022_milit 2022_oncon
## 1 Afghanistan 4.127 2.472 3.650
## 2 Albania 2.120 1.666 1.403
## 3 Algeria 2.302 2.041 2.068
## 4 Angola 2.413 1.706 1.666
## 5 Argentina 2.656 1.611 1.201
## 6 Armenia 1.977 2.041 1.990
## 7 Australia 1.657 1.848 1.266
## 8 Austria 1.452 1.331 1.097
## 9 Azerbaijan 2.528 2.108 2.579
## 10 Bahrain 2.405 1.717 1.955
## 11 Bangladesh 2.438 1.534 2.004
## 12 Belarus 2.526 1.896 2.208
## 13 Belgium 1.806 1.602 1.141
## 14 Benin 2.574 2.022 1.666
## 15 Bhutan 1.601 1.336 1.436
## 16 Bolivia 2.503 1.934 1.403
## 17 Bosnia and Herzegovina 2.044 1.526 1.835
## 18 Botswana 2.441 1.812 1.000
## 19 Brazil 3.284 1.965 1.853
## 20 Bulgaria 1.929 1.633 1.000
## 21 Burkina Faso 2.993 2.189 3.023
## 22 Burundi 3.024 1.556 2.443
## 23 Cambodia 2.080 1.933 1.638
## 24 Cameroon 3.014 1.877 2.967
## 25 Canada 1.637 1.465 1.030
## 26 Central African Republic 3.579 2.273 2.874
## 27 Chad 2.903 2.189 2.552
## 28 Chile 2.361 1.585 1.403
## 29 China 2.107 2.022 1.858
## 30 Colombia 3.596 2.052 2.099
## 31 Costa Rica 2.229 1.614 1.201
## 32 Cote d' Ivoire 2.764 1.724 1.694
## 33 Croatia 1.564 1.565 1.201
## 34 Cuba 2.614 1.533 1.805
## 35 Cyprus 2.251 1.605 1.604
## 36 Czech Republic 1.566 1.263 1.034
## 37 Democratic Republic of the Congo 3.747 1.881 3.413
## 38 Denmark 1.295 1.368 1.242
## 39 Djibouti 2.561 2.140 1.856
## 40 Dominican Republic 2.614 1.720 1.403
## 41 Ecuador 2.509 1.866 1.426
## 42 Egypt 2.503 2.014 2.426
## 43 El Salvador 3.062 1.879 1.436
## 44 Equatorial Guinea 2.200 1.638 1.604
## 45 Eritrea 3.359 1.914 1.705
## 46 Estonia 1.703 1.607 1.638
## 47 Eswatini 2.576 1.706 1.604
## 48 Ethiopia 2.917 1.809 3.457
## 49 Finland 1.349 1.598 1.436
## 50 France 1.867 2.773 1.305
## 51 Gabon 2.378 1.807 1.604
## 52 Georgia 2.413 1.790 1.805
## 53 Germany 1.619 1.750 1.062
## 54 Ghana 2.026 1.726 1.472
## 55 Greece 1.773 2.036 1.805
## 56 Guatemala 2.819 1.698 1.626
## 57 Guinea 2.587 2.069 2.262
## 58 Guinea-Bissau 2.586 2.218 1.615
## 59 Guyana 2.895 1.551 1.604
## 60 Haiti 2.735 1.740 2.077
## 61 Honduras 3.064 1.812 1.604
## 62 Hungary 1.528 1.184 1.421
## 63 Iceland 1.237 1.020 1.000
## 64 India 2.373 2.461 2.982
## 65 Indonesia 2.107 1.496 1.644
## 66 Iran 2.838 2.123 2.923
## 67 Iraq 3.646 2.298 3.207
## 68 Ireland 1.525 1.259 1.015
## 69 Israel 2.237 3.813 2.167
## 70 Italy 1.927 2.013 1.034
## 71 Jamaica 2.791 1.721 1.201
## 72 Japan 1.283 1.308 1.403
## 73 Jordan 2.037 1.964 1.542
## 74 Kazakhstan 2.492 1.781 1.805
## 75 Kenya 2.599 1.794 2.345
## 76 Kosovo 2.266 1.575 1.805
## 77 Kuwait 1.762 2.184 1.431
## 78 Kyrgyz Republic 2.282 1.671 2.006
## 79 Laos 2.076 1.949 1.403
## 80 Latvia 1.798 1.479 1.634
## 81 Lebanon 2.748 2.317 2.701
## 82 Lesotho 2.528 1.748 1.805
## 83 Liberia 2.491 1.804 1.462
## 84 Libya 3.093 2.433 3.160
## 85 Lithuania 1.775 1.735 1.638
## 86 Madagascar 2.612 1.444 1.630
## 87 Malawi 2.144 1.723 1.739
## 88 Malaysia 1.973 1.205 1.015
## 89 Mali 3.589 1.873 2.861
## 90 Mauritania 2.782 1.969 1.638
## 91 Mauritius 2.132 1.373 1.000
## 92 Mexico 3.158 1.736 2.642
## 93 Moldova 2.142 1.243 2.006
## 94 Mongolia 2.459 1.348 1.201
## 95 Montenegro 2.243 1.602 1.403
## 96 Morocco 2.158 1.752 1.891
## 97 Mozambique 2.803 1.677 2.215
## 98 Myanmar 3.116 1.744 2.700
## 99 Namibia 2.569 1.755 1.201
## 100 Nepal 2.493 1.706 1.436
## 101 Netherlands 1.525 2.206 1.045
## 102 New Zealand 1.530 1.168 1.008
## 103 Nicaragua 2.966 1.723 2.006
## 104 Niger 2.957 1.957 2.839
## 105 Nigeria 3.107 2.059 2.806
## 106 North Korea 3.113 3.120 2.610
## 107 North Macedonia 1.928 1.771 1.403
## 108 Norway 1.266 2.184 1.235
## 109 Oman 1.892 2.651 1.403
## 110 Pakistan 2.653 2.581 3.172
## 111 Palestine 2.950 1.991 2.612
## 112 Panama 2.524 1.547 1.403
## 113 Papua New Guinea 2.802 1.697 1.605
## 114 Paraguay 2.534 2.004 1.403
## 115 Peru 2.625 1.938 1.420
## 116 Philippines 2.855 1.700 2.410
## 117 Poland 1.933 1.527 1.403
## 118 Portugal 1.390 1.312 1.007
## 119 Qatar 1.495 2.196 1.246
## 120 Republic of the Congo 2.903 2.194 1.620
## 121 Romania 1.837 1.773 1.007
## 122 Russia 2.912 3.234 2.844
## 123 Rwanda 2.374 1.512 1.858
## 124 Saudi Arabia 2.254 2.614 2.425
## 125 Senegal 2.337 1.701 1.418
## 126 Serbia 1.976 1.796 1.604
## 127 Sierra Leone 2.313 1.605 1.413
## 128 Singapore 1.238 1.943 1.000
## 129 Slovakia 1.835 1.274 1.403
## 130 Slovenia 1.407 1.129 1.403
## 131 Somalia 3.624 2.155 3.474
## 132 South Africa 3.234 1.758 1.613
## 133 South Korea 1.591 2.370 1.805
## 134 South Sudan 3.957 2.594 3.267
## 135 Spain 1.808 1.898 1.218
## 136 Sri Lanka 2.377 2.047 1.855
## 137 Sudan 3.303 2.312 3.040
## 138 Sweden 1.475 1.802 1.221
## 139 Switzerland 1.236 1.929 1.005
## 140 Syria 3.741 2.287 3.828
## 141 Taiwan 1.731 1.704 1.604
## 142 Tajikistan 2.428 1.707 2.057
## 143 Tanzania 2.353 1.687 1.479
## 144 Thailand 2.863 1.562 1.916
## 145 The Gambia 2.373 1.830 1.409
## 146 Timor-Leste 2.369 1.667 1.403
## 147 Togo 2.615 2.020 1.818
## 148 Trinidad and Tobago 2.736 2.098 1.201
## 149 Tunisia 2.508 1.769 1.820
## 150 Turkey 3.217 2.058 3.159
## 151 Turkmenistan 2.501 2.383 1.604
## 152 Uganda 2.762 1.760 1.765
## 153 Ukraine 3.230 2.044 2.765
## 154 United Arab Emirates 1.712 2.652 1.464
## 155 United Kingdom 1.729 2.489 1.042
## 156 United States of America 2.225 3.172 1.770
## 157 Uruguay 2.323 1.582 1.000
## 158 Uzbekistan 2.215 2.213 1.805
## 159 Venezuela 4.007 2.002 2.103
## 160 Vietnam 2.289 1.876 1.403
## 161 Yemen 3.771 2.272 3.559
## 162 Zambia 2.511 1.519 1.604
## 163 Zimbabwe 2.990 2.044 2.112data1 = data1[complete.cases(data1),]
data1## Country 2022_safesec 2022_milit 2022_oncon
## 1 Afghanistan 4.127 2.472 3.650
## 2 Albania 2.120 1.666 1.403
## 3 Algeria 2.302 2.041 2.068
## 4 Angola 2.413 1.706 1.666
## 5 Argentina 2.656 1.611 1.201
## 6 Armenia 1.977 2.041 1.990
## 7 Australia 1.657 1.848 1.266
## 8 Austria 1.452 1.331 1.097
## 9 Azerbaijan 2.528 2.108 2.579
## 10 Bahrain 2.405 1.717 1.955
## 11 Bangladesh 2.438 1.534 2.004
## 12 Belarus 2.526 1.896 2.208
## 13 Belgium 1.806 1.602 1.141
## 14 Benin 2.574 2.022 1.666
## 15 Bhutan 1.601 1.336 1.436
## 16 Bolivia 2.503 1.934 1.403
## 17 Bosnia and Herzegovina 2.044 1.526 1.835
## 18 Botswana 2.441 1.812 1.000
## 19 Brazil 3.284 1.965 1.853
## 20 Bulgaria 1.929 1.633 1.000
## 21 Burkina Faso 2.993 2.189 3.023
## 22 Burundi 3.024 1.556 2.443
## 23 Cambodia 2.080 1.933 1.638
## 24 Cameroon 3.014 1.877 2.967
## 25 Canada 1.637 1.465 1.030
## 26 Central African Republic 3.579 2.273 2.874
## 27 Chad 2.903 2.189 2.552
## 28 Chile 2.361 1.585 1.403
## 29 China 2.107 2.022 1.858
## 30 Colombia 3.596 2.052 2.099
## 31 Costa Rica 2.229 1.614 1.201
## 32 Cote d' Ivoire 2.764 1.724 1.694
## 33 Croatia 1.564 1.565 1.201
## 34 Cuba 2.614 1.533 1.805
## 35 Cyprus 2.251 1.605 1.604
## 36 Czech Republic 1.566 1.263 1.034
## 37 Democratic Republic of the Congo 3.747 1.881 3.413
## 38 Denmark 1.295 1.368 1.242
## 39 Djibouti 2.561 2.140 1.856
## 40 Dominican Republic 2.614 1.720 1.403
## 41 Ecuador 2.509 1.866 1.426
## 42 Egypt 2.503 2.014 2.426
## 43 El Salvador 3.062 1.879 1.436
## 44 Equatorial Guinea 2.200 1.638 1.604
## 45 Eritrea 3.359 1.914 1.705
## 46 Estonia 1.703 1.607 1.638
## 47 Eswatini 2.576 1.706 1.604
## 48 Ethiopia 2.917 1.809 3.457
## 49 Finland 1.349 1.598 1.436
## 50 France 1.867 2.773 1.305
## 51 Gabon 2.378 1.807 1.604
## 52 Georgia 2.413 1.790 1.805
## 53 Germany 1.619 1.750 1.062
## 54 Ghana 2.026 1.726 1.472
## 55 Greece 1.773 2.036 1.805
## 56 Guatemala 2.819 1.698 1.626
## 57 Guinea 2.587 2.069 2.262
## 58 Guinea-Bissau 2.586 2.218 1.615
## 59 Guyana 2.895 1.551 1.604
## 60 Haiti 2.735 1.740 2.077
## 61 Honduras 3.064 1.812 1.604
## 62 Hungary 1.528 1.184 1.421
## 63 Iceland 1.237 1.020 1.000
## 64 India 2.373 2.461 2.982
## 65 Indonesia 2.107 1.496 1.644
## 66 Iran 2.838 2.123 2.923
## 67 Iraq 3.646 2.298 3.207
## 68 Ireland 1.525 1.259 1.015
## 69 Israel 2.237 3.813 2.167
## 70 Italy 1.927 2.013 1.034
## 71 Jamaica 2.791 1.721 1.201
## 72 Japan 1.283 1.308 1.403
## 73 Jordan 2.037 1.964 1.542
## 74 Kazakhstan 2.492 1.781 1.805
## 75 Kenya 2.599 1.794 2.345
## 76 Kosovo 2.266 1.575 1.805
## 77 Kuwait 1.762 2.184 1.431
## 78 Kyrgyz Republic 2.282 1.671 2.006
## 79 Laos 2.076 1.949 1.403
## 80 Latvia 1.798 1.479 1.634
## 81 Lebanon 2.748 2.317 2.701
## 82 Lesotho 2.528 1.748 1.805
## 83 Liberia 2.491 1.804 1.462
## 84 Libya 3.093 2.433 3.160
## 85 Lithuania 1.775 1.735 1.638
## 86 Madagascar 2.612 1.444 1.630
## 87 Malawi 2.144 1.723 1.739
## 88 Malaysia 1.973 1.205 1.015
## 89 Mali 3.589 1.873 2.861
## 90 Mauritania 2.782 1.969 1.638
## 91 Mauritius 2.132 1.373 1.000
## 92 Mexico 3.158 1.736 2.642
## 93 Moldova 2.142 1.243 2.006
## 94 Mongolia 2.459 1.348 1.201
## 95 Montenegro 2.243 1.602 1.403
## 96 Morocco 2.158 1.752 1.891
## 97 Mozambique 2.803 1.677 2.215
## 98 Myanmar 3.116 1.744 2.700
## 99 Namibia 2.569 1.755 1.201
## 100 Nepal 2.493 1.706 1.436
## 101 Netherlands 1.525 2.206 1.045
## 102 New Zealand 1.530 1.168 1.008
## 103 Nicaragua 2.966 1.723 2.006
## 104 Niger 2.957 1.957 2.839
## 105 Nigeria 3.107 2.059 2.806
## 106 North Korea 3.113 3.120 2.610
## 107 North Macedonia 1.928 1.771 1.403
## 108 Norway 1.266 2.184 1.235
## 109 Oman 1.892 2.651 1.403
## 110 Pakistan 2.653 2.581 3.172
## 111 Palestine 2.950 1.991 2.612
## 112 Panama 2.524 1.547 1.403
## 113 Papua New Guinea 2.802 1.697 1.605
## 114 Paraguay 2.534 2.004 1.403
## 115 Peru 2.625 1.938 1.420
## 116 Philippines 2.855 1.700 2.410
## 117 Poland 1.933 1.527 1.403
## 118 Portugal 1.390 1.312 1.007
## 119 Qatar 1.495 2.196 1.246
## 120 Republic of the Congo 2.903 2.194 1.620
## 121 Romania 1.837 1.773 1.007
## 122 Russia 2.912 3.234 2.844
## 123 Rwanda 2.374 1.512 1.858
## 124 Saudi Arabia 2.254 2.614 2.425
## 125 Senegal 2.337 1.701 1.418
## 126 Serbia 1.976 1.796 1.604
## 127 Sierra Leone 2.313 1.605 1.413
## 128 Singapore 1.238 1.943 1.000
## 129 Slovakia 1.835 1.274 1.403
## 130 Slovenia 1.407 1.129 1.403
## 131 Somalia 3.624 2.155 3.474
## 132 South Africa 3.234 1.758 1.613
## 133 South Korea 1.591 2.370 1.805
## 134 South Sudan 3.957 2.594 3.267
## 135 Spain 1.808 1.898 1.218
## 136 Sri Lanka 2.377 2.047 1.855
## 137 Sudan 3.303 2.312 3.040
## 138 Sweden 1.475 1.802 1.221
## 139 Switzerland 1.236 1.929 1.005
## 140 Syria 3.741 2.287 3.828
## 141 Taiwan 1.731 1.704 1.604
## 142 Tajikistan 2.428 1.707 2.057
## 143 Tanzania 2.353 1.687 1.479
## 144 Thailand 2.863 1.562 1.916
## 145 The Gambia 2.373 1.830 1.409
## 146 Timor-Leste 2.369 1.667 1.403
## 147 Togo 2.615 2.020 1.818
## 148 Trinidad and Tobago 2.736 2.098 1.201
## 149 Tunisia 2.508 1.769 1.820
## 150 Turkey 3.217 2.058 3.159
## 151 Turkmenistan 2.501 2.383 1.604
## 152 Uganda 2.762 1.760 1.765
## 153 Ukraine 3.230 2.044 2.765
## 154 United Arab Emirates 1.712 2.652 1.464
## 155 United Kingdom 1.729 2.489 1.042
## 156 United States of America 2.225 3.172 1.770
## 157 Uruguay 2.323 1.582 1.000
## 158 Uzbekistan 2.215 2.213 1.805
## 159 Venezuela 4.007 2.002 2.103
## 160 Vietnam 2.289 1.876 1.403
## 161 Yemen 3.771 2.272 3.559
## 162 Zambia 2.511 1.519 1.604
## 163 Zimbabwe 2.990 2.044 2.112library(rio)
data2 = import("~/PUCP/PUCP FACU/6TO CICLO/ESTADÍSTICA/ETR_data.xlsx")
data2## Country foodr_score naturald_score
## 1 Burundi 5 5
## 2 Central African Republic 5 5
## 3 Republic of the Congo 5 5
## 4 Kenya 5 5
## 5 Mozambique 5 5
## 6 Malawi 5 5
## 7 Niger 5 5
## 8 Somalia 5 5
## 9 South Sudan 5 5
## 10 Chad 5 5
## 11 Uganda 5 5
## 12 Yemen 5 5
## 13 Afghanistan 4 5
## 14 Benin 5 4
## 15 Cameroon 5 4
## 16 Democratic Republic of the Congo 5 4
## 17 Comoros 4 5
## 18 The Gambia 4 5
## 19 Guinea-Bissau 5 4
## 20 Namibia 5 5
## 21 Nigeria 5 4
## 22 Zimbabwe 5 4
## 23 Angola 5 3
## 24 Ethiopia 4 4
## 25 Guinea 5 3
## 26 Haiti 5 5
## 27 Iraq 3 5
## 28 Madagascar 5 3
## 29 Mauritania 4 4
## 30 Rwanda 5 3
## 31 Sudan 4 4
## 32 Senegal 4 4
## 33 Sierra Leone 5 4
## 34 São Tomé and Príncipe 5 3
## 35 Tanzania 4 4
## 36 Zambia 5 3
## 37 Burkina Faso 4 3
## 38 Botswana 4 5
## 39 Cote d' Ivoire 4 3
## 40 Cape Verde 5 4
## 41 Ecuador 4 5
## 42 Ghana 4 3
## 43 Guatemala 4 5
## 44 Cambodia 4 5
## 45 Laos 4 5
## 46 Lesotho 5 4
## 47 Mali 4 3
## 48 Philippines 4 5
## 49 Solomon Islands 2 5
## 50 Syria 5 5
## 51 Togo 4 3
## 52 Tajikistan 4 3
## 53 Timor-Leste 2 5
## 54 Venezuela 5 4
## 55 Djibouti 3 5
## 56 Eritrea 5 1
## 57 Equatorial Guinea 5 1
## 58 Honduras 4 4
## 59 Liberia 5 1
## 60 Libya 3 5
## 61 Myanmar 4 5
## 62 Mayotte 5 1
## 63 Nepal 4 5
## 64 Pakistan 3 5
## 65 Peru 3 5
## 66 Papua New Guinea 2 5
## 67 Belize 2 5
## 68 Bolivia 3 4
## 69 Dominican Republic 3 5
## 70 Gabon 5 1
## 71 Morocco 3 4
## 72 Mongolia 2 5
## 73 Nicaragua 4 4
## 74 Eswatini 5 2
## 75 Vanuatu 2 5
## 76 Bhutan 2 5
## 77 Egypt 3 5
## 78 Indonesia 2 5
## 79 Sri Lanka 4 5
## 80 El Salvador 4 5
## 81 Turks and Caicos Islands 3 5
## 82 Vietnam 2 5
## 83 Antigua and Barbuda 3 5
## 84 Colombia 3 4
## 85 Cuba 3 5
## 86 Algeria 2 5
## 87 Federated States of Micronesia 2 5
## 88 French Guiana 3 2
## 89 Iran 3 5
## 90 Mauritius 3 4
## 91 New Caledonia 2 5
## 92 Panama 2 3
## 93 Palau 2 5
## 94 Suriname 2 5
## 95 Tonga 2 5
## 96 Tuvalu 2 5
## 97 South Africa 3 2
## 98 Anguilla 3 5
## 99 The Bahamas 3 5
## 100 Brazil 2 4
## 101 Dominica 3 5
## 102 Western Sahara 5 1
## 103 Fiji 2 4
## 104 Georgia 2 5
## 105 Guyana 2 5
## 106 India 3 4
## 107 Kyrgyz Republic 2 4
## 108 Saint Lucia 3 5
## 109 Malaysia 2 4
## 110 Paraguay 3 4
## 111 Palestine 3 1
## 112 Réunion 5 1
## 113 Russia 2 5
## 114 St. Helena 5 1
## 115 Seychelles 5 1
## 116 Thailand 2 5
## 117 Turkey 3 5
## 118 Saint Vincent and the Grenadines 3 5
## 119 British Virgin Islands 3 5
## 120 Samoa 2 5
## 121 Bangladesh 4 4
## 122 Kazakhstan 2 4
## 123 Moldova 2 3
## 124 Northern Mariana Islands 2 5
## 125 Puerto Rico 2 5
## 126 Romania 2 5
## 127 Tokelau 2 1
## 128 Uruguay 2 5
## 129 Argentina 2 4
## 130 Australia 1 4
## 131 Barbados 3 4
## 132 Costa Rica 2 4
## 133 Jamaica 3 3
## 134 Jordan 3 3
## 135 Saint Kitts and Nevis 3 4
## 136 Mexico 2 4
## 137 North Macedonia 2 5
## 138 French Polynesia 2 4
## 139 Trinidad and Tobago 3 3
## 140 Tunisia 2 4
## 141 U.S. Virgin Islands 3 4
## 142 Albania 2 4
## 143 Azerbaijan 2 1
## 144 China 1 4
## 145 Israel 1 3
## 146 New Zealand 1 4
## 147 Oman 2 4
## 148 Saudi Arabia 2 3
## 149 Ukraine 2 4
## 150 United States of America 1 4
## 151 Uzbekistan 3 2
## 152 United Arab Emirates 2 2
## 153 Armenia 2 2
## 154 Brunei 2 1
## 155 Canada 1 4
## 156 Chile 1 4
## 157 Croatia 1 5
## 158 Japan 1 5
## 159 Lebanon 3 4
## 160 North Korea 2 4
## 161 Slovenia 1 5
## 162 Bulgaria 2 4
## 163 Bahrain 2 1
## 164 St. Barthélemy 3 1
## 165 Cayman Islands 3 1
## 166 Guadeloupe 3 2
## 167 Greece 1 4
## 168 South Korea 2 4
## 169 Norway 1 4
## 170 Serbia 2 4
## 171 Taiwan 2 3
## 172 Caribbean Netherlands 3 1
## 173 Bermuda 3 1
## 174 Switzerland 1 4
## 175 Grenada 3 1
## 176 Kuwait 2 1
## 177 Lithuania 1 1
## 178 Luxembourg 1 3
## 179 Marshall Islands 2 1
## 180 Montserrat 3 1
## 181 Martinique 3 1
## 182 Poland 1 3
## 183 Portugal 1 4
## 184 Sweden 1 4
## 185 American Samoa 2 1
## 186 French Southern Territories 2 1
## 187 Cook Islands 2 1
## 188 Czech Republic 1 3
## 189 Guam 2 1
## 190 Ireland 1 3
## 191 Nauru 2 1
## 192 Qatar 2 1
## 193 Turkmenistan 1 1
## 194 United States Minor Outlying Islands (the) 2 1
## 195 Wallis & Futuna 2 1
## 196 Austria 1 3
## 197 Spain 1 3
## 198 Italy 1 3
## 199 Slovakia 2 2
## 200 Bosnia and Herzegovina 2 2
## 201 Belarus 2 1
## 202 Estonia 1 2
## 203 Latvia 1 2
## 204 Montenegro 2 2
## 205 Åland Islands 2 1
## 206 Andorra 2 1
## 207 Belgium 1 2
## 208 Cyprus 1 2
## 209 Germany 1 2
## 210 Finland 1 1
## 211 France 1 2
## 212 Faroe Islands 2 1
## 213 Guernsey 2 1
## 214 Greenland 2 1
## 215 Hungary 1 2
## 216 Isle of Man 2 1
## 217 Jersey 2 1
## 218 Liechtenstein 2 1
## 219 Netherlands 1 2
## 220 Svalbard & Jan Mayen 2 1
## 221 San Marino 2 1
## 222 St. Pierre & Miquelon 1 1
## 223 Kosovo 2 1
## 224 Denmark 1 1
## 225 United Kingdom 1 1
## 226 Iceland 1 1
## 227 Malta 1 1
## 228 Singapore 1 1
## RPG_score waterisk_score
## 1 5 5
## 2 5 5
## 3 5 5
## 4 5 5
## 5 5 5
## 6 5 5
## 7 5 5
## 8 5 5
## 9 5 5
## 10 5 5
## 11 5 5
## 12 5 5
## 13 5 5
## 14 5 5
## 15 5 5
## 16 5 5
## 17 5 5
## 18 5 5
## 19 5 5
## 20 4 5
## 21 5 5
## 22 5 5
## 23 5 5
## 24 5 5
## 25 5 5
## 26 3 5
## 27 5 5
## 28 5 5
## 29 5 5
## 30 5 5
## 31 5 5
## 32 5 5
## 33 4 5
## 34 5 5
## 35 5 5
## 36 5 5
## 37 5 5
## 38 3 5
## 39 5 5
## 40 3 5
## 41 3 5
## 42 5 5
## 43 4 4
## 44 3 5
## 45 3 5
## 46 3 5
## 47 5 5
## 48 3 5
## 49 5 5
## 50 5 2
## 51 5 5
## 52 5 5
## 53 5 5
## 54 3 5
## 55 3 5
## 56 5 5
## 57 5 5
## 58 3 5
## 59 5 5
## 60 5 3
## 61 2 5
## 62 5 5
## 63 2 5
## 64 4 4
## 65 3 5
## 66 4 5
## 67 4 4
## 68 3 5
## 69 2 5
## 70 4 5
## 71 3 5
## 72 3 5
## 73 2 5
## 74 3 5
## 75 4 4
## 76 3 4
## 77 4 2
## 78 2 5
## 79 1 4
## 80 1 4
## 81 3 3
## 82 2 5
## 83 2 3
## 84 1 5
## 85 1 4
## 86 3 3
## 87 3 3
## 88 5 3
## 89 1 4
## 90 1 5
## 91 3 3
## 92 3 5
## 93 3 3
## 94 1 5
## 95 3 3
## 96 3 3
## 97 3 5
## 98 1 3
## 99 1 3
## 100 1 5
## 101 1 3
## 102 4 2
## 103 1 5
## 104 1 4
## 105 1 4
## 106 2 4
## 107 3 4
## 108 1 3
## 109 3 4
## 110 2 4
## 111 5 3
## 112 1 5
## 113 1 4
## 114 1 5
## 115 1 5
## 116 1 4
## 117 1 3
## 118 1 3
## 119 1 3
## 120 2 3
## 121 2 2
## 122 2 4
## 123 1 5
## 124 1 3
## 125 1 3
## 126 1 3
## 127 5 3
## 128 1 3
## 129 2 3
## 130 3 3
## 131 1 3
## 132 1 4
## 133 1 4
## 134 3 3
## 135 1 3
## 136 2 3
## 137 1 2
## 138 2 3
## 139 1 4
## 140 2 3
## 141 1 3
## 142 1 3
## 143 1 5
## 144 2 3
## 145 4 2
## 146 2 3
## 147 3 1
## 148 3 3
## 149 1 3
## 150 2 3
## 151 3 3
## 152 3 3
## 153 1 4
## 154 2 4
## 155 2 2
## 156 1 3
## 157 1 1
## 158 1 1
## 159 1 1
## 160 1 2
## 161 1 1
## 162 1 1
## 163 3 3
## 164 2 3
## 165 2 3
## 166 1 3
## 167 1 2
## 168 1 1
## 169 2 1
## 170 1 1
## 171 1 3
## 172 1 3
## 173 1 3
## 174 1 1
## 175 1 3
## 176 3 2
## 177 1 4
## 178 3 1
## 179 2 3
## 180 1 3
## 181 1 3
## 182 1 3
## 183 1 1
## 184 1 1
## 185 1 3
## 186 1 3
## 187 1 3
## 188 1 2
## 189 1 3
## 190 2 1
## 191 1 3
## 192 3 1
## 193 3 2
## 194 1 3
## 195 1 3
## 196 1 1
## 197 1 1
## 198 1 1
## 199 1 2
## 200 1 1
## 201 1 2
## 202 1 2
## 203 1 2
## 204 1 1
## 205 1 1
## 206 1 1
## 207 1 1
## 208 1 1
## 209 1 1
## 210 1 2
## 211 1 1
## 212 1 1
## 213 1 1
## 214 1 1
## 215 1 1
## 216 1 1
## 217 1 1
## 218 1 1
## 219 1 1
## 220 1 1
## 221 1 1
## 222 1 2
## 223 1 1
## 224 1 1
## 225 1 1
## 226 1 1
## 227 1 1
## 228 1 1data2 = data2[complete.cases(data2),]
data2## Country foodr_score naturald_score
## 1 Burundi 5 5
## 2 Central African Republic 5 5
## 3 Republic of the Congo 5 5
## 4 Kenya 5 5
## 5 Mozambique 5 5
## 6 Malawi 5 5
## 7 Niger 5 5
## 8 Somalia 5 5
## 9 South Sudan 5 5
## 10 Chad 5 5
## 11 Uganda 5 5
## 12 Yemen 5 5
## 13 Afghanistan 4 5
## 14 Benin 5 4
## 15 Cameroon 5 4
## 16 Democratic Republic of the Congo 5 4
## 17 Comoros 4 5
## 18 The Gambia 4 5
## 19 Guinea-Bissau 5 4
## 20 Namibia 5 5
## 21 Nigeria 5 4
## 22 Zimbabwe 5 4
## 23 Angola 5 3
## 24 Ethiopia 4 4
## 25 Guinea 5 3
## 26 Haiti 5 5
## 27 Iraq 3 5
## 28 Madagascar 5 3
## 29 Mauritania 4 4
## 30 Rwanda 5 3
## 31 Sudan 4 4
## 32 Senegal 4 4
## 33 Sierra Leone 5 4
## 34 São Tomé and Príncipe 5 3
## 35 Tanzania 4 4
## 36 Zambia 5 3
## 37 Burkina Faso 4 3
## 38 Botswana 4 5
## 39 Cote d' Ivoire 4 3
## 40 Cape Verde 5 4
## 41 Ecuador 4 5
## 42 Ghana 4 3
## 43 Guatemala 4 5
## 44 Cambodia 4 5
## 45 Laos 4 5
## 46 Lesotho 5 4
## 47 Mali 4 3
## 48 Philippines 4 5
## 49 Solomon Islands 2 5
## 50 Syria 5 5
## 51 Togo 4 3
## 52 Tajikistan 4 3
## 53 Timor-Leste 2 5
## 54 Venezuela 5 4
## 55 Djibouti 3 5
## 56 Eritrea 5 1
## 57 Equatorial Guinea 5 1
## 58 Honduras 4 4
## 59 Liberia 5 1
## 60 Libya 3 5
## 61 Myanmar 4 5
## 62 Mayotte 5 1
## 63 Nepal 4 5
## 64 Pakistan 3 5
## 65 Peru 3 5
## 66 Papua New Guinea 2 5
## 67 Belize 2 5
## 68 Bolivia 3 4
## 69 Dominican Republic 3 5
## 70 Gabon 5 1
## 71 Morocco 3 4
## 72 Mongolia 2 5
## 73 Nicaragua 4 4
## 74 Eswatini 5 2
## 75 Vanuatu 2 5
## 76 Bhutan 2 5
## 77 Egypt 3 5
## 78 Indonesia 2 5
## 79 Sri Lanka 4 5
## 80 El Salvador 4 5
## 81 Turks and Caicos Islands 3 5
## 82 Vietnam 2 5
## 83 Antigua and Barbuda 3 5
## 84 Colombia 3 4
## 85 Cuba 3 5
## 86 Algeria 2 5
## 87 Federated States of Micronesia 2 5
## 88 French Guiana 3 2
## 89 Iran 3 5
## 90 Mauritius 3 4
## 91 New Caledonia 2 5
## 92 Panama 2 3
## 93 Palau 2 5
## 94 Suriname 2 5
## 95 Tonga 2 5
## 96 Tuvalu 2 5
## 97 South Africa 3 2
## 98 Anguilla 3 5
## 99 The Bahamas 3 5
## 100 Brazil 2 4
## 101 Dominica 3 5
## 102 Western Sahara 5 1
## 103 Fiji 2 4
## 104 Georgia 2 5
## 105 Guyana 2 5
## 106 India 3 4
## 107 Kyrgyz Republic 2 4
## 108 Saint Lucia 3 5
## 109 Malaysia 2 4
## 110 Paraguay 3 4
## 111 Palestine 3 1
## 112 Réunion 5 1
## 113 Russia 2 5
## 114 St. Helena 5 1
## 115 Seychelles 5 1
## 116 Thailand 2 5
## 117 Turkey 3 5
## 118 Saint Vincent and the Grenadines 3 5
## 119 British Virgin Islands 3 5
## 120 Samoa 2 5
## 121 Bangladesh 4 4
## 122 Kazakhstan 2 4
## 123 Moldova 2 3
## 124 Northern Mariana Islands 2 5
## 125 Puerto Rico 2 5
## 126 Romania 2 5
## 127 Tokelau 2 1
## 128 Uruguay 2 5
## 129 Argentina 2 4
## 130 Australia 1 4
## 131 Barbados 3 4
## 132 Costa Rica 2 4
## 133 Jamaica 3 3
## 134 Jordan 3 3
## 135 Saint Kitts and Nevis 3 4
## 136 Mexico 2 4
## 137 North Macedonia 2 5
## 138 French Polynesia 2 4
## 139 Trinidad and Tobago 3 3
## 140 Tunisia 2 4
## 141 U.S. Virgin Islands 3 4
## 142 Albania 2 4
## 143 Azerbaijan 2 1
## 144 China 1 4
## 145 Israel 1 3
## 146 New Zealand 1 4
## 147 Oman 2 4
## 148 Saudi Arabia 2 3
## 149 Ukraine 2 4
## 150 United States of America 1 4
## 151 Uzbekistan 3 2
## 152 United Arab Emirates 2 2
## 153 Armenia 2 2
## 154 Brunei 2 1
## 155 Canada 1 4
## 156 Chile 1 4
## 157 Croatia 1 5
## 158 Japan 1 5
## 159 Lebanon 3 4
## 160 North Korea 2 4
## 161 Slovenia 1 5
## 162 Bulgaria 2 4
## 163 Bahrain 2 1
## 164 St. Barthélemy 3 1
## 165 Cayman Islands 3 1
## 166 Guadeloupe 3 2
## 167 Greece 1 4
## 168 South Korea 2 4
## 169 Norway 1 4
## 170 Serbia 2 4
## 171 Taiwan 2 3
## 172 Caribbean Netherlands 3 1
## 173 Bermuda 3 1
## 174 Switzerland 1 4
## 175 Grenada 3 1
## 176 Kuwait 2 1
## 177 Lithuania 1 1
## 178 Luxembourg 1 3
## 179 Marshall Islands 2 1
## 180 Montserrat 3 1
## 181 Martinique 3 1
## 182 Poland 1 3
## 183 Portugal 1 4
## 184 Sweden 1 4
## 185 American Samoa 2 1
## 186 French Southern Territories 2 1
## 187 Cook Islands 2 1
## 188 Czech Republic 1 3
## 189 Guam 2 1
## 190 Ireland 1 3
## 191 Nauru 2 1
## 192 Qatar 2 1
## 193 Turkmenistan 1 1
## 194 United States Minor Outlying Islands (the) 2 1
## 195 Wallis & Futuna 2 1
## 196 Austria 1 3
## 197 Spain 1 3
## 198 Italy 1 3
## 199 Slovakia 2 2
## 200 Bosnia and Herzegovina 2 2
## 201 Belarus 2 1
## 202 Estonia 1 2
## 203 Latvia 1 2
## 204 Montenegro 2 2
## 205 Åland Islands 2 1
## 206 Andorra 2 1
## 207 Belgium 1 2
## 208 Cyprus 1 2
## 209 Germany 1 2
## 210 Finland 1 1
## 211 France 1 2
## 212 Faroe Islands 2 1
## 213 Guernsey 2 1
## 214 Greenland 2 1
## 215 Hungary 1 2
## 216 Isle of Man 2 1
## 217 Jersey 2 1
## 218 Liechtenstein 2 1
## 219 Netherlands 1 2
## 220 Svalbard & Jan Mayen 2 1
## 221 San Marino 2 1
## 222 St. Pierre & Miquelon 1 1
## 223 Kosovo 2 1
## 224 Denmark 1 1
## 225 United Kingdom 1 1
## 226 Iceland 1 1
## 227 Malta 1 1
## 228 Singapore 1 1
## RPG_score waterisk_score
## 1 5 5
## 2 5 5
## 3 5 5
## 4 5 5
## 5 5 5
## 6 5 5
## 7 5 5
## 8 5 5
## 9 5 5
## 10 5 5
## 11 5 5
## 12 5 5
## 13 5 5
## 14 5 5
## 15 5 5
## 16 5 5
## 17 5 5
## 18 5 5
## 19 5 5
## 20 4 5
## 21 5 5
## 22 5 5
## 23 5 5
## 24 5 5
## 25 5 5
## 26 3 5
## 27 5 5
## 28 5 5
## 29 5 5
## 30 5 5
## 31 5 5
## 32 5 5
## 33 4 5
## 34 5 5
## 35 5 5
## 36 5 5
## 37 5 5
## 38 3 5
## 39 5 5
## 40 3 5
## 41 3 5
## 42 5 5
## 43 4 4
## 44 3 5
## 45 3 5
## 46 3 5
## 47 5 5
## 48 3 5
## 49 5 5
## 50 5 2
## 51 5 5
## 52 5 5
## 53 5 5
## 54 3 5
## 55 3 5
## 56 5 5
## 57 5 5
## 58 3 5
## 59 5 5
## 60 5 3
## 61 2 5
## 62 5 5
## 63 2 5
## 64 4 4
## 65 3 5
## 66 4 5
## 67 4 4
## 68 3 5
## 69 2 5
## 70 4 5
## 71 3 5
## 72 3 5
## 73 2 5
## 74 3 5
## 75 4 4
## 76 3 4
## 77 4 2
## 78 2 5
## 79 1 4
## 80 1 4
## 81 3 3
## 82 2 5
## 83 2 3
## 84 1 5
## 85 1 4
## 86 3 3
## 87 3 3
## 88 5 3
## 89 1 4
## 90 1 5
## 91 3 3
## 92 3 5
## 93 3 3
## 94 1 5
## 95 3 3
## 96 3 3
## 97 3 5
## 98 1 3
## 99 1 3
## 100 1 5
## 101 1 3
## 102 4 2
## 103 1 5
## 104 1 4
## 105 1 4
## 106 2 4
## 107 3 4
## 108 1 3
## 109 3 4
## 110 2 4
## 111 5 3
## 112 1 5
## 113 1 4
## 114 1 5
## 115 1 5
## 116 1 4
## 117 1 3
## 118 1 3
## 119 1 3
## 120 2 3
## 121 2 2
## 122 2 4
## 123 1 5
## 124 1 3
## 125 1 3
## 126 1 3
## 127 5 3
## 128 1 3
## 129 2 3
## 130 3 3
## 131 1 3
## 132 1 4
## 133 1 4
## 134 3 3
## 135 1 3
## 136 2 3
## 137 1 2
## 138 2 3
## 139 1 4
## 140 2 3
## 141 1 3
## 142 1 3
## 143 1 5
## 144 2 3
## 145 4 2
## 146 2 3
## 147 3 1
## 148 3 3
## 149 1 3
## 150 2 3
## 151 3 3
## 152 3 3
## 153 1 4
## 154 2 4
## 155 2 2
## 156 1 3
## 157 1 1
## 158 1 1
## 159 1 1
## 160 1 2
## 161 1 1
## 162 1 1
## 163 3 3
## 164 2 3
## 165 2 3
## 166 1 3
## 167 1 2
## 168 1 1
## 169 2 1
## 170 1 1
## 171 1 3
## 172 1 3
## 173 1 3
## 174 1 1
## 175 1 3
## 176 3 2
## 177 1 4
## 178 3 1
## 179 2 3
## 180 1 3
## 181 1 3
## 182 1 3
## 183 1 1
## 184 1 1
## 185 1 3
## 186 1 3
## 187 1 3
## 188 1 2
## 189 1 3
## 190 2 1
## 191 1 3
## 192 3 1
## 193 3 2
## 194 1 3
## 195 1 3
## 196 1 1
## 197 1 1
## 198 1 1
## 199 1 2
## 200 1 1
## 201 1 2
## 202 1 2
## 203 1 2
## 204 1 1
## 205 1 1
## 206 1 1
## 207 1 1
## 208 1 1
## 209 1 1
## 210 1 2
## 211 1 1
## 212 1 1
## 213 1 1
## 214 1 1
## 215 1 1
## 216 1 1
## 217 1 1
## 218 1 1
## 219 1 1
## 220 1 1
## 221 1 1
## 222 1 2
## 223 1 1
## 224 1 1
## 225 1 1
## 226 1 1
## 227 1 1
## 228 1 1MERGEdatas =merge(data1, data2, by = "Country")
str(MERGEdatas)## 'data.frame': 163 obs. of 8 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ 2022_safesec : num 4.13 2.12 2.3 2.41 2.66 ...
## $ 2022_milit : num 2.47 1.67 2.04 1.71 1.61 ...
## $ 2022_oncon : num 3.65 1.4 2.07 1.67 1.2 ...
## $ foodr_score : num 4 2 2 5 2 2 1 1 2 2 ...
## $ naturald_score: num 5 4 5 3 4 2 4 3 1 1 ...
## $ RPG_score : num 5 1 3 5 2 1 3 1 1 3 ...
## $ waterisk_score: num 5 3 3 5 3 4 3 1 5 3 ...head(MERGEdatas)## Country 2022_safesec 2022_milit 2022_oncon foodr_score naturald_score
## 1 Afghanistan 4.127 2.472 3.650 4 5
## 2 Albania 2.120 1.666 1.403 2 4
## 3 Algeria 2.302 2.041 2.068 2 5
## 4 Angola 2.413 1.706 1.666 5 3
## 5 Argentina 2.656 1.611 1.201 2 4
## 6 Armenia 1.977 2.041 1.990 2 2
## RPG_score waterisk_score
## 1 5 5
## 2 1 3
## 3 3 3
## 4 5 5
## 5 2 3
## 6 1 4Clusterización con PAM sin tipificar:
clusMERGEdatas2=MERGEdatas[,c(2:8)]
row.names(clusMERGEdatas2)=MERGEdatas$Countrylibrary(cluster)
g.dist2 = daisy(clusMERGEdatas2, metric="gower")library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_nbclust(clusMERGEdatas2, pam,diss=g.dist2,method ="gap_stat",k.max =10,verbose =F)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsset.seed(123)
res.pam2=pam(g.dist2,3,cluster.only=F) #el número refleja la cantidad de clusters
#nueva columna
clusMERGEdatas2$pam=res.pam2$cluster
# ver
head(clusMERGEdatas2,15)%>%kbl()%>%kable_styling()| 2022_safesec | 2022_milit | 2022_oncon | foodr_score | naturald_score | RPG_score | waterisk_score | pam | |
|---|---|---|---|---|---|---|---|---|
| Afghanistan | 4.127 | 2.472 | 3.650 | 4 | 5 | 5 | 5 | 1 |
| Albania | 2.120 | 1.666 | 1.403 | 2 | 4 | 1 | 3 | 2 |
| Algeria | 2.302 | 2.041 | 2.068 | 2 | 5 | 3 | 3 | 2 |
| Angola | 2.413 | 1.706 | 1.666 | 5 | 3 | 5 | 5 | 1 |
| Argentina | 2.656 | 1.611 | 1.201 | 2 | 4 | 2 | 3 | 2 |
| Armenia | 1.977 | 2.041 | 1.990 | 2 | 2 | 1 | 4 | 2 |
| Australia | 1.657 | 1.848 | 1.266 | 1 | 4 | 3 | 3 | 2 |
| Austria | 1.452 | 1.331 | 1.097 | 1 | 3 | 1 | 1 | 3 |
| Azerbaijan | 2.528 | 2.108 | 2.579 | 2 | 1 | 1 | 5 | 2 |
| Bahrain | 2.405 | 1.717 | 1.955 | 2 | 1 | 3 | 3 | 2 |
| Bangladesh | 2.438 | 1.534 | 2.004 | 4 | 4 | 2 | 2 | 2 |
| Belarus | 2.526 | 1.896 | 2.208 | 2 | 1 | 1 | 2 | 3 |
| Belgium | 1.806 | 1.602 | 1.141 | 1 | 2 | 1 | 1 | 3 |
| Benin | 2.574 | 2.022 | 1.666 | 5 | 4 | 5 | 5 | 1 |
| Bhutan | 1.601 | 1.336 | 1.436 | 2 | 5 | 3 | 4 | 2 |
fviz_silhouette(res.pam2,print.summary = F) #barras para abajo están mal clusterizadas
Preparación de datos para la clusterización tipificada:
boxplot(MERGEdatas[,c(2:8)],horizontal = F,las=2,cex.axis =0.5)
library(BBmisc)##
## Attaching package: 'BBmisc'## The following objects are masked from 'package:dplyr':
##
## coalesce, collapse## The following object is masked from 'package:base':
##
## isFALSEboxplot(normalize(MERGEdatas[,c(2:8)],method='range',range=c(0,10))) #CAMBIAR ORIENTACIÓN DE NOMBRES
library(BBmisc)
boxplot(normalize(MERGEdatas[,c(2:8)],method='standardize'))
MERGEdatas[,c(2:8)]=normalize(MERGEdatas[,c(2:8)],method='standardize') #preguntar si nos quedamos con este o cambiamos por el de rangecor(MERGEdatas[,c(2:8)])## 2022_safesec 2022_milit 2022_oncon foodr_score naturald_score
## 2022_safesec 1.0000000 0.32475365 0.7341753 0.66058571 0.35251645
## 2022_milit 0.3247536 1.00000000 0.4495004 0.08883541 0.05969936
## 2022_oncon 0.7341753 0.44950043 1.0000000 0.44921226 0.25637791
## foodr_score 0.6605857 0.08883541 0.4492123 1.00000000 0.28711933
## naturald_score 0.3525165 0.05969936 0.2563779 0.28711933 1.00000000
## RPG_score 0.5078034 0.18837463 0.4321162 0.76202029 0.17405917
## waterisk_score 0.6370051 0.01704344 0.3393531 0.75129538 0.36454740
## RPG_score waterisk_score
## 2022_safesec 0.5078034 0.63700512
## 2022_milit 0.1883746 0.01704344
## 2022_oncon 0.4321162 0.33935309
## foodr_score 0.7620203 0.75129538
## naturald_score 0.1740592 0.36454740
## RPG_score 1.0000000 0.63011932
## waterisk_score 0.6301193 1.00000000Clusterización con PAM:
clusMERGEdatas=MERGEdatas[,c(2:8)]
row.names(clusMERGEdatas)=MERGEdatas$Countrylibrary(cluster)
g.dist = daisy(clusMERGEdatas, metric="gower")library(factoextra)
fviz_nbclust(clusMERGEdatas, pam,diss=g.dist,method ="gap_stat",k.max =10,verbose =F)
library(dplyr)
library(kableExtra)
set.seed(123)
res.pam=pam(g.dist,8,cluster.only=F) #el número refleja la cantidad de clusters
#nueva columna
clusMERGEdatas$pam=res.pam$cluster
# ver
head(clusMERGEdatas,15)%>%kbl()%>%kable_styling()| 2022_safesec | 2022_milit | 2022_oncon | foodr_score | naturald_score | RPG_score | waterisk_score | pam | |
|---|---|---|---|---|---|---|---|---|
| Afghanistan | 2.7198722 | 1.4692293 | 2.7432847 | 0.775457 | 1.0174633 | 1.3896665 | 0.9195792 | 1 |
| Albania | -0.4643791 | -0.5017953 | -0.6274257 | -0.591024 | 0.2636155 | -1.0525507 | -0.3560924 | 2 |
| Algeria | -0.1756229 | 0.4152447 | 0.3701366 | -0.591024 | 1.0174633 | 0.1685579 | -0.3560924 | 2 |
| Angola | 0.0004867 | -0.4039777 | -0.2329010 | 1.458697 | -0.4902323 | 1.3896665 | 0.9195792 | 3 |
| Argentina | 0.3860238 | -0.6362945 | -0.9304446 | -0.591024 | 0.2636155 | -0.4419964 | -0.3560924 | 2 |
| Armenia | -0.6912590 | 0.4152447 | 0.2531293 | -0.591024 | -1.2440801 | -1.0525507 | 0.2817434 | 4 |
| Australia | -1.1989622 | -0.0567252 | -0.8329385 | -1.274264 | 0.2636155 | 0.1685579 | -0.3560924 | 2 |
| Austria | -1.5242096 | -1.3210176 | -1.0864543 | -1.274264 | -0.4902323 | -1.0525507 | -1.6317640 | 5 |
| Azerbaijan | 0.1829425 | 0.5790892 | 1.1366845 | -0.591024 | -1.9979279 | -1.0525507 | 0.9195792 | 4 |
| Bahrain | -0.0122059 | -0.3770778 | 0.2006260 | -0.591024 | -1.9979279 | 0.1685579 | -0.3560924 | 6 |
| Bangladesh | 0.0401510 | -0.8245933 | 0.2741306 | 0.775457 | 0.2636155 | -0.4419964 | -0.9939282 | 2 |
| Belarus | 0.1797694 | 0.0606559 | 0.5801497 | -0.591024 | -1.9979279 | -1.0525507 | -0.9939282 | 6 |
| Belgium | -0.9625629 | -0.6583034 | -1.0204502 | -1.274264 | -1.2440801 | -1.0525507 | -1.6317640 | 7 |
| Benin | 0.2559249 | 0.3687813 | -0.2329010 | 1.458697 | 0.2636155 | 1.3896665 | 0.9195792 | 3 |
| Bhutan | -1.2878103 | -1.3087904 | -0.5779226 | -0.591024 | 1.0174633 | 0.1685579 | 0.2817434 | 4 |
fviz_silhouette(res.pam,print.summary = F) #barras para abajo están mal clusterizadas