KuisMandarel
Muhammad Aqil Siradj
2023-12-02
#Mengambil data dari format csv ke Rstudio
Rawdata <- read.csv(file = "C:/Users/User/OneDrive/Documents/Mandarel/avgIQpercountry.csv", header = TRUE, sep=",")
Rawdata## Rank Country Average.IQ Continent
## 1 1 Japan 106.48 Asia
## 2 2 Taiwan 106.47 Asia
## 3 3 Singapore 105.89 Asia
## 4 4 Hong Kong 105.37 Asia
## 5 5 China 104.10 Asia
## 6 6 South Korea 102.35 Asia
## 7 7 Belarus 101.60 Europe
## 8 8 Finland 101.20 Europe
## 9 9 Liechtenstein 101.07 Europe
## 10 10 Germany 100.74 Europe
## 11 11 Netherlands 100.74 Europe
## 12 12 Estonia 100.72 Europe
## 13 13 Luxembourg 99.87 Europe
## 14 14 Macao 99.82 Asia
## 15 15 Cambodia 99.75 Asia
## 16 16 Canada 99.52 North America
## 17 17 Australia 99.24 Oceania
## 18 18 Hungary 99.24 Europe
## 19 19 Switzerland 99.24 Europe
## 20 20 United Kingdom 99.12 Europe
## 21 21 North Korea 98.82 Asia
## 22 22 Slovenia 98.60 Europe
## 23 23 New Zealand 98.57 Oceania
## 24 24 Austria 98.38 Europe
## 25 25 Iceland 98.26 Europe
## 26 26 Denmark 97.83 Europe
## 27 27 Belgium 97.49 Europe
## 28 28 United States 97.43 North America
## 29 29 Norway 97.13 Europe
## 30 30 Sweden 97.00 Europe
## 31 31 France 96.69 Europe
## 32 32 Poland 96.35 Europe
## 33 33 Slovakia 96.32 Europe
## 34 34 Russia 96.29 Europe/Asia
## 35 35 Lithuania 95.89 Europe
## 36 36 Croatia 95.75 Europe
## 37 37 Andorra 95.20 Europe
## 38 38 Ireland 95.13 Europe
## 39 39 Czech republic 94.92 Europe
## 40 40 Latvia 94.79 Europe
## 41 41 Italy 94.23 Europe
## 42 42 New Caledonia 93.92 Oceania
## 43 43 Vanuatu 93.92 Oceania
## 44 44 Spain 93.90 Europe
## 45 45 Bermuda 93.48 North America
## 46 46 Cyprus 93.39 Europe
## 47 47 Portugal 92.77 Europe
## 48 48 Israel 92.43 Asia
## 49 49 Barbados 91.60 Central America
## 50 50 Malta 91.27 Europe
## 51 51 Myanmar 91.18 Asia
## 52 52 Mongolia 91.03 Asia
## 53 53 Bulgaria 90.99 Europe
## 54 54 Greece 90.77 Europe
## 55 55 Suriname 90.29 South America
## 56 56 Ukraine 90.07 Europe
## 57 57 Moldavia 89.98 Europe
## 58 58 Serbia 89.60 Europe
## 59 59 Vietnam 89.53 Asia
## 60 60 Iraq 89.28 Asia
## 61 61 Uzbekistan 89.01 Asia
## 62 62 Kazakhstan 88.89 Asia
## 63 63 Thailand 88.87 Asia
## 64 64 Armenia 88.82 Asia
## 65 65 Bosnia and Herzegovina 88.54 Europe
## 66 66 Costa Rica 88.34 Central America
## 67 67 Bhutan 87.94 Asia
## 68 68 Chile 87.89 South America
## 69 69 Mexico 87.73 North America
## 70 70 Tajikistan 87.71 Asia
## 71 71 Uruguay 87.59 South America
## 72 72 Brunei 87.58 Asia
## 73 73 Malaysia 87.58 Asia
## 74 74 Bahamas 86.99 Central America
## 75 75 Romania 86.88 Europe
## 76 76 Türkiye 86.80 Europe/Asia
## 77 77 Argentina 86.63 South America
## 78 78 Sri Lanka 86.62 Asia
## 79 79 Mauritius 86.56 Africa
## 80 80 Turkmenistan 85.86 Asia
## 81 81 Montenegro 85.78 Europe
## 82 82 Trinidad and Tobago 85.63 Central America
## 83 83 Azerbaijan 84.81 Asia
## 84 84 Georgia 84.50 Europe/Asia
## 85 85 Turks and Caicos Islands 84.29 Central America
## 86 86 Paraguay 84.04 South America
## 87 87 Federated States of Micronesia 83.96 Oceania
## 88 88 Fiji 83.96 Oceania
## 89 89 Marshall Islands 83.96 Oceania
## 90 90 Solomon Islands 83.96 Oceania
## 91 91 Cuba 83.90 Central America
## 92 92 Bahrain 83.60 Asia
## 93 93 Brazil 83.38 South America
## 94 94 Guyana 83.23 South America
## 95 95 Colombia 83.13 South America
## 96 96 Venezuela 82.99 South America
## 97 97 Cayman Islands 82.24 Central America
## 98 98 Afghanistan 82.12 Asia
## 99 99 Haiti 82.10 Central America
## 100 100 Dominican Republic 82.05 Central America
## 101 101 United Arab Emirates 82.05 Asia
## 102 102 Puerto Rico 81.99 Central America
## 103 103 North Macedonia 81.91 Europe
## 104 104 Albania 81.75 Europe
## 105 105 Lebanon 81.70 Asia
## 106 106 Philippines 81.64 Asia
## 107 107 Peru 81.44 South America
## 108 108 Northern Mariana Islands 81.36 Oceania
## 109 109 Laos 80.99 Asia
## 110 110 Libya 80.92 Africa
## 111 111 Qatar 80.78 Asia
## 112 112 Jordan 80.70 Asia
## 113 113 Maldives 80.54 Asia
## 114 114 Iran 80.01 Asia
## 115 115 Pakistan 80.00 Asia
## 116 116 Grenade 79.34 Central America
## 117 117 Tunisia 79.22 Africa
## 118 118 Kyrgyzstan 79.09 Asia
## 119 119 Panama 79.00 Central America
## 120 120 Chad 78.87 Africa
## 121 121 Sudan 78.87 Africa
## 122 122 Seychelles 78.76 Africa
## 123 123 Oman 78.70 Asia
## 124 124 Kuwait 78.64 Asia
## 125 125 East Timor 78.49 Asia
## 126 126 Indonesia 78.49 Asia
## 127 127 Papua New Guinea 78.49 Oceania
## 128 128 Ecuador 78.26 South America
## 129 129 Palestine 77.69 Asia
## 130 130 Senegal 77.37 Africa
## 131 131 Comoros 77.07 Africa
## 132 132 Madagascar 76.79 Africa
## 133 133 British Virgin Islands 76.69 Central America
## 134 134 Bolivia 76.53 South America
## 135 135 Uganda 76.42 Africa
## 136 136 Saudi Arabia 76.36 Asia
## 137 137 Egypt 76.32 Africa
## 138 138 India 76.24 Asia
## 139 139 Algeria 76.00 Africa
## 140 140 Kenya 75.20 Africa
## 141 141 Angola 75.10 Africa
## 142 142 Jamaica 75.08 Central America
## 143 143 Tanzania 74.95 Africa
## 144 144 Syria 74.41 Asia
## 145 145 Bangladesh 74.33 Asia
## 146 146 Zimbabwe 74.01 Africa
## 147 147 Burkina Faso 73.80 Africa
## 148 148 Saint Lucia 73.68 Central America
## 149 149 Mozambique 72.50 Africa
## 150 150 Burundi 72.09 Africa
## 151 151 Niger 70.82 Africa
## 152 152 Antigua and Barbuda 70.48 Central America
## 153 153 Rwanda 69.95 Africa
## 154 154 Benin 69.71 Africa
## 155 155 Malawi 69.70 Africa
## 156 156 El Salvador 69.63 Central America
## 157 157 Botswana 69.45 Africa
## 158 158 Lesotho 68.87 Africa
## 159 159 South Africa 68.87 Africa
## 160 160 Eswatini 68.87 Africa
## 161 161 Eritrea 68.77 Africa
## 162 162 Saint Helena 68.74 Africa
## 163 163 Zambia 68.43 Africa
## 164 164 Ethiopia 68.42 Africa
## 165 165 Djibouti 68.41 Africa
## 166 166 Cameroon 67.76 Africa
## 167 167 Nigeria 67.76 Africa
## 168 168 Somalia 67.67 Africa
## 169 169 Morocco 67.03 Africa
## 170 170 Namibia 66.19 Africa
## 171 171 Dominica 66.03 Central America
## 172 172 Sao Tome and Principe 65.22 Africa
## 173 173 Congo 64.92 Africa
## 174 174 Saint Vincent and the Grenadines 63.42 Central America
## 175 175 Gabon 62.97 Africa
## 176 176 Congo Republic 62.97 Africa
## 177 177 Yemen 62.86 Asia
## 178 178 Belize 62.55 Central America
## 179 179 Central African Republic 62.55 Africa
## 180 180 Honduras 62.16 Central America
## 181 181 Togo 59.83 Africa
## 182 182 Mali 59.76 Africa
## 183 183 Mauritania 59.76 Africa
## 184 184 South Sudan 58.61 Africa
## 185 185 Ghana 58.16 Africa
## 186 186 Costa do Marfim 58.16 Africa
## 187 187 Guinea 53.48 Africa
## 188 188 Nicaragua 52.69 Central America
## 189 189 Gambia 52.68 Africa
## 190 190 Guatemala 47.72 Central America
## 191 191 Liberia 45.07 Africa
## 192 192 Sierra Leone 45.07 Africa
## 193 193 Nepal 42.99 Asia
## Literacy.Rate Nobel.Prices HDI..2021. Mean.years.of.schooling...2021
## 1 0.99 29 0.925 13.4
## 2 0.96 4 NA NA
## 3 0.97 0 0.939 11.9
## 4 0.94 1 0.952 12.2
## 5 0.96 8 0.768 7.6
## 6 0.98 0 0.925 12.5
## 7 1.00 2 0.808 12.1
## 8 1.00 5 0.940 12.9
## 9 1.00 0 0.935 12.5
## 10 0.99 111 0.942 14.1
## 11 0.99 22 0.941 12.6
## 12 1.00 0 0.890 13.5
## 13 1.00 2 0.930 13.0
## 14 0.97 0 NA NA
## 15 0.78 0 0.593 5.1
## 16 0.99 28 0.936 13.8
## 17 0.99 12 0.951 12.7
## 18 0.99 13 0.846 12.2
## 19 0.99 27 0.962 13.9
## 20 0.99 137 0.929 13.4
## 21 1.00 0 NA NA
## 22 1.00 1 0.918 12.8
## 23 0.99 3 0.937 12.9
## 24 0.98 22 0.916 12.3
## 25 0.99 1 0.959 13.8
## 26 0.99 13 0.948 13.0
## 27 0.99 11 0.937 12.4
## 28 0.99 400 0.921 13.7
## 29 1.00 13 0.961 13.0
## 30 0.99 32 0.947 12.6
## 31 0.99 71 0.903 11.6
## 32 1.00 19 0.876 13.2
## 33 1.00 0 0.848 12.9
## 34 1.00 0 0.822 12.8
## 35 1.00 3 0.875 13.5
## 36 0.99 2 0.858 12.2
## 37 1.00 0 0.858 10.6
## 38 0.99 11 0.945 11.6
## 39 0.99 6 0.889 12.9
## 40 1.00 1 0.863 13.3
## 41 0.99 21 0.895 10.7
## 42 0.97 0 NA NA
## 43 0.85 0 0.607 7.1
## 44 0.98 8 0.905 10.6
## 45 0.98 0 NA NA
## 46 0.99 1 0.896 12.4
## 47 0.95 2 0.866 9.6
## 48 0.97 13 0.919 13.3
## 49 1.00 0 0.790 9.9
## 50 0.94 0 0.918 12.2
## 51 0.93 1 0.585 6.4
## 52 0.98 0 0.739 9.4
## 53 0.98 1 0.795 11.4
## 54 0.95 2 0.887 11.4
## 55 0.96 0 0.730 9.8
## 56 1.00 6 0.773 11.1
## 57 0.99 0 0.767 11.8
## 58 0.98 0 0.802 11.4
## 59 0.95 1 0.703 8.4
## 60 0.80 1 0.686 7.9
## 61 1.00 0 0.727 11.9
## 62 1.00 0 0.811 12.3
## 63 0.94 0 0.800 8.7
## 64 1.00 0 0.759 11.3
## 65 0.98 2 0.780 10.5
## 66 0.98 1 0.809 8.8
## 67 0.64 0 0.666 5.2
## 68 0.97 2 0.855 10.9
## 69 0.95 3 0.758 9.2
## 70 1.00 0 0.685 11.3
## 71 0.98 0 0.809 9.0
## 72 0.97 0 0.829 9.2
## 73 0.95 0 0.803 10.6
## 74 0.96 0 0.812 12.6
## 75 0.99 5 0.821 11.3
## 76 0.96 2 0.838 8.6
## 77 0.98 5 0.842 11.1
## 78 0.93 0 0.782 10.8
## 79 0.91 0 0.802 10.4
## 80 1.00 0 0.745 11.3
## 81 0.99 0 0.832 12.2
## 82 0.99 1 0.810 11.6
## 83 1.00 1 0.745 10.5
## 84 1.00 0 0.802 12.8
## 85 0.98 0 NA NA
## 86 0.96 0 0.717 8.9
## 87 0.89 0 0.628 7.8
## 88 0.94 0 0.730 10.9
## 89 0.98 0 0.639 10.9
## 90 0.84 0 0.564 5.7
## 91 1.00 0 0.764 12.5
## 92 0.96 0 0.875 11.0
## 93 0.93 0 0.754 8.1
## 94 0.88 0 0.714 8.6
## 95 0.95 2 0.752 8.9
## 96 0.95 1 0.691 11.1
## 97 0.99 0 NA NA
## 98 0.38 0 0.478 3.0
## 99 0.61 0 0.535 5.6
## 100 0.92 0 0.767 9.3
## 101 0.93 0 0.911 12.7
## 102 0.93 0 NA NA
## 103 0.98 0 0.770 10.2
## 104 0.98 0 0.796 11.3
## 105 0.94 0 0.706 8.7
## 106 0.97 1 0.699 9.0
## 107 0.94 1 0.762 9.9
## 108 0.97 0 NA NA
## 109 0.80 0 0.607 5.4
## 110 0.91 0 0.718 7.6
## 111 0.98 0 0.855 10.0
## 112 0.98 0 0.720 10.4
## 113 0.99 0 0.747 7.3
## 114 0.87 1 0.774 10.6
## 115 0.56 2 0.544 4.5
## 116 0.96 0 0.795 9.0
## 117 0.81 1 0.731 7.4
## 118 0.99 0 0.692 11.4
## 119 0.95 0 0.805 10.5
## 120 0.40 0 0.394 2.6
## 121 0.59 0 0.508 3.8
## 122 0.95 0 0.785 10.3
## 123 0.94 0 0.816 11.7
## 124 0.96 0 0.831 7.3
## 125 0.64 0 NA NA
## 126 0.95 0 0.705 8.6
## 127 0.63 0 0.558 4.7
## 128 0.95 0 0.740 8.8
## 129 0.97 1 0.715 9.9
## 130 0.56 0 0.511 2.9
## 131 0.78 0 0.558 5.1
## 132 0.65 0 0.501 5.1
## 133 0.98 0 NA NA
## 134 0.95 0 0.692 9.8
## 135 0.74 0 0.525 5.7
## 136 0.95 0 0.875 11.3
## 137 0.76 4 0.731 9.6
## 138 0.72 12 0.633 6.7
## 139 0.80 2 0.745 8.1
## 140 0.78 1 0.575 6.7
## 141 0.71 0 0.586 5.4
## 142 0.89 0 0.709 9.2
## 143 0.80 1 0.549 6.4
## 144 0.86 0 0.577 5.1
## 145 0.61 2 0.661 7.4
## 146 0.87 0 0.593 8.7
## 147 0.38 0 0.449 2.1
## 148 0.90 2 0.715 8.5
## 149 0.59 0 0.446 3.2
## 150 0.85 0 0.426 3.1
## 151 0.19 0 0.400 2.1
## 152 0.99 0 0.788 9.3
## 153 0.71 0 0.534 4.4
## 154 0.38 0 0.525 4.3
## 155 0.66 0 0.512 4.5
## 156 0.88 0 0.675 7.2
## 157 0.88 0 0.693 10.3
## 158 0.79 0 0.514 6.0
## 159 0.95 11 0.713 11.4
## 160 0.87 0 0.597 5.6
## 161 0.74 0 0.492 4.9
## 162 0.97 0 NA NA
## 163 0.85 0 0.565 7.2
## 164 0.49 1 0.498 3.2
## 165 0.68 0 0.509 4.1
## 166 0.75 0 0.576 6.2
## 167 0.60 1 0.535 7.2
## 168 0.38 0 NA NA
## 169 0.72 1 0.683 5.9
## 170 0.91 0 0.615 7.2
## 171 0.94 0 0.720 8.1
## 172 0.92 0 0.618 6.2
## 173 0.77 0 0.571 6.2
## 174 0.96 0 0.751 10.8
## 175 0.83 0 0.706 9.4
## 176 0.79 0 0.479 7.0
## 177 0.70 1 0.455 3.2
## 178 0.83 0 0.683 8.8
## 179 0.37 0 0.404 4.3
## 180 0.88 0 0.621 7.1
## 181 0.67 0 0.539 5.0
## 182 0.33 0 0.428 2.3
## 183 0.52 0 0.556 4.9
## 184 0.32 0 0.385 5.7
## 185 0.77 1 0.632 8.3
## 186 0.43 0 NA NA
## 187 0.30 0 0.465 2.2
## 188 0.82 0 0.667 7.1
## 189 0.58 0 0.500 4.6
## 190 0.79 2 0.627 5.7
## 191 0.48 2 0.481 5.1
## 192 0.48 0 0.477 4.6
## 193 0.65 0 0.602 5.1
## GNI...2021 Population...2023
## 1 42274 123294513
## 2 NA 10143543
## 3 90919 6014723
## 4 62607 7491609
## 5 17504 1425671352
## 6 44501 51784059
## 7 18849 9498238
## 8 49452 5545475
## 9 146830 \t39315
## 10 54534 83294633
## 11 55979 17618299
## 12 38048 1322766
## 13 84649 654.768
## 14 NA 704.15
## 15 4079 16944826
## 16 46808 38781292
## 17 49238 26439112
## 18 32789 10156239
## 19 66933 8796669
## 20 45225 67736802
## 21 NA 26160822
## 22 39746 2119675
## 23 44057 5228100
## 24 53619 8958961
## 25 55782 375.319
## 26 60365 5910913
## 27 52293 11686140
## 28 64765 339996564
## 29 64660 5474360
## 30 54489 10612086
## 31 45937 64756584
## 32 33034 41026068
## 33 30690 5795199
## 34 27166 144444359
## 35 37931 2718352
## 36 30132 4008617
## 37 51167 76.965
## 38 76169 5056935
## 39 38745 10495295
## 40 32803 1830212
## 41 42840 58870763
## 42 NA 292.991
## 43 3085 334.506
## 44 38354 47519628
## 45 NA 63.837
## 46 38188 1260138
## 47 33155 10247605
## 48 41524 9174520
## 49 12306 281.996
## 50 38884 535.065
## 51 3851 54577997
## 52 10588 3447157
## 53 23079 6687717
## 54 29002 10341277
## 55 12672 623.237
## 56 13256 36744634
## 57 14875 3435931
## 58 19123 7149077
## 59 7867 98858950
## 60 9977 45504560
## 61 7917 35163944
## 62 23943 19606634
## 63 17030 71801279
## 64 13158 2777971
## 65 15242 3210848
## 66 19974 5212173
## 67 9438 787.425
## 68 24563 19629590
## 69 17896 128455567
## 70 4548 10143543
## 71 21269 3423109
## 72 64490 452.524
## 73 26658 34308525
## 74 30486 412.624
## 75 30027 19892812
## 76 31033 85816199
## 77 20925 45773884
## 78 12578 21893579
## 79 22025 1300557
## 80 13021 6516100
## 81 20839 626.485
## 82 23392 1534937
## 83 14257 10412652
## 84 14664 3728282
## 85 NA 44.104
## 86 12349 6861524
## 87 3696 115.224
## 88 9980 936.375
## 89 4620 41.996
## 90 2482 740.425
## 91 7879 11194449
## 92 39497 1485510
## 93 14370 216422446
## 94 22465 813.834
## 95 14384 52085168
## 96 4811 28838499
## 97 NA 69310
## 98 1824 42239854
## 99 2848 11724764
## 100 17990 11332973
## 101 62574 9516871
## 102 NA 3260314
## 103 15918 2085679
## 104 14131 2832439
## 105 9526 5353930
## 106 8920 117337368
## 107 12246 34352719
## 108 NA 49.796
## 109 7700 7633779
## 110 15336 6888388
## 111 87134 2716391
## 112 9924 11337053
## 113 15448 521.022
## 114 13001 89172767
## 115 4624 240485658
## 116 13484 126.184
## 117 10258 12458223
## 118 4566 6735348
## 119 26957 4468087
## 120 1364 18278568
## 121 3575 48109006
## 122 25831 107.66
## 123 27054 4644384
## 124 52920 4310108
## 125 NA 1360596
## 126 11466 277534123
## 127 4009 10329931
## 128 10312 18190484
## 129 6583 5040000
## 130 3344 17763163
## 131 3142 852.075
## 132 1484 30325732
## 133 NA 32291
## 134 8111 12388571
## 135 2181 48582334
## 136 46112 36947025
## 137 11732 112716599
## 138 6590 1428627663
## 139 10800 45606481
## 140 4474 55100587
## 141 5466 36684203
## 142 8834 2825544
## 143 2664 67438106
## 144 4192 23227014
## 145 5472 172954319
## 146 3810 16665409
## 147 2118 23251485
## 148 12048 180.251
## 149 1198 33897354
## 150 732 13238559
## 151 1240 27202843
## 152 16792 94.298
## 153 2210 14094683
## 154 3409 13712828
## 155 1466 20931751
## 156 8296 6364943
## 157 16198 2675353
## 158 2700 2330318
## 159 12948 60414495
## 160 7679 1210822
## 161 1729 3748902
## 162 NA 6.115
## 163 3218 20569738
## 164 2361 126527060
## 165 5025 1136455
## 166 3621 28647293
## 167 4790 223804632
## 168 NA 18143379
## 169 7303 37840044
## 170 8634 2604172
## 171 11488 73.161
## 172 4021 231.856
## 173 2889 6106869
## 174 11961 103.699
## 175 13367 2436567
## 176 1076 102262809
## 177 1314 34449825
## 178 6309 410.825
## 179 966 5742316
## 180 5298 10593798
## 181 2167 9053799
## 182 2133 23293699
## 183 5075 4862989
## 184 768 11088796
## 185 5745 34121985
## 186 NA 28873034
## 187 2481 14190612
## 188 5625 7046311
## 189 2172 2773168
## 190 8723 18092026
## 191 1289 5418377
## 192 1622 8791092
## 193 3877 30896590#Membuat Rata-rata dari IQ masing-masing benua
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionhasil_rata_rata <- Rawdata %>%
group_by(Continent) %>%
summarize(rata_rata_iq = mean(Average.IQ, na.rm = TRUE)) %>%
arrange(desc(rata_rata_iq))
print(hasil_rata_rata)## # A tibble: 8 × 2
## Continent rata_rata_iq
## <chr> <dbl>
## 1 Europe 94.9
## 2 North America 94.5
## 3 Europe/Asia 89.2
## 4 Oceania 88.1
## 5 Asia 85.8
## 6 South America 83.8
## 7 Central America 75.1
## 8 Africa 68.6#Membuat histogram rata-rata iq berdasarkan benua
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.2#Memunculkan 8 warna sesuai benua
custom_colors <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a", "#a6cee3", "#b2df8a", "#fb9a99")
#Pembentukan histogram
ggplot(hasil_rata_rata, aes(x = hasil_rata_rata$Continent, y = hasil_rata_rata$rata_rata_iq, fill = Continent)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = custom_colors) +
labs(title = "Rata-rata IQ Berdasarkan Benua",
x = "Continent",
y = "Average IQ") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) ## Warning: Use of `hasil_rata_rata$Continent` is discouraged.
## ℹ Use `Continent` instead.## Warning: Use of `hasil_rata_rata$rata_rata_iq` is discouraged.
## ℹ Use `rata_rata_iq` instead.
#Membuat Regresi antara Literacy Rate dengan Populasi di Benua Asia
dataAsia <- Rawdata %>%
filter(Continent == "Asia")
dataAsia## Rank Country Average.IQ Continent Literacy.Rate Nobel.Prices
## 1 1 Japan 106.48 Asia 0.99 29
## 2 2 Taiwan 106.47 Asia 0.96 4
## 3 3 Singapore 105.89 Asia 0.97 0
## 4 4 Hong Kong 105.37 Asia 0.94 1
## 5 5 China 104.10 Asia 0.96 8
## 6 6 South Korea 102.35 Asia 0.98 0
## 7 14 Macao 99.82 Asia 0.97 0
## 8 15 Cambodia 99.75 Asia 0.78 0
## 9 21 North Korea 98.82 Asia 1.00 0
## 10 48 Israel 92.43 Asia 0.97 13
## 11 51 Myanmar 91.18 Asia 0.93 1
## 12 52 Mongolia 91.03 Asia 0.98 0
## 13 59 Vietnam 89.53 Asia 0.95 1
## 14 60 Iraq 89.28 Asia 0.80 1
## 15 61 Uzbekistan 89.01 Asia 1.00 0
## 16 62 Kazakhstan 88.89 Asia 1.00 0
## 17 63 Thailand 88.87 Asia 0.94 0
## 18 64 Armenia 88.82 Asia 1.00 0
## 19 67 Bhutan 87.94 Asia 0.64 0
## 20 70 Tajikistan 87.71 Asia 1.00 0
## 21 72 Brunei 87.58 Asia 0.97 0
## 22 73 Malaysia 87.58 Asia 0.95 0
## 23 78 Sri Lanka 86.62 Asia 0.93 0
## 24 80 Turkmenistan 85.86 Asia 1.00 0
## 25 83 Azerbaijan 84.81 Asia 1.00 1
## 26 92 Bahrain 83.60 Asia 0.96 0
## 27 98 Afghanistan 82.12 Asia 0.38 0
## 28 101 United Arab Emirates 82.05 Asia 0.93 0
## 29 105 Lebanon 81.70 Asia 0.94 0
## 30 106 Philippines 81.64 Asia 0.97 1
## 31 109 Laos 80.99 Asia 0.80 0
## 32 111 Qatar 80.78 Asia 0.98 0
## 33 112 Jordan 80.70 Asia 0.98 0
## 34 113 Maldives 80.54 Asia 0.99 0
## 35 114 Iran 80.01 Asia 0.87 1
## 36 115 Pakistan 80.00 Asia 0.56 2
## 37 118 Kyrgyzstan 79.09 Asia 0.99 0
## 38 123 Oman 78.70 Asia 0.94 0
## 39 124 Kuwait 78.64 Asia 0.96 0
## 40 125 East Timor 78.49 Asia 0.64 0
## 41 126 Indonesia 78.49 Asia 0.95 0
## 42 129 Palestine 77.69 Asia 0.97 1
## 43 136 Saudi Arabia 76.36 Asia 0.95 0
## 44 138 India 76.24 Asia 0.72 12
## 45 144 Syria 74.41 Asia 0.86 0
## 46 145 Bangladesh 74.33 Asia 0.61 2
## 47 177 Yemen 62.86 Asia 0.70 1
## 48 193 Nepal 42.99 Asia 0.65 0
## HDI..2021. Mean.years.of.schooling...2021 GNI...2021 Population...2023
## 1 0.925 13.4 42274 123294513
## 2 NA NA NA 10143543
## 3 0.939 11.9 90919 6014723
## 4 0.952 12.2 62607 7491609
## 5 0.768 7.6 17504 1425671352
## 6 0.925 12.5 44501 51784059
## 7 NA NA NA 704.15
## 8 0.593 5.1 4079 16944826
## 9 NA NA NA 26160822
## 10 0.919 13.3 41524 9174520
## 11 0.585 6.4 3851 54577997
## 12 0.739 9.4 10588 3447157
## 13 0.703 8.4 7867 98858950
## 14 0.686 7.9 9977 45504560
## 15 0.727 11.9 7917 35163944
## 16 0.811 12.3 23943 19606634
## 17 0.800 8.7 17030 71801279
## 18 0.759 11.3 13158 2777971
## 19 0.666 5.2 9438 787.425
## 20 0.685 11.3 4548 10143543
## 21 0.829 9.2 64490 452.524
## 22 0.803 10.6 26658 34308525
## 23 0.782 10.8 12578 21893579
## 24 0.745 11.3 13021 6516100
## 25 0.745 10.5 14257 10412652
## 26 0.875 11.0 39497 1485510
## 27 0.478 3.0 1824 42239854
## 28 0.911 12.7 62574 9516871
## 29 0.706 8.7 9526 5353930
## 30 0.699 9.0 8920 117337368
## 31 0.607 5.4 7700 7633779
## 32 0.855 10.0 87134 2716391
## 33 0.720 10.4 9924 11337053
## 34 0.747 7.3 15448 521.022
## 35 0.774 10.6 13001 89172767
## 36 0.544 4.5 4624 240485658
## 37 0.692 11.4 4566 6735348
## 38 0.816 11.7 27054 4644384
## 39 0.831 7.3 52920 4310108
## 40 NA NA NA 1360596
## 41 0.705 8.6 11466 277534123
## 42 0.715 9.9 6583 5040000
## 43 0.875 11.3 46112 36947025
## 44 0.633 6.7 6590 1428627663
## 45 0.577 5.1 4192 23227014
## 46 0.661 7.4 5472 172954319
## 47 0.455 3.2 1314 34449825
## 48 0.602 5.1 3877 30896590SelectedTabelAsia <- dataAsia %>%
select(Country, Literacy.Rate, Population...2023)
SelectedTabelAsia## Country Literacy.Rate Population...2023
## 1 Japan 0.99 123294513
## 2 Taiwan 0.96 10143543
## 3 Singapore 0.97 6014723
## 4 Hong Kong 0.94 7491609
## 5 China 0.96 1425671352
## 6 South Korea 0.98 51784059
## 7 Macao 0.97 704.15
## 8 Cambodia 0.78 16944826
## 9 North Korea 1.00 26160822
## 10 Israel 0.97 9174520
## 11 Myanmar 0.93 54577997
## 12 Mongolia 0.98 3447157
## 13 Vietnam 0.95 98858950
## 14 Iraq 0.80 45504560
## 15 Uzbekistan 1.00 35163944
## 16 Kazakhstan 1.00 19606634
## 17 Thailand 0.94 71801279
## 18 Armenia 1.00 2777971
## 19 Bhutan 0.64 787.425
## 20 Tajikistan 1.00 10143543
## 21 Brunei 0.97 452.524
## 22 Malaysia 0.95 34308525
## 23 Sri Lanka 0.93 21893579
## 24 Turkmenistan 1.00 6516100
## 25 Azerbaijan 1.00 10412652
## 26 Bahrain 0.96 1485510
## 27 Afghanistan 0.38 42239854
## 28 United Arab Emirates 0.93 9516871
## 29 Lebanon 0.94 5353930
## 30 Philippines 0.97 117337368
## 31 Laos 0.80 7633779
## 32 Qatar 0.98 2716391
## 33 Jordan 0.98 11337053
## 34 Maldives 0.99 521.022
## 35 Iran 0.87 89172767
## 36 Pakistan 0.56 240485658
## 37 Kyrgyzstan 0.99 6735348
## 38 Oman 0.94 4644384
## 39 Kuwait 0.96 4310108
## 40 East Timor 0.64 1360596
## 41 Indonesia 0.95 277534123
## 42 Palestine 0.97 5040000
## 43 Saudi Arabia 0.95 36947025
## 44 India 0.72 1428627663
## 45 Syria 0.86 23227014
## 46 Bangladesh 0.61 172954319
## 47 Yemen 0.70 34449825
## 48 Nepal 0.65 30896590#Memasukkan Scatter Plot populasi dengan literacy rate Benua Asia
SelectedTabelAsia$Population...2023 <- as.numeric(SelectedTabelAsia$Population...2023) #Mengubah tipe data char menjadi numerik
str(SelectedTabelAsia)## 'data.frame': 48 obs. of 3 variables:
## $ Country : chr "Japan" "Taiwan" "Singapore" "Hong Kong" ...
## $ Literacy.Rate : num 0.99 0.96 0.97 0.94 0.96 0.98 0.97 0.78 1 0.97 ...
## $ Population...2023: num 1.23e+08 1.01e+07 6.01e+06 7.49e+06 1.43e+09 ...model <- lm(SelectedTabelAsia$Literacy.Rate ~ SelectedTabelAsia$Population...2023, SelectedTableAsia = SelectedTabelAsia)## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'SelectedTableAsia' will be disregardedsummary(model) #Melihat ringkasan model##
## Call:
## lm(formula = SelectedTabelAsia$Literacy.Rate ~ SelectedTabelAsia$Population...2023,
## SelectedTableAsia = SelectedTabelAsia)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51758 -0.02806 0.05982 0.08105 0.15416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.004e-01 2.182e-02 41.272 <2e-16 ***
## SelectedTabelAsia$Population...2023 -6.631e-11 7.281e-11 -0.911 0.367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.143 on 46 degrees of freedom
## Multiple R-squared: 0.01771, Adjusted R-squared: -0.003644
## F-statistic: 0.8294 on 1 and 46 DF, p-value: 0.3672#scatter plot
plot(SelectedTabelAsia$Population...2023, SelectedTabelAsia$Literacy.Rate, main = "Scatter Plot", xlab = "Populasi", ylab = "Literacy Rate")
#Korelasi antara populasi dengan literasi
SortedSelectedTabelAsia <- SelectedTabelAsia[-1]
SortedSelectedTabelAsia## Literacy.Rate Population...2023
## 1 0.99 1.232945e+08
## 2 0.96 1.014354e+07
## 3 0.97 6.014723e+06
## 4 0.94 7.491609e+06
## 5 0.96 1.425671e+09
## 6 0.98 5.178406e+07
## 7 0.97 7.041500e+02
## 8 0.78 1.694483e+07
## 9 1.00 2.616082e+07
## 10 0.97 9.174520e+06
## 11 0.93 5.457800e+07
## 12 0.98 3.447157e+06
## 13 0.95 9.885895e+07
## 14 0.80 4.550456e+07
## 15 1.00 3.516394e+07
## 16 1.00 1.960663e+07
## 17 0.94 7.180128e+07
## 18 1.00 2.777971e+06
## 19 0.64 7.874250e+02
## 20 1.00 1.014354e+07
## 21 0.97 4.525240e+02
## 22 0.95 3.430853e+07
## 23 0.93 2.189358e+07
## 24 1.00 6.516100e+06
## 25 1.00 1.041265e+07
## 26 0.96 1.485510e+06
## 27 0.38 4.223985e+07
## 28 0.93 9.516871e+06
## 29 0.94 5.353930e+06
## 30 0.97 1.173374e+08
## 31 0.80 7.633779e+06
## 32 0.98 2.716391e+06
## 33 0.98 1.133705e+07
## 34 0.99 5.210220e+02
## 35 0.87 8.917277e+07
## 36 0.56 2.404857e+08
## 37 0.99 6.735348e+06
## 38 0.94 4.644384e+06
## 39 0.96 4.310108e+06
## 40 0.64 1.360596e+06
## 41 0.95 2.775341e+08
## 42 0.97 5.040000e+06
## 43 0.95 3.694703e+07
## 44 0.72 1.428628e+09
## 45 0.86 2.322701e+07
## 46 0.61 1.729543e+08
## 47 0.70 3.444983e+07
## 48 0.65 3.089659e+07cor(SortedSelectedTabelAsia)## Literacy.Rate Population...2023
## Literacy.Rate 1.0000000 -0.1330801
## Population...2023 -0.1330801 1.0000000#Membuat histogram nobel prize
#Membuat tabel baru untuk melihat nobel dari masing-masing benua
TabelNobel <- Rawdata %>%
group_by(Continent) %>%
summarize(jumlahnobel = sum(Nobel.Prices, na.rm = TRUE)) %>%
arrange(desc(jumlahnobel)) #mengurutkan dari besar ke kecil
print(TabelNobel)## # A tibble: 8 × 2
## Continent jumlahnobel
## <chr> <int>
## 1 Europe 573
## 2 North America 431
## 3 Asia 79
## 4 Africa 26
## 5 Oceania 15
## 6 South America 11
## 7 Central America 6
## 8 Europe/Asia 2#Memasukkan rumus histogram
library(ggplot2)
#Urutkan data berdasarkan jumlah Nobel secara menurun
Nobelsorted <- TabelNobel[order(-TabelNobel$jumlahnobel), ]
#Pembentukan Histogram
ggplot(Nobelsorted, aes(x = reorder(Continent, -jumlahnobel), y = jumlahnobel, fill = jumlahnobel)) +
geom_bar(stat = "identity") +
labs(title = "Histogram Jumlah Nobel per Benua", x = "Benua", y = "Jumlah Nobel") +
scale_fill_viridis_c(direction = -1) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
#Boxplot Benua mengenai rata-rata Lama belajar Sekolah
#Membuat tabel yang berisi benua, negara, dan lamanya belajar
TabelBelajarIQ <- Rawdata %>%
group_by(Continent) %>%
select(Continent, Country, Mean.years.of.schooling...2021)
TabelBelajarIQ## # A tibble: 193 × 3
## # Groups: Continent [8]
## Continent Country Mean.years.of.schooling...2021
## <chr> <chr> <dbl>
## 1 Asia Japan 13.4
## 2 Asia Taiwan NA
## 3 Asia Singapore 11.9
## 4 Asia Hong Kong 12.2
## 5 Asia China 7.6
## 6 Asia South Korea 12.5
## 7 Europe Belarus 12.1
## 8 Europe Finland 12.9
## 9 Europe Liechtenstein 12.5
## 10 Europe Germany 14.1
## # ℹ 183 more rowsClearTabelBelajarIQ <- na.omit(TabelBelajarIQ [-2]) #Menghilangkan Kolom negara dan menghilangkan data NA
#Mengurtukan tabel sesuai benua
BelajarIQsorted <- ClearTabelBelajarIQ %>% arrange(Continent, desc(Mean.years.of.schooling...2021))
BelajarIQsorted## # A tibble: 179 × 2
## # Groups: Continent [8]
## Continent Mean.years.of.schooling...2021
## <chr> <dbl>
## 1 Africa 11.4
## 2 Africa 10.4
## 3 Africa 10.3
## 4 Africa 10.3
## 5 Africa 9.6
## 6 Africa 9.4
## 7 Africa 8.7
## 8 Africa 8.3
## 9 Africa 8.1
## 10 Africa 7.6
## # ℹ 169 more rows#Pembentukan Boxplot
ggplot(BelajarIQsorted, aes(x = Continent, y = Mean.years.of.schooling...2021, fill = Mean.years.of.schooling...2021)) +
geom_boxplot() +
labs(title = "Boxplot Rata rata tahun belajar per Benua", x = "Benua", y = "Rata-rata Lama Belajar") +
theme_minimal()## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?