Kuis MDR PRAK
HARIYOL
2023-12-01
Memanggil Package
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'ggplot2' was built under R version 4.3.2## Warning: package 'readr' was built under R version 4.3.2## Warning: package 'forcats' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsImport Data Perangkat
data20 <- read.csv("C:/1 SEMESTER 3 IPB UNIVERSITY/MATA KULIAH/MANAJEMEN DATA RELASIONAL [ MANDAREL ]/#UAS/Global_Education.csv")
data20## Countries.and.areas Latitude Longitude
## 1 Afghanistan 33.939110 67.709953
## 2 Albania 41.153332 20.168331
## 3 Algeria 28.033886 1.659626
## 4 Andorra 42.506285 1.521801
## 5 Angola 11.202692 17.873887
## 6 Anguilla 18.220554 63.068615
## 7 Antigua and Barbuda 17.060816 61.796428
## 8 Argentina 38.416097 63.616672
## 9 Armenia 40.069099 45.038189
## 10 Australia 25.274398 133.775136
## 11 Austria 47.516231 14.550072
## 12 Azerbaijan 40.143105 47.576927
## 13 The Bahamas 25.034280 77.396280
## 14 Bahrain 26.066700 50.557700
## 15 Bangladesh 23.684994 90.356331
## 16 Barbados 13.193887 59.543198
## 17 Belarus 53.709807 27.953389
## 18 Belgium 50.503887 4.469936
## 19 Belize 17.189877 88.497650
## 20 Benin 9.307690 2.315834
## 21 Bhutan 27.514162 90.433601
## 22 Bolivia 16.290154 63.588653
## 23 Bosnia and Herzegovina 43.915886 17.679076
## 24 Botswana 22.328474 24.684866
## 25 Brazil 14.235004 51.925280
## 26 British Virgin Islands 18.420695 64.639968
## 27 Brunei 4.535277 114.727669
## 28 Bulgaria 42.733883 25.485830
## 29 Burkina Faso 12.238333 1.561593
## 30 Burundi 3.373056 29.918886
## 31 Cape Verde 16.538800 23.041800
## 32 Cambodia 12.565679 104.990963
## 33 Cameroon 7.369722 12.354722
## 34 Canada 56.130366 106.346771
## 35 Central African Republic 6.611111 20.939444
## 36 Chad 15.454166 18.732207
## 37 Chile 35.675147 71.542969
## 38 China 35.861660 104.195397
## 39 Colombia 4.570868 74.297333
## 40 Comoros 11.645500 43.333300
## 41 Republic of the Congo 0.228021 15.827659
## 42 Cook Islands 21.236736 159.777671
## 43 Costa Rica 9.748917 83.753428
## 44 Ivory Coast 7.539989 5.547080
## 45 Croatia 45.100000 15.200000
## 46 Cuba 21.521757 77.781167
## 47 Cyprus 35.126413 33.429859
## 48 Czech Republic 49.817492 15.472962
## 49 North Korea 40.339852 127.510093
## 50 Democratic Republic of the Congo 4.038333 21.758664
## 51 Denmark 56.263920 9.501785
## 52 Djibouti 11.825138 42.590275
## 53 Dominica 15.414999 61.370976
## 54 Dominican Republic 18.735693 70.162651
## 55 Ecuador 1.831239 78.183406
## 56 Egypt 26.820553 30.802498
## 57 El Salvador 13.794185 88.896530
## 58 Equatorial Guinea 1.650801 10.267895
## 59 Eritrea 15.179384 39.782334
## 60 Estonia 58.595272 25.013607
## 61 Eswatini 26.522503 31.465866
## 62 Ethiopia 9.145000 40.489673
## 63 Fiji 17.713371 178.065032
## 64 Finland 61.924110 25.748151
## 65 France 46.227638 2.213749
## 66 Gabon 0.803689 11.609444
## 67 The Gambia 13.443182 15.310139
## 68 Georgia 42.315407 43.356892
## 69 Germany 51.165691 10.451526
## 70 Ghana 7.946527 1.023194
## 71 Greece 39.074208 21.824312
## 72 Grenada 12.116500 61.679000
## 73 Guatemala 15.783471 90.230759
## 74 Guinea 9.945587 9.696645
## 75 Guinea0Bissau 11.803749 15.180413
## 76 Guyana 4.860416 58.930180
## 77 Haiti 18.971187 72.285215
## 78 Vatican City 41.902916 12.453389
## 79 Honduras 15.199999 86.241905
## 80 Hungary 47.162494 19.503304
## 81 Iceland 64.963051 19.020835
## 82 India 20.593684 78.962880
## 83 Indonesia 0.789275 113.921327
## 84 Iran 32.427908 53.688046
## 85 Iraq 33.223191 43.679291
## 86 Republic of Ireland 53.412910 8.243890
## 87 Israel 31.046051 34.851612
## 88 Italy 41.871940 12.567380
## 89 Jamaica 18.109581 77.297508
## 90 Japan 36.204824 138.252924
## 91 Jordan 31.302848 36.786839
## 92 Kazakhstan 48.019573 66.923684
## 93 Kenya 0.023559 37.906193
## 94 Kiribati 1.836898 157.376832
## 95 Kuwait 29.311660 47.481766
## 96 Kyrgyzstan 41.204380 74.766098
## 97 Laos 19.856270 102.495496
## 98 Latvia 56.879635 24.603189
## 99 Lebanon 33.854721 35.862285
## 100 Lesotho 29.609988 28.233608
## 101 Liberia 6.428055 9.429499
## 102 Libya 26.335100 17.228331
## 103 Liechtenstein 47.141039 9.520935
## 104 Lithuania 55.169438 23.881275
## 105 Luxembourg 49.815273 6.129583
## 106 Madagascar 18.766947 46.869107
## 107 Malawi 13.254308 34.301525
## 108 Malaysia 4.210484 101.975766
## 109 Maldives 3.202778 73.220680
## 110 Mali 17.570692 3.996166
## 111 Malta 35.937496 14.375416
## 112 Marshall Islands 7.131474 171.184478
## 113 Mauritania 21.007890 10.940835
## 114 Mauritius 20.348404 57.552152
## 115 Mexico 23.634501 102.552784
## 116 Federated States of Micronesia 7.425554 150.550812
## 117 Monaco 43.738418 7.424616
## 118 Mongolia 46.862496 103.846656
## 119 Montenegro 42.708678 19.374390
## 120 Montserrat 16.742498 62.187366
## 121 Morocco 31.791702 7.092620
## 122 Mozambique 18.665695 35.529562
## 123 Myanmar 21.916221 95.955974
## 124 Namibia 22.957640 18.490410
## 125 Nauru 0.522778 166.931503
## 126 Nepal 28.394857 84.124008
## 127 Netherlands 52.132633 5.291266
## 128 New Zealand 40.900557 174.885971
## 129 Nicaragua 12.865416 85.207229
## 130 Niger 17.607789 8.081666
## 131 Nigeria 9.081999 8.675277
## 132 Niue 19.054445 169.867233
## 133 North Macedonia 41.608635 21.745275
## 134 Norway 60.472024 8.468946
## 135 Oman 21.473533 55.975413
## 136 Pakistan 30.375321 69.345116
## 137 Palau 7.514980 134.582520
## 138 Panama 8.537981 80.782127
## 139 Papua New Guinea 6.314993 143.955550
## 140 Paraguay 23.442503 58.443832
## 141 Peru 9.189967 75.015152
## 142 Philippines 12.879721 121.774017
## 143 Poland 51.919438 19.145136
## 144 Portugal 39.399872 8.224454
## 145 Qatar 25.354826 51.183884
## 146 South Korea 35.907757 127.766922
## 147 Moldova 47.411631 28.369885
## 148 Romania 45.943161 24.966760
## 149 Russia 61.524010 105.318756
## 150 Rwanda 1.940278 29.873888
## 151 Saint Kitts and Nevis 17.357822 62.782998
## 152 Saint Lucia 13.909444 60.978893
## 153 Saint Vincent and the Grenadines 12.984305 61.287228
## 154 Samoa 13.759029 172.104629
## 155 San Marino 43.942360 12.457777
## 156 S�������\xef\xbf 0.186360 6.613081
## 157 Saudi Arabia 23.885942 45.079162
## 158 Senegal 14.497401 14.452362
## 159 Serbia 44.016521 21.005859
## 160 Seychelles 4.679574 55.491977
## 161 Sierra Leone 8.460555 11.779889
## 162 Singapore 1.352083 103.819836
## 163 Slovakia 48.669026 19.699024
## 164 Slovenia 46.151241 14.995463
## 165 Solomon Islands 9.645710 160.156194
## 166 Somalia 5.152149 46.199616
## 167 South Africa 30.559482 22.937506
## 168 South Sudan 6.876992 31.306979
## 169 Spain 40.463667 3.749220
## 170 Sri Lanka 7.873054 80.771797
## 171 Palestinian National Authority 31.952162 35.233154
## 172 Sudan 12.862807 30.217636
## 173 Suriname 3.919305 56.027783
## 174 Sweden 60.128161 18.643501
## 175 Switzerland 46.818188 8.227512
## 176 Syria 34.802075 38.996815
## 177 Tajikistan 38.861034 71.276093
## 178 Thailand 15.870032 100.992541
## 179 East Timor 8.874217 125.727539
## 180 Togo 8.619543 0.824782
## 181 Tokelau 9.200200 171.848400
## 182 Tonga 21.178986 175.198242
## 183 Trinidad and Tobago 10.691803 61.222503
## 184 Tunisia 33.886917 9.537499
## 185 Turkey 38.963745 35.243322
## 186 Turkmenistan 38.969719 59.556278
## 187 Turks and Caicos Islands 21.694025 71.797928
## 188 Tuvalu 7.109535 177.649330
## 189 Uganda 1.373333 32.290275
## 190 Ukraine 48.379433 31.165580
## 191 United Arab Emirates 23.424076 53.847818
## 192 United Kingdom 55.378051 3.435973
## 193 Tanzania 6.369028 34.888822
## 194 United States 37.090240 95.712891
## 195 Uruguay 32.522779 55.765835
## 196 Uzbekistan 41.377491 64.585262
## 197 Vanuatu 15.376706 166.959158
## 198 Venezuela 6.423750 66.589730
## 199 Vietnam 14.058324 108.277199
## 200 Yemen 15.552727 48.516388
## 201 Zambia 13.133897 27.849332
## 202 Zimbabwe 19.015438 29.154857
## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 1 0 0 0
## 2 4 2 6
## 3 0 0 0
## 4 0 0 0
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## 11 0 0 0
## 12 32 19 10
## 13 0 0 0
## 14 31 28 2
## 15 0 0 0
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## 18 3 2 1
## 19 18 16 1
## 20 15 16 3
## 21 58 59 5
## 22 9 8 5
## 23 72 73 0
## 24 79 78 12
## 25 0 1 1
## 26 9 2 4
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## 28 19 20 13
## 29 81 81 21
## 30 52 50 9
## 31 19 19 6
## 32 48 44 9
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## 34 0 0 0
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## 37 8 8 1
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## 63 0 1 1
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## 68 0 0 2
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## 173 12 10 14
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## 186 0 0 0
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## 189 0 0 6
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## 194 12 8 1
## 195 2 1 3
## 196 53 55 0
## 197 38 38 8
## 198 14 14 10
## 199 0 0 0
## 200 96 96 10
## 201 0 0 17
## 202 60 58 0
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## 1 0 0
## 2 3 6
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 9 11
## 10 0 2
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## 13 0 23
## 14 3 7
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## 17 2 1
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## 21 2 18
## 22 5 13
## 23 0 0
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## 25 1 2
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## 27 0 0
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## 29 22 48
## 30 6 36
## 31 7 14
## 32 9 12
## 33 13 33
## 34 0 0
## 35 0 0
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## 37 2 5
## 38 0 0
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## 41 16 0
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## 45 0 3
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## 47 1 2
## 48 0 1
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## 50 0 0
## 51 0 0
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## 63 1 0
## 64 1 1
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## 71 1 3
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## 75 0 0
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## 80 5 3
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## 84 0 3
## 85 0 0
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## 89 0 19
## 90 0 0
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## 92 9 0
## 93 0 0
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## 98 1 1
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## 101 21 17
## 102 0 0
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## 104 0 0
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## 107 0 19
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## 194 1 3
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## 196 2 0
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## 199 0 0
## 200 21 23
## 201 13 0
## 202 0 0
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## 1 0 44
## 2 1 21
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 2 14
## 8 0 15
## 9 9 16
## 10 3 10
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## 12 0 0
## 13 21 29
## 14 0 18
## 15 0 41
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## 21 6 33
## 22 14 22
## 23 0 22
## 24 0 0
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## 27 0 19
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## 31 13 29
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## 199 0 0
## 200 34 46
## 201 0 0
## 202 0 45
## OOSR_Upper_Secondary_Age_Female Completion_Rate_Primary_Male
## 1 69 67
## 2 15 94
## 3 0 93
## 4 0 0
## 5 0 63
## 6 0 0
## 7 12 0
## 8 7 91
## 9 4 99
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## 12 0 0
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## 14 6 0
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## 129 0 0
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## 199 0 96
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## 201 0 71
## 202 50 86
## Completion_Rate_Primary_Female Completion_Rate_Lower_Secondary_Male
## 1 40 49
## 2 96 98
## 3 93 49
## 4 0 0
## 5 57 42
## 6 0 0
## 7 0 0
## 8 94 70
## 9 99 95
## 10 0 0
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## 19 96 55
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## 29 29 13
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## 31 0 0
## 32 79 41
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## 34 0 0
## 35 33 16
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## 51 0 0
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## 57 89 73
## 58 0 0
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## 61 77 47
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## 71 0 0
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## 76 99 80
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## 84 0 0
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## 93 82 61
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## 98 0 0
## 99 0 0
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## 103 0 0
## 104 0 0
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## 118 98 87
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## 121 0 0
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## 151 0 0
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## 153 0 0
## 154 0 0
## 155 0 0
## 156 86 32
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## 159 100 99
## 160 0 0
## 161 65 47
## 162 0 0
## 163 0 0
## 164 0 0
## 165 0 0
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## 169 0 0
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## 174 0 0
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## 179 85 63
## 180 76 55
## 181 0 0
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## 183 0 0
## 184 97 68
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## 187 0 0
## 188 0 0
## 189 43 27
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## 192 0 0
## 193 84 31
## 194 0 0
## 195 98 66
## 196 0 0
## 197 0 0
## 198 0 0
## 199 97 81
## 200 55 55
## 201 73 54
## 202 92 45
## Completion_Rate_Lower_Secondary_Female Completion_Rate_Upper_Secondary_Male
## 1 26 32
## 2 97 76
## 3 65 22
## 4 0 0
## 5 32 24
## 6 0 0
## 7 0 0
## 8 79 46
## 9 99 69
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 71 32
## 16 98 91
## 17 100 91
## 18 0 0
## 19 66 48
## 20 13 12
## 21 38 25
## 22 0 0
## 23 97 92
## 24 92 55
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## 29 6 6
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## 31 0 0
## 32 39 20
## 33 43 26
## 34 0 0
## 35 8 8
## 36 10 15
## 37 97 83
## 38 93 63
## 39 81 69
## 40 45 24
## 41 45 28
## 42 0 0
## 43 76 56
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## 45 0 0
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## 50 52 36
## 51 0 0
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## 56 81 43
## 57 74 34
## 58 0 0
## 59 0 0
## 60 0 0
## 61 54 31
## 62 22 11
## 63 0 0
## 64 0 0
## 65 0 0
## 66 33 14
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## 69 0 0
## 70 50 12
## 71 0 0
## 72 0 0
## 73 45 27
## 74 20 22
## 75 16 14
## 76 88 49
## 77 38 17
## 78 0 0
## 79 52 29
## 80 0 0
## 81 0 0
## 82 79 46
## 83 59 40
## 84 0 0
## 85 47 45
## 86 0 0
## 87 0 0
## 88 0 0
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## 90 0 0
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## 94 88 13
## 95 0 0
## 96 99 89
## 97 53 32
## 98 0 0
## 99 0 0
## 100 55 27
## 101 23 18
## 102 0 0
## 103 0 0
## 104 0 0
## 105 0 0
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## 107 21 15
## 108 0 0
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## 116 0 0
## 117 0 0
## 118 93 60
## 119 97 83
## 120 0 0
## 121 0 0
## 122 11 8
## 123 45 14
## 124 62 33
## 125 0 0
## 126 75 27
## 127 0 0
## 128 0 0
## 129 0 0
## 130 4 4
## 131 59 57
## 132 0 0
## 133 95 86
## 134 0 0
## 135 0 0
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## 137 0 0
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## 139 0 0
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## 141 83 78
## 142 88 74
## 143 0 0
## 144 0 0
## 145 0 0
## 146 0 0
## 147 98 63
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## 153 0 0
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## 155 0 0
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## 160 0 0
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## 163 0 0
## 164 0 0
## 165 0 0
## 166 0 0
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## 196 0 0
## 197 0 0
## 198 0 0
## 199 87 50
## 200 39 37
## 201 50 33
## 202 53 17
## Completion_Rate_Upper_Secondary_Female Grade_2_3_Proficiency_Reading
## 1 14 22
## 2 80 0
## 3 37 0
## 4 0 0
## 5 15 0
## 6 0 0
## 7 0 0
## 8 53 76
## 9 79 0
## 10 0 94
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## 13 0 0
## 14 0 69
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## 17 94 0
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## 43 60 89
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## 47 0 0
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## 57 36 0
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## 59 0 0
## 60 0 0
## 61 33 0
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## 64 0 98
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## 69 0 0
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## 81 0 0
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## 84 0 66
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## 120 0 0
## 121 0 36
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## 130 1 9
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## 143 0 98
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## 148 0 0
## 149 0 0
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## 153 0 0
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## 166 0 0
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## 176 0 0
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## 188 0 0
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## 191 0 68
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## 193 27 0
## 194 0 0
## 195 29 80
## 196 0 0
## 197 0 0
## 198 0 0
## 199 61 0
## 200 23 0
## 201 27 0
## 202 14 20
## Grade_2_3_Proficiency_Math Primary_End_Proficiency_Reading
## 1 25 13
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 71 46
## 9 0 0
## 10 70 0
## 11 0 98
## 12 0 81
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## 22 0 0
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## 25 71 53
## 26 0 0
## 27 0 0
## 28 0 95
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## 30 97 7
## 31 0 0
## 32 0 50
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## 34 69 0
## 35 0 0
## 36 48 3
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## 38 85 0
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## 40 0 0
## 41 72 17
## 42 0 0
## 43 84 68
## 44 33 22
## 45 0 0
## 46 0 0
## 47 74 0
## 48 78 0
## 49 0 0
## 50 0 0
## 51 80 0
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## 53 0 0
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## 120 0 0
## 121 16 0
## 122 0 0
## 123 0 0
## 124 0 35
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## 126 0 0
## 127 83 0
## 128 59 0
## 129 44 31
## 130 27 2
## 131 0 0
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## 135 32 0
## 136 0 0
## 137 0 0
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## 142 0 0
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## 144 82 0
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## 147 0 0
## 148 0 0
## 149 0 99
## 150 0 0
## 151 0 0
## 152 0 0
## 153 0 0
## 154 0 0
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## 165 0 0
## 166 0 0
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## 168 0 0
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## 170 0 0
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## 196 0 0
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## 198 0 0
## 199 0 55
## 200 0 0
## 201 0 0
## 202 0 0
## Primary_End_Proficiency_Math Lower_Secondary_End_Proficiency_Reading
## 1 11 0
## 2 0 48
## 3 0 21
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 56 48
## 9 55 0
## 10 64 80
## 11 0 76
## 12 0 0
## 13 0 0
## 14 0 0
## 15 32 54
## 16 0 0
## 17 0 77
## 18 0 79
## 19 0 0
## 20 11 0
## 21 0 56
## 22 0 0
## 23 0 46
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## 25 52 50
## 26 0 0
## 27 0 48
## 28 75 53
## 29 22 0
## 30 40 0
## 31 0 0
## 32 0 8
## 33 12 0
## 34 0 86
## 35 0 0
## 36 3 0
## 37 75 68
## 38 0 80
## 39 48 50
## 40 0 0
## 41 6 0
## 42 0 0
## 43 60 58
## 44 3 0
## 45 67 78
## 46 0 0
## 47 0 56
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## 49 0 0
## 50 0 0
## 51 0 84
## 52 0 0
## 53 0 0
## 54 12 21
## 55 48 49
## 56 0 0
## 57 0 0
## 58 0 0
## 59 0 0
## 60 0 89
## 61 0 0
## 62 0 0
## 63 0 0
## 64 0 86
## 65 58 79
## 66 0 0
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## 70 0 0
## 71 0 69
## 72 0 0
## 73 34 30
## 74 0 0
## 75 0 0
## 76 0 0
## 77 0 0
## 78 0 0
## 79 32 30
## 80 75 75
## 81 0 74
## 82 44 0
## 83 0 30
## 84 0 0
## 85 0 0
## 86 0 88
## 87 0 69
## 88 0 77
## 89 0 0
## 90 0 83
## 91 0 59
## 92 80 36
## 93 0 0
## 94 0 0
## 95 0 0
## 96 35 0
## 97 0 0
## 98 0 78
## 99 0 32
## 100 0 0
## 101 0 0
## 102 0 0
## 103 0 0
## 104 81 76
## 105 0 71
## 106 5 0
## 107 0 0
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## 109 0 0
## 110 0 0
## 111 0 64
## 112 0 0
## 113 0 0
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## 116 0 0
## 117 0 0
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## 121 0 27
## 122 0 0
## 123 0 0
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## 125 0 0
## 126 0 0
## 127 0 76
## 128 0 81
## 129 20 0
## 130 1 0
## 131 0 0
## 132 0 0
## 133 0 45
## 134 70 81
## 135 0 0
## 136 0 0
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## 140 23 32
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## 142 0 19
## 143 0 85
## 144 0 80
## 145 0 49
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## 148 0 59
## 149 89 78
## 150 0 0
## 151 0 0
## 152 0 0
## 153 0 0
## 154 0 0
## 155 0 0
## 156 0 0
## 157 0 48
## 158 29 9
## 159 72 62
## 160 0 0
## 161 0 0
## 162 0 89
## 163 65 69
## 164 0 82
## 165 0 0
## 166 0 0
## 167 13 0
## 168 0 0
## 169 0 84
## 170 0 0
## 171 0 0
## 172 0 0
## 173 0 0
## 174 0 82
## 175 0 76
## 176 0 0
## 177 0 0
## 178 0 40
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## 180 20 0
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## 182 0 0
## 183 0 58
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## 185 57 74
## 186 0 0
## 187 0 0
## 188 0 0
## 189 0 0
## 190 0 74
## 191 0 57
## 192 0 83
## 193 0 0
## 194 0 81
## 195 68 58
## 196 0 0
## 197 0 0
## 198 0 0
## 199 51 86
## 200 0 0
## 201 0 5
## 202 0 0
## Lower_Secondary_End_Proficiency_Math Youth_15_24_Literacy_Rate_Male
## 1 0 74
## 2 58 99
## 3 19 98
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 31 99
## 9 50 0
## 10 78 0
## 11 79 0
## 12 0 0
## 13 0 0
## 14 39 100
## 15 57 94
## 16 0 0
## 17 71 100
## 18 80 0
## 19 0 0
## 20 0 70
## 21 0 0
## 22 0 0
## 23 42 0
## 24 0 0
## 25 32 99
## 26 0 0
## 27 52 100
## 28 56 0
## 29 0 62
## 30 0 0
## 31 0 0
## 32 10 0
## 33 0 88
## 34 84 0
## 35 0 48
## 36 0 0
## 37 28 0
## 38 79 100
## 39 35 99
## 40 0 78
## 41 0 85
## 42 0 0
## 43 40 99
## 44 0 64
## 45 69 0
## 46 0 0
## 47 63 0
## 48 80 0
## 49 0 0
## 50 0 0
## 51 85 0
## 52 0 0
## 53 0 0
## 54 9 0
## 55 29 0
## 56 21 0
## 57 0 98
## 58 0 0
## 59 0 94
## 60 90 0
## 61 0 94
## 62 0 0
## 63 0 0
## 64 85 0
## 65 79 0
## 66 0 88
## 67 0 0
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## 69 79 0
## 70 0 93
## 71 64 99
## 72 0 0
## 73 11 0
## 74 0 70
## 75 0 0
## 76 0 0
## 77 0 0
## 78 0 0
## 79 15 95
## 80 67 0
## 81 79 0
## 82 0 93
## 83 28 100
## 84 34 0
## 85 0 0
## 86 84 0
## 87 66 0
## 88 62 100
## 89 0 0
## 90 89 0
## 91 41 99
## 92 51 100
## 93 0 88
## 94 0 0
## 95 18 99
## 96 0 100
## 97 0 0
## 98 83 100
## 99 35 100
## 100 0 0
## 101 0 0
## 102 0 0
## 103 0 0
## 104 74 0
## 105 73 0
## 106 0 82
## 107 0 0
## 108 59 97
## 109 0 0
## 110 0 58
## 111 62 99
## 112 0 0
## 113 0 0
## 114 0 99
## 115 44 99
## 116 0 0
## 117 0 0
## 118 0 98
## 119 54 99
## 120 0 0
## 121 14 98
## 122 0 0
## 123 0 0
## 124 0 94
## 125 0 0
## 126 0 94
## 127 84 0
## 128 78 0
## 129 0 0
## 130 0 51
## 131 0 82
## 132 0 0
## 133 39 0
## 134 81 0
## 135 23 98
## 136 0 0
## 137 0 0
## 138 19 99
## 139 0 0
## 140 8 98
## 141 40 99
## 142 19 0
## 143 85 0
## 144 77 100
## 145 36 0
## 146 85 0
## 147 50 0
## 148 53 99
## 149 78 100
## 150 0 84
## 151 0 0
## 152 0 0
## 153 0 0
## 154 0 99
## 155 0 100
## 156 0 98
## 157 11 0
## 158 8 0
## 159 60 0
## 160 0 99
## 161 0 71
## 162 94 100
## 163 75 0
## 164 84 0
## 165 0 0
## 166 0 0
## 167 0 0
## 168 0 48
## 169 75 100
## 170 0 99
## 171 0 99
## 172 0 73
## 173 0 99
## 174 81 0
## 175 83 0
## 176 0 0
## 177 0 0
## 178 47 98
## 179 0 82
## 180 0 0
## 181 0 0
## 182 0 99
## 183 48 0
## 184 25 0
## 185 42 0
## 186 0 0
## 187 0 0
## 188 0 0
## 189 0 89
## 190 64 0
## 191 46 0
## 192 81 0
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## 195 49 99
## 196 0 100
## 197 0 96
## 198 0 0
## 199 81 98
## 200 0 0
## 201 2 93
## 202 0 0
## Youth_15_24_Literacy_Rate_Female Birth_Rate
## 1 56 32.49
## 2 100 11.78
## 3 97 24.28
## 4 0 7.20
## 5 0 40.73
## 6 0 0.00
## 7 0 15.33
## 8 100 17.02
## 9 0 13.99
## 10 0 12.60
## 11 0 9.70
## 12 0 14.00
## 13 0 13.97
## 14 99 13.99
## 15 96 18.18
## 16 0 10.65
## 17 100 9.90
## 18 0 10.30
## 19 0 20.79
## 20 52 36.22
## 21 0 17.26
## 22 0 21.75
## 23 0 8.11
## 24 0 24.82
## 25 99 13.92
## 26 0 0.00
## 27 100 14.90
## 28 0 8.90
## 29 55 37.93
## 30 0 39.01
## 31 0 19.49
## 32 0 22.46
## 33 82 35.39
## 34 0 10.10
## 35 29 35.35
## 36 0 42.17
## 37 0 12.43
## 38 100 10.90
## 39 99 14.88
## 40 78 31.88
## 41 79 32.86
## 42 0 0.00
## 43 100 13.97
## 44 53 35.74
## 45 0 9.00
## 46 0 10.17
## 47 0 10.46
## 48 0 10.70
## 49 0 13.89
## 50 0 41.18
## 51 0 10.60
## 52 0 21.47
## 53 0 12.00
## 54 0 19.51
## 55 0 19.72
## 56 0 26.38
## 57 98 18.25
## 58 0 33.24
## 59 93 30.30
## 60 0 10.90
## 61 97 0.00
## 62 0 32.34
## 63 0 21.28
## 64 0 8.60
## 65 0 11.30
## 66 91 31.61
## 67 0 38.54
## 68 0 13.47
## 69 0 9.50
## 70 92 29.41
## 71 99 8.10
## 72 0 16.47
## 73 0 24.56
## 74 43 36.36
## 75 0 35.13
## 76 0 19.97
## 77 0 24.35
## 78 0 0.00
## 79 98 21.60
## 80 0 9.60
## 81 0 12.00
## 82 90 17.86
## 83 100 18.07
## 84 0 18.78
## 85 0 29.08
## 86 0 12.50
## 87 0 20.80
## 88 100 7.30
## 89 0 16.10
## 90 0 7.40
## 91 99 21.98
## 92 100 21.77
## 93 88 28.75
## 94 0 27.89
## 95 100 13.94
## 96 100 27.10
## 97 0 23.55
## 98 100 10.00
## 99 100 17.55
## 100 0 26.81
## 101 0 33.04
## 102 0 18.83
## 103 0 9.90
## 104 0 10.00
## 105 0 10.30
## 106 81 32.66
## 107 0 34.12
## 108 97 16.75
## 109 0 14.20
## 110 43 41.54
## 111 100 9.20
## 112 0 29.03
## 113 0 33.69
## 114 99 10.20
## 115 99 17.60
## 116 0 22.82
## 117 0 5.90
## 118 99 24.13
## 119 99 11.73
## 120 0 0.00
## 121 97 18.94
## 122 0 37.52
## 123 0 17.55
## 124 96 28.64
## 125 0 0.00
## 126 91 19.89
## 127 0 9.70
## 128 0 11.98
## 129 0 20.64
## 130 36 46.08
## 131 68 37.91
## 132 0 0.00
## 133 0 0.00
## 134 0 10.40
## 135 99 19.19
## 136 0 28.25
## 137 0 14.00
## 138 99 18.98
## 139 0 27.07
## 140 99 20.57
## 141 99 17.95
## 142 0 20.55
## 143 0 10.20
## 144 100 8.50
## 145 0 9.54
## 146 0 6.40
## 147 0 10.10
## 148 99 9.60
## 149 100 11.50
## 150 89 31.70
## 151 0 12.60
## 152 0 12.00
## 153 0 14.24
## 154 99 24.38
## 155 100 6.80
## 156 98 31.54
## 157 0 17.80
## 158 0 34.52
## 159 0 9.20
## 160 100 17.10
## 161 63 33.41
## 162 100 8.80
## 163 0 10.60
## 164 0 9.40
## 165 0 32.44
## 166 0 41.75
## 167 0 20.51
## 168 47 35.01
## 169 100 7.90
## 170 99 15.83
## 171 99 0.00
## 172 73 32.18
## 173 98 18.54
## 174 0 11.40
## 175 0 10.00
## 176 0 23.69
## 177 0 30.76
## 178 99 10.34
## 179 85 29.42
## 180 0 33.11
## 181 0 0.00
## 182 100 24.30
## 183 0 12.94
## 184 0 17.56
## 185 0 16.03
## 186 0 23.83
## 187 0 0.00
## 188 0 0.00
## 189 90 38.14
## 190 0 8.70
## 191 0 10.33
## 192 0 11.00
## 193 0 36.70
## 194 0 11.60
## 195 99 13.86
## 196 100 23.30
## 197 97 29.60
## 198 0 17.88
## 199 98 16.75
## 200 0 30.45
## 201 92 36.19
## 202 0 30.68
## Gross_Primary_Education_Enrollment Gross_Tertiary_Education_Enrollment
## 1 104.0 9.7
## 2 107.0 55.0
## 3 109.9 51.4
## 4 106.4 0.0
## 5 113.5 9.3
## 6 0.0 0.0
## 7 105.0 24.8
## 8 109.7 90.0
## 9 92.7 54.6
## 10 100.3 113.1
## 11 103.1 85.1
## 12 99.7 27.7
## 13 81.4 15.1
## 14 99.4 50.5
## 15 116.5 20.6
## 16 99.4 65.4
## 17 100.5 87.4
## 18 103.9 79.7
## 19 111.7 24.7
## 20 122.0 12.3
## 21 100.1 15.6
## 22 98.2 0.0
## 23 0.0 23.3
## 24 103.2 24.9
## 25 115.4 51.3
## 26 0.0 0.0
## 27 103.2 31.4
## 28 89.3 71.0
## 29 96.1 6.5
## 30 121.4 6.1
## 31 104.0 23.6
## 32 107.4 13.7
## 33 103.4 12.8
## 34 100.9 68.9
## 35 102.0 3.0
## 36 86.8 3.3
## 37 101.4 88.5
## 38 100.2 50.6
## 39 114.5 55.3
## 40 99.5 9.0
## 41 106.6 12.7
## 42 0.0 0.0
## 43 113.3 55.2
## 44 99.8 9.3
## 45 96.5 67.9
## 46 101.9 41.4
## 47 99.3 75.9
## 48 100.7 64.1
## 49 112.8 27.0
## 50 108.0 6.6
## 51 101.3 80.6
## 52 75.3 5.3
## 53 114.7 7.2
## 54 105.7 59.9
## 55 103.3 44.9
## 56 106.3 35.2
## 57 94.8 29.4
## 58 61.8 1.9
## 59 68.4 3.4
## 60 97.2 69.6
## 61 0.0 0.0
## 62 101.0 8.1
## 63 106.4 16.1
## 64 100.2 88.2
## 65 102.5 65.6
## 66 139.9 8.3
## 67 98.0 2.7
## 68 98.6 63.9
## 69 104.0 70.2
## 70 104.8 15.7
## 71 99.6 136.6
## 72 106.9 104.6
## 73 101.9 21.8
## 74 91.5 11.6
## 75 118.7 2.6
## 76 97.8 11.6
## 77 113.6 1.1
## 78 0.0 0.0
## 79 91.5 26.2
## 80 100.8 48.5
## 81 100.4 71.8
## 82 113.0 28.1
## 83 106.4 36.3
## 84 110.7 68.1
## 85 108.7 16.2
## 86 100.9 77.8
## 87 104.9 63.4
## 88 101.9 61.9
## 89 91.0 27.1
## 90 98.8 63.2
## 91 81.5 34.4
## 92 104.4 61.7
## 93 103.2 11.5
## 94 101.3 0.0
## 95 92.4 54.4
## 96 107.6 41.3
## 97 102.4 15.0
## 98 99.4 88.1
## 99 95.1 26.3
## 100 120.9 10.2
## 101 85.1 11.9
## 102 109.0 60.5
## 103 104.7 35.6
## 104 103.9 72.4
## 105 102.3 19.2
## 106 142.5 5.4
## 107 142.5 0.8
## 108 105.3 45.1
## 109 97.1 31.2
## 110 75.6 4.5
## 111 105.0 54.3
## 112 84.7 23.7
## 113 99.9 5.0
## 114 101.1 40.6
## 115 105.8 40.2
## 116 97.2 14.1
## 117 0.0 0.0
## 118 104.0 65.6
## 119 100.0 56.1
## 120 0.0 0.0
## 121 113.9 35.9
## 122 112.6 7.3
## 123 112.3 18.8
## 124 124.2 22.9
## 125 0.0 0.0
## 126 142.1 12.4
## 127 104.2 85.0
## 128 100.0 82.0
## 129 120.6 17.4
## 130 74.7 4.4
## 131 84.7 10.2
## 132 0.0 0.0
## 133 0.0 0.0
## 134 100.3 82.0
## 135 103.4 38.0
## 136 94.3 9.0
## 137 112.6 54.7
## 138 94.4 47.8
## 139 108.5 1.8
## 140 104.4 34.6
## 141 106.9 70.7
## 142 107.5 35.5
## 143 100.0 67.8
## 144 106.2 63.9
## 145 103.8 17.9
## 146 98.1 94.3
## 147 90.6 39.8
## 148 85.2 49.4
## 149 102.6 81.9
## 150 133.0 6.7
## 151 108.7 86.7
## 152 102.6 14.1
## 153 113.4 23.7
## 154 110.5 7.6
## 155 108.1 42.5
## 156 106.8 13.4
## 157 99.8 68.0
## 158 81.0 12.8
## 159 100.3 67.2
## 160 100.4 17.1
## 161 112.8 2.0
## 162 100.6 84.8
## 163 98.7 46.6
## 164 100.4 78.6
## 165 106.2 0.0
## 166 23.4 2.5
## 167 100.9 22.4
## 168 73.0 0.0
## 169 102.7 88.9
## 170 100.2 19.6
## 171 0.0 0.0
## 172 76.8 16.9
## 173 108.8 12.6
## 174 126.6 67.0
## 175 105.2 59.6
## 176 81.7 40.1
## 177 100.9 31.3
## 178 99.8 49.3
## 179 115.3 17.8
## 180 123.8 14.5
## 181 0.0 0.0
## 182 116.3 6.4
## 183 106.2 12.0
## 184 115.4 31.7
## 185 93.2 23.9
## 186 88.4 8.0
## 187 0.0 0.0
## 188 0.0 0.0
## 189 102.7 4.8
## 190 99.0 82.7
## 191 108.4 36.8
## 192 101.2 60.0
## 193 94.2 4.0
## 194 101.8 88.2
## 195 108.5 63.1
## 196 104.2 10.1
## 197 109.3 4.7
## 198 97.2 79.3
## 199 110.6 28.5
## 200 93.6 10.2
## 201 98.7 4.1
## 202 109.9 10.0
## Unemployment_Rate
## 1 11.12
## 2 12.33
## 3 11.70
## 4 0.00
## 5 6.89
## 6 0.00
## 7 0.00
## 8 9.79
## 9 16.99
## 10 5.27
## 11 4.67
## 12 5.51
## 13 10.36
## 14 0.71
## 15 4.19
## 16 10.33
## 17 4.59
## 18 5.59
## 19 6.41
## 20 2.23
## 21 2.34
## 22 3.50
## 23 18.42
## 24 18.19
## 25 12.08
## 26 0.00
## 27 9.12
## 28 4.34
## 29 6.26
## 30 1.43
## 31 12.25
## 32 0.68
## 33 3.38
## 34 5.56
## 35 3.68
## 36 1.89
## 37 7.09
## 38 4.32
## 39 9.71
## 40 4.34
## 41 9.47
## 42 0.00
## 43 11.85
## 44 3.32
## 45 6.93
## 46 1.64
## 47 7.27
## 48 1.93
## 49 2.74
## 50 4.24
## 51 4.91
## 52 10.30
## 53 0.00
## 54 5.84
## 55 3.97
## 56 10.76
## 57 4.11
## 58 6.43
## 59 5.14
## 60 5.11
## 61 0.00
## 62 2.08
## 63 4.10
## 64 6.59
## 65 8.43
## 66 20.00
## 67 9.06
## 68 14.40
## 69 3.04
## 70 4.33
## 71 17.24
## 72 0.00
## 73 2.46
## 74 4.30
## 75 2.47
## 76 11.85
## 77 13.78
## 78 0.00
## 79 5.39
## 80 3.40
## 81 2.84
## 82 5.36
## 83 4.69
## 84 11.38
## 85 12.82
## 86 4.93
## 87 3.86
## 88 9.89
## 89 8.00
## 90 2.29
## 91 14.72
## 92 4.59
## 93 2.64
## 94 0.00
## 95 2.18
## 96 6.33
## 97 0.63
## 98 6.52
## 99 6.23
## 100 23.41
## 101 2.81
## 102 18.56
## 103 0.00
## 104 6.35
## 105 5.36
## 106 1.76
## 107 5.65
## 108 3.32
## 109 6.14
## 110 7.22
## 111 3.47
## 112 0.00
## 113 9.55
## 114 6.67
## 115 3.42
## 116 0.00
## 117 0.00
## 118 6.01
## 119 14.88
## 120 0.00
## 121 9.02
## 122 3.24
## 123 1.58
## 124 20.27
## 125 0.00
## 126 1.41
## 127 3.20
## 128 4.07
## 129 6.84
## 130 0.47
## 131 8.10
## 132 0.00
## 133 0.00
## 134 3.35
## 135 2.67
## 136 4.45
## 137 0.00
## 138 3.90
## 139 2.46
## 140 4.81
## 141 3.31
## 142 2.15
## 143 3.47
## 144 6.33
## 145 0.09
## 146 4.15
## 147 5.47
## 148 3.98
## 149 4.59
## 150 1.03
## 151 0.00
## 152 20.71
## 153 18.88
## 154 8.36
## 155 0.00
## 156 13.37
## 157 5.93
## 158 6.60
## 159 12.69
## 160 0.00
## 161 4.43
## 162 4.11
## 163 5.56
## 164 4.20
## 165 0.58
## 166 11.35
## 167 28.18
## 168 12.24
## 169 13.96
## 170 4.20
## 171 0.00
## 172 16.53
## 173 7.33
## 174 6.48
## 175 4.58
## 176 8.37
## 177 11.02
## 178 0.75
## 179 4.55
## 180 2.04
## 181 0.00
## 182 1.12
## 183 2.69
## 184 16.02
## 185 13.49
## 186 3.91
## 187 0.00
## 188 0.00
## 189 1.84
## 190 8.88
## 191 2.35
## 192 3.85
## 193 1.98
## 194 14.70
## 195 8.73
## 196 5.92
## 197 4.39
## 198 8.80
## 199 2.01
## 200 12.91
## 201 11.43
## 202 4.95Mengenal fungsi glimpse
fungsi glimpse adalah untuk memberikan gambaran umum tentang data yang kita gunakan. Fungsi glimpse hampir mirip dengan fungsi print tetapi ditransposisikan: kolom berjalan kebawah halaman,dan data berjalan melintas
glimpse(data20)## Rows: 202
## Columns: 29
## $ Countries.and.areas <chr> "Afghanistan", "Albania", "Alg…
## $ Latitude <dbl> 33.93911, 41.15333, 28.03389, …
## $ Longitude <dbl> 67.709953, 20.168331, 1.659626…
## $ OOSR_Pre0Primary_Age_Male <int> 0, 4, 0, 0, 31, 14, 14, 2, 52,…
## $ OOSR_Pre0Primary_Age_Female <int> 0, 2, 0, 0, 39, 0, 4, 2, 50, 1…
## $ OOSR_Primary_Age_Male <int> 0, 6, 0, 0, 0, 0, 4, 0, 9, 0, …
## $ OOSR_Primary_Age_Female <int> 0, 3, 0, 0, 0, 0, 1, 0, 9, 0, …
## $ OOSR_Lower_Secondary_Age_Male <int> 0, 6, 0, 0, 0, 0, 1, 0, 11, 2,…
## $ OOSR_Lower_Secondary_Age_Female <int> 0, 1, 0, 0, 0, 0, 2, 0, 9, 3, …
## $ OOSR_Upper_Secondary_Age_Male <int> 44, 21, 0, 0, 0, 0, 14, 15, 16…
## $ OOSR_Upper_Secondary_Age_Female <int> 69, 15, 0, 0, 0, 0, 12, 7, 4, …
## $ Completion_Rate_Primary_Male <int> 67, 94, 93, 0, 63, 0, 0, 91, 9…
## $ Completion_Rate_Primary_Female <int> 40, 96, 93, 0, 57, 0, 0, 94, 9…
## $ Completion_Rate_Lower_Secondary_Male <int> 49, 98, 49, 0, 42, 0, 0, 70, 9…
## $ Completion_Rate_Lower_Secondary_Female <int> 26, 97, 65, 0, 32, 0, 0, 79, 9…
## $ Completion_Rate_Upper_Secondary_Male <int> 32, 76, 22, 0, 24, 0, 0, 46, 6…
## $ Completion_Rate_Upper_Secondary_Female <int> 14, 80, 37, 0, 15, 0, 0, 53, 7…
## $ Grade_2_3_Proficiency_Reading <int> 22, 0, 0, 0, 0, 0, 0, 76, 0, 9…
## $ Grade_2_3_Proficiency_Math <int> 25, 0, 0, 0, 0, 0, 0, 71, 0, 7…
## $ Primary_End_Proficiency_Reading <int> 13, 0, 0, 0, 0, 0, 0, 46, 0, 0…
## $ Primary_End_Proficiency_Math <int> 11, 0, 0, 0, 0, 0, 0, 56, 55, …
## $ Lower_Secondary_End_Proficiency_Reading <int> 0, 48, 21, 0, 0, 0, 0, 48, 0, …
## $ Lower_Secondary_End_Proficiency_Math <int> 0, 58, 19, 0, 0, 0, 0, 31, 50,…
## $ Youth_15_24_Literacy_Rate_Male <int> 74, 99, 98, 0, 0, 0, 0, 99, 0,…
## $ Youth_15_24_Literacy_Rate_Female <int> 56, 100, 97, 0, 0, 0, 0, 100, …
## $ Birth_Rate <dbl> 32.49, 11.78, 24.28, 7.20, 40.…
## $ Gross_Primary_Education_Enrollment <dbl> 104.0, 107.0, 109.9, 106.4, 11…
## $ Gross_Tertiary_Education_Enrollment <dbl> 9.7, 55.0, 51.4, 0.0, 9.3, 0.0…
## $ Unemployment_Rate <dbl> 11.12, 12.33, 11.70, 0.00, 6.8…Mengenal fungsi View
Fungsi View adalah untuk membuka tampilan interaktif yang memungkinkan untuk menjelajahi data frame yang ada. Kita bisa melihat struktur data,nilai-nilai dan sebagiannya
view(data20)Mengenal fungsi head
fungsi head menampilkan beberapa baris pertama dari objek data yang kita gunakan. Ini memberikan gambaran cepat tentang jenis data dan nilai yang ada di dalamnya.
head(data20)## Countries.and.areas Latitude Longitude OOSR_Pre0Primary_Age_Male
## 1 Afghanistan 33.93911 67.709953 0
## 2 Albania 41.15333 20.168331 4
## 3 Algeria 28.03389 1.659626 0
## 4 Andorra 42.50628 1.521801 0
## 5 Angola 11.20269 17.873887 31
## 6 Anguilla 18.22055 63.068615 14
## OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male OOSR_Primary_Age_Female
## 1 0 0 0
## 2 2 6 3
## 3 0 0 0
## 4 0 0 0
## 5 39 0 0
## 6 0 0 0
## OOSR_Lower_Secondary_Age_Male OOSR_Lower_Secondary_Age_Female
## 1 0 0
## 2 6 1
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## OOSR_Upper_Secondary_Age_Male OOSR_Upper_Secondary_Age_Female
## 1 44 69
## 2 21 15
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Completion_Rate_Primary_Male Completion_Rate_Primary_Female
## 1 67 40
## 2 94 96
## 3 93 93
## 4 0 0
## 5 63 57
## 6 0 0
## Completion_Rate_Lower_Secondary_Male Completion_Rate_Lower_Secondary_Female
## 1 49 26
## 2 98 97
## 3 49 65
## 4 0 0
## 5 42 32
## 6 0 0
## Completion_Rate_Upper_Secondary_Male Completion_Rate_Upper_Secondary_Female
## 1 32 14
## 2 76 80
## 3 22 37
## 4 0 0
## 5 24 15
## 6 0 0
## Grade_2_3_Proficiency_Reading Grade_2_3_Proficiency_Math
## 1 22 25
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Primary_End_Proficiency_Reading Primary_End_Proficiency_Math
## 1 13 11
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Lower_Secondary_End_Proficiency_Reading Lower_Secondary_End_Proficiency_Math
## 1 0 0
## 2 48 58
## 3 21 19
## 4 0 0
## 5 0 0
## 6 0 0
## Youth_15_24_Literacy_Rate_Male Youth_15_24_Literacy_Rate_Female Birth_Rate
## 1 74 56 32.49
## 2 99 100 11.78
## 3 98 97 24.28
## 4 0 0 7.20
## 5 0 0 40.73
## 6 0 0 0.00
## Gross_Primary_Education_Enrollment Gross_Tertiary_Education_Enrollment
## 1 104.0 9.7
## 2 107.0 55.0
## 3 109.9 51.4
## 4 106.4 0.0
## 5 113.5 9.3
## 6 0.0 0.0
## Unemployment_Rate
## 1 11.12
## 2 12.33
## 3 11.70
## 4 0.00
## 5 6.89
## 6 0.00Mengenal fungsi summary
Fungsi summary memberikan ringkasan statistik singkat dari data. Untuk data frame, ini akan memberikan informasi seperti rata-rata, median, nilai minimum dan maksimum, dan quartil.
summary(data20)## Countries.and.areas Latitude Longitude
## Length:202 Min. : 0.02356 Min. : 0.8248
## Class :character 1st Qu.:11.68506 1st Qu.: 18.6657
## Mode :character Median :21.20786 Median : 43.5181
## Mean :25.08142 Mean : 55.1669
## 3rd Qu.:39.90179 3rd Qu.: 77.6850
## Max. :64.96305 Max. :178.0650
## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 9.00 Median : 7.00 Median : 1.000
## Mean :19.66 Mean :19.28 Mean : 5.282
## 3rd Qu.:31.00 3rd Qu.:30.00 3rd Qu.: 6.000
## Max. :96.00 Max. :96.00 Max. :58.000
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 2.000
## Mean : 5.569 Mean : 8.708
## 3rd Qu.: 6.750 3rd Qu.:12.750
## Max. :67.000 Max. :61.000
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.25
## Median : 2.000 Median :15.00
## Mean : 8.832 Mean :20.29
## 3rd Qu.:10.750 3rd Qu.:32.75
## Max. :70.000 Max. :84.00
## OOSR_Upper_Secondary_Age_Female Completion_Rate_Primary_Male
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.25 1st Qu.: 0.00
## Median :12.00 Median : 37.50
## Mean :19.98 Mean : 41.72
## 3rd Qu.:30.00 3rd Qu.: 87.50
## Max. :89.00 Max. :100.00
## Completion_Rate_Primary_Female Completion_Rate_Lower_Secondary_Male
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 33.00 Median : 18.50
## Mean : 42.13 Mean : 32.74
## 3rd Qu.: 92.00 3rd Qu.: 64.75
## Max. :100.00 Max. :100.00
## Completion_Rate_Lower_Secondary_Female Completion_Rate_Upper_Secondary_Male
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 12.00 Median : 9.50
## Mean : 33.17 Mean : 22.68
## 3rd Qu.: 70.75 3rd Qu.: 40.00
## Max. :100.00 Max. :100.00
## Completion_Rate_Upper_Secondary_Female Grade_2_3_Proficiency_Reading
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 5.50 Median : 0.00
## Mean : 23.07 Mean :21.98
## 3rd Qu.: 38.75 3rd Qu.:38.75
## Max. :100.00 Max. :99.00
## Grade_2_3_Proficiency_Math Primary_End_Proficiency_Reading
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean :17.44 Mean :10.72
## 3rd Qu.:32.75 3rd Qu.: 0.00
## Max. :97.00 Max. :99.00
## Primary_End_Proficiency_Math Lower_Secondary_End_Proficiency_Reading
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean :10.38 Mean :25.79
## 3rd Qu.: 0.00 3rd Qu.:56.75
## Max. :89.00 Max. :89.00
## Lower_Secondary_End_Proficiency_Math Youth_15_24_Literacy_Rate_Male
## Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.0
## Median : 0.00 Median : 0.0
## Mean :24.45 Mean : 35.8
## 3rd Qu.:50.75 3rd Qu.: 94.0
## Max. :94.00 Max. :100.0
## Youth_15_24_Literacy_Rate_Female Birth_Rate
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.:10.36
## Median : 0.00 Median :17.55
## Mean : 35.08 Mean :18.91
## 3rd Qu.: 96.75 3rd Qu.:27.69
## Max. :100.00 Max. :46.08
## Gross_Primary_Education_Enrollment Gross_Tertiary_Education_Enrollment
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 97.20 1st Qu.: 9.00
## Median :101.85 Median : 24.85
## Mean : 94.94 Mean : 34.39
## 3rd Qu.:107.30 3rd Qu.: 59.98
## Max. :142.50 Max. :136.60
## Unemployment_Rate
## Min. : 0.000
## 1st Qu.: 2.303
## Median : 4.585
## Mean : 6.000
## 3rd Qu.: 8.655
## Max. :28.180Mengenal fungsi str
Fungsi str memberikan struktur objek. Ini mencakup informasi tentang tipe data, jumlah observasi, dan nama-nama kolom beserta tipe data masing-masing kolom.
str(data20)## 'data.frame': 202 obs. of 29 variables:
## $ Countries.and.areas : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Latitude : num 33.9 41.2 28 42.5 11.2 ...
## $ Longitude : num 67.71 20.17 1.66 1.52 17.87 ...
## $ OOSR_Pre0Primary_Age_Male : int 0 4 0 0 31 14 14 2 52 13 ...
## $ OOSR_Pre0Primary_Age_Female : int 0 2 0 0 39 0 4 2 50 14 ...
## $ OOSR_Primary_Age_Male : int 0 6 0 0 0 0 4 0 9 0 ...
## $ OOSR_Primary_Age_Female : int 0 3 0 0 0 0 1 0 9 0 ...
## $ OOSR_Lower_Secondary_Age_Male : int 0 6 0 0 0 0 1 0 11 2 ...
## $ OOSR_Lower_Secondary_Age_Female : int 0 1 0 0 0 0 2 0 9 3 ...
## $ OOSR_Upper_Secondary_Age_Male : int 44 21 0 0 0 0 14 15 16 10 ...
## $ OOSR_Upper_Secondary_Age_Female : int 69 15 0 0 0 0 12 7 4 6 ...
## $ Completion_Rate_Primary_Male : int 67 94 93 0 63 0 0 91 99 0 ...
## $ Completion_Rate_Primary_Female : int 40 96 93 0 57 0 0 94 99 0 ...
## $ Completion_Rate_Lower_Secondary_Male : int 49 98 49 0 42 0 0 70 95 0 ...
## $ Completion_Rate_Lower_Secondary_Female : int 26 97 65 0 32 0 0 79 99 0 ...
## $ Completion_Rate_Upper_Secondary_Male : int 32 76 22 0 24 0 0 46 69 0 ...
## $ Completion_Rate_Upper_Secondary_Female : int 14 80 37 0 15 0 0 53 79 0 ...
## $ Grade_2_3_Proficiency_Reading : int 22 0 0 0 0 0 0 76 0 94 ...
## $ Grade_2_3_Proficiency_Math : int 25 0 0 0 0 0 0 71 0 70 ...
## $ Primary_End_Proficiency_Reading : int 13 0 0 0 0 0 0 46 0 0 ...
## $ Primary_End_Proficiency_Math : int 11 0 0 0 0 0 0 56 55 64 ...
## $ Lower_Secondary_End_Proficiency_Reading: int 0 48 21 0 0 0 0 48 0 80 ...
## $ Lower_Secondary_End_Proficiency_Math : int 0 58 19 0 0 0 0 31 50 78 ...
## $ Youth_15_24_Literacy_Rate_Male : int 74 99 98 0 0 0 0 99 0 0 ...
## $ Youth_15_24_Literacy_Rate_Female : int 56 100 97 0 0 0 0 100 0 0 ...
## $ Birth_Rate : num 32.5 11.8 24.3 7.2 40.7 ...
## $ Gross_Primary_Education_Enrollment : num 104 107 110 106 114 ...
## $ Gross_Tertiary_Education_Enrollment : num 9.7 55 51.4 0 9.3 ...
## $ Unemployment_Rate : num 11.12 12.33 11.7 0 6.89 ...Inspeksi Data Frame
Selelum melakukan data wrangling lebih lanjut, hal utama yang dikerjakan ada inspeksi terhadap dataframe, diantaranya menampilkan jumlah baris dan kolom, melihat statistik ringkasan, melihat struktur dataframe serta melihat beberapa baris data (baik baris teratas maupun terbawah)
#Menghitung Jumlah Baris
nrow(data20)## [1] 202#Menghitung Jumlah Kolom
ncol(data20)## [1] 29#Menghitung dimensi
dim(data20)## [1] 202 29#Mengambil nilai dari baris ke 1 sampai dengan 20 dengan kolom 4 sampai kolom 10
data20 [1:20,4:10] ## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 1 0 0 0
## 2 4 2 6
## 3 0 0 0
## 4 0 0 0
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## 11 0 0 0
## 12 32 19 10
## 13 0 0 0
## 14 31 28 2
## 15 0 0 0
## 16 6 10 1
## 17 0 4 1
## 18 3 2 1
## 19 18 16 1
## 20 15 16 3
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## 1 0 0
## 2 3 6
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 9 11
## 10 0 2
## 11 0 1
## 12 7 0
## 13 0 23
## 14 3 7
## 15 0 0
## 16 2 7
## 17 2 1
## 18 0 1
## 19 1 9
## 20 10 27
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## 1 0 44
## 2 1 21
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 2 14
## 8 0 15
## 9 9 16
## 10 3 10
## 11 0 10
## 12 0 0
## 13 21 29
## 14 0 18
## 15 0 41
## 16 3 7
## 17 1 6
## 18 1 1
## 19 11 38
## 20 43 46Membuat Data baru
membuat sebuah data baru dengan hanya mengambil beberapa peubah dari data sebelumnya
data21 <- data20 [1:20,4:10]
data21## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 1 0 0 0
## 2 4 2 6
## 3 0 0 0
## 4 0 0 0
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## 11 0 0 0
## 12 32 19 10
## 13 0 0 0
## 14 31 28 2
## 15 0 0 0
## 16 6 10 1
## 17 0 4 1
## 18 3 2 1
## 19 18 16 1
## 20 15 16 3
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## 1 0 0
## 2 3 6
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 9 11
## 10 0 2
## 11 0 1
## 12 7 0
## 13 0 23
## 14 3 7
## 15 0 0
## 16 2 7
## 17 2 1
## 18 0 1
## 19 1 9
## 20 10 27
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## 1 0 44
## 2 1 21
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 2 14
## 8 0 15
## 9 9 16
## 10 3 10
## 11 0 10
## 12 0 0
## 13 21 29
## 14 0 18
## 15 0 41
## 16 3 7
## 17 1 6
## 18 1 1
## 19 11 38
## 20 43 46Matriks Korelasi
data23 <- data21 [5:10,1:4]
data23## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## OOSR_Primary_Age_Female
## 5 0
## 6 0
## 7 1
## 8 0
## 9 9
## 10 0#Matriks korelasi Pearson
matriks_korelasi_person <- cor(data23,method = 'pearson')
matriks_korelasi_person## OOSR_Pre0Primary_Age_Male
## OOSR_Pre0Primary_Age_Male 1.0000000
## OOSR_Pre0Primary_Age_Female 0.9319199
## OOSR_Primary_Age_Male 0.7601455
## OOSR_Primary_Age_Female 0.8455200
## OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## OOSR_Pre0Primary_Age_Male 0.9319199 0.7601455
## OOSR_Pre0Primary_Age_Female 1.0000000 0.5831280
## OOSR_Primary_Age_Male 0.5831280 1.0000000
## OOSR_Primary_Age_Female 0.7092244 0.9444208
## OOSR_Primary_Age_Female
## OOSR_Pre0Primary_Age_Male 0.8455200
## OOSR_Pre0Primary_Age_Female 0.7092244
## OOSR_Primary_Age_Male 0.9444208
## OOSR_Primary_Age_Female 1.0000000#matriks korelasi spearman
matriks_korelasi_spearman <- cor(data23,method = 'spearman')
matriks_korelasi_spearman## OOSR_Pre0Primary_Age_Male
## OOSR_Pre0Primary_Age_Male 1.0000000
## OOSR_Pre0Primary_Age_Female 0.6667367
## OOSR_Primary_Age_Male 0.6002450
## OOSR_Primary_Age_Female 0.6002450
## OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## OOSR_Pre0Primary_Age_Male 0.6667367 0.6002450
## OOSR_Pre0Primary_Age_Female 1.0000000 0.5070926
## OOSR_Primary_Age_Male 0.5070926 1.0000000
## OOSR_Primary_Age_Female 0.5070926 1.0000000
## OOSR_Primary_Age_Female
## OOSR_Pre0Primary_Age_Male 0.6002450
## OOSR_Pre0Primary_Age_Female 0.5070926
## OOSR_Primary_Age_Male 1.0000000
## OOSR_Primary_Age_Female 1.0000000Korelasi
#korelasi pearson
korelasi<- cor(data23$OOSR_Pre0Primary_Age_Female,data23$OOSR_Primary_Age_Female,method = 'pearson')
korelasi## [1] 0.7092244#korelasi spearman
korelasi <- cor(data23$OOSR_Pre0Primary_Age_Female,data23$OOSR_Primary_Age_Female,method = 'spearman')
korelasi## [1] 0.5070926Boxplot
boxplot(data21, main="Bxplot", col=rainbow(length(data21)))
histogram
library(viridis)## Warning: package 'viridis' was built under R version 4.3.2## Loading required package: viridisLitehist(data20$Longitude, main="histogram",col =viridis(8),xlab="longitude")
Pie Chart
# Membuat pie chart
pie(data21$OOSR_Upper_Secondary_Age_Male, main = " Pie Chart(OOSR_Upper_Secondary_Age_Male)", col =viridis(7))
# Mengganti label
new_labels <- c("Afghanistan", "Albania", "Algeria", "Andora","Angola","Anguilla","Antigua dan Barbuda","Argentina","Armenia","Australia","Autria","Azerbaijan","The Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin")
pie(data21$OOSR_Upper_Secondary_Age_Male, labels = new_labels, main = "OOSR_Upper_Secondary_Age_Male", col = rainbow(length(data21$OOSR_Upper_Secondary_Age_Male)))
model regresi linear
library(ggplot2)
data24 <- data20 [1:20,4:10]
data24## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 1 0 0 0
## 2 4 2 6
## 3 0 0 0
## 4 0 0 0
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## 11 0 0 0
## 12 32 19 10
## 13 0 0 0
## 14 31 28 2
## 15 0 0 0
## 16 6 10 1
## 17 0 4 1
## 18 3 2 1
## 19 18 16 1
## 20 15 16 3
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## 1 0 0
## 2 3 6
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 9 11
## 10 0 2
## 11 0 1
## 12 7 0
## 13 0 23
## 14 3 7
## 15 0 0
## 16 2 7
## 17 2 1
## 18 0 1
## 19 1 9
## 20 10 27
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## 1 0 44
## 2 1 21
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 2 14
## 8 0 15
## 9 9 16
## 10 3 10
## 11 0 10
## 12 0 0
## 13 21 29
## 14 0 18
## 15 0 41
## 16 3 7
## 17 1 6
## 18 1 1
## 19 11 38
## 20 43 46# Membuat model regresi linear
model <- lm(OOSR_Upper_Secondary_Age_Male ~ OOSR_Pre0Primary_Age_Male, data = data24)
# Plot data dengan ggplot2
p <- ggplot(data24, aes(x = OOSR_Pre0Primary_Age_Male, y = OOSR_Upper_Secondary_Age_Male)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "blue") +
labs(title = "Grafik Regresi Linear Sederhana", x = "OOSR_Pre0Primary_Age_Male", y = "OOSR_Upper_Secondary_Age_Male")
# Menambahkan teks dengan nilai slope dan intercept
p + geom_text(x = 25, y = 25, label = paste("Slope =", round(coef(model)[2], 2), "\nIntercept =", round(coef(model)[1], 2)), col = "black", hjust = 0)## `geom_smooth()` using formula = 'y ~ x'
Filter data
fungsi filter memilih beberapa kolom tertentu, dan kemudian menyaring baris berdasarkan apa yang kita mau. Hasilnya adalah subset dari data frame yang hanya berisi kolom-kolom yang diinginkan dan baris-baris yang sesuai dengan kondisi filter.
data20 %>%
select(Countries.and.areas,Birth_Rate,Gross_Primary_Education_Enrollment) %>%
filter(Countries.and.areas%in%c("Indonesia","Iran","Japan","South Korea","Russia","Portugal"))## Countries.and.areas Birth_Rate Gross_Primary_Education_Enrollment
## 1 Indonesia 18.07 106.4
## 2 Iran 18.78 110.7
## 3 Japan 7.40 98.8
## 4 Portugal 8.50 106.2
## 5 South Korea 6.40 98.1
## 6 Russia 11.50 102.6Mengurutkan data
mengurutkan sesuai kolom yang dipilih, dengan nilai kolom dari kecil ke besar atau sebaliknya
data20 %>%
select(Countries.and.areas,Gross_Primary_Education_Enrollment) %>%
filter(Gross_Primary_Education_Enrollment > 80 & Gross_Primary_Education_Enrollment <= 130) %>%
arrange(Gross_Primary_Education_Enrollment)## Countries.and.areas Gross_Primary_Education_Enrollment
## 1 Senegal 81.0
## 2 The Bahamas 81.4
## 3 Jordan 81.5
## 4 Syria 81.7
## 5 Marshall Islands 84.7
## 6 Nigeria 84.7
## 7 Liberia 85.1
## 8 Romania 85.2
## 9 Chad 86.8
## 10 Turkmenistan 88.4
## 11 Bulgaria 89.3
## 12 Moldova 90.6
## 13 Jamaica 91.0
## 14 Guinea 91.5
## 15 Honduras 91.5
## 16 Kuwait 92.4
## 17 Armenia 92.7
## 18 Turkey 93.2
## 19 Yemen 93.6
## 20 Tanzania 94.2
## 21 Pakistan 94.3
## 22 Panama 94.4
## 23 El Salvador 94.8
## 24 Lebanon 95.1
## 25 Burkina Faso 96.1
## 26 Croatia 96.5
## 27 Maldives 97.1
## 28 Estonia 97.2
## 29 Federated States of Micronesia 97.2
## 30 Venezuela 97.2
## 31 Guyana 97.8
## 32 The Gambia 98.0
## 33 South Korea 98.1
## 34 Bolivia 98.2
## 35 Georgia 98.6
## 36 Slovakia 98.7
## 37 Zambia 98.7
## 38 Japan 98.8
## 39 Ukraine 99.0
## 40 Cyprus 99.3
## 41 Bahrain 99.4
## 42 Barbados 99.4
## 43 Latvia 99.4
## 44 Comoros 99.5
## 45 Greece 99.6
## 46 Azerbaijan 99.7
## 47 Ivory Coast 99.8
## 48 Saudi Arabia 99.8
## 49 Thailand 99.8
## 50 Mauritania 99.9
## 51 Montenegro 100.0
## 52 New Zealand 100.0
## 53 Poland 100.0
## 54 Bhutan 100.1
## 55 China 100.2
## 56 Finland 100.2
## 57 Sri Lanka 100.2
## 58 Australia 100.3
## 59 Norway 100.3
## 60 Serbia 100.3
## 61 Iceland 100.4
## 62 Seychelles 100.4
## 63 Slovenia 100.4
## 64 Belarus 100.5
## 65 Singapore 100.6
## 66 Czech Republic 100.7
## 67 Hungary 100.8
## 68 Canada 100.9
## 69 Republic of Ireland 100.9
## 70 South Africa 100.9
## 71 Tajikistan 100.9
## 72 Ethiopia 101.0
## 73 Mauritius 101.1
## 74 United Kingdom 101.2
## 75 Denmark 101.3
## 76 Kiribati 101.3
## 77 Chile 101.4
## 78 United States 101.8
## 79 Cuba 101.9
## 80 Guatemala 101.9
## 81 Italy 101.9
## 82 Central African Republic 102.0
## 83 Luxembourg 102.3
## 84 Laos 102.4
## 85 France 102.5
## 86 Russia 102.6
## 87 Saint Lucia 102.6
## 88 Spain 102.7
## 89 Uganda 102.7
## 90 Austria 103.1
## 91 Botswana 103.2
## 92 Brunei 103.2
## 93 Kenya 103.2
## 94 Ecuador 103.3
## 95 Cameroon 103.4
## 96 Oman 103.4
## 97 Qatar 103.8
## 98 Belgium 103.9
## 99 Lithuania 103.9
## 100 Afghanistan 104.0
## 101 Cape Verde 104.0
## 102 Germany 104.0
## 103 Mongolia 104.0
## 104 Netherlands 104.2
## 105 Uzbekistan 104.2
## 106 Kazakhstan 104.4
## 107 Paraguay 104.4
## 108 Liechtenstein 104.7
## 109 Ghana 104.8
## 110 Israel 104.9
## 111 Antigua and Barbuda 105.0
## 112 Malta 105.0
## 113 Switzerland 105.2
## 114 Malaysia 105.3
## 115 Dominican Republic 105.7
## 116 Mexico 105.8
## 117 Portugal 106.2
## 118 Solomon Islands 106.2
## 119 Trinidad and Tobago 106.2
## 120 Egypt 106.3
## 121 Andorra 106.4
## 122 Fiji 106.4
## 123 Indonesia 106.4
## 124 Republic of the Congo 106.6
## 125 S�������\xef\xbf 106.8
## 126 Grenada 106.9
## 127 Peru 106.9
## 128 Albania 107.0
## 129 Cambodia 107.4
## 130 Philippines 107.5
## 131 Kyrgyzstan 107.6
## 132 Democratic Republic of the Congo 108.0
## 133 San Marino 108.1
## 134 United Arab Emirates 108.4
## 135 Papua New Guinea 108.5
## 136 Uruguay 108.5
## 137 Iraq 108.7
## 138 Saint Kitts and Nevis 108.7
## 139 Suriname 108.8
## 140 Libya 109.0
## 141 Vanuatu 109.3
## 142 Argentina 109.7
## 143 Algeria 109.9
## 144 Zimbabwe 109.9
## 145 Samoa 110.5
## 146 Vietnam 110.6
## 147 Iran 110.7
## 148 Belize 111.7
## 149 Myanmar 112.3
## 150 Mozambique 112.6
## 151 Palau 112.6
## 152 North Korea 112.8
## 153 Sierra Leone 112.8
## 154 India 113.0
## 155 Costa Rica 113.3
## 156 Saint Vincent and the Grenadines 113.4
## 157 Angola 113.5
## 158 Haiti 113.6
## 159 Morocco 113.9
## 160 Colombia 114.5
## 161 Dominica 114.7
## 162 East Timor 115.3
## 163 Brazil 115.4
## 164 Tunisia 115.4
## 165 Tonga 116.3
## 166 Bangladesh 116.5
## 167 Guinea0Bissau 118.7
## 168 Nicaragua 120.6
## 169 Lesotho 120.9
## 170 Burundi 121.4
## 171 Benin 122.0
## 172 Togo 123.8
## 173 Namibia 124.2
## 174 Sweden 126.6Summerize
Fungsi summerize digunakan untuk merangkum banyak baris (amatan) menjadi satu baris, rangkuman ini bisa berupa mean, median,variance,sd(standar deviasi).
data20 %>%
summarize(mean = mean(Birth_Rate,na.rm = TRUE),
median = median(Birth_Rate,na.rm = TRUE),
sd = sd(Birth_Rate,na.rm=TRUE),
q1 = quantile(Birth_Rate,probs = 0.25,na.rm = TRUE))## mean median sd q1
## 1 18.91401 17.55 10.82818 10.355Heatmap Heatmap korelasi dari 7 peubah pada data25
library(ggplot2)
library(GGally)## Warning: package 'GGally' was built under R version 4.3.2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(viridis)
data25 <- data20 [1:20,4:10]
data25## OOSR_Pre0Primary_Age_Male OOSR_Pre0Primary_Age_Female OOSR_Primary_Age_Male
## 1 0 0 0
## 2 4 2 6
## 3 0 0 0
## 4 0 0 0
## 5 31 39 0
## 6 14 0 0
## 7 14 4 4
## 8 2 2 0
## 9 52 50 9
## 10 13 14 0
## 11 0 0 0
## 12 32 19 10
## 13 0 0 0
## 14 31 28 2
## 15 0 0 0
## 16 6 10 1
## 17 0 4 1
## 18 3 2 1
## 19 18 16 1
## 20 15 16 3
## OOSR_Primary_Age_Female OOSR_Lower_Secondary_Age_Male
## 1 0 0
## 2 3 6
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 9 11
## 10 0 2
## 11 0 1
## 12 7 0
## 13 0 23
## 14 3 7
## 15 0 0
## 16 2 7
## 17 2 1
## 18 0 1
## 19 1 9
## 20 10 27
## OOSR_Lower_Secondary_Age_Female OOSR_Upper_Secondary_Age_Male
## 1 0 44
## 2 1 21
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 2 14
## 8 0 15
## 9 9 16
## 10 3 10
## 11 0 10
## 12 0 0
## 13 21 29
## 14 0 18
## 15 0 41
## 16 3 7
## 17 1 6
## 18 1 1
## 19 11 38
## 20 43 46ggcorr(data25,method =c("pairwise.complete.obs","pearson"),
low =viridis(5),mid ="yellow",high = "#3B9AB2" )
Menyimpan Data
write.csv(data20, "datacontoh.csv", row.names = F)
write.csv(data21, "datacontoh.csv", row.names = F)
write.csv(data23, "datacontoh.csv", row.names = F)