Pengenalan Tidyverse
Freditasari P.H.
2025-08-28
Materi ini akan membahas pengenalan meta-package
tidyverse yang merupakan kumpulan dari 8 package inti,
yaitu
readrdigunakan untuk membaca data tabular seperti csv,tsv dan fwfdplyrdigunakan untuk memanipulasi dataggplot2digunakan untuk visualisasi data berbasiskan Grammar of Graphicstidyrdigunakan untuk merapihkan (tidying) datapurrrdigunakan untuk functional programmingtibbledigunakan sebagai alternatifdata.frameyang lebih konsistenforcatsdigunakan untuk memanipulasi data berupa yang bertipefactorstringrdigunakan untuk memanipulasi data bertipestring
Mengenal dplyr
dplyr adalah package yang dapat digunakan untuk
melakukan manipulasi data seperti melihat gambaran umum data, membuat
kolom baru, menyeleksi kolom, menyaring baris (filtering), melakukan
agregasi data dan masih banyak lagi.
Memanggil library tidyverse
Data
Global Country Information Dataset 2023
Dataset komprehensif ini menyediakan banyak informasi tentang semua negara di seluruh dunia, yang mencakup berbagai indikator dan atribut. Data ini mencakup statistik demografi, indikator ekonomi, faktor lingkungan, metrik perawatan kesehatan, statistik pendidikan, dan masih banyak lagi. Dengan setiap negara terwakili, dataset ini menawarkan perspektif global yang lengkap tentang berbagai aspek negara, memungkinkan analisis mendalam dan perbandingan lintas negara.
Data dapat diperoleh dengan mendownload link dibawah ini
Melakukan Input Data
country_data <- read.csv(file.choose(), header = TRUE, sep = ",")Fungsi Glimpse
Fungsi glimpse digunakan untuk mendapatkan gambaran umum data seperti tipe data (dbl,int,chr,factor,lgl), snapshoot amatan-amatan awal, banyaknya baris dan banyaknya kolom
glimpse(country_data)## Rows: 195
## Columns: 35
## $ Country <chr> "Afghanistan", "Albania", "A~
## $ Density..P.Km2. <chr> "60", "105", "18", "164", "2~
## $ Abbreviation <chr> "AF", "AL", "DZ", "AD", "AO"~
## $ Agricultural.Land.... <chr> "58.10%", "43.10%", "17.40%"~
## $ Land.Area.Km2. <chr> "652,230", "28,748", "2,381,~
## $ Armed.Forces.size <chr> "323,000", "9,000", "317,000~
## $ Birth.Rate <dbl> 32.49, 11.78, 24.28, 7.20, 4~
## $ Calling.Code <int> 93, 355, 213, 376, 244, 1, 5~
## $ Capital.Major.City <chr> "Kabul", "Tirana", "Algiers"~
## $ Co2.Emissions <chr> "8,672", "4,536", "150,006",~
## $ CPI <chr> "149.9", "119.05", "151.36",~
## $ CPI.Change.... <chr> "2.30%", "1.40%", "2.00%", "~
## $ Currency.Code <chr> "AFN", "ALL", "DZD", "EUR", ~
## $ Fertility.Rate <dbl> 4.47, 1.62, 3.02, 1.27, 5.52~
## $ Forested.Area.... <chr> "2.10%", "28.10%", "0.80%", ~
## $ Gasoline.Price <chr> "$0.70 ", "$1.36 ", "$0.28 "~
## $ GDP <chr> "$19,101,353,833 ", "$15,278~
## $ Gross.primary.education.enrollment.... <chr> "104.00%", "107.00%", "109.9~
## $ Gross.tertiary.education.enrollment.... <chr> "9.70%", "55.00%", "51.40%",~
## $ Infant.mortality <dbl> 47.9, 7.8, 20.1, 2.7, 51.6, ~
## $ Largest.city <chr> "Kabul", "Tirana", "Algiers"~
## $ Life.expectancy <dbl> 64.5, 78.5, 76.7, NA, 60.8, ~
## $ Maternal.mortality.ratio <int> 638, 15, 112, NA, 241, 42, 3~
## $ Minimum.wage <chr> "$0.43 ", "$1.12 ", "$0.95 "~
## $ Official.language <chr> "Pashto", "Albanian", "Arabi~
## $ Out.of.pocket.health.expenditure <chr> "78.40%", "56.90%", "28.10%"~
## $ Physicians.per.thousand <dbl> 0.28, 1.20, 1.72, 3.33, 0.21~
## $ Population <chr> "38,041,754", "2,854,191", "~
## $ Population..Labor.force.participation.... <chr> "48.90%", "55.70%", "41.20%"~
## $ Tax.revenue.... <chr> "9.30%", "18.60%", "37.20%",~
## $ Total.tax.rate <chr> "71.40%", "36.60%", "66.10%"~
## $ Unemployment.rate <chr> "11.12%", "12.33%", "11.70%"~
## $ Urban_population <chr> "9,797,273", "1,747,593", "3~
## $ Latitude <dbl> 33.939110, 41.153332, 28.033~
## $ Longitude <dbl> 67.709953, 20.168331, 1.6596~Fungsi Select
Fungsi select digunakan untuk menyeleksi kolom dari dataset yang ada
select(.data = country_data,Country,`Birth.Rate`,Urban_population,GDP, `Gasoline.Price`)## Country Birth.Rate Urban_population
## 1 Afghanistan 32.49 9,797,273
## 2 Albania 11.78 1,747,593
## 3 Algeria 24.28 31,510,100
## 4 Andorra 7.20 67,873
## 5 Angola 40.73 21,061,025
## 6 Antigua and Barbuda 15.33 23,800
## 7 Argentina 17.02 41,339,571
## 8 Armenia 13.99 1,869,848
## 9 Australia 12.60 21,844,756
## 10 Austria 9.70 5,194,416
## 11 Azerbaijan 14.00 5,616,165
## 12 The Bahamas 13.97 323,784
## 13 Bahrain 13.99 1,467,109
## 14 Bangladesh 18.18 60,987,417
## 15 Barbados 10.65 89,431
## 16 Belarus 9.90 7,482,982
## 17 Belgium 10.30 11,259,082
## 18 Belize 20.79 179,039
## 19 Benin 36.22 5,648,149
## 20 Bhutan 17.26 317,538
## 21 Bolivia 21.75 8,033,035
## 22 Bosnia and Herzegovina 8.11 1,605,144
## 23 Botswana 24.82 1,616,550
## 24 Brazil 13.92 183,241,641
## 25 Brunei 14.90 337,711
## 26 Bulgaria 8.90 5,256,027
## 27 Burkina Faso 37.93 6,092,349
## 28 Burundi 39.01 1,541,177
## 29 Ivory Coast 35.74 13,176,900
## 30 Cape Verde 19.49 364,029
## 31 Cambodia 22.46 3,924,621
## 32 Cameroon 35.39 14,741,256
## 33 Canada 10.10 30,628,482
## 34 Central African Republic 35.35 1,982,064
## 35 Chad 42.17 3,712,273
## 36 Chile 12.43 16,610,135
## 37 China 10.90 842,933,962
## 38 Colombia 14.88 40,827,302
## 39 Comoros 31.88 248,152
## 40 Republic of the Congo 32.86 3,625,010
## 41 Costa Rica 13.97 4,041,885
## 42 Croatia 9.00 2,328,318
## 43 Cuba 10.17 8,739,135
## 44 Cyprus 10.46 800,708
## 45 Czech Republic 10.70 7,887,156
## 46 Democratic Republic of the Congo 41.18 39,095,679
## 47 Denmark 10.60 5,119,978
## 48 Djibouti 21.47 758,549
## 49 Dominica 12.00 50,830
## 50 Dominican Republic 19.51 8,787,475
## 51 Ecuador 19.72 11,116,711
## 52 Egypt 26.38 42,895,824
## 53 El Salvador 18.25 4,694,702
## 54 Equatorial Guinea 33.24 984,812
## 55 Eritrea 30.30 1,149,670
## 56 Estonia 10.90 916,024
## 57 Eswatini NA
## 58 Ethiopia 32.34 23,788,710
## 59 Fiji 21.28 505,048
## 60 Finland 8.60 4,716,888
## 61 France 11.30 54,123,364
## 62 Gabon 31.61 1,949,694
## 63 The Gambia 38.54 1,453,958
## 64 Georgia 13.47 2,196,476
## 65 Germany 9.50 64,324,835
## 66 Ghana 29.41 17,249,054
## 67 Greece 8.10 8,507,474
## 68 Grenada 16.47 40,765
## 69 Guatemala 24.56 8,540,945
## 70 Guinea 36.36 4,661,505
## 71 Guinea-Bissau 35.13 840,922
## 72 Guyana 19.97 208,912
## 73 Haiti 24.35 6,328,948
## 74 Vatican City NA
## 75 Honduras 21.60 5,626,433
## 76 Hungary 9.60 6,999,582
## 77 Iceland 12.00 339,110
## 78 India 17.86 471,031,528
## 79 Indonesia 18.07 151,509,724
## 80 Iran 18.78 62,509,623
## 81 Iraq 29.08 27,783,368
## 82 Republic of Ireland 12.50 3,133,123
## 83 Israel 20.80 8,374,393
## 84 Italy 7.30 42,651,966
## 85 Jamaica 16.10 1,650,594
## 86 Japan 7.40 115,782,416
## 87 Jordan 21.98 9,213,048
## 88 Kazakhstan 21.77 10,652,915
## 89 Kenya 28.75 14,461,523
## 90 Kiribati 27.89 64,489
## 91 Kuwait 13.94 4,207,083
## 92 Kyrgyzstan 27.10 2,362,644
## 93 Laos 23.55 2,555,552
## 94 Latvia 10.00 1,304,943
## 95 Lebanon 17.55 6,084,994
## 96 Lesotho 26.81 607,508
## 97 Liberia 33.04 2,548,426
## 98 Libya 18.83 5,448,597
## 99 Liechtenstein 9.90 5,464
## 100 Lithuania 10.00 1,891,013
## 101 Luxembourg 10.30 565,488
## 102 Madagascar 32.66 10,210,849
## 103 Malawi 34.12 3,199,301
## 104 Malaysia 16.75 24,475,766
## 105 Maldives 14.20 213,645
## 106 Mali 41.54 8,479,688
## 107 Malta 9.20 475,902
## 108 Marshall Islands 29.03 45,514
## 109 Mauritania 33.69 2,466,821
## 110 Mauritius 10.20 515,980
## 111 Mexico 17.60 102,626,859
## 112 Federated States of Micronesia 22.82 25,963
## 113 Moldova 10.10 1,135,502
## 114 Monaco 5.90 38,964
## 115 Mongolia 24.13 2,210,626
## 116 Montenegro 11.73 417,765
## 117 Morocco 18.94 22,975,026
## 118 Mozambique 37.52 11,092,106
## 119 Myanmar 17.55 16,674,093
## 120 Namibia 28.64 1,273,258
## 121 Nauru NA
## 122 Nepal 19.89 5,765,513
## 123 Netherlands 9.70 15,924,729
## 124 New Zealand 11.98 4,258,860
## 125 Nicaragua 20.64 3,846,137
## 126 Niger 46.08 3,850,231
## 127 Nigeria 37.91 102,806,948
## 128 North Korea 13.89 15,947,412
## 129 North Macedonia NA
## 130 Norway 10.40 4,418,218
## 131 Oman 19.19 4,250,777
## 132 Pakistan 28.25 79,927,762
## 133 Palau 14.00 14,491
## 134 Palestinian National Authority NA
## 135 Panama 18.98 2,890,084
## 136 Papua New Guinea 27.07 1,162,834
## 137 Paraguay 20.57 4,359,150
## 138 Peru 17.95 25,390,339
## 139 Philippines 20.55 50,975,903
## 140 Poland 10.20 22,796,574
## 141 Portugal 8.50 6,753,579
## 142 Qatar 9.54 2,809,071
## 143 Romania 9.60 10,468,793
## 144 Russia 11.50 107,683,889
## 145 Rwanda 31.70 2,186,104
## 146 Saint Kitts and Nevis 12.60 16,269
## 147 Saint Lucia 12.00 34,280
## 148 Saint Vincent and the Grenadines 14.24 58,185
## 149 Samoa 24.38 35,588
## 150 San Marino 6.80 32,969
## 151 S����������� 31.54 158,277
## 152 Saudi Arabia 17.80 28,807,838
## 153 Senegal 34.52 7,765,706
## 154 Serbia 9.20 3,907,243
## 155 Seychelles 17.10 55,762
## 156 Sierra Leone 33.41 3,319,366
## 157 Singapore 8.80 5,703,569
## 158 Slovakia 10.60 2,930,419
## 159 Slovenia 9.40 1,144,654
## 160 Solomon Islands 32.44 162,164
## 161 Somalia 41.75 7,034,861
## 162 South Africa 20.51 39,149,717
## 163 South Korea 6.40 42,106,719
## 164 South Sudan 35.01 2,201,250
## 165 Spain 7.90 37,927,409
## 166 Sri Lanka 15.83 4,052,088
## 167 Sudan 32.18 14,957,233
## 168 Suriname 18.54 384,258
## 169 Sweden 11.40 9,021,165
## 170 Switzerland 10.00 6,332,428
## 171 Syria 23.69 9,358,019
## 172 Tajikistan 30.76 2,545,477
## 173 Tanzania 36.70 20,011,885
## 174 Thailand 10.34 35,294,600
## 175 East Timor 29.42 400,182
## 176 Togo 33.11 3,414,638
## 177 Tonga 24.30 24,145
## 178 Trinidad and Tobago 12.94 741,944
## 179 Tunisia 17.56 8,099,061
## 180 Turkey 16.03 63,097,818
## 181 Turkmenistan 23.83 3,092,738
## 182 Tuvalu NA 7,362
## 183 Uganda 38.14 10,784,516
## 184 Ukraine 8.70 30,835,699
## 185 United Arab Emirates 10.33 8,479,744
## 186 United Kingdom 11.00 55,908,316
## 187 United States 11.60 270,663,028
## 188 Uruguay 13.86 3,303,394
## 189 Uzbekistan 23.30 16,935,729
## 190 Vanuatu 29.60 76,152
## 191 Venezuela 17.88 25,162,368
## 192 Vietnam 16.75 35,332,140
## 193 Yemen 30.45 10,869,523
## 194 Zambia 36.19 7,871,713
## 195 Zimbabwe 30.68 4,717,305
## GDP Gasoline.Price
## 1 $19,101,353,833 $0.70
## 2 $15,278,077,447 $1.36
## 3 $169,988,236,398 $0.28
## 4 $3,154,057,987 $1.51
## 5 $94,635,415,870 $0.97
## 6 $1,727,759,259 $0.99
## 7 $449,663,446,954 $1.10
## 8 $13,672,802,158 $0.77
## 9 $1,392,680,589,329 $0.93
## 10 $446,314,739,528 $1.20
## 11 $39,207,000,000 $0.56
## 12 $12,827,000,000 $0.92
## 13 $38,574,069,149 $0.43
## 14 $302,571,254,131 $1.12
## 15 $5,209,000,000 $1.81
## 16 $63,080,457,023 $0.60
## 17 $529,606,710,418 $1.43
## 18 $1,879,613,600 $1.13
## 19 $14,390,709,095 $0.72
## 20 $2,446,674,101 $0.98
## 21 $40,895,322,865 $0.71
## 22 $20,047,848,435 $1.05
## 23 $18,340,510,789 $0.71
## 24 $1,839,758,040,766 $1.02
## 25 $13,469,422,941 $0.37
## 26 $86,000,000,000 $1.11
## 27 $15,745,810,235 $0.98
## 28 $3,012,334,882 $1.21
## 29 $58,792,205,642 $0.93
## 30 $1,981,845,741 $1.02
## 31 $27,089,389,787 $0.90
## 32 $38,760,467,033 $1.03
## 33 $1,736,425,629,520 $0.81
## 34 $2,220,307,369 $1.41
## 35 $11,314,951,343 $0.78
## 36 $282,318,159,745 $1.03
## 37 $19,910,000,000,000 $0.96
## 38 $323,802,808,108 $0.68
## 39 $1,185,728,677
## 40 $10,820,591,131 $0.97
## 41 $61,773,944,174 $0.98
## 42 $60,415,553,039 $1.26
## 43 $100,023,000,000 $1.40
## 44 $24,564,647,935 $1.23
## 45 $246,489,245,495 $1.17
## 46 $47,319,624,204 $1.49
## 47 $348,078,018,464 $1.55
## 48 $3,318,716,359 $1.32
## 49 $596,033,333
## 50 $88,941,298,258 $1.07
## 51 $107,435,665,000 $0.61
## 52 $303,175,127,598 $0.40
## 53 $27,022,640,000 $0.83
## 54 $11,026,774,945
## 55 $2,065,001,626 $2.00
## 56 $31,386,949,981 $1.14
## 57 $3,791,304,348
## 58 $96,107,662,398 $0.75
## 59 $5,535,548,972 $0.82
## 60 $268,761,201,365 $1.45
## 61 $2,715,518,274,227 $1.39
## 62 $16,657,960,228 $0.92
## 63 $1,763,819,048 $1.18
## 64 $17,743,195,770 $0.76
## 65 $3,845,630,030,824 $1.39
## 66 $66,983,634,224 $0.92
## 67 $209,852,761,469 $1.54
## 68 $1,228,170,370 $1.12
## 69 $76,710,385,880 $0.79
## 70 $13,590,281,809 $0.90
## 71 $1,340,389,411
## 72 $4,280,443,645 $0.90
## 73 $8,498,981,821 $0.81
## 74
## 75 $25,095,395,475 $0.98
## 76 $160,967,157,504 $1.18
## 77 $24,188,035,739 $1.69
## 78 $2,611,000,000,000 $0.97
## 79 $1,119,190,780,753 $0.63
## 80 $445,345,282,123 $0.40
## 81 $234,094,042,939 $0.61
## 82 $388,698,711,348 $1.37
## 83 $395,098,666,122 $1.57
## 84 $2,001,244,392,042 $1.61
## 85 $16,458,071,068 $1.11
## 86 $5,081,769,542,380 $1.06
## 87 $43,743,661,972 $1.10
## 88 $180,161,741,180 $0.42
## 89 $95,503,088,538 $0.95
## 90 $194,647,202
## 91 $134,761,198,946 $0.35
## 92 $8,454,619,608 $0.56
## 93 $18,173,839,128 $0.93
## 94 $34,117,202,555 $1.16
## 95 $53,367,042,272 $0.74
## 96 $2,460,072,444 $0.70
## 97 $3,070,518,100 $0.80
## 98 $52,076,250,948 $0.11
## 99 $6,552,858,739 $1.74
## 100 $54,219,315,600 $1.16
## 101 $71,104,919,108 $1.19
## 102 $14,083,906,357 $1.11
## 103 $7,666,704,427 $1.15
## 104 $364,701,517,788 $0.45
## 105 $5,729,248,472 $1.63
## 106 $17,510,141,171 $1.12
## 107 $14,786,156,563 $1.36
## 108 $221,278,000 $1.44
## 109 $7,593,752,450 $1.13
## 110 $14,180,444,557 $1.12
## 111 $1,258,286,717,125 $0.73
## 112 $401,932,279
## 113 $11,955,435,457 $0.80
## 114 $7,184,844,193 $2.00
## 115 $13,852,850,259 $0.72
## 116 $5,494,736,901 $1.16
## 117 $118,725,279,596 $0.99
## 118 $14,934,159,926 $0.65
## 119 $76,085,852,617 $0.54
## 120 $12,366,527,719 $0.76
## 121 $133,000,000
## 122 $30,641,380,604 $0.91
## 123 $909,070,395,161 $1.68
## 124 $206,928,765,544 $1.40
## 125 $12,520,915,291 $0.91
## 126 $12,928,145,120 $0.88
## 127 $448,120,428,859 $0.46
## 128 $32,100,000,000 $0.58
## 129 $10,220,781,069
## 130 $403,336,363,636 $1.78
## 131 $76,983,094,928 $0.45
## 132 $304,400,000,000 $0.79
## 133 $283,994,900
## 134
## 135 $66,800,800,000 $0.74
## 136 $24,969,611,435 $1.36
## 137 $38,145,288,940 $1.04
## 138 $226,848,050,820 $0.99
## 139 $376,795,508,680 $0.86
## 140 $592,164,400,688 $1.07
## 141 $237,686,075,635 $1.54
## 142 $183,466,208,791 $0.40
## 143 $250,077,444,017 $1.16
## 144 $1,699,876,578,871 $0.59
## 145 $10,122,472,590 $1.17
## 146 $1,050,992,593
## 147 $2,122,450,630 $1.30
## 148 $825,385,185
## 149 $850,655,017 $0.91
## 150 $1,637,931,034
## 151 $429,016,605
## 152 $792,966,838,162 $0.24
## 153 $23,578,084,052 $1.14
## 154 $51,409,167,351 $1.16
## 155 $1,698,843,063
## 156 $3,941,474,311 $1.08
## 157 $372,062,527,489 $1.25
## 158 $105,422,304,976 $1.32
## 159 $53,742,159,517 $1.32
## 160 $1,425,074,226
## 161 $4,720,727,278 $1.41
## 162 $351,431,649,241 $0.92
## 163 $2,029,000,000,000 $1.22
## 164 $11,997,800,751 $0.28
## 165 $1,394,116,310,769 $1.26
## 166 $84,008,783,756 $0.88
## 167 $18,902,284,476 $0.95
## 168 $3,985,250,737 $1.29
## 169 $530,832,908,738 $1.42
## 170 $703,082,435,360 $1.45
## 171 $40,405,006,007 $0.83
## 172 $8,116,626,794 $0.71
## 173 $63,177,068,175 $0.87
## 174 $543,649,976,166 $0.71
## 175 $1,673,540,300 $1.10
## 176 $5,459,979,417 $0.71
## 177 $450,353,314
## 178 $24,100,202,834 $0.54
## 179 $38,797,709,924 $0.73
## 180 $754,411,708,203 $1.42
## 181 $40,761,142,857 $0.29
## 182 $47,271,463
## 183 $34,387,229,486 $0.94
## 184 $153,781,069,118 $0.83
## 185 $421,142,267,938 $0.49
## 186 $2,827,113,184,696 $1.46
## 187 $21,427,700,000,000 $0.71
## 188 $56,045,912,952 $1.50
## 189 $57,921,286,440 $1.03
## 190 $917,058,851 $1.31
## 191 $482,359,318,768 $0.00
## 192 $261,921,244,843 $0.80
## 193 $26,914,402,224 $0.92
## 194 $23,064,722,446 $1.40
## 195 $21,440,758,800 $1.34sintaks diatas dapat ditulis dalam bentuk lain yaitu
country_data %>%
select(Country,`Birth.Rate`,Urban_population,GDP, `Gasoline.Price`)## Country Birth.Rate Urban_population
## 1 Afghanistan 32.49 9,797,273
## 2 Albania 11.78 1,747,593
## 3 Algeria 24.28 31,510,100
## 4 Andorra 7.20 67,873
## 5 Angola 40.73 21,061,025
## 6 Antigua and Barbuda 15.33 23,800
## 7 Argentina 17.02 41,339,571
## 8 Armenia 13.99 1,869,848
## 9 Australia 12.60 21,844,756
## 10 Austria 9.70 5,194,416
## 11 Azerbaijan 14.00 5,616,165
## 12 The Bahamas 13.97 323,784
## 13 Bahrain 13.99 1,467,109
## 14 Bangladesh 18.18 60,987,417
## 15 Barbados 10.65 89,431
## 16 Belarus 9.90 7,482,982
## 17 Belgium 10.30 11,259,082
## 18 Belize 20.79 179,039
## 19 Benin 36.22 5,648,149
## 20 Bhutan 17.26 317,538
## 21 Bolivia 21.75 8,033,035
## 22 Bosnia and Herzegovina 8.11 1,605,144
## 23 Botswana 24.82 1,616,550
## 24 Brazil 13.92 183,241,641
## 25 Brunei 14.90 337,711
## 26 Bulgaria 8.90 5,256,027
## 27 Burkina Faso 37.93 6,092,349
## 28 Burundi 39.01 1,541,177
## 29 Ivory Coast 35.74 13,176,900
## 30 Cape Verde 19.49 364,029
## 31 Cambodia 22.46 3,924,621
## 32 Cameroon 35.39 14,741,256
## 33 Canada 10.10 30,628,482
## 34 Central African Republic 35.35 1,982,064
## 35 Chad 42.17 3,712,273
## 36 Chile 12.43 16,610,135
## 37 China 10.90 842,933,962
## 38 Colombia 14.88 40,827,302
## 39 Comoros 31.88 248,152
## 40 Republic of the Congo 32.86 3,625,010
## 41 Costa Rica 13.97 4,041,885
## 42 Croatia 9.00 2,328,318
## 43 Cuba 10.17 8,739,135
## 44 Cyprus 10.46 800,708
## 45 Czech Republic 10.70 7,887,156
## 46 Democratic Republic of the Congo 41.18 39,095,679
## 47 Denmark 10.60 5,119,978
## 48 Djibouti 21.47 758,549
## 49 Dominica 12.00 50,830
## 50 Dominican Republic 19.51 8,787,475
## 51 Ecuador 19.72 11,116,711
## 52 Egypt 26.38 42,895,824
## 53 El Salvador 18.25 4,694,702
## 54 Equatorial Guinea 33.24 984,812
## 55 Eritrea 30.30 1,149,670
## 56 Estonia 10.90 916,024
## 57 Eswatini NA
## 58 Ethiopia 32.34 23,788,710
## 59 Fiji 21.28 505,048
## 60 Finland 8.60 4,716,888
## 61 France 11.30 54,123,364
## 62 Gabon 31.61 1,949,694
## 63 The Gambia 38.54 1,453,958
## 64 Georgia 13.47 2,196,476
## 65 Germany 9.50 64,324,835
## 66 Ghana 29.41 17,249,054
## 67 Greece 8.10 8,507,474
## 68 Grenada 16.47 40,765
## 69 Guatemala 24.56 8,540,945
## 70 Guinea 36.36 4,661,505
## 71 Guinea-Bissau 35.13 840,922
## 72 Guyana 19.97 208,912
## 73 Haiti 24.35 6,328,948
## 74 Vatican City NA
## 75 Honduras 21.60 5,626,433
## 76 Hungary 9.60 6,999,582
## 77 Iceland 12.00 339,110
## 78 India 17.86 471,031,528
## 79 Indonesia 18.07 151,509,724
## 80 Iran 18.78 62,509,623
## 81 Iraq 29.08 27,783,368
## 82 Republic of Ireland 12.50 3,133,123
## 83 Israel 20.80 8,374,393
## 84 Italy 7.30 42,651,966
## 85 Jamaica 16.10 1,650,594
## 86 Japan 7.40 115,782,416
## 87 Jordan 21.98 9,213,048
## 88 Kazakhstan 21.77 10,652,915
## 89 Kenya 28.75 14,461,523
## 90 Kiribati 27.89 64,489
## 91 Kuwait 13.94 4,207,083
## 92 Kyrgyzstan 27.10 2,362,644
## 93 Laos 23.55 2,555,552
## 94 Latvia 10.00 1,304,943
## 95 Lebanon 17.55 6,084,994
## 96 Lesotho 26.81 607,508
## 97 Liberia 33.04 2,548,426
## 98 Libya 18.83 5,448,597
## 99 Liechtenstein 9.90 5,464
## 100 Lithuania 10.00 1,891,013
## 101 Luxembourg 10.30 565,488
## 102 Madagascar 32.66 10,210,849
## 103 Malawi 34.12 3,199,301
## 104 Malaysia 16.75 24,475,766
## 105 Maldives 14.20 213,645
## 106 Mali 41.54 8,479,688
## 107 Malta 9.20 475,902
## 108 Marshall Islands 29.03 45,514
## 109 Mauritania 33.69 2,466,821
## 110 Mauritius 10.20 515,980
## 111 Mexico 17.60 102,626,859
## 112 Federated States of Micronesia 22.82 25,963
## 113 Moldova 10.10 1,135,502
## 114 Monaco 5.90 38,964
## 115 Mongolia 24.13 2,210,626
## 116 Montenegro 11.73 417,765
## 117 Morocco 18.94 22,975,026
## 118 Mozambique 37.52 11,092,106
## 119 Myanmar 17.55 16,674,093
## 120 Namibia 28.64 1,273,258
## 121 Nauru NA
## 122 Nepal 19.89 5,765,513
## 123 Netherlands 9.70 15,924,729
## 124 New Zealand 11.98 4,258,860
## 125 Nicaragua 20.64 3,846,137
## 126 Niger 46.08 3,850,231
## 127 Nigeria 37.91 102,806,948
## 128 North Korea 13.89 15,947,412
## 129 North Macedonia NA
## 130 Norway 10.40 4,418,218
## 131 Oman 19.19 4,250,777
## 132 Pakistan 28.25 79,927,762
## 133 Palau 14.00 14,491
## 134 Palestinian National Authority NA
## 135 Panama 18.98 2,890,084
## 136 Papua New Guinea 27.07 1,162,834
## 137 Paraguay 20.57 4,359,150
## 138 Peru 17.95 25,390,339
## 139 Philippines 20.55 50,975,903
## 140 Poland 10.20 22,796,574
## 141 Portugal 8.50 6,753,579
## 142 Qatar 9.54 2,809,071
## 143 Romania 9.60 10,468,793
## 144 Russia 11.50 107,683,889
## 145 Rwanda 31.70 2,186,104
## 146 Saint Kitts and Nevis 12.60 16,269
## 147 Saint Lucia 12.00 34,280
## 148 Saint Vincent and the Grenadines 14.24 58,185
## 149 Samoa 24.38 35,588
## 150 San Marino 6.80 32,969
## 151 S����������� 31.54 158,277
## 152 Saudi Arabia 17.80 28,807,838
## 153 Senegal 34.52 7,765,706
## 154 Serbia 9.20 3,907,243
## 155 Seychelles 17.10 55,762
## 156 Sierra Leone 33.41 3,319,366
## 157 Singapore 8.80 5,703,569
## 158 Slovakia 10.60 2,930,419
## 159 Slovenia 9.40 1,144,654
## 160 Solomon Islands 32.44 162,164
## 161 Somalia 41.75 7,034,861
## 162 South Africa 20.51 39,149,717
## 163 South Korea 6.40 42,106,719
## 164 South Sudan 35.01 2,201,250
## 165 Spain 7.90 37,927,409
## 166 Sri Lanka 15.83 4,052,088
## 167 Sudan 32.18 14,957,233
## 168 Suriname 18.54 384,258
## 169 Sweden 11.40 9,021,165
## 170 Switzerland 10.00 6,332,428
## 171 Syria 23.69 9,358,019
## 172 Tajikistan 30.76 2,545,477
## 173 Tanzania 36.70 20,011,885
## 174 Thailand 10.34 35,294,600
## 175 East Timor 29.42 400,182
## 176 Togo 33.11 3,414,638
## 177 Tonga 24.30 24,145
## 178 Trinidad and Tobago 12.94 741,944
## 179 Tunisia 17.56 8,099,061
## 180 Turkey 16.03 63,097,818
## 181 Turkmenistan 23.83 3,092,738
## 182 Tuvalu NA 7,362
## 183 Uganda 38.14 10,784,516
## 184 Ukraine 8.70 30,835,699
## 185 United Arab Emirates 10.33 8,479,744
## 186 United Kingdom 11.00 55,908,316
## 187 United States 11.60 270,663,028
## 188 Uruguay 13.86 3,303,394
## 189 Uzbekistan 23.30 16,935,729
## 190 Vanuatu 29.60 76,152
## 191 Venezuela 17.88 25,162,368
## 192 Vietnam 16.75 35,332,140
## 193 Yemen 30.45 10,869,523
## 194 Zambia 36.19 7,871,713
## 195 Zimbabwe 30.68 4,717,305
## GDP Gasoline.Price
## 1 $19,101,353,833 $0.70
## 2 $15,278,077,447 $1.36
## 3 $169,988,236,398 $0.28
## 4 $3,154,057,987 $1.51
## 5 $94,635,415,870 $0.97
## 6 $1,727,759,259 $0.99
## 7 $449,663,446,954 $1.10
## 8 $13,672,802,158 $0.77
## 9 $1,392,680,589,329 $0.93
## 10 $446,314,739,528 $1.20
## 11 $39,207,000,000 $0.56
## 12 $12,827,000,000 $0.92
## 13 $38,574,069,149 $0.43
## 14 $302,571,254,131 $1.12
## 15 $5,209,000,000 $1.81
## 16 $63,080,457,023 $0.60
## 17 $529,606,710,418 $1.43
## 18 $1,879,613,600 $1.13
## 19 $14,390,709,095 $0.72
## 20 $2,446,674,101 $0.98
## 21 $40,895,322,865 $0.71
## 22 $20,047,848,435 $1.05
## 23 $18,340,510,789 $0.71
## 24 $1,839,758,040,766 $1.02
## 25 $13,469,422,941 $0.37
## 26 $86,000,000,000 $1.11
## 27 $15,745,810,235 $0.98
## 28 $3,012,334,882 $1.21
## 29 $58,792,205,642 $0.93
## 30 $1,981,845,741 $1.02
## 31 $27,089,389,787 $0.90
## 32 $38,760,467,033 $1.03
## 33 $1,736,425,629,520 $0.81
## 34 $2,220,307,369 $1.41
## 35 $11,314,951,343 $0.78
## 36 $282,318,159,745 $1.03
## 37 $19,910,000,000,000 $0.96
## 38 $323,802,808,108 $0.68
## 39 $1,185,728,677
## 40 $10,820,591,131 $0.97
## 41 $61,773,944,174 $0.98
## 42 $60,415,553,039 $1.26
## 43 $100,023,000,000 $1.40
## 44 $24,564,647,935 $1.23
## 45 $246,489,245,495 $1.17
## 46 $47,319,624,204 $1.49
## 47 $348,078,018,464 $1.55
## 48 $3,318,716,359 $1.32
## 49 $596,033,333
## 50 $88,941,298,258 $1.07
## 51 $107,435,665,000 $0.61
## 52 $303,175,127,598 $0.40
## 53 $27,022,640,000 $0.83
## 54 $11,026,774,945
## 55 $2,065,001,626 $2.00
## 56 $31,386,949,981 $1.14
## 57 $3,791,304,348
## 58 $96,107,662,398 $0.75
## 59 $5,535,548,972 $0.82
## 60 $268,761,201,365 $1.45
## 61 $2,715,518,274,227 $1.39
## 62 $16,657,960,228 $0.92
## 63 $1,763,819,048 $1.18
## 64 $17,743,195,770 $0.76
## 65 $3,845,630,030,824 $1.39
## 66 $66,983,634,224 $0.92
## 67 $209,852,761,469 $1.54
## 68 $1,228,170,370 $1.12
## 69 $76,710,385,880 $0.79
## 70 $13,590,281,809 $0.90
## 71 $1,340,389,411
## 72 $4,280,443,645 $0.90
## 73 $8,498,981,821 $0.81
## 74
## 75 $25,095,395,475 $0.98
## 76 $160,967,157,504 $1.18
## 77 $24,188,035,739 $1.69
## 78 $2,611,000,000,000 $0.97
## 79 $1,119,190,780,753 $0.63
## 80 $445,345,282,123 $0.40
## 81 $234,094,042,939 $0.61
## 82 $388,698,711,348 $1.37
## 83 $395,098,666,122 $1.57
## 84 $2,001,244,392,042 $1.61
## 85 $16,458,071,068 $1.11
## 86 $5,081,769,542,380 $1.06
## 87 $43,743,661,972 $1.10
## 88 $180,161,741,180 $0.42
## 89 $95,503,088,538 $0.95
## 90 $194,647,202
## 91 $134,761,198,946 $0.35
## 92 $8,454,619,608 $0.56
## 93 $18,173,839,128 $0.93
## 94 $34,117,202,555 $1.16
## 95 $53,367,042,272 $0.74
## 96 $2,460,072,444 $0.70
## 97 $3,070,518,100 $0.80
## 98 $52,076,250,948 $0.11
## 99 $6,552,858,739 $1.74
## 100 $54,219,315,600 $1.16
## 101 $71,104,919,108 $1.19
## 102 $14,083,906,357 $1.11
## 103 $7,666,704,427 $1.15
## 104 $364,701,517,788 $0.45
## 105 $5,729,248,472 $1.63
## 106 $17,510,141,171 $1.12
## 107 $14,786,156,563 $1.36
## 108 $221,278,000 $1.44
## 109 $7,593,752,450 $1.13
## 110 $14,180,444,557 $1.12
## 111 $1,258,286,717,125 $0.73
## 112 $401,932,279
## 113 $11,955,435,457 $0.80
## 114 $7,184,844,193 $2.00
## 115 $13,852,850,259 $0.72
## 116 $5,494,736,901 $1.16
## 117 $118,725,279,596 $0.99
## 118 $14,934,159,926 $0.65
## 119 $76,085,852,617 $0.54
## 120 $12,366,527,719 $0.76
## 121 $133,000,000
## 122 $30,641,380,604 $0.91
## 123 $909,070,395,161 $1.68
## 124 $206,928,765,544 $1.40
## 125 $12,520,915,291 $0.91
## 126 $12,928,145,120 $0.88
## 127 $448,120,428,859 $0.46
## 128 $32,100,000,000 $0.58
## 129 $10,220,781,069
## 130 $403,336,363,636 $1.78
## 131 $76,983,094,928 $0.45
## 132 $304,400,000,000 $0.79
## 133 $283,994,900
## 134
## 135 $66,800,800,000 $0.74
## 136 $24,969,611,435 $1.36
## 137 $38,145,288,940 $1.04
## 138 $226,848,050,820 $0.99
## 139 $376,795,508,680 $0.86
## 140 $592,164,400,688 $1.07
## 141 $237,686,075,635 $1.54
## 142 $183,466,208,791 $0.40
## 143 $250,077,444,017 $1.16
## 144 $1,699,876,578,871 $0.59
## 145 $10,122,472,590 $1.17
## 146 $1,050,992,593
## 147 $2,122,450,630 $1.30
## 148 $825,385,185
## 149 $850,655,017 $0.91
## 150 $1,637,931,034
## 151 $429,016,605
## 152 $792,966,838,162 $0.24
## 153 $23,578,084,052 $1.14
## 154 $51,409,167,351 $1.16
## 155 $1,698,843,063
## 156 $3,941,474,311 $1.08
## 157 $372,062,527,489 $1.25
## 158 $105,422,304,976 $1.32
## 159 $53,742,159,517 $1.32
## 160 $1,425,074,226
## 161 $4,720,727,278 $1.41
## 162 $351,431,649,241 $0.92
## 163 $2,029,000,000,000 $1.22
## 164 $11,997,800,751 $0.28
## 165 $1,394,116,310,769 $1.26
## 166 $84,008,783,756 $0.88
## 167 $18,902,284,476 $0.95
## 168 $3,985,250,737 $1.29
## 169 $530,832,908,738 $1.42
## 170 $703,082,435,360 $1.45
## 171 $40,405,006,007 $0.83
## 172 $8,116,626,794 $0.71
## 173 $63,177,068,175 $0.87
## 174 $543,649,976,166 $0.71
## 175 $1,673,540,300 $1.10
## 176 $5,459,979,417 $0.71
## 177 $450,353,314
## 178 $24,100,202,834 $0.54
## 179 $38,797,709,924 $0.73
## 180 $754,411,708,203 $1.42
## 181 $40,761,142,857 $0.29
## 182 $47,271,463
## 183 $34,387,229,486 $0.94
## 184 $153,781,069,118 $0.83
## 185 $421,142,267,938 $0.49
## 186 $2,827,113,184,696 $1.46
## 187 $21,427,700,000,000 $0.71
## 188 $56,045,912,952 $1.50
## 189 $57,921,286,440 $1.03
## 190 $917,058,851 $1.31
## 191 $482,359,318,768 $0.00
## 192 $261,921,244,843 $0.80
## 193 $26,914,402,224 $0.92
## 194 $23,064,722,446 $1.40
## 195 $21,440,758,800 $1.34Hasil dari kedua sintaks tersebut sama. Simbol %>% dinamakan pipe-operator
##Operator Operator %>% digunakan untuk mengantarkan kita dari step satu ke step yang lainnya. Misalnya saja sintkas dibawah ini
country_data_cleanup <- country_data
country_data_cleanup$Urban_population <- as.numeric(gsub(",", "", country_data$Urban_population))
glimpse(country_data_cleanup)## Rows: 195
## Columns: 35
## $ Country <chr> "Afghanistan", "Albania", "A~
## $ Density..P.Km2. <chr> "60", "105", "18", "164", "2~
## $ Abbreviation <chr> "AF", "AL", "DZ", "AD", "AO"~
## $ Agricultural.Land.... <chr> "58.10%", "43.10%", "17.40%"~
## $ Land.Area.Km2. <chr> "652,230", "28,748", "2,381,~
## $ Armed.Forces.size <chr> "323,000", "9,000", "317,000~
## $ Birth.Rate <dbl> 32.49, 11.78, 24.28, 7.20, 4~
## $ Calling.Code <int> 93, 355, 213, 376, 244, 1, 5~
## $ Capital.Major.City <chr> "Kabul", "Tirana", "Algiers"~
## $ Co2.Emissions <chr> "8,672", "4,536", "150,006",~
## $ CPI <chr> "149.9", "119.05", "151.36",~
## $ CPI.Change.... <chr> "2.30%", "1.40%", "2.00%", "~
## $ Currency.Code <chr> "AFN", "ALL", "DZD", "EUR", ~
## $ Fertility.Rate <dbl> 4.47, 1.62, 3.02, 1.27, 5.52~
## $ Forested.Area.... <chr> "2.10%", "28.10%", "0.80%", ~
## $ Gasoline.Price <chr> "$0.70 ", "$1.36 ", "$0.28 "~
## $ GDP <chr> "$19,101,353,833 ", "$15,278~
## $ Gross.primary.education.enrollment.... <chr> "104.00%", "107.00%", "109.9~
## $ Gross.tertiary.education.enrollment.... <chr> "9.70%", "55.00%", "51.40%",~
## $ Infant.mortality <dbl> 47.9, 7.8, 20.1, 2.7, 51.6, ~
## $ Largest.city <chr> "Kabul", "Tirana", "Algiers"~
## $ Life.expectancy <dbl> 64.5, 78.5, 76.7, NA, 60.8, ~
## $ Maternal.mortality.ratio <int> 638, 15, 112, NA, 241, 42, 3~
## $ Minimum.wage <chr> "$0.43 ", "$1.12 ", "$0.95 "~
## $ Official.language <chr> "Pashto", "Albanian", "Arabi~
## $ Out.of.pocket.health.expenditure <chr> "78.40%", "56.90%", "28.10%"~
## $ Physicians.per.thousand <dbl> 0.28, 1.20, 1.72, 3.33, 0.21~
## $ Population <chr> "38,041,754", "2,854,191", "~
## $ Population..Labor.force.participation.... <chr> "48.90%", "55.70%", "41.20%"~
## $ Tax.revenue.... <chr> "9.30%", "18.60%", "37.20%",~
## $ Total.tax.rate <chr> "71.40%", "36.60%", "66.10%"~
## $ Unemployment.rate <chr> "11.12%", "12.33%", "11.70%"~
## $ Urban_population <dbl> 9797273, 1747593, 31510100, ~
## $ Latitude <dbl> 33.939110, 41.153332, 28.033~
## $ Longitude <dbl> 67.709953, 20.168331, 1.6596~country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population < 10000)## Country Urban_population
## 1 Liechtenstein 5464
## 2 Tuvalu 7362country_data %>%
select(Country, Urban_population)## Country Urban_population
## 1 Afghanistan 9,797,273
## 2 Albania 1,747,593
## 3 Algeria 31,510,100
## 4 Andorra 67,873
## 5 Angola 21,061,025
## 6 Antigua and Barbuda 23,800
## 7 Argentina 41,339,571
## 8 Armenia 1,869,848
## 9 Australia 21,844,756
## 10 Austria 5,194,416
## 11 Azerbaijan 5,616,165
## 12 The Bahamas 323,784
## 13 Bahrain 1,467,109
## 14 Bangladesh 60,987,417
## 15 Barbados 89,431
## 16 Belarus 7,482,982
## 17 Belgium 11,259,082
## 18 Belize 179,039
## 19 Benin 5,648,149
## 20 Bhutan 317,538
## 21 Bolivia 8,033,035
## 22 Bosnia and Herzegovina 1,605,144
## 23 Botswana 1,616,550
## 24 Brazil 183,241,641
## 25 Brunei 337,711
## 26 Bulgaria 5,256,027
## 27 Burkina Faso 6,092,349
## 28 Burundi 1,541,177
## 29 Ivory Coast 13,176,900
## 30 Cape Verde 364,029
## 31 Cambodia 3,924,621
## 32 Cameroon 14,741,256
## 33 Canada 30,628,482
## 34 Central African Republic 1,982,064
## 35 Chad 3,712,273
## 36 Chile 16,610,135
## 37 China 842,933,962
## 38 Colombia 40,827,302
## 39 Comoros 248,152
## 40 Republic of the Congo 3,625,010
## 41 Costa Rica 4,041,885
## 42 Croatia 2,328,318
## 43 Cuba 8,739,135
## 44 Cyprus 800,708
## 45 Czech Republic 7,887,156
## 46 Democratic Republic of the Congo 39,095,679
## 47 Denmark 5,119,978
## 48 Djibouti 758,549
## 49 Dominica 50,830
## 50 Dominican Republic 8,787,475
## 51 Ecuador 11,116,711
## 52 Egypt 42,895,824
## 53 El Salvador 4,694,702
## 54 Equatorial Guinea 984,812
## 55 Eritrea 1,149,670
## 56 Estonia 916,024
## 57 Eswatini
## 58 Ethiopia 23,788,710
## 59 Fiji 505,048
## 60 Finland 4,716,888
## 61 France 54,123,364
## 62 Gabon 1,949,694
## 63 The Gambia 1,453,958
## 64 Georgia 2,196,476
## 65 Germany 64,324,835
## 66 Ghana 17,249,054
## 67 Greece 8,507,474
## 68 Grenada 40,765
## 69 Guatemala 8,540,945
## 70 Guinea 4,661,505
## 71 Guinea-Bissau 840,922
## 72 Guyana 208,912
## 73 Haiti 6,328,948
## 74 Vatican City
## 75 Honduras 5,626,433
## 76 Hungary 6,999,582
## 77 Iceland 339,110
## 78 India 471,031,528
## 79 Indonesia 151,509,724
## 80 Iran 62,509,623
## 81 Iraq 27,783,368
## 82 Republic of Ireland 3,133,123
## 83 Israel 8,374,393
## 84 Italy 42,651,966
## 85 Jamaica 1,650,594
## 86 Japan 115,782,416
## 87 Jordan 9,213,048
## 88 Kazakhstan 10,652,915
## 89 Kenya 14,461,523
## 90 Kiribati 64,489
## 91 Kuwait 4,207,083
## 92 Kyrgyzstan 2,362,644
## 93 Laos 2,555,552
## 94 Latvia 1,304,943
## 95 Lebanon 6,084,994
## 96 Lesotho 607,508
## 97 Liberia 2,548,426
## 98 Libya 5,448,597
## 99 Liechtenstein 5,464
## 100 Lithuania 1,891,013
## 101 Luxembourg 565,488
## 102 Madagascar 10,210,849
## 103 Malawi 3,199,301
## 104 Malaysia 24,475,766
## 105 Maldives 213,645
## 106 Mali 8,479,688
## 107 Malta 475,902
## 108 Marshall Islands 45,514
## 109 Mauritania 2,466,821
## 110 Mauritius 515,980
## 111 Mexico 102,626,859
## 112 Federated States of Micronesia 25,963
## 113 Moldova 1,135,502
## 114 Monaco 38,964
## 115 Mongolia 2,210,626
## 116 Montenegro 417,765
## 117 Morocco 22,975,026
## 118 Mozambique 11,092,106
## 119 Myanmar 16,674,093
## 120 Namibia 1,273,258
## 121 Nauru
## 122 Nepal 5,765,513
## 123 Netherlands 15,924,729
## 124 New Zealand 4,258,860
## 125 Nicaragua 3,846,137
## 126 Niger 3,850,231
## 127 Nigeria 102,806,948
## 128 North Korea 15,947,412
## 129 North Macedonia
## 130 Norway 4,418,218
## 131 Oman 4,250,777
## 132 Pakistan 79,927,762
## 133 Palau 14,491
## 134 Palestinian National Authority
## 135 Panama 2,890,084
## 136 Papua New Guinea 1,162,834
## 137 Paraguay 4,359,150
## 138 Peru 25,390,339
## 139 Philippines 50,975,903
## 140 Poland 22,796,574
## 141 Portugal 6,753,579
## 142 Qatar 2,809,071
## 143 Romania 10,468,793
## 144 Russia 107,683,889
## 145 Rwanda 2,186,104
## 146 Saint Kitts and Nevis 16,269
## 147 Saint Lucia 34,280
## 148 Saint Vincent and the Grenadines 58,185
## 149 Samoa 35,588
## 150 San Marino 32,969
## 151 S����������� 158,277
## 152 Saudi Arabia 28,807,838
## 153 Senegal 7,765,706
## 154 Serbia 3,907,243
## 155 Seychelles 55,762
## 156 Sierra Leone 3,319,366
## 157 Singapore 5,703,569
## 158 Slovakia 2,930,419
## 159 Slovenia 1,144,654
## 160 Solomon Islands 162,164
## 161 Somalia 7,034,861
## 162 South Africa 39,149,717
## 163 South Korea 42,106,719
## 164 South Sudan 2,201,250
## 165 Spain 37,927,409
## 166 Sri Lanka 4,052,088
## 167 Sudan 14,957,233
## 168 Suriname 384,258
## 169 Sweden 9,021,165
## 170 Switzerland 6,332,428
## 171 Syria 9,358,019
## 172 Tajikistan 2,545,477
## 173 Tanzania 20,011,885
## 174 Thailand 35,294,600
## 175 East Timor 400,182
## 176 Togo 3,414,638
## 177 Tonga 24,145
## 178 Trinidad and Tobago 741,944
## 179 Tunisia 8,099,061
## 180 Turkey 63,097,818
## 181 Turkmenistan 3,092,738
## 182 Tuvalu 7,362
## 183 Uganda 10,784,516
## 184 Ukraine 30,835,699
## 185 United Arab Emirates 8,479,744
## 186 United Kingdom 55,908,316
## 187 United States 270,663,028
## 188 Uruguay 3,303,394
## 189 Uzbekistan 16,935,729
## 190 Vanuatu 76,152
## 191 Venezuela 25,162,368
## 192 Vietnam 35,332,140
## 193 Yemen 10,869,523
## 194 Zambia 7,871,713
## 195 Zimbabwe 4,717,305Operator %>% juga memastikan bahwa objek data selalu berada di step 1. Kemudian, Operator %>% bisa dikeluarkan dengan shortcut keyboard ctrl+shift+m pada windows (macos menyesuaikan).
##Fungsi Filter fungsi filter digunakan untuk menyaring baris berdasarkan pernyataan logika tertentu. Pernyataan logika adalah suatu pernyataan yang menghasilkan TRUE atau FALSE dan biasanya menggunakan operator logika seperti <,>,<=,>=,!=, &,| dan ==.
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population == 1747593)## Country Urban_population
## 1 Albania 1747593country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population > 1747593)## Country Urban_population
## 1 Afghanistan 9797273
## 2 Algeria 31510100
## 3 Angola 21061025
## 4 Argentina 41339571
## 5 Armenia 1869848
## 6 Australia 21844756
## 7 Austria 5194416
## 8 Azerbaijan 5616165
## 9 Bangladesh 60987417
## 10 Belarus 7482982
## 11 Belgium 11259082
## 12 Benin 5648149
## 13 Bolivia 8033035
## 14 Brazil 183241641
## 15 Bulgaria 5256027
## 16 Burkina Faso 6092349
## 17 Ivory Coast 13176900
## 18 Cambodia 3924621
## 19 Cameroon 14741256
## 20 Canada 30628482
## 21 Central African Republic 1982064
## 22 Chad 3712273
## 23 Chile 16610135
## 24 China 842933962
## 25 Colombia 40827302
## 26 Republic of the Congo 3625010
## 27 Costa Rica 4041885
## 28 Croatia 2328318
## 29 Cuba 8739135
## 30 Czech Republic 7887156
## 31 Democratic Republic of the Congo 39095679
## 32 Denmark 5119978
## 33 Dominican Republic 8787475
## 34 Ecuador 11116711
## 35 Egypt 42895824
## 36 El Salvador 4694702
## 37 Ethiopia 23788710
## 38 Finland 4716888
## 39 France 54123364
## 40 Gabon 1949694
## 41 Georgia 2196476
## 42 Germany 64324835
## 43 Ghana 17249054
## 44 Greece 8507474
## 45 Guatemala 8540945
## 46 Guinea 4661505
## 47 Haiti 6328948
## 48 Honduras 5626433
## 49 Hungary 6999582
## 50 India 471031528
## 51 Indonesia 151509724
## 52 Iran 62509623
## 53 Iraq 27783368
## 54 Republic of Ireland 3133123
## 55 Israel 8374393
## 56 Italy 42651966
## 57 Japan 115782416
## 58 Jordan 9213048
## 59 Kazakhstan 10652915
## 60 Kenya 14461523
## 61 Kuwait 4207083
## 62 Kyrgyzstan 2362644
## 63 Laos 2555552
## 64 Lebanon 6084994
## 65 Liberia 2548426
## 66 Libya 5448597
## 67 Lithuania 1891013
## 68 Madagascar 10210849
## 69 Malawi 3199301
## 70 Malaysia 24475766
## 71 Mali 8479688
## 72 Mauritania 2466821
## 73 Mexico 102626859
## 74 Mongolia 2210626
## 75 Morocco 22975026
## 76 Mozambique 11092106
## 77 Myanmar 16674093
## 78 Nepal 5765513
## 79 Netherlands 15924729
## 80 New Zealand 4258860
## 81 Nicaragua 3846137
## 82 Niger 3850231
## 83 Nigeria 102806948
## 84 North Korea 15947412
## 85 Norway 4418218
## 86 Oman 4250777
## 87 Pakistan 79927762
## 88 Panama 2890084
## 89 Paraguay 4359150
## 90 Peru 25390339
## 91 Philippines 50975903
## 92 Poland 22796574
## 93 Portugal 6753579
## 94 Qatar 2809071
## 95 Romania 10468793
## 96 Russia 107683889
## 97 Rwanda 2186104
## 98 Saudi Arabia 28807838
## 99 Senegal 7765706
## 100 Serbia 3907243
## 101 Sierra Leone 3319366
## 102 Singapore 5703569
## 103 Slovakia 2930419
## 104 Somalia 7034861
## 105 South Africa 39149717
## 106 South Korea 42106719
## 107 South Sudan 2201250
## 108 Spain 37927409
## 109 Sri Lanka 4052088
## 110 Sudan 14957233
## 111 Sweden 9021165
## 112 Switzerland 6332428
## 113 Syria 9358019
## 114 Tajikistan 2545477
## 115 Tanzania 20011885
## 116 Thailand 35294600
## 117 Togo 3414638
## 118 Tunisia 8099061
## 119 Turkey 63097818
## 120 Turkmenistan 3092738
## 121 Uganda 10784516
## 122 Ukraine 30835699
## 123 United Arab Emirates 8479744
## 124 United Kingdom 55908316
## 125 United States 270663028
## 126 Uruguay 3303394
## 127 Uzbekistan 16935729
## 128 Venezuela 25162368
## 129 Vietnam 35332140
## 130 Yemen 10869523
## 131 Zambia 7871713
## 132 Zimbabwe 4717305country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Country=="Indonesia")## Country Urban_population
## 1 Indonesia 151509724Mencari negara ASEAN dengan filter berikut ini
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Country%in%c("Indonesia","Malaysia","Thailand","Singapore","Papua"))## Country Urban_population
## 1 Indonesia 151509724
## 2 Malaysia 24475766
## 3 Singapore 5703569
## 4 Thailand 35294600Selain menggunakan operator logika, kita bisa menggunakan
fungsi str_detect dari package stringr untuk
melakukan filtering jika kita tidak yakin dengan nama persis negaranya.
Dalam sintaks sebelumnya dapat diperhatikan bahwa
negara Papua tidak muncul karena mungkin
nama Papua salah atau kurang lengkap. Berikut ilustrasi
penggunaan str_detect.
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(str_detect(Country,"Papua"))## Country Urban_population
## 1 Papua New Guinea 1162834country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(str_detect(Country,"Papua")|str_detect(Country,"Timor"))## Country Urban_population
## 1 Papua New Guinea 1162834
## 2 East Timor 400182Fungsi arrange
Fungsi arrange digunakan untuk mengurutkan kolom besar
ke kecil atau sebaliknya.
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population > 1747593 & Urban_population <= 6084994) %>%
arrange(Urban_population)## Country Urban_population
## 1 Armenia 1869848
## 2 Lithuania 1891013
## 3 Gabon 1949694
## 4 Central African Republic 1982064
## 5 Rwanda 2186104
## 6 Georgia 2196476
## 7 South Sudan 2201250
## 8 Mongolia 2210626
## 9 Croatia 2328318
## 10 Kyrgyzstan 2362644
## 11 Mauritania 2466821
## 12 Tajikistan 2545477
## 13 Liberia 2548426
## 14 Laos 2555552
## 15 Qatar 2809071
## 16 Panama 2890084
## 17 Slovakia 2930419
## 18 Turkmenistan 3092738
## 19 Republic of Ireland 3133123
## 20 Malawi 3199301
## 21 Uruguay 3303394
## 22 Sierra Leone 3319366
## 23 Togo 3414638
## 24 Republic of the Congo 3625010
## 25 Chad 3712273
## 26 Nicaragua 3846137
## 27 Niger 3850231
## 28 Serbia 3907243
## 29 Cambodia 3924621
## 30 Costa Rica 4041885
## 31 Sri Lanka 4052088
## 32 Kuwait 4207083
## 33 Oman 4250777
## 34 New Zealand 4258860
## 35 Paraguay 4359150
## 36 Norway 4418218
## 37 Guinea 4661505
## 38 El Salvador 4694702
## 39 Finland 4716888
## 40 Zimbabwe 4717305
## 41 Denmark 5119978
## 42 Austria 5194416
## 43 Bulgaria 5256027
## 44 Libya 5448597
## 45 Azerbaijan 5616165
## 46 Honduras 5626433
## 47 Benin 5648149
## 48 Singapore 5703569
## 49 Nepal 5765513
## 50 Lebanon 6084994jika ingin mengurutkan dari besar ke kecil cukup
tambahkan desc
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population > 1747593 & Urban_population <= 6084994) %>%
arrange(desc(Urban_population))## Country Urban_population
## 1 Lebanon 6084994
## 2 Nepal 5765513
## 3 Singapore 5703569
## 4 Benin 5648149
## 5 Honduras 5626433
## 6 Azerbaijan 5616165
## 7 Libya 5448597
## 8 Bulgaria 5256027
## 9 Austria 5194416
## 10 Denmark 5119978
## 11 Zimbabwe 4717305
## 12 Finland 4716888
## 13 El Salvador 4694702
## 14 Guinea 4661505
## 15 Norway 4418218
## 16 Paraguay 4359150
## 17 New Zealand 4258860
## 18 Oman 4250777
## 19 Kuwait 4207083
## 20 Sri Lanka 4052088
## 21 Costa Rica 4041885
## 22 Cambodia 3924621
## 23 Serbia 3907243
## 24 Niger 3850231
## 25 Nicaragua 3846137
## 26 Chad 3712273
## 27 Republic of the Congo 3625010
## 28 Togo 3414638
## 29 Sierra Leone 3319366
## 30 Uruguay 3303394
## 31 Malawi 3199301
## 32 Republic of Ireland 3133123
## 33 Turkmenistan 3092738
## 34 Slovakia 2930419
## 35 Panama 2890084
## 36 Qatar 2809071
## 37 Laos 2555552
## 38 Liberia 2548426
## 39 Tajikistan 2545477
## 40 Mauritania 2466821
## 41 Kyrgyzstan 2362644
## 42 Croatia 2328318
## 43 Mongolia 2210626
## 44 South Sudan 2201250
## 45 Georgia 2196476
## 46 Rwanda 2186104
## 47 Central African Republic 1982064
## 48 Gabon 1949694
## 49 Lithuania 1891013
## 50 Armenia 1869848Mengurutkan berdasarkan abjad
country_data_cleanup %>%
select(Country, Urban_population) %>%
filter(Urban_population > 1747593 & Urban_population <= 6084994) %>%
arrange(desc(Country))## Country Urban_population
## 1 Zimbabwe 4717305
## 2 Uruguay 3303394
## 3 Turkmenistan 3092738
## 4 Togo 3414638
## 5 Tajikistan 2545477
## 6 Sri Lanka 4052088
## 7 South Sudan 2201250
## 8 Slovakia 2930419
## 9 Singapore 5703569
## 10 Sierra Leone 3319366
## 11 Serbia 3907243
## 12 Rwanda 2186104
## 13 Republic of the Congo 3625010
## 14 Republic of Ireland 3133123
## 15 Qatar 2809071
## 16 Paraguay 4359150
## 17 Panama 2890084
## 18 Oman 4250777
## 19 Norway 4418218
## 20 Niger 3850231
## 21 Nicaragua 3846137
## 22 New Zealand 4258860
## 23 Nepal 5765513
## 24 Mongolia 2210626
## 25 Mauritania 2466821
## 26 Malawi 3199301
## 27 Lithuania 1891013
## 28 Libya 5448597
## 29 Liberia 2548426
## 30 Lebanon 6084994
## 31 Laos 2555552
## 32 Kyrgyzstan 2362644
## 33 Kuwait 4207083
## 34 Honduras 5626433
## 35 Guinea 4661505
## 36 Georgia 2196476
## 37 Gabon 1949694
## 38 Finland 4716888
## 39 El Salvador 4694702
## 40 Denmark 5119978
## 41 Croatia 2328318
## 42 Costa Rica 4041885
## 43 Chad 3712273
## 44 Central African Republic 1982064
## 45 Cambodia 3924621
## 46 Bulgaria 5256027
## 47 Benin 5648149
## 48 Azerbaijan 5616165
## 49 Austria 5194416
## 50 Armenia 1869848arrange juga bisa digunakan untuk melakukan pengurutan
berdasarkan kriteria lebih dari satu kolom
country_data_cleanup %>%
select(Country, Urban_population,`Official.language`) %>%
filter(Urban_population > 1747593 & Urban_population <= 6084994) %>%
arrange(`Official.language`,desc(Urban_population))## Country Urban_population Official.language
## 1 Lebanon 6084994 Arabic
## 2 Libya 5448597 Arabic
## 3 Oman 4250777 Arabic
## 4 Qatar 2809071 Arabic
## 5 Mauritania 2466821 Arabic
## 6 Armenia 1869848 Armenian
## 7 Azerbaijan 5616165 Azerbaijani language
## 8 Bulgaria 5256027 Bulgarian
## 9 Croatia 2328318 Croatian
## 10 Denmark 5119978 Danish
## 11 New Zealand 4258860 English
## 12 Sierra Leone 3319366 English
## 13 Malawi 3199301 English
## 14 Liberia 2548426 English
## 15 South Sudan 2201250 English
## 16 Benin 5648149 French
## 17 Guinea 4661505 French
## 18 Niger 3850231 French
## 19 Chad 3712273 French
## 20 Republic of the Congo 3625010 French
## 21 Togo 3414638 French
## 22 Central African Republic 1982064 French
## 23 Gabon 1949694 French
## 24 Georgia 2196476 Georgian
## 25 Austria 5194416 German
## 26 Republic of Ireland 3133123 Irish
## 27 Cambodia 3924621 Khmer language
## 28 Laos 2555552 Lao
## 29 Lithuania 1891013 Lithuanian
## 30 Singapore 5703569 Malay
## 31 Kuwait 4207083 Modern Standard Arabic
## 32 Mongolia 2210626 Mongolian
## 33 Nepal 5765513 Nepali
## 34 Norway 4418218 Norwegian
## 35 Tajikistan 2545477 Persian
## 36 Kyrgyzstan 2362644 Russian
## 37 Serbia 3907243 Serbian
## 38 Zimbabwe 4717305 Shona
## 39 Slovakia 2930419 Slovak
## 40 Honduras 5626433 Spanish
## 41 El Salvador 4694702 Spanish
## 42 Paraguay 4359150 Spanish
## 43 Costa Rica 4041885 Spanish
## 44 Nicaragua 3846137 Spanish
## 45 Uruguay 3303394 Spanish
## 46 Panama 2890084 Spanish
## 47 Rwanda 2186104 Swahili
## 48 Finland 4716888 Swedish
## 49 Sri Lanka 4052088 Tamil
## 50 Turkmenistan 3092738 TurkmenFungsi mutate
Fungsi mutate digunakan untuk membuat, memodifikasi dan
menghapus kolom dari dataset.
Misal kita ingin membuat kolom baru yakni kolom bernama
Persentase_urban_pop yang berasal dari rumus sebagai
berikut
#Mengubah tipe data Population menjadi dbl/numerik
country_data_cleanup$Population <- as.numeric(gsub(",", "", country_data$Population))
country_data_cleanup %>%
mutate(Persentase_urban_pop= (Urban_population*100) / Population)## Country Density..P.Km2. Abbreviation
## 1 Afghanistan 60 AF
## 2 Albania 105 AL
## 3 Algeria 18 DZ
## 4 Andorra 164 AD
## 5 Angola 26 AO
## 6 Antigua and Barbuda 223 AG
## 7 Argentina 17 AR
## 8 Armenia 104 AM
## 9 Australia 3 AU
## 10 Austria 109 AT
## 11 Azerbaijan 123 AZ
## 12 The Bahamas 39 BS
## 13 Bahrain 2,239 BH
## 14 Bangladesh 1,265 BD
## 15 Barbados 668 BB
## 16 Belarus 47 BY
## 17 Belgium 383 BE
## 18 Belize 17 BZ
## 19 Benin 108 BJ
## 20 Bhutan 20 BT
## 21 Bolivia 11 BO
## 22 Bosnia and Herzegovina 64 BA
## 23 Botswana 4 BW
## 24 Brazil 25 BR
## 25 Brunei 83 BN
## 26 Bulgaria 64 BG
## 27 Burkina Faso 76 BF
## 28 Burundi 463 BI
## 29 Ivory Coast 83 CI
## 30 Cape Verde 138 CV
## 31 Cambodia 95 KH
## 32 Cameroon 56 CM
## 33 Canada 4 CA
## 34 Central African Republic 8 CF
## 35 Chad 13 TD
## 36 Chile 26 CL
## 37 China 153 CN
## 38 Colombia 46 CO
## 39 Comoros 467 KM
## 40 Republic of the Congo 16
## 41 Costa Rica 100 CR
## 42 Croatia 73 HR
## 43 Cuba 106 CU
## 44 Cyprus 131 CY
## 45 Czech Republic 139 CZ
## 46 Democratic Republic of the Congo 40 CD
## 47 Denmark 137 DK
## 48 Djibouti 43 DJ
## 49 Dominica 96 DM
## 50 Dominican Republic 225 DO
## 51 Ecuador 71 EC
## 52 Egypt 103 EG
## 53 El Salvador 313 SV
## 54 Equatorial Guinea 50 GQ
## 55 Eritrea 35 ER
## 56 Estonia 31 EE
## 57 Eswatini 67
## 58 Ethiopia 115 ET
## 59 Fiji 49 FJ
## 60 Finland 18 FI
## 61 France 119 FR
## 62 Gabon 9 GA
## 63 The Gambia 239 GM
## 64 Georgia 57 GE
## 65 Germany 240 DE
## 66 Ghana 137 GH
## 67 Greece 81 GR
## 68 Grenada 331 GD
## 69 Guatemala 167 GT
## 70 Guinea 53 GN
## 71 Guinea-Bissau 70 GW
## 72 Guyana 4 GY
## 73 Haiti 414 HT
## 74 Vatican City 2,003
## 75 Honduras 89 HN
## 76 Hungary 107 HU
## 77 Iceland 3 IS
## 78 India 464 IN
## 79 Indonesia 151 ID
## 80 Iran 52 IR
## 81 Iraq 93 IQ
## 82 Republic of Ireland 72
## 83 Israel 400 IL
## 84 Italy 206 IT
## 85 Jamaica 273 JM
## 86 Japan 347 JP
## 87 Jordan 115 JO
## 88 Kazakhstan 7 KZ
## 89 Kenya 94 KE
## 90 Kiribati 147 KI
## 91 Kuwait 240 KW
## 92 Kyrgyzstan 34 KG
## 93 Laos 32 LA
## 94 Latvia 30 LV
## 95 Lebanon 667 LB
## 96 Lesotho 71 LS
## 97 Liberia 53 LR
## 98 Libya 4 LY
## 99 Liechtenstein 238 LI
## 100 Lithuania 43 LT
## 101 Luxembourg 242 LU
## 102 Madagascar 48 MG
## 103 Malawi 203 MW
## 104 Malaysia 99 MY
## 105 Maldives 1,802 MV
## 106 Mali 17 ML
## 107 Malta 1,380 MT
## 108 Marshall Islands 329 MH
## 109 Mauritania 5 MR
## 110 Mauritius 626 MU
## 111 Mexico 66 MX
## 112 Federated States of Micronesia 784 FM
## 113 Moldova 123 MD
## 114 Monaco 26,337 MC
## 115 Mongolia 2 MN
## 116 Montenegro 47 ME
## 117 Morocco 83 MA
## 118 Mozambique 40 MZ
## 119 Myanmar 83 MM
## 120 Namibia 3
## 121 Nauru 541 NR
## 122 Nepal 203 NP
## 123 Netherlands 508 NL
## 124 New Zealand 18 NZ
## 125 Nicaragua 55 NI
## 126 Niger 19 NE
## 127 Nigeria 226 NG
## 128 North Korea 214 KP
## 129 North Macedonia 83
## 130 Norway 15 NO
## 131 Oman 16 OM
## 132 Pakistan 287 PK
## 133 Palau 39 PW
## 134 Palestinian National Authority 847
## 135 Panama 58 PA
## 136 Papua New Guinea 20 PG
## 137 Paraguay 18 PY
## 138 Peru 26 PE
## 139 Philippines 368 PH
## 140 Poland 124 PL
## 141 Portugal 111 PT
## 142 Qatar 248 QA
## 143 Romania 84 RO
## 144 Russia 9 RU
## 145 Rwanda 525 RW
## 146 Saint Kitts and Nevis 205 KN
## 147 Saint Lucia 301 LC
## 148 Saint Vincent and the Grenadines 284 VC
## 149 Samoa 70 WS
## 150 San Marino 566 SM
## 151 S����������� 228 ST
## 152 Saudi Arabia 16 SA
## 153 Senegal 87 SN
## 154 Serbia 100 RS
## 155 Seychelles 214 SC
## 156 Sierra Leone 111 SL
## 157 Singapore 8,358 SG
## 158 Slovakia 114 SK
## 159 Slovenia 103 SI
## 160 Solomon Islands 25 SB
## 161 Somalia 25 SO
## 162 South Africa 49 ZA
## 163 South Korea 527 KR
## 164 South Sudan 18 SS
## 165 Spain 94 ES
## 166 Sri Lanka 341 LK
## 167 Sudan 25 SD
## 168 Suriname 4 SR
## 169 Sweden 25 SE
## 170 Switzerland 219 CH
## 171 Syria 95 SY
## 172 Tajikistan 68 TJ
## 173 Tanzania 67 TZ
## 174 Thailand 137 TH
## 175 East Timor 89 TL
## 176 Togo 152 TG
## 177 Tonga 147 TO
## 178 Trinidad and Tobago 273 TT
## 179 Tunisia 76 TN
## 180 Turkey 110 TR
## 181 Turkmenistan 13 TM
## 182 Tuvalu 393 TV
## 183 Uganda 229 UG
## 184 Ukraine 75 UA
## 185 United Arab Emirates 118 AE
## 186 United Kingdom 281 GB
## 187 United States 36 US
## 188 Uruguay 20 UY
## 189 Uzbekistan 79 UZ
## 190 Vanuatu 25 VU
## 191 Venezuela 32 VE
## 192 Vietnam 314 VN
## 193 Yemen 56 YE
## 194 Zambia 25 ZM
## 195 Zimbabwe 38 ZW
## Agricultural.Land.... Land.Area.Km2. Armed.Forces.size Birth.Rate
## 1 58.10% 652,230 323,000 32.49
## 2 43.10% 28,748 9,000 11.78
## 3 17.40% 2,381,741 317,000 24.28
## 4 40.00% 468 7.20
## 5 47.50% 1,246,700 117,000 40.73
## 6 20.50% 443 0 15.33
## 7 54.30% 2,780,400 105,000 17.02
## 8 58.90% 29,743 49,000 13.99
## 9 48.20% 7,741,220 58,000 12.60
## 10 32.40% 83,871 21,000 9.70
## 11 57.70% 86,600 82,000 14.00
## 12 1.40% 13,880 1,000 13.97
## 13 11.10% 765 19,000 13.99
## 14 70.60% 148,460 221,000 18.18
## 15 23.30% 430 1,000 10.65
## 16 42.00% 207,600 155,000 9.90
## 17 44.60% 30,528 32,000 10.30
## 18 7.00% 22,966 2,000 20.79
## 19 33.30% 112,622 12,000 36.22
## 20 13.60% 38,394 6,000 17.26
## 21 34.80% 1,098,581 71,000 21.75
## 22 43.10% 51,197 11,000 8.11
## 23 45.60% 581,730 9,000 24.82
## 24 33.90% 8,515,770 730,000 13.92
## 25 2.70% 5,765 8,000 14.90
## 26 46.30% 110,879 31,000 8.90
## 27 44.20% 274,200 11,000 37.93
## 28 79.20% 27,830 31,000 39.01
## 29 64.80% 322,463 27,000 35.74
## 30 19.60% 4,033 1,000 19.49
## 31 30.90% 181,035 191,000 22.46
## 32 20.60% 475,440 24,000 35.39
## 33 6.90% 9,984,670 72,000 10.10
## 34 8.20% 622,984 8,000 35.35
## 35 39.70% 1,284,000 35,000 42.17
## 36 21.20% 756,096 122,000 12.43
## 37 56.20% 9,596,960 2,695,000 10.90
## 38 40.30% 1,138,910 481,000 14.88
## 39 71.50% 2,235 31.88
## 40 31.10% 342,000 12,000 32.86
## 41 34.50% 51,100 10,000 13.97
## 42 27.60% 56,594 18,000 9.00
## 43 59.90% 110,860 76,000 10.17
## 44 12.20% 9,251 16,000 10.46
## 45 45.20% 78,867 23,000 10.70
## 46 11.60% 2,344,858 134,000 41.18
## 47 62.00% 43,094 15,000 10.60
## 48 73.40% 23,200 13,000 21.47
## 49 33.30% 751 12.00
## 50 48.70% 48,670 71,000 19.51
## 51 22.20% 283,561 41,000 19.72
## 52 3.80% 1,001,450 836,000 26.38
## 53 76.40% 21,041 42,000 18.25
## 54 10.10% 28,051 1,000 33.24
## 55 75.20% 117,600 202,000 30.30
## 56 23.10% 45,228 6,000 10.90
## 57 17,364 NA
## 58 36.30% 1,104,300 138,000 32.34
## 59 23.30% 18,274 4,000 21.28
## 60 7.50% 338,145 25,000 8.60
## 61 52.40% 643,801 307,000 11.30
## 62 20.00% 267,667 7,000 31.61
## 63 59.80% 11,300 1,000 38.54
## 64 34.50% 69,700 26,000 13.47
## 65 47.70% 357,022 180,000 9.50
## 66 69.00% 238,533 16,000 29.41
## 67 47.60% 131,957 146,000 8.10
## 68 23.50% 349 16.47
## 69 36.00% 108,889 43,000 24.56
## 70 59.00% 245,857 13,000 36.36
## 71 58.00% 36,125 4,000 35.13
## 72 8.60% 214,969 3,000 19.97
## 73 66.80% 27,750 0 24.35
## 74 0 NA
## 75 28.90% 112,090 23,000 21.60
## 76 58.40% 93,028 40,000 9.60
## 77 18.70% 103,000 0 12.00
## 78 60.40% 3,287,263 3,031,000 17.86
## 79 31.50% 1,904,569 676,000 18.07
## 80 28.20% 1,648,195 563,000 18.78
## 81 21.40% 438,317 209,000 29.08
## 82 64.50% 70,273 9,000 12.50
## 83 24.60% 20,770 178,000 20.80
## 84 43.20% 301,340 347,000 7.30
## 85 41.00% 10,991 4,000 16.10
## 86 12.30% 377,944 261,000 7.40
## 87 12.00% 89,342 116,000 21.98
## 88 80.40% 2,724,900 71,000 21.77
## 89 48.50% 580,367 29,000 28.75
## 90 42.00% 811 27.89
## 91 8.40% 17,818 25,000 13.94
## 92 55.00% 199,951 21,000 27.10
## 93 10.30% 236,800 129,000 23.55
## 94 31.10% 64,589 6,000 10.00
## 95 64.30% 10,400 80,000 17.55
## 96 77.60% 30,355 2,000 26.81
## 97 28.00% 111,369 2,000 33.04
## 98 8.70% 1,759,540 0 18.83
## 99 32.20% 160 9.90
## 100 47.20% 65,300 34,000 10.00
## 101 53.70% 2,586 2,000 10.30
## 102 71.20% 587,041 22,000 32.66
## 103 61.40% 118,484 15,000 34.12
## 104 26.30% 329,847 136,000 16.75
## 105 26.30% 298 5,000 14.20
## 106 33.80% 1,240,192 18,000 41.54
## 107 32.40% 316 2,000 9.20
## 108 63.90% 181 29.03
## 109 38.50% 1,030,700 21,000 33.69
## 110 42.40% 2,040 3,000 10.20
## 111 54.60% 1,964,375 336,000 17.60
## 112 31.40% 702 22.82
## 113 74.20% 33,851 7,000 10.10
## 114 2 5.90
## 115 71.50% 1,564,116 18,000 24.13
## 116 19.00% 13,812 12,000 11.73
## 117 68.50% 446,550 246,000 18.94
## 118 63.50% 799,380 11,000 37.52
## 119 19.50% 676,578 513,000 17.55
## 120 47.10% 824,292 16,000 28.64
## 121 21 NA
## 122 28.70% 147,181 112,000 19.89
## 123 53.30% 41,543 41,000 9.70
## 124 40.50% 268,838 9,000 11.98
## 125 42.10% 130,370 12,000 20.64
## 126 36.10% 1,267,000 10,000 46.08
## 127 77.70% 923,768 215,000 37.91
## 128 21.80% 120,538 1,469,000 13.89
## 129 25,713 NA
## 130 2.70% 323,802 23,000 10.40
## 131 4.60% 309,500 47,000 19.19
## 132 47.80% 796,095 936,000 28.25
## 133 10.90% 459 14.00
## 134 NA
## 135 30.40% 75,420 26,000 18.98
## 136 2.60% 462,840 4,000 27.07
## 137 55.10% 406,752 27,000 20.57
## 138 18.50% 1,285,216 158,000 17.95
## 139 41.70% 300,000 153,000 20.55
## 140 46.90% 312,685 191,000 10.20
## 141 39.50% 92,212 52,000 8.50
## 142 5.80% 11,586 22,000 9.54
## 143 58.80% 238,391 126,000 9.60
## 144 13.30% 17,098,240 1,454,000 11.50
## 145 73.40% 26,338 35,000 31.70
## 146 23.10% 261 12.60
## 147 17.40% 616 12.00
## 148 25.60% 389 14.24
## 149 12.40% 2,831 24.38
## 150 16.70% 61 6.80
## 151 50.70% 964 1,000 31.54
## 152 80.80% 2,149,690 252,000 17.80
## 153 46.10% 196,722 19,000 34.52
## 154 39.30% 77,474 32,000 9.20
## 155 3.40% 455 0 17.10
## 156 54.70% 71,740 9,000 33.41
## 157 0.90% 716 81,000 8.80
## 158 39.20% 49,035 16,000 10.60
## 159 30.70% 20,273 7,000 9.40
## 160 3.90% 28,896 32.44
## 161 70.30% 637,657 20,000 41.75
## 162 79.80% 1,219,090 80,000 20.51
## 163 17.40% 99,720 634,000 6.40
## 164 644,329 185,000 35.01
## 165 52.60% 505,370 196,000 7.90
## 166 43.70% 65,610 317,000 15.83
## 167 28.70% 1,861,484 124,000 32.18
## 168 0.60% 163,820 2,000 18.54
## 169 7.40% 450,295 30,000 11.40
## 170 38.40% 41,277 21,000 10.00
## 171 75.80% 185,180 239,000 23.69
## 172 34.10% 144,100 17,000 30.76
## 173 44.80% 947,300 28,000 36.70
## 174 43.30% 513,120 455,000 10.34
## 175 25.60% 14,874 2,000 29.42
## 176 70.20% 56,785 10,000 33.11
## 177 45.80% 747 24.30
## 178 10.50% 5,128 4,000 12.94
## 179 64.80% 163,610 48,000 17.56
## 180 49.80% 783,562 512,000 16.03
## 181 72.00% 488,100 42,000 23.83
## 182 60.00% 26 NA
## 183 71.90% 241,038 46,000 38.14
## 184 71.70% 603,550 297,000 8.70
## 185 5.50% 83,600 63,000 10.33
## 186 71.70% 243,610 148,000 11.00
## 187 44.40% 9,833,517 1,359,000 11.60
## 188 82.60% 176,215 22,000 13.86
## 189 62.90% 447,400 68,000 23.30
## 190 15.30% 12,189 29.60
## 191 24.50% 912,050 343,000 17.88
## 192 39.30% 331,210 522,000 16.75
## 193 44.60% 527,968 40,000 30.45
## 194 32.10% 752,618 16,000 36.19
## 195 41.90% 390,757 51,000 30.68
## Calling.Code Capital.Major.City Co2.Emissions CPI
## 1 93 Kabul 8,672 149.9
## 2 355 Tirana 4,536 119.05
## 3 213 Algiers 150,006 151.36
## 4 376 Andorra la Vella 469
## 5 244 Luanda 34,693 261.73
## 6 1 St. John's, Saint John 557 113.81
## 7 54 Buenos Aires 201,348 232.75
## 8 374 Yerevan 5,156 129.18
## 9 61 Canberra 375,908 119.8
## 10 43 Vienna 61,448 118.06
## 11 994 Baku 37,620 156.32
## 12 1 Nassau, Bahamas 1,786 116.22
## 13 973 Manama 31,694 117.59
## 14 880 Dhaka 84,246 179.68
## 15 1 Bridgetown 1,276 134.09
## 16 375 Minsk 58,280
## 17 32 City of Brussels 96,889 117.11
## 18 501 Belmopan 568 105.68
## 19 229 Porto-Novo 6,476 110.71
## 20 975 Thimphu 1,261 167.18
## 21 591 Sucre 21,606 148.32
## 22 387 Sarajevo 21,848 104.9
## 23 267 Gaborone 6,340 149.75
## 24 55 Bras��� 462,299 167.4
## 25 673 Bandar Seri Begawan 7,664 99.03
## 26 359 Sofia 41,708 114.42
## 27 226 Ouagadougou 3,418 106.58
## 28 257 Bujumbura 495 182.11
## 29 225 Yamoussoukro 9,674 111.61
## 30 238 Praia 543 110.5
## 31 855 Phnom Penh 9,919 127.63
## 32 237 Yaound� 8,291 118.65
## 33 1 Ottawa 544,894 116.76
## 34 236 Bangui 297 186.86
## 35 235 N'Djamena 1,016 117.7
## 36 56 Santiago 85,822 131.91
## 37 86 Beijing 9,893,038 125.08
## 38 57 Bogot� 97,814 140.95
## 39 269 Moroni, Comoros 202 103.62
## 40 242 Brazzaville 3,282 124.74
## 41 506 San Jos������ 8,023 128.85
## 42 385 Zagreb 17,488 109.82
## 43 53 Havana 28,284
## 44 357 Nicosia 6,626 102.51
## 45 420 Prague 102,218 116.48
## 46 243 Kinshasa 2,021 133.85
## 47 45 Copenhagen 31,786 110.35
## 48 253 Djibouti City 620 120.25
## 49 1 Roseau 180 103.87
## 50 1 Santo Domingo 25,258 135.5
## 51 593 Quito 41,155 124.14
## 52 20 Cairo 238,560 288.57
## 53 503 San Salvador 7,169 111.23
## 54 240 Malabo 5,655 124.35
## 55 291 Asmara 711
## 56 372 Tallinn 16,590 122.14
## 57 268 Mbabane
## 58 251 Addis Ababa 14,870 143.86
## 59 679 Suva 2,046 132.3
## 60 358 Helsinki 45,871 112.33
## 61 33 Paris 303,276 110.05
## 62 241 Libreville 5,321 122.19
## 63 220 Banjul 532 172.73
## 64 995 Tbilisi 10,128 133.61
## 65 49 Berlin 727,973 112.85
## 66 233 Accra 16,670 268.36
## 67 30 Athens 62,434 101.87
## 68 1 St. George's, Grenada 268 107.43
## 69 502 Guatemala City 16,777 142.92
## 70 224 Conakry 2,996 262.95
## 71 245 Bissau 293 111.65
## 72 592 Georgetown, Guyana 2,384 116.19
## 73 509 Port-au-Prince 2,978 179.29
## 74 379 Vatican City
## 75 504 Tegucigalpa 9,813 150.34
## 76 36 Budapest 45,537 121.64
## 77 354 Reykjav�� 2,065 129
## 78 91 New Delhi 2,407,672 180.44
## 79 62 Jakarta 563,325 151.18
## 80 98 Tehran 661,710 550.93
## 81 964 Baghdad 190,061 119.86
## 82 353 Dublin 37,711 106.58
## 83 972 Jerusalem 65,166 108.15
## 84 39 Rome 320,411 110.62
## 85 1876 Kingston, Jamaica 8,225 162.47
## 86 81 Tokyo 1,135,886 105.48
## 87 962 Amman 25,108 125.6
## 88 7 Astana 247,207 182.75
## 89 254 Nairobi 17,910 180.51
## 90 686 South Tarawa 66 99.55
## 91 965 Kuwait City 98,734 126.6
## 92 996 Bishkek 9,787 155.68
## 93 856 Vientiane 17,763 135.87
## 94 371 Riga 7,004 116.86
## 95 961 Beirut 24,796 130.02
## 96 266 Maseru 2,512 155.86
## 97 231 Monrovia 1,386 223.13
## 98 218 50,564 125.71
## 99 423 Vaduz 51
## 100 370 Vilnius 12,963 118.38
## 101 352 Luxembourg City 8,988 115.09
## 102 261 Antananarivo 3,905 184.33
## 103 265 Lilongwe 1,298 418.34
## 104 60 Kuala Lumpur 248,289 121.46
## 105 960 Mal� 1,445 99.7
## 106 223 Bamako 3,179 108.73
## 107 356 Valletta 1,342 113.45
## 108 692 Majuro 143
## 109 222 Nouakchott 2,739 135.02
## 110 230 Port Louis 4,349 129.91
## 111 52 Mexico City 486,406 141.54
## 112 691 Palikir 143 112.1
## 113 373 Chi���� 5,115 166.2
## 114 377 Monaco City
## 115 976 Ulaanbaatar 25,368 195.76
## 116 382 Podgorica 2,017 116.32
## 117 212 Rabat 61,276 111.07
## 118 258 Maputo 7,943 182.31
## 119 95 Naypyidaw 25,280 168.18
## 120 264 Windhoek 4,228 157.97
## 121 674 Yaren District
## 122 977 Kathmandu 9,105 188.73
## 123 31 Amsterdam 170,780 115.91
## 124 64 Wellington 34,382 114.24
## 125 505 Managua 5,592 162.74
## 126 227 Niamey 2,017 109.32
## 127 234 Abuja 120,369 267.51
## 128 850 Pyongyang 28,284
## 129 389 Skopje
## 130 47 Oslo 41,023 120.27
## 131 968 Muscat 63,457 113.53
## 132 92 Islamabad 201,150 182.32
## 133 680 Ngerulmud 224 118.17
## 134 NA
## 135 507 Panama City 10,715 122.07
## 136 675 Port Moresby 7,536 155.99
## 137 595 Asunci�� 7,407 143.82
## 138 51 Lima 57,414 129.78
## 139 63 Manila 122,287 129.61
## 140 48 Warsaw 299,037 114.11
## 141 351 Lisbon 48,742 110.62
## 142 974 Doha 103,259 115.38
## 143 40 Bucharest 69,259 123.78
## 144 7 Moscow 1,732,027 180.75
## 145 250 Kigali 1,115 151.09
## 146 1 Basseterre 238 104.57
## 147 1 Castries 414 110.13
## 148 1 Kingstown 220 109.67
## 149 685 Apia 246 117.56
## 150 378 City of San Marino 110.63
## 151 239 S���� 121 185.09
## 152 966 Riyadh 563,449 118.4
## 153 221 Dakar 10,902 109.25
## 154 381 Belgrade 45,221 144
## 155 248 Victoria, Seychelles 605 129.96
## 156 232 Freetown 1,093 234.16
## 157 65 37,535 114.41
## 158 421 Bratislava 32,424 115.34
## 159 386 Ljubljana 12,633 111.05
## 160 677 Honiara 169 133.06
## 161 252 Mogadishu 645
## 162 27 Pretoria 476,644 158.93
## 163 82 Seoul 620,302 115.16
## 164 211 Juba 1,727 4,583.71
## 165 34 Madrid 244,002 110.96
## 166 94 Colombo 23,362 155.53
## 167 249 Khartoum 20,000 1,344.19
## 168 597 Paramaribo 1,738 294.66
## 169 46 Stockholm 43,252 110.51
## 170 41 Bern 34,477 99.55
## 171 963 Damascus 28,830 143.2
## 172 992 Dushanbe 5,310 148.57
## 173 255 Dodoma 11,973 187.43
## 174 66 Bangkok 283,763 113.27
## 175 670 Dili 495 145.38
## 176 228 Lom� 3,000 113.3
## 177 676 Nuku���� 128 121.09
## 178 1 Port of Spain 43,868 141.75
## 179 216 Tunis 29,937 155.33
## 180 90 Ankara 372,725 234.44
## 181 993 Ashgabat 70,630
## 182 688 Funafuti 11
## 183 256 Kampala 5,680 173.87
## 184 380 Kyiv 202,250 281.66
## 185 971 Abu Dhabi 206,324 114.52
## 186 44 London 379,025 119.62
## 187 1 Washington, D.C. 5,006,302 117.24
## 188 598 Montevideo 6,766 202.92
## 189 998 Tashkent 91,811
## 190 678 Port Vila 147 117.13
## 191 58 Caracas 164,175 2,740.27
## 192 84 Hanoi 192,668 163.52
## 193 967 Sanaa 10,609 157.58
## 194 260 Lusaka 5,141 212.31
## 195 263 Harare 10,983 105.51
## CPI.Change.... Currency.Code Fertility.Rate Forested.Area....
## 1 2.30% AFN 4.47 2.10%
## 2 1.40% ALL 1.62 28.10%
## 3 2.00% DZD 3.02 0.80%
## 4 EUR 1.27 34.00%
## 5 17.10% AOA 5.52 46.30%
## 6 1.20% XCD 1.99 22.30%
## 7 53.50% ARS 2.26 9.80%
## 8 1.40% AMD 1.76 11.70%
## 9 1.60% AUD 1.74 16.30%
## 10 1.50% EUR 1.47 46.90%
## 11 2.60% AZN 1.73 14.10%
## 12 2.50% 1.75 51.40%
## 13 2.10% BHD 1.99 0.80%
## 14 5.60% BDT 2.04 11.00%
## 15 4.10% BBD 1.62 14.70%
## 16 5.60% BYN 1.45 42.60%
## 17 1.40% EUR 1.62 22.60%
## 18 -0.90% BZD 2.31 59.70%
## 19 -0.90% XOF 4.84 37.80%
## 20 2.70% 1.98 72.50%
## 21 1.80% BOB 2.73 50.30%
## 22 0.60% BAM 1.27 42.70%
## 23 2.80% BWP 2.87 18.90%
## 24 3.70% BRL 1.73 58.90%
## 25 -0.40% BND 1.85 72.10%
## 26 3.10% BGN 1.56 35.40%
## 27 -3.20% XOF 5.19 19.30%
## 28 -0.70% BIF 5.41 10.90%
## 29 -0.90% XOF 4.65 32.70%
## 30 1.10% CVE 2.27 22.50%
## 31 2.50% 2.50 52.90%
## 32 2.50% XAF 4.57 39.30%
## 33 1.90% CAD 1.50 38.20%
## 34 37.10% 4.72 35.60%
## 35 -1.00% XAF 5.75 3.80%
## 36 2.60% CLP 1.65 24.30%
## 37 2.90% CNY 1.69 22.40%
## 38 3.50% COP 1.81 52.70%
## 39 -4.30% KMF 4.21 19.70%
## 40 2.20% XAF 4.43 65.40%
## 41 2.10% CRC 1.75 54.60%
## 42 0.80% HRK 1.47 34.40%
## 43 CUP 1.62 31.30%
## 44 0.30% EUR 1.33 18.70%
## 45 2.80% CZK 1.69 34.60%
## 46 2.90% CDF 5.92 67.20%
## 47 0.80% DKK 1.73 14.70%
## 48 3.30% DJF 2.73 0.20%
## 49 1.00% XCD 1.90 57.40%
## 50 1.80% DOP 2.35 41.70%
## 51 0.30% USD 2.43 50.20%
## 52 9.20% EGP 3.33 0.10%
## 53 0.10% 2.04 12.60%
## 54 1.20% XAF 4.51 55.50%
## 55 ERN 4.06 14.90%
## 56 2.30% EUR 1.59 51.30%
## 57 NA
## 58 15.80% ETB 4.25 12.50%
## 59 1.80% FJD 2.77 55.90%
## 60 1.00% EUR 1.41 73.10%
## 61 1.10% EUR 1.88 31.20%
## 62 2.10% XAF 3.97 90.00%
## 63 7.10% GMD 5.22 48.40%
## 64 4.90% GEL 2.06 40.60%
## 65 1.40% EUR 1.56 32.70%
## 66 7.20% GHS 3.87 41.20%
## 67 0.20% EUR 1.35 31.70%
## 68 0.80% XCD 2.06 50.00%
## 69 3.70% GTQ 2.87 32.70%
## 70 9.50% GNF 4.70 25.80%
## 71 1.40% XOF 4.48 69.80%
## 72 2.10% GYD 2.46 83.90%
## 73 12.50% HTG 2.94 3.50%
## 74 EUR NA
## 75 4.40% HNL 2.46 40.00%
## 76 3.30% HUF 1.54 22.90%
## 77 3.00% ISK 1.71 0.50%
## 78 7.70% INR 2.22 23.80%
## 79 3.00% IDR 2.31 49.90%
## 80 39.90% IRR 2.14 6.60%
## 81 0.40% IQD 3.67 1.90%
## 82 0.90% EUR 1.75 11.00%
## 83 0.80% ILS 3.09 7.70%
## 84 0.60% EUR 1.29 31.80%
## 85 3.90% JMD 1.98 30.90%
## 86 0.50% 1.42 68.50%
## 87 0.80% JOD 2.76 1.10%
## 88 5.20% KZT 2.84 1.20%
## 89 4.70% KES 3.49 7.80%
## 90 0.60% AUD 3.57 15.00%
## 91 1.10% KWD 2.08 0.40%
## 92 1.10% KGS 3.30 3.30%
## 93 3.30% LAK 2.67 82.10%
## 94 2.80% EUR 1.60 54.00%
## 95 3.00% LBP 2.09 13.40%
## 96 5.20% 3.14 1.60%
## 97 23.60% 4.32 43.10%
## 98 2.60% LYD 2.24 0.10%
## 99 CHF 1.44 43.10%
## 100 2.30% EUR 1.63 34.80%
## 101 1.70% EUR 1.37 35.70%
## 102 5.60% MGA 4.08 21.40%
## 103 9.40% MWK 4.21 33.20%
## 104 0.70% MYR 2.00 67.60%
## 105 0.20% 1.87 3.30%
## 106 -1.70% XOF 5.88 3.80%
## 107 1.60% EUR 1.23 1.10%
## 108 USD 4.05 70.20%
## 109 2.30% MRU 4.56 0.20%
## 110 0.40% MUR 1.41 19.00%
## 111 3.60% MXN 2.13 33.90%
## 112 0.50% USD 3.05 91.90%
## 113 4.80% MDL 1.26 12.60%
## 114 EUR NA
## 115 7.30% MNT 2.90 8.00%
## 116 2.60% EUR 1.75 61.50%
## 117 0.20% MAD 2.42 12.60%
## 118 2.80% MZN 4.85 48.00%
## 119 8.80% MMK 2.15 43.60%
## 120 3.70% 3.40 8.30%
## 121 AUD NA
## 122 5.60% NPR 1.92 25.40%
## 123 2.60% 1.59 11.20%
## 124 1.60% NZD 1.71 38.60%
## 125 5.40% NIO 2.40 25.90%
## 126 -2.50% XOF 6.91 0.90%
## 127 11.40% NGN 5.39 7.20%
## 128 KPW 1.90 40.70%
## 129 MKD NA
## 130 2.20% NOK 1.56 33.20%
## 131 0.10% OMR 2.89 0.00%
## 132 10.60% PKR 3.51 1.90%
## 133 1.30% USD 2.21 87.60%
## 134 NA
## 135 -0.40% 2.46 61.90%
## 136 3.60% PGK 3.56 74.10%
## 137 2.80% PYG 2.43 37.70%
## 138 2.10% PEN 2.25 57.70%
## 139 2.50% PHP 2.58 27.80%
## 140 2.20% PLN 1.46 30.90%
## 141 0.30% EUR 1.38 34.60%
## 142 -0.70% QAR 1.87 0.00%
## 143 3.80% RON 1.71 30.10%
## 144 4.50% RUB 1.57 49.80%
## 145 3.40% RWF 4.04 19.70%
## 146 -1.00% XCD 2.11 42.30%
## 147 1.90% XCD 1.44 33.20%
## 148 2.30% XCD 1.89 69.20%
## 149 1.00% WST 3.88 60.40%
## 150 1.00% EUR 1.26 0.00%
## 151 7.90% STN 4.32 55.80%
## 152 -1.20% SAR 2.32 0.50%
## 153 1.80% XOF 4.63 42.80%
## 154 1.80% RSD 1.49 31.10%
## 155 1.80% SCR 2.41 88.40%
## 156 14.80% SLL 4.26 43.10%
## 157 0.60% SGD 1.14 23.10%
## 158 2.70% EUR 1.52 40.40%
## 159 1.60% EUR 1.60 62.00%
## 160 1.60% SBD 4.40 77.90%
## 161 SOS 6.07 10.00%
## 162 4.10% ZAR 2.41 7.60%
## 163 0.40% KRW 0.98 63.40%
## 164 187.90% SSP 4.70
## 165 0.70% EUR 1.26 36.90%
## 166 3.50% LKR 2.20 32.90%
## 167 51.00% SDG 4.41 8.10%
## 168 22.00% SRD 2.42 98.30%
## 169 1.80% SEK 1.76 68.90%
## 170 0.40% CHF 1.52 31.80%
## 171 36.70% SYP 2.81 2.70%
## 172 6.00% TJS 3.59 3.00%
## 173 3.50% TZS 4.89 51.60%
## 174 0.70% THB 1.53 32.20%
## 175 2.60% USD 4.02 45.40%
## 176 0.70% XOF 4.32 3.10%
## 177 7.40% TOP 3.56 12.50%
## 178 1.00% TTD 1.73 46.00%
## 179 6.70% TND 2.20 6.80%
## 180 15.20% TRY 2.07 15.40%
## 181 TMT 2.79 8.80%
## 182 AUD NA 33.30%
## 183 2.90% UGX 4.96 9.70%
## 184 7.90% UAH 1.30 16.70%
## 185 -1.90% AED 1.41 4.60%
## 186 1.70% GBP 1.68 13.10%
## 187 7.50% USD 1.73 33.90%
## 188 7.90% UYU 1.97 10.70%
## 189 UZS 2.42 7.50%
## 190 2.80% VUV 3.78 36.10%
## 191 254.90% VED 2.27 52.70%
## 192 2.80% VND 2.05 48.10%
## 193 8.10% YER 3.79 1.00%
## 194 9.20% ZMW 4.63 65.20%
## 195 0.90% 3.62 35.50%
## Gasoline.Price GDP Gross.primary.education.enrollment....
## 1 $0.70 $19,101,353,833 104.00%
## 2 $1.36 $15,278,077,447 107.00%
## 3 $0.28 $169,988,236,398 109.90%
## 4 $1.51 $3,154,057,987 106.40%
## 5 $0.97 $94,635,415,870 113.50%
## 6 $0.99 $1,727,759,259 105.00%
## 7 $1.10 $449,663,446,954 109.70%
## 8 $0.77 $13,672,802,158 92.70%
## 9 $0.93 $1,392,680,589,329 100.30%
## 10 $1.20 $446,314,739,528 103.10%
## 11 $0.56 $39,207,000,000 99.70%
## 12 $0.92 $12,827,000,000 81.40%
## 13 $0.43 $38,574,069,149 99.40%
## 14 $1.12 $302,571,254,131 116.50%
## 15 $1.81 $5,209,000,000 99.40%
## 16 $0.60 $63,080,457,023 100.50%
## 17 $1.43 $529,606,710,418 103.90%
## 18 $1.13 $1,879,613,600 111.70%
## 19 $0.72 $14,390,709,095 122.00%
## 20 $0.98 $2,446,674,101 100.10%
## 21 $0.71 $40,895,322,865 98.20%
## 22 $1.05 $20,047,848,435
## 23 $0.71 $18,340,510,789 103.20%
## 24 $1.02 $1,839,758,040,766 115.40%
## 25 $0.37 $13,469,422,941 103.20%
## 26 $1.11 $86,000,000,000 89.30%
## 27 $0.98 $15,745,810,235 96.10%
## 28 $1.21 $3,012,334,882 121.40%
## 29 $0.93 $58,792,205,642 99.80%
## 30 $1.02 $1,981,845,741 104.00%
## 31 $0.90 $27,089,389,787 107.40%
## 32 $1.03 $38,760,467,033 103.40%
## 33 $0.81 $1,736,425,629,520 100.90%
## 34 $1.41 $2,220,307,369 102.00%
## 35 $0.78 $11,314,951,343 86.80%
## 36 $1.03 $282,318,159,745 101.40%
## 37 $0.96 $19,910,000,000,000 100.20%
## 38 $0.68 $323,802,808,108 114.50%
## 39 $1,185,728,677 99.50%
## 40 $0.97 $10,820,591,131 106.60%
## 41 $0.98 $61,773,944,174 113.30%
## 42 $1.26 $60,415,553,039 96.50%
## 43 $1.40 $100,023,000,000 101.90%
## 44 $1.23 $24,564,647,935 99.30%
## 45 $1.17 $246,489,245,495 100.70%
## 46 $1.49 $47,319,624,204 108.00%
## 47 $1.55 $348,078,018,464 101.30%
## 48 $1.32 $3,318,716,359 75.30%
## 49 $596,033,333 114.70%
## 50 $1.07 $88,941,298,258 105.70%
## 51 $0.61 $107,435,665,000 103.30%
## 52 $0.40 $303,175,127,598 106.30%
## 53 $0.83 $27,022,640,000 94.80%
## 54 $11,026,774,945 61.80%
## 55 $2.00 $2,065,001,626 68.40%
## 56 $1.14 $31,386,949,981 97.20%
## 57 $3,791,304,348
## 58 $0.75 $96,107,662,398 101.00%
## 59 $0.82 $5,535,548,972 106.40%
## 60 $1.45 $268,761,201,365 100.20%
## 61 $1.39 $2,715,518,274,227 102.50%
## 62 $0.92 $16,657,960,228 139.90%
## 63 $1.18 $1,763,819,048 98.00%
## 64 $0.76 $17,743,195,770 98.60%
## 65 $1.39 $3,845,630,030,824 104.00%
## 66 $0.92 $66,983,634,224 104.80%
## 67 $1.54 $209,852,761,469 99.60%
## 68 $1.12 $1,228,170,370 106.90%
## 69 $0.79 $76,710,385,880 101.90%
## 70 $0.90 $13,590,281,809 91.50%
## 71 $1,340,389,411 118.70%
## 72 $0.90 $4,280,443,645 97.80%
## 73 $0.81 $8,498,981,821 113.60%
## 74
## 75 $0.98 $25,095,395,475 91.50%
## 76 $1.18 $160,967,157,504 100.80%
## 77 $1.69 $24,188,035,739 100.40%
## 78 $0.97 $2,611,000,000,000 113.00%
## 79 $0.63 $1,119,190,780,753 106.40%
## 80 $0.40 $445,345,282,123 110.70%
## 81 $0.61 $234,094,042,939 108.70%
## 82 $1.37 $388,698,711,348 100.90%
## 83 $1.57 $395,098,666,122 104.90%
## 84 $1.61 $2,001,244,392,042 101.90%
## 85 $1.11 $16,458,071,068 91.00%
## 86 $1.06 $5,081,769,542,380 98.80%
## 87 $1.10 $43,743,661,972 81.50%
## 88 $0.42 $180,161,741,180 104.40%
## 89 $0.95 $95,503,088,538 103.20%
## 90 $194,647,202 101.30%
## 91 $0.35 $134,761,198,946 92.40%
## 92 $0.56 $8,454,619,608 107.60%
## 93 $0.93 $18,173,839,128 102.40%
## 94 $1.16 $34,117,202,555 99.40%
## 95 $0.74 $53,367,042,272 95.10%
## 96 $0.70 $2,460,072,444 120.90%
## 97 $0.80 $3,070,518,100 85.10%
## 98 $0.11 $52,076,250,948 109.00%
## 99 $1.74 $6,552,858,739 104.70%
## 100 $1.16 $54,219,315,600 103.90%
## 101 $1.19 $71,104,919,108 102.30%
## 102 $1.11 $14,083,906,357 142.50%
## 103 $1.15 $7,666,704,427 142.50%
## 104 $0.45 $364,701,517,788 105.30%
## 105 $1.63 $5,729,248,472 97.10%
## 106 $1.12 $17,510,141,171 75.60%
## 107 $1.36 $14,786,156,563 105.00%
## 108 $1.44 $221,278,000 84.70%
## 109 $1.13 $7,593,752,450 99.90%
## 110 $1.12 $14,180,444,557 101.10%
## 111 $0.73 $1,258,286,717,125 105.80%
## 112 $401,932,279 97.20%
## 113 $0.80 $11,955,435,457 90.60%
## 114 $2.00 $7,184,844,193
## 115 $0.72 $13,852,850,259 104.00%
## 116 $1.16 $5,494,736,901 100.00%
## 117 $0.99 $118,725,279,596 113.90%
## 118 $0.65 $14,934,159,926 112.60%
## 119 $0.54 $76,085,852,617 112.30%
## 120 $0.76 $12,366,527,719 124.20%
## 121 $133,000,000
## 122 $0.91 $30,641,380,604 142.10%
## 123 $1.68 $909,070,395,161 104.20%
## 124 $1.40 $206,928,765,544 100.00%
## 125 $0.91 $12,520,915,291 120.60%
## 126 $0.88 $12,928,145,120 74.70%
## 127 $0.46 $448,120,428,859 84.70%
## 128 $0.58 $32,100,000,000 112.80%
## 129 $10,220,781,069
## 130 $1.78 $403,336,363,636 100.30%
## 131 $0.45 $76,983,094,928 103.40%
## 132 $0.79 $304,400,000,000 94.30%
## 133 $283,994,900 112.60%
## 134
## 135 $0.74 $66,800,800,000 94.40%
## 136 $1.36 $24,969,611,435 108.50%
## 137 $1.04 $38,145,288,940 104.40%
## 138 $0.99 $226,848,050,820 106.90%
## 139 $0.86 $376,795,508,680 107.50%
## 140 $1.07 $592,164,400,688 100.00%
## 141 $1.54 $237,686,075,635 106.20%
## 142 $0.40 $183,466,208,791 103.80%
## 143 $1.16 $250,077,444,017 85.20%
## 144 $0.59 $1,699,876,578,871 102.60%
## 145 $1.17 $10,122,472,590 133.00%
## 146 $1,050,992,593 108.70%
## 147 $1.30 $2,122,450,630 102.60%
## 148 $825,385,185 113.40%
## 149 $0.91 $850,655,017 110.50%
## 150 $1,637,931,034 108.10%
## 151 $429,016,605 106.80%
## 152 $0.24 $792,966,838,162 99.80%
## 153 $1.14 $23,578,084,052 81.00%
## 154 $1.16 $51,409,167,351 100.30%
## 155 $1,698,843,063 100.40%
## 156 $1.08 $3,941,474,311 112.80%
## 157 $1.25 $372,062,527,489 100.60%
## 158 $1.32 $105,422,304,976 98.70%
## 159 $1.32 $53,742,159,517 100.40%
## 160 $1,425,074,226 106.20%
## 161 $1.41 $4,720,727,278 23.40%
## 162 $0.92 $351,431,649,241 100.90%
## 163 $1.22 $2,029,000,000,000 98.10%
## 164 $0.28 $11,997,800,751 73.00%
## 165 $1.26 $1,394,116,310,769 102.70%
## 166 $0.88 $84,008,783,756 100.20%
## 167 $0.95 $18,902,284,476 76.80%
## 168 $1.29 $3,985,250,737 108.80%
## 169 $1.42 $530,832,908,738 126.60%
## 170 $1.45 $703,082,435,360 105.20%
## 171 $0.83 $40,405,006,007 81.70%
## 172 $0.71 $8,116,626,794 100.90%
## 173 $0.87 $63,177,068,175 94.20%
## 174 $0.71 $543,649,976,166 99.80%
## 175 $1.10 $1,673,540,300 115.30%
## 176 $0.71 $5,459,979,417 123.80%
## 177 $450,353,314 116.30%
## 178 $0.54 $24,100,202,834 106.20%
## 179 $0.73 $38,797,709,924 115.40%
## 180 $1.42 $754,411,708,203 93.20%
## 181 $0.29 $40,761,142,857 88.40%
## 182 $47,271,463 86.00%
## 183 $0.94 $34,387,229,486 102.70%
## 184 $0.83 $153,781,069,118 99.00%
## 185 $0.49 $421,142,267,938 108.40%
## 186 $1.46 $2,827,113,184,696 101.20%
## 187 $0.71 $21,427,700,000,000 101.80%
## 188 $1.50 $56,045,912,952 108.50%
## 189 $1.03 $57,921,286,440 104.20%
## 190 $1.31 $917,058,851 109.30%
## 191 $0.00 $482,359,318,768 97.20%
## 192 $0.80 $261,921,244,843 110.60%
## 193 $0.92 $26,914,402,224 93.60%
## 194 $1.40 $23,064,722,446 98.70%
## 195 $1.34 $21,440,758,800 109.90%
## Gross.tertiary.education.enrollment.... Infant.mortality
## 1 9.70% 47.9
## 2 55.00% 7.8
## 3 51.40% 20.1
## 4 2.7
## 5 9.30% 51.6
## 6 24.80% 5.0
## 7 90.00% 8.8
## 8 54.60% 11.0
## 9 113.10% 3.1
## 10 85.10% 2.9
## 11 27.70% 19.2
## 12 15.10% 8.3
## 13 50.50% 6.1
## 14 20.60% 25.1
## 15 65.40% 11.3
## 16 87.40% 2.6
## 17 79.70% 2.9
## 18 24.70% 11.2
## 19 12.30% 60.5
## 20 15.60% 24.8
## 21 21.8
## 22 23.30% 5.0
## 23 24.90% 30.0
## 24 51.30% 12.8
## 25 31.40% 9.8
## 26 71.00% 5.9
## 27 6.50% 49.0
## 28 6.10% 41.0
## 29 9.30% 59.4
## 30 23.60% 16.7
## 31 13.70% 24.0
## 32 12.80% 50.6
## 33 68.90% 4.3
## 34 3.00% 84.5
## 35 3.30% 71.4
## 36 88.50% 6.2
## 37 50.60% 7.4
## 38 55.30% 12.2
## 39 9.00% 51.3
## 40 12.70% 36.2
## 41 55.20% 7.6
## 42 67.90% 4.0
## 43 41.40% 3.7
## 44 75.90% 1.9
## 45 64.10% 2.7
## 46 6.60% 68.2
## 47 80.60% 3.6
## 48 5.30% 49.8
## 49 7.20% 32.9
## 50 59.90% 24.1
## 51 44.90% 12.2
## 52 35.20% 18.1
## 53 29.40% 11.8
## 54 1.90% 62.6
## 55 3.40% 31.3
## 56 69.60% 2.1
## 57 NA
## 58 8.10% 39.1
## 59 16.10% 21.6
## 60 88.20% 1.4
## 61 65.60% 3.4
## 62 8.30% 32.7
## 63 2.70% 39.0
## 64 63.90% 8.7
## 65 70.20% 3.1
## 66 15.70% 34.9
## 67 136.60% 3.6
## 68 104.60% 13.7
## 69 21.80% 22.1
## 70 11.60% 64.9
## 71 2.60% 54.0
## 72 11.60% 25.1
## 73 1.10% 49.5
## 74 NA
## 75 26.20% 15.1
## 76 48.50% 3.6
## 77 71.80% 1.5
## 78 28.10% 29.9
## 79 36.30% 21.1
## 80 68.10% 12.4
## 81 16.20% 22.5
## 82 77.80% 3.1
## 83 63.40% 3.0
## 84 61.90% 2.6
## 85 27.10% 12.4
## 86 63.20% 1.8
## 87 34.40% 13.9
## 88 61.70% 8.8
## 89 11.50% 30.6
## 90 41.2
## 91 54.40% 6.7
## 92 41.30% 16.9
## 93 15.00% 37.6
## 94 88.10% 3.3
## 95 26.30% 6.4
## 96 10.20% 65.7
## 97 11.90% 53.5
## 98 60.50% 10.2
## 99 35.60% NA
## 100 72.40% 3.3
## 101 19.20% 1.9
## 102 5.40% 38.2
## 103 0.80% 35.3
## 104 45.10% 6.7
## 105 31.20% 7.4
## 106 4.50% 62.0
## 107 54.30% 6.1
## 108 23.70% 27.4
## 109 5.00% 51.5
## 110 40.60% 13.6
## 111 40.20% 11.0
## 112 14.10% 25.6
## 113 39.80% 13.6
## 114 2.6
## 115 65.60% 14.0
## 116 56.10% 2.3
## 117 35.90% 19.2
## 118 7.30% 54.0
## 119 18.80% 36.8
## 120 22.90% 29.0
## 121 NA
## 122 12.40% 26.7
## 123 85.00% 3.3
## 124 82.00% 4.7
## 125 17.40% 15.7
## 126 4.40% 48.0
## 127 10.20% 75.7
## 128 27.00% 13.7
## 129 NA
## 130 82.00% 2.1
## 131 38.00% 9.8
## 132 9.00% 57.2
## 133 54.70% 16.6
## 134 NA
## 135 47.80% 13.1
## 136 1.80% 38.0
## 137 34.60% 17.2
## 138 70.70% 11.1
## 139 35.50% 22.5
## 140 67.80% 3.8
## 141 63.90% 3.1
## 142 17.90% 5.8
## 143 49.40% 6.1
## 144 81.90% 6.1
## 145 6.70% 27.0
## 146 86.70% 9.8
## 147 14.10% 14.9
## 148 23.70% 14.8
## 149 7.60% 13.6
## 150 42.50% 1.7
## 151 13.40% 24.4
## 152 68.00% 6.0
## 153 12.80% 31.8
## 154 67.20% 4.8
## 155 17.10% 12.4
## 156 2.00% 78.5
## 157 84.80% 2.3
## 158 46.60% 4.6
## 159 78.60% 1.7
## 160 17.1
## 161 2.50% 76.6
## 162 22.40% 28.5
## 163 94.30% 2.7
## 164 63.7
## 165 88.90% 2.5
## 166 19.60% 6.4
## 167 16.90% 42.1
## 168 12.60% 16.9
## 169 67.00% 2.2
## 170 59.60% 3.7
## 171 40.10% 14.0
## 172 31.30% 30.4
## 173 4.00% 37.6
## 174 49.30% 7.8
## 175 17.80% 39.3
## 176 14.50% 47.4
## 177 6.40% 13.4
## 178 12.00% 16.4
## 179 31.70% 14.6
## 180 23.90% 9.1
## 181 8.00% 39.3
## 182 20.6
## 183 4.80% 33.8
## 184 82.70% 7.5
## 185 36.80% 6.5
## 186 60.00% 3.6
## 187 88.20% 5.6
## 188 63.10% 6.4
## 189 10.10% 19.1
## 190 4.70% 22.3
## 191 79.30% 21.4
## 192 28.50% 16.5
## 193 10.20% 42.9
## 194 4.10% 40.4
## 195 10.00% 33.9
## Largest.city Life.expectancy Maternal.mortality.ratio
## 1 Kabul 64.5 638
## 2 Tirana 78.5 15
## 3 Algiers 76.7 112
## 4 Andorra la Vella NA NA
## 5 Luanda 60.8 241
## 6 St. John's, Saint John 76.9 42
## 7 Buenos Aires 76.5 39
## 8 Yerevan 74.9 26
## 9 Sydney 82.7 6
## 10 Vienna 81.6 5
## 11 Baku 72.9 26
## 12 Nassau, Bahamas 73.8 70
## 13 Riffa 77.2 14
## 14 Dhaka 72.3 173
## 15 Bridgetown 79.1 27
## 16 Minsk 74.2 2
## 17 Brussels 81.6 5
## 18 Belize City 74.5 36
## 19 Cotonou 61.5 397
## 20 Thimphu 71.5 183
## 21 Santa Cruz de la Sierra 71.2 155
## 22 Tuzla Canton 77.3 10
## 23 Gaborone 69.3 144
## 24 S���� 75.7 60
## 25 75.7 31
## 26 Sofia 74.9 10
## 27 Ouagadougou 61.2 320
## 28 Bujumbura 61.2 548
## 29 Abidjan 57.4 617
## 30 Praia 72.8 58
## 31 Phnom Penh 69.6 160
## 32 Douala 58.9 529
## 33 Toronto 81.9 10
## 34 Bangui 52.8 829
## 35 N'Djamena 54.0 1140
## 36 Santiago 80.0 13
## 37 Shanghai 77.0 29
## 38 Bogot� 77.1 83
## 39 Moroni, Comoros 64.1 273
## 40 Brazzaville 64.3 378
## 41 San Jos������ 80.1 27
## 42 Zagreb 78.1 8
## 43 Havana 78.7 36
## 44 Statos������� 80.8 6
## 45 Prague 79.0 3
## 46 Kinshasa 60.4 473
## 47 Copenhagen 81.0 4
## 48 Djibouti City 66.6 248
## 49 Roseau 76.6 NA
## 50 Santo Domingo 73.9 95
## 51 Quito 76.8 59
## 52 Cairo 71.8 37
## 53 San Salvador 73.1 46
## 54 Malabo 58.4 301
## 55 Asmara 65.9 480
## 56 Tallinn 78.2 9
## 57 Mbabane NA NA
## 58 Addis Ababa 66.2 401
## 59 Suva 67.3 34
## 60 Helsinki 81.7 3
## 61 Paris 82.5 8
## 62 Libreville 66.2 252
## 63 Serekunda 61.7 597
## 64 Tbilisi 73.6 25
## 65 Berlin 80.9 7
## 66 Accra 63.8 308
## 67 Macedonia 81.3 3
## 68 St. George's, Grenada 72.4 25
## 69 Guatemala City 74.1 95
## 70 Kankan 61.2 576
## 71 Bissau 58.0 667
## 72 Georgetown, Guyana 69.8 169
## 73 Port-au-Prince 63.7 480
## 74 NA NA
## 75 Tegucigalpa 75.1 65
## 76 Budapest 75.8 12
## 77 Reykjav�� 82.7 4
## 78 Kurebhar 69.4 145
## 79 Kalimantan 71.5 177
## 80 Tehran 76.5 16
## 81 Baghdad 70.5 79
## 82 Connacht 82.3 5
## 83 Jerusalem 82.8 3
## 84 Rome 82.9 2
## 85 Kingston, Jamaica 74.4 80
## 86 Tokyo 84.2 5
## 87 Amman 74.4 46
## 88 Almaty 73.2 10
## 89 Nairobi 66.3 342
## 90 South Tarawa 68.1 92
## 91 Kuwait City 75.4 12
## 92 Bishkek 71.4 60
## 93 Vientiane 67.6 185
## 94 Riga 74.7 19
## 95 Tripoli, Lebanon 78.9 29
## 96 Maseru 53.7 544
## 97 Monrovia 63.7 661
## 98 72.7 72
## 99 Schaan 83.0 NA
## 100 Vilnius 75.7 8
## 101 Luxembourg City 82.1 5
## 102 Antananarivo 66.7 335
## 103 Lilongwe 63.8 349
## 104 Johor Bahru 76.0 29
## 105 Mal� 78.6 53
## 106 Bamako 58.9 562
## 107 Birkirkara 82.3 6
## 108 Majuro 65.2 NA
## 109 Nouakchott 64.7 766
## 110 Port Louis 74.4 61
## 111 Mexico City 75.0 33
## 112 Palikir 67.8 88
## 113 Chi���� 71.8 19
## 114 Monaco City NA NA
## 115 Ulaanbaatar 69.7 45
## 116 Podgorica 76.8 6
## 117 Casablanca 76.5 70
## 118 Maputo 60.2 289
## 119 Yangon 66.9 250
## 120 Windhoek 63.4 195
## 121 NA NA
## 122 Kathmandu 70.5 186
## 123 Amsterdam 81.8 5
## 124 Auckland 81.9 9
## 125 Managua 74.3 98
## 126 Niamey 62.0 509
## 127 Lagos 54.3 917
## 128 Pyongyang 72.1 89
## 129 Skopje NA NA
## 130 Oslo 82.8 2
## 131 Seeb 77.6 19
## 132 Karachi 67.1 140
## 133 Koror 69.1 NA
## 134 NA NA
## 135 Panama City 78.3 52
## 136 Port Moresby 64.3 145
## 137 Ciudad del Este 74.1 129
## 138 Lima 76.5 88
## 139 Manila 71.1 121
## 140 Warsaw 77.6 2
## 141 Lisbon 81.3 8
## 142 Doha 80.1 9
## 143 Bucharest 75.4 19
## 144 Moscow 72.7 17
## 145 Kigali 68.7 248
## 146 Basseterre 71.3 NA
## 147 Castries 76.1 117
## 148 Calliaqua 72.4 68
## 149 Apia 73.2 43
## 150 City of San Marino 85.4 NA
## 151 S���� 70.2 130
## 152 Riyadh 75.0 17
## 153 Pikine 67.7 315
## 154 Belgrade 75.5 12
## 155 Victoria, Seychelles 72.8 53
## 156 Freetown 54.3 1120
## 157 83.1 8
## 158 Bratislava 77.2 5
## 159 Ljubljana 81.0 7
## 160 Honiara 72.8 104
## 161 Bosaso 57.1 829
## 162 Johannesburg 63.9 119
## 163 Seoul 82.6 11
## 164 Juba 57.6 1150
## 165 Madrid 83.3 4
## 166 Colombo 76.8 36
## 167 Omdurman 65.1 295
## 168 Paramaribo 71.6 120
## 169 S����� 82.5 4
## 170 Z��� 83.6 5
## 171 Damascus 71.8 31
## 172 Dushanbe 70.9 17
## 173 Dar es Salaam 65.0 524
## 174 Bangkok 76.9 37
## 175 Dili 69.3 142
## 176 Lom� 60.8 396
## 177 Nuku���� 70.8 52
## 178 Chaguanas 73.4 67
## 179 Tunis 76.5 43
## 180 Istanbul 77.4 17
## 181 Ashgabat 68.1 7
## 182 Singapore NA NA
## 183 Buganda 63.0 375
## 184 Kyiv 71.6 19
## 185 Dubai 77.8 3
## 186 London 81.3 7
## 187 New York City 78.5 19
## 188 Montevideo 77.8 17
## 189 Tashkent 71.6 29
## 190 Port Vila 70.3 72
## 191 Caracas 72.1 125
## 192 Ho Chi Minh City 75.3 43
## 193 Sanaa 66.1 164
## 194 Lusaka 63.5 213
## 195 Harare 61.2 458
## Minimum.wage Official.language Out.of.pocket.health.expenditure
## 1 $0.43 Pashto 78.40%
## 2 $1.12 Albanian 56.90%
## 3 $0.95 Arabic 28.10%
## 4 $6.63 Catalan 36.40%
## 5 $0.71 Portuguese 33.40%
## 6 $3.04 English 24.30%
## 7 $3.35 Spanish 17.60%
## 8 $0.66 Armenian 81.60%
## 9 $13.59 None 19.60%
## 10 German 17.90%
## 11 $0.47 Azerbaijani language 78.60%
## 12 $5.25 English 27.80%
## 13 Arabic 25.10%
## 14 $0.51 Bengali 71.80%
## 15 $3.13 English 45.20%
## 16 $1.49 Russian 34.50%
## 17 $10.31 French 17.60%
## 18 $1.65 English 22.70%
## 19 $0.39 French 40.50%
## 20 $0.32 Dzongkha 19.80%
## 21 $1.36 Spanish 25.90%
## 22 $1.04 Bosnian 28.60%
## 23 $0.29 English 5.30%
## 24 $1.53 Portuguese 28.30%
## 25 Malay 6.00%
## 26 $1.57 Bulgarian 47.70%
## 27 $0.34 French 36.10%
## 28 Kirundi 19.10%
## 29 $0.36 French 36.00%
## 30 $0.68 Portuguese 23.20%
## 31 Khmer language 59.40%
## 32 $0.35 French 69.70%
## 33 $9.51 French 14.60%
## 34 $0.37 French 39.60%
## 35 $0.60 French 56.40%
## 36 $2.00 Spanish 32.20%
## 37 $0.87 Standard Chinese 32.40%
## 38 $1.23 Spanish 18.30%
## 39 $0.71 French 74.80%
## 40 $0.88 French 43.80%
## 41 $1.84 Spanish 21.50%
## 42 $2.92 Croatian 15.20%
## 43 $0.05 Spanish
## 44 Greek 43.90%
## 45 $3.00 Czech 14.80%
## 46 $0.18 French 37.40%
## 47 Danish 13.70%
## 48 French 20.40%
## 49 $1.48 English 28.40%
## 50 $0.40 Spanish 43.70%
## 51 $2.46 Spanish 43.70%
## 52 Modern Standard Arabic 62.00%
## 53 $0.50 Spanish 27.90%
## 54 $1.05 Spanish 72.00%
## 55 Tigrinya 52.40%
## 56 $3.14 Estonian 22.80%
## 57 English 11.30%
## 58 Amharic 37.80%
## 59 $1.28 Fiji Hindi 21.40%
## 60 Swedish 19.90%
## 61 $11.16 French 6.80%
## 62 $1.46 French 25.90%
## 63 $0.13 English 20.30%
## 64 $0.05 Georgian 57.30%
## 65 $9.99 German 12.50%
## 66 $0.27 English 36.10%
## 67 $4.46 Greek 35.50%
## 68 English 57.00%
## 69 $1.60 Spanish 55.80%
## 70 French 54.50%
## 71 $0.16 Portuguese 37.20%
## 72 $0.98 English 40.50%
## 73 $0.25 French 36.30%
## 74 Italian
## 75 $1.01 Spanish 49.10%
## 76 $2.62 Hungarian 29.00%
## 77 Icelandic 17.00%
## 78 $0.30 Hindi 65.10%
## 79 $0.48 Indonesian 48.30%
## 80 $1.58 Persian 39.70%
## 81 $1.24 Arabic 76.50%
## 82 $10.79 Irish 15.20%
## 83 $7.58 Hebrew 24.40%
## 84 Italian 22.80%
## 85 $1.33 Jamaican English 23.70%
## 86 $6.77 None 13.10%
## 87 $1.49 Arabic 25.10%
## 88 $0.41 Russian 38.80%
## 89 $0.25 Swahili 33.40%
## 90 English 0.20%
## 91 $0.95 Modern Standard Arabic 14.40%
## 92 $0.09 Russian 48.20%
## 93 $0.83 Lao 45.40%
## 94 $2.80 Latvian 41.60%
## 95 $2.15 Arabic 32.10%
## 96 $0.41 English 16.90%
## 97 $0.17 English 19.60%
## 98 $1.88 Arabic 36.70%
## 99 German
## 100 $2.41 Lithuanian 32.10%
## 101 $13.05 Luxembourgish 10.60%
## 102 $0.21 French 21.70%
## 103 $0.12 English 11.00%
## 104 $0.93 Malaysian language 36.70%
## 105 Divehi 16.40%
## 106 $0.23 French 46.30%
## 107 $5.07 Maltese 37.10%
## 108 $2.00 Marshallese 10.00%
## 109 $0.53 Arabic 48.20%
## 110 $0.38 French 50.70%
## 111 $0.49 None 41.40%
## 112 English 2.50%
## 113 $0.31 Romanian 46.20%
## 114 $11.72 French 6.10%
## 115 $0.65 Mongolian 39.30%
## 116 $1.23 Montenegrin language 31.80%
## 117 $1.60 Arabic 53.10%
## 118 $0.27 Portuguese 6.80%
## 119 $0.39 Burmese 73.90%
## 120 English 8.30%
## 121 English
## 122 $0.36 Nepali 60.40%
## 123 $10.29 Dutch 12.30%
## 124 $11.49 English 12.60%
## 125 $0.54 Spanish 36.00%
## 126 $0.29 French 52.30%
## 127 $0.54 English 72.20%
## 128 Korean
## 129 Macedonian 35.60%
## 130 Norwegian 14.30%
## 131 $4.33 Arabic 6.40%
## 132 $0.69 Urdu 66.50%
## 133 $3.00 English 21.80%
## 134 Arabic
## 135 $1.53 Spanish 30.50%
## 136 $1.16 Tok Pisin 5.80%
## 137 $1.55 Spanish 36.50%
## 138 $1.28 Spanish 30.90%
## 139 $1.12 English 53.50%
## 140 $2.93 Polish 23.20%
## 141 $3.78 Portuguese 27.70%
## 142 Arabic 6.20%
## 143 $2.25 Romanian 21.30%
## 144 $0.53 Russian 36.40%
## 145 Swahili 26.00%
## 146 $3.33 English 56.60%
## 147 English 48.40%
## 148 $1.16 English 21.40%
## 149 $0.78 Samoan 11.50%
## 150 Italian 18.30%
## 151 11.70%
## 152 $3.85 Arabic 15.00%
## 153 $0.31 French 44.20%
## 154 $1.57 Serbian 40.60%
## 155 $2.00 French 2.50%
## 156 $0.57 English 38.20%
## 157 Malay 36.70%
## 158 $3.11 Slovak 18.40%
## 159 $5.25 Slovene language 12.50%
## 160 $0.40 English 3.30%
## 161 Arabic
## 162 Afrikaans 7.70%
## 163 $6.49 Korean 36.80%
## 164 English 61.30%
## 165 $5.60 Spanish 24.20%
## 166 $0.35 Tamil 38.40%
## 167 $0.41 Arabic 63.20%
## 168 Dutch 10.10%
## 169 Swedish 15.20%
## 170 German 28.30%
## 171 $1.02 Arabic 53.70%
## 172 $0.23 Persian 63.10%
## 173 $0.09 Swahili 26.10%
## 174 $1.06 Thai 11.80%
## 175 $0.60 Portuguese 10.20%
## 176 $0.34 French 51.00%
## 177 Tongan Language 10.20%
## 178 $2.25 English 37.30%
## 179 $0.47 Arabic 39.80%
## 180 $3.45 Turkish 16.90%
## 181 $0.88 Turkmen 71.10%
## 182 Tuvaluan Language 0.70%
## 183 $0.01 Swahili 40.50%
## 184 $0.84 Ukrainian 47.80%
## 185 Arabic 17.80%
## 186 $10.13 English 14.80%
## 187 $7.25 None 11.10%
## 188 $1.66 Spanish 16.20%
## 189 $0.24 Uzbek 42.70%
## 190 $1.56 French 8.90%
## 191 $0.01 Spanish 45.80%
## 192 $0.73 Vietnamese 43.50%
## 193 Arabic 81.00%
## 194 $0.24 English 27.50%
## 195 Shona 25.80%
## Physicians.per.thousand Population
## 1 0.28 38041754
## 2 1.20 2854191
## 3 1.72 43053054
## 4 3.33 77142
## 5 0.21 31825295
## 6 2.76 97118
## 7 3.96 44938712
## 8 4.40 2957731
## 9 3.68 25766605
## 10 5.17 8877067
## 11 3.45 10023318
## 12 1.94 389482
## 13 0.93 1501635
## 14 0.58 167310838
## 15 2.48 287025
## 16 5.19 9466856
## 17 3.07 11484055
## 18 1.12 390353
## 19 0.08 11801151
## 20 0.42 727145
## 21 1.59 11513100
## 22 2.16 3301000
## 23 0.37 2346179
## 24 2.15 212559417
## 25 1.61 433285
## 26 4.03 6975761
## 27 0.08 20321378
## 28 0.10 11530580
## 29 0.23 25716544
## 30 0.77 483628
## 31 0.17 16486542
## 32 0.09 25876380
## 33 2.61 36991981
## 34 0.06 4745185
## 35 0.04 15946876
## 36 2.59 18952038
## 37 1.98 1397715000
## 38 2.18 50339443
## 39 0.27 850886
## 40 0.12 5380508
## 41 2.89 5047561
## 42 3.00 4067500
## 43 8.42 11333483
## 44 1.95 1198575
## 45 4.12 10669709
## 46 0.07 86790567
## 47 4.01 5818553
## 48 0.22 973560
## 49 1.08 71808
## 50 1.56 10738958
## 51 2.04 17373662
## 52 0.45 100388073
## 53 1.57 6453553
## 54 0.40 1355986
## 55 0.06 6333135
## 56 4.48 1331824
## 57 NA 1093238
## 58 0.08 112078730
## 59 0.84 889953
## 60 3.81 5520314
## 61 3.27 67059887
## 62 0.68 2172579
## 63 0.10 2347706
## 64 7.12 3720382
## 65 4.25 83132799
## 66 0.14 30792608
## 67 5.48 10716322
## 68 1.41 112003
## 69 0.35 16604026
## 70 0.08 12771246
## 71 0.13 1920922
## 72 0.80 782766
## 73 0.23 11263077
## 74 NA 836
## 75 0.31 9746117
## 76 3.41 9769949
## 77 4.08 361313
## 78 0.86 1366417754
## 79 0.43 270203917
## 80 1.58 82913906
## 81 0.71 39309783
## 82 3.31 5007069
## 83 4.62 9053300
## 84 3.98 60297396
## 85 1.31 2948279
## 86 2.41 126226568
## 87 2.32 10101694
## 88 3.25 18513930
## 89 0.16 52573973
## 90 0.20 117606
## 91 2.58 4207083
## 92 1.88 6456900
## 93 0.37 7169455
## 94 3.19 1912789
## 95 2.10 6855713
## 96 0.07 2125268
## 97 0.04 4937374
## 98 2.09 6777452
## 99 NA 38019
## 100 6.35 2786844
## 101 3.01 645397
## 102 0.18 26969307
## 103 0.04 18628747
## 104 1.51 32447385
## 105 4.56 530953
## 106 0.13 19658031
## 107 2.86 502653
## 108 0.42 58791
## 109 0.19 4525696
## 110 2.53 1265711
## 111 2.38 126014024
## 112 0.18 113815
## 113 3.21 2657637
## 114 6.56 38964
## 115 2.86 3225167
## 116 2.76 622137
## 117 0.73 36910560
## 118 0.08 30366036
## 119 0.68 54045420
## 120 0.42 2494530
## 121 NA 10084
## 122 0.75 28608710
## 123 3.61 17332850
## 124 3.59 4841000
## 125 0.98 6545502
## 126 0.04 23310715
## 127 0.38 200963599
## 128 3.67 25666161
## 129 NA 1836713
## 130 2.92 5347896
## 131 2.00 5266535
## 132 0.98 216565318
## 133 1.18 18233
## 134 NA NA
## 135 1.57 4246439
## 136 0.07 8776109
## 137 1.35 7044636
## 138 1.27 32510453
## 139 0.60 108116615
## 140 2.38 37970874
## 141 5.12 10269417
## 142 2.49 2832067
## 143 2.98 19356544
## 144 4.01 144373535
## 145 0.13 12626950
## 146 2.52 52823
## 147 0.64 182790
## 148 0.66 100455
## 149 0.34 202506
## 150 6.11 33860
## 151 0.05 215056
## 152 2.61 34268528
## 153 0.07 16296364
## 154 3.11 6944975
## 155 0.95 97625
## 156 0.03 7813215
## 157 2.29 5703569
## 158 3.42 5454073
## 159 3.09 2087946
## 160 0.19 669823
## 161 0.02 15442905
## 162 0.91 58558270
## 163 2.36 51709098
## 164 NA 11062113
## 165 3.87 47076781
## 166 1.00 21803000
## 167 0.26 42813238
## 168 1.21 581372
## 169 3.98 10285453
## 170 4.30 8574832
## 171 1.22 17070135
## 172 1.70 9321018
## 173 0.01 58005463
## 174 0.81 69625582
## 175 0.72 3500000
## 176 0.08 8082366
## 177 0.52 100209
## 178 4.17 1394973
## 179 1.30 11694719
## 180 1.85 83429615
## 181 2.22 5942089
## 182 0.92 11646
## 183 0.17 44269594
## 184 2.99 44385155
## 185 2.53 9770529
## 186 2.81 66834405
## 187 2.61 328239523
## 188 5.05 3461734
## 189 2.37 33580650
## 190 0.17 299882
## 191 1.92 28515829
## 192 0.82 96462106
## 193 0.31 29161922
## 194 1.19 17861030
## 195 0.21 14645468
## Population..Labor.force.participation.... Tax.revenue.... Total.tax.rate
## 1 48.90% 9.30% 71.40%
## 2 55.70% 18.60% 36.60%
## 3 41.20% 37.20% 66.10%
## 4
## 5 77.50% 9.20% 49.10%
## 6 16.50% 43.00%
## 7 61.30% 10.10% 106.30%
## 8 55.60% 20.90% 22.60%
## 9 65.50% 23.00% 47.40%
## 10 60.70% 25.40% 51.40%
## 11 66.50% 13.00% 40.70%
## 12 74.60% 14.80% 33.80%
## 13 73.40% 4.20% 13.80%
## 14 59.00% 8.80% 33.40%
## 15 65.20% 27.50% 35.60%
## 16 64.10% 14.70% 53.30%
## 17 53.60% 24.00% 55.40%
## 18 65.10% 26.30% 31.10%
## 19 70.90% 10.80% 48.90%
## 20 66.70% 16.00% 35.30%
## 21 71.80% 17.00% 83.70%
## 22 46.40% 20.40% 23.70%
## 23 70.80% 19.50% 25.10%
## 24 63.90% 14.20% 65.10%
## 25 64.70% 8.00%
## 26 55.40% 20.20% 28.30%
## 27 66.40% 15.00% 41.30%
## 28 79.20% 13.60% 41.20%
## 29 57.00% 11.80% 50.10%
## 30 60.50% 20.10% 37.50%
## 31 82.30% 17.10% 23.10%
## 32 76.10% 12.80% 57.70%
## 33 65.10% 12.80% 24.50%
## 34 72.00% 8.60% 73.30%
## 35 70.70% 63.50%
## 36 62.60% 18.20% 34.00%
## 37 68.00% 9.40% 59.20%
## 38 68.80% 14.40% 71.20%
## 39 43.30% 219.60%
## 40 69.40% 9.00% 54.30%
## 41 62.10% 13.60% 58.30%
## 42 51.20% 22.00% 20.50%
## 43 53.60%
## 44 63.10% 24.50% 22.40%
## 45 60.60% 14.90% 46.10%
## 46 63.50% 10.70% 50.70%
## 47 62.20% 32.40% 23.80%
## 48 60.20% 37.90%
## 49 22.10% 32.60%
## 50 64.30% 13.00% 48.80%
## 51 68.00% 34.40%
## 52 46.40% 12.50% 44.40%
## 53 59.10% 18.10% 36.40%
## 54 62.00% 6.10% 79.40%
## 55 78.40% 83.70%
## 56 63.60% 20.90% 47.80%
## 57 28.60%
## 58 79.60% 7.50% 37.70%
## 59 57.60% 24.20% 32.10%
## 60 59.10% 20.80% 36.60%
## 61 55.10% 24.20% 60.70%
## 62 52.90% 10.20% 47.10%
## 63 59.40% 9.40% 48.40%
## 64 68.30% 21.70% 9.90%
## 65 60.80% 11.50% 48.80%
## 66 67.80% 12.60% 55.40%
## 67 51.80% 26.20% 51.90%
## 68 19.40% 47.80%
## 69 62.30% 10.60% 35.20%
## 70 61.50% 10.80% 69.30%
## 71 72.00% 10.30% 45.50%
## 72 56.20% 30.60%
## 73 67.20% 42.70%
## 74
## 75 68.80% 17.30% 39.10%
## 76 56.50% 23.00% 37.90%
## 77 75.00% 23.30% 31.90%
## 78 49.30% 11.20% 49.70%
## 79 67.50% 10.20% 30.10%
## 80 44.70% 7.40% 44.70%
## 81 43.00% 2.00% 30.80%
## 82 62.10% 18.30% 26.10%
## 83 64.00% 23.10% 25.30%
## 84 49.60% 24.30% 59.10%
## 85 66.00% 26.80% 35.10%
## 86 61.70% 11.90% 46.70%
## 87 39.30% 15.10% 28.60%
## 88 68.80% 11.70% 28.40%
## 89 74.70% 15.10% 37.20%
## 90 22.00% 32.70%
## 91 73.50% 1.40% 13.00%
## 92 59.80% 18.00% 29.00%
## 93 78.50% 12.90% 24.10%
## 94 61.40% 22.90% 38.10%
## 95 47.00% 15.30% 32.20%
## 96 67.90% 31.60% 13.60%
## 97 76.30% 12.90% 46.20%
## 98 49.70% 32.60%
## 99 21.60%
## 100 61.60% 16.90% 42.60%
## 101 59.30% 26.50% 20.40%
## 102 86.10% 10.20% 38.30%
## 103 76.70% 17.30% 34.50%
## 104 64.30% 12.00% 38.70%
## 105 69.80% 19.50% 30.20%
## 106 70.80% 11.60% 54.50%
## 107 56.50% 26.20% 44.00%
## 108 17.80% 65.90%
## 109 45.90% 67.00%
## 110 58.30% 19.10% 22.20%
## 111 60.70% 13.10% 55.10%
## 112 25.20% 60.50%
## 113 43.10% 17.70% 38.70%
## 114
## 115 59.70% 16.80% 25.70%
## 116 54.40% 22.20%
## 117 45.30% 21.90% 45.80%
## 118 78.10% 0.00% 36.10%
## 119 61.70% 5.40% 31.20%
## 120 59.50% 27.10% 20.70%
## 121
## 122 83.80% 20.70% 41.80%
## 123 63.60% 23.00% 41.20%
## 124 69.90% 29.00% 34.60%
## 125 66.40% 15.60% 60.60%
## 126 72.00% 11.80% 47.20%
## 127 52.90% 1.50% 34.80%
## 128 80.40%
## 129
## 130 63.80% 23.90% 36.20%
## 131 72.40% 2.50% 27.40%
## 132 52.60% 9.20% 33.90%
## 133 21.30% 76.60%
## 134
## 135 66.60% 37.20%
## 136 47.20% 13.60% 37.10%
## 137 72.10% 10.00% 35.00%
## 138 77.60% 14.30% 36.80%
## 139 59.60% 14.00% 43.10%
## 140 56.70% 17.40% 40.80%
## 141 58.80% 22.80% 39.80%
## 142 86.80% 14.70% 11.30%
## 143 54.70% 14.60% 20.00%
## 144 61.80% 11.40% 46.20%
## 145 83.70% 14.30% 33.20%
## 146 18.50% 49.70%
## 147 67.10% 18.20% 34.70%
## 148 65.90% 25.40% 37.00%
## 149 43.70% 25.50% 19.30%
## 150 18.10% 36.20%
## 151 57.80% 14.60% 37.00%
## 152 55.90% 8.90% 15.70%
## 153 45.70% 16.30% 44.80%
## 154 54.90% 18.60% 36.60%
## 155 34.10% 30.10%
## 156 57.90% 8.60% 30.70%
## 157 70.50% 13.10% 21.00%
## 158 59.50% 18.70% 49.70%
## 159 58.40% 18.60% 31.00%
## 160 83.80% 29.50% 32.00%
## 161 47.40% 0.00%
## 162 56.00% 27.50% 29.20%
## 163 63.00% 15.60% 33.20%
## 164 72.40% 31.40%
## 165 57.50% 14.20% 47.00%
## 166 53.90% 11.90% 55.20%
## 167 48.40% 8.00% 45.40%
## 168 51.10% 19.50% 27.90%
## 169 64.60% 27.90% 49.10%
## 170 68.30% 10.10% 28.80%
## 171 44.10% 14.20% 42.70%
## 172 42.00% 9.80% 67.30%
## 173 83.40% 11.50% 43.80%
## 174 67.30% 14.90% 29.50%
## 175 67.30% 25.00% 17.30%
## 176 77.60% 16.90% 48.20%
## 177 59.80% 22.30% 27.50%
## 178 60.00% 19.50% 40.50%
## 179 46.10% 21.10% 60.70%
## 180 52.80% 17.90% 42.30%
## 181 64.50%
## 182
## 183 70.30% 11.70% 33.70%
## 184 54.20% 20.10% 45.20%
## 185 82.10% 0.10% 15.90%
## 186 62.80% 25.50% 30.60%
## 187 62.00% 9.60% 36.60%
## 188 64.00% 20.10% 41.80%
## 189 65.10% 14.80% 31.60%
## 190 69.90% 17.80% 8.50%
## 191 59.70% 73.30%
## 192 77.40% 19.10% 37.60%
## 193 38.00% 26.60%
## 194 74.60% 16.20% 15.60%
## 195 83.10% 20.70% 31.60%
## Unemployment.rate Urban_population Latitude Longitude
## 1 11.12% 9797273 33.939110 67.709953
## 2 12.33% 1747593 41.153332 20.168331
## 3 11.70% 31510100 28.033886 1.659626
## 4 67873 42.506285 1.521801
## 5 6.89% 21061025 -11.202692 17.873887
## 6 23800 17.060816 -61.796428
## 7 9.79% 41339571 -38.416097 -63.616672
## 8 16.99% 1869848 40.069099 45.038189
## 9 5.27% 21844756 -25.274398 133.775136
## 10 4.67% 5194416 47.516231 14.550072
## 11 5.51% 5616165 40.143105 47.576927
## 12 10.36% 323784 25.034280 -77.396280
## 13 0.71% 1467109 26.066700 50.557700
## 14 4.19% 60987417 23.684994 90.356331
## 15 10.33% 89431 13.193887 -59.543198
## 16 4.59% 7482982 53.709807 27.953389
## 17 5.59% 11259082 50.503887 4.469936
## 18 6.41% 179039 17.189877 -88.497650
## 19 2.23% 5648149 9.307690 2.315834
## 20 2.34% 317538 27.514162 90.433601
## 21 3.50% 8033035 -16.290154 -63.588653
## 22 18.42% 1605144 43.915886 17.679076
## 23 18.19% 1616550 -22.328474 24.684866
## 24 12.08% 183241641 -14.235004 -51.925280
## 25 9.12% 337711 4.535277 114.727669
## 26 4.34% 5256027 42.733883 25.485830
## 27 6.26% 6092349 12.238333 -1.561593
## 28 1.43% 1541177 -3.373056 29.918886
## 29 3.32% 13176900 7.539989 -5.547080
## 30 12.25% 364029 16.538800 -23.041800
## 31 0.68% 3924621 12.565679 104.990963
## 32 3.38% 14741256 7.369722 12.354722
## 33 5.56% 30628482 56.130366 -106.346771
## 34 3.68% 1982064 6.611111 20.939444
## 35 1.89% 3712273 15.454166 18.732207
## 36 7.09% 16610135 -35.675147 -71.542969
## 37 4.32% 842933962 35.861660 104.195397
## 38 9.71% 40827302 4.570868 -74.297333
## 39 4.34% 248152 -11.645500 43.333300
## 40 9.47% 3625010 -0.228021 15.827659
## 41 11.85% 4041885 9.748917 -83.753428
## 42 6.93% 2328318 45.100000 15.200000
## 43 1.64% 8739135 21.521757 -77.781167
## 44 7.27% 800708 35.126413 33.429859
## 45 1.93% 7887156 49.817492 15.472962
## 46 4.24% 39095679 -4.038333 21.758664
## 47 4.91% 5119978 56.263920 9.501785
## 48 10.30% 758549 11.825138 42.590275
## 49 50830 15.414999 -61.370976
## 50 5.84% 8787475 18.735693 -70.162651
## 51 3.97% 11116711 -1.831239 -78.183406
## 52 10.76% 42895824 26.820553 30.802498
## 53 4.11% 4694702 13.794185 -88.896530
## 54 6.43% 984812 1.650801 10.267895
## 55 5.14% 1149670 15.179384 39.782334
## 56 5.11% 916024 58.595272 25.013607
## 57 NA -26.522503 31.465866
## 58 2.08% 23788710 9.145000 40.489673
## 59 4.10% 505048 -17.713371 178.065032
## 60 6.59% 4716888 61.924110 25.748151
## 61 8.43% 54123364 46.227638 2.213749
## 62 20.00% 1949694 -0.803689 11.609444
## 63 9.06% 1453958 13.443182 -15.310139
## 64 14.40% 2196476 42.315407 43.356892
## 65 3.04% 64324835 51.165691 10.451526
## 66 4.33% 17249054 7.946527 -1.023194
## 67 17.24% 8507474 39.074208 21.824312
## 68 40765 12.116500 -61.679000
## 69 2.46% 8540945 15.783471 -90.230759
## 70 4.30% 4661505 9.945587 -9.696645
## 71 2.47% 840922 11.803749 -15.180413
## 72 11.85% 208912 4.860416 -58.930180
## 73 13.78% 6328948 18.971187 -72.285215
## 74 NA 41.902916 12.453389
## 75 5.39% 5626433 15.199999 -86.241905
## 76 3.40% 6999582 47.162494 19.503304
## 77 2.84% 339110 64.963051 -19.020835
## 78 5.36% 471031528 20.593684 78.962880
## 79 4.69% 151509724 -0.789275 113.921327
## 80 11.38% 62509623 32.427908 53.688046
## 81 12.82% 27783368 33.223191 43.679291
## 82 4.93% 3133123 53.412910 -8.243890
## 83 3.86% 8374393 31.046051 34.851612
## 84 9.89% 42651966 41.871940 12.567380
## 85 8.00% 1650594 18.109581 -77.297508
## 86 2.29% 115782416 36.204824 138.252924
## 87 14.72% 9213048 30.585164 36.238414
## 88 4.59% 10652915 48.019573 66.923684
## 89 2.64% 14461523 -0.023559 37.906193
## 90 64489 1.836898 -157.376832
## 91 2.18% 4207083 29.311660 47.481766
## 92 6.33% 2362644 41.204380 74.766098
## 93 0.63% 2555552 19.856270 102.495496
## 94 6.52% 1304943 56.879635 24.603189
## 95 6.23% 6084994 33.854721 35.862285
## 96 23.41% 607508 -29.609988 28.233608
## 97 2.81% 2548426 6.428055 -9.429499
## 98 18.56% 5448597 26.335100 17.228331
## 99 5464 47.141039 9.520935
## 100 6.35% 1891013 55.169438 23.881275
## 101 5.36% 565488 49.815273 6.129583
## 102 1.76% 10210849 -18.766947 46.869107
## 103 5.65% 3199301 -13.254308 34.301525
## 104 3.32% 24475766 4.210484 101.975766
## 105 6.14% 213645 3.202778 73.220680
## 106 7.22% 8479688 17.570692 -3.996166
## 107 3.47% 475902 35.937496 14.375416
## 108 45514 7.131474 171.184478
## 109 9.55% 2466821 21.007890 -10.940835
## 110 6.67% 515980 -20.348404 57.552152
## 111 3.42% 102626859 23.634501 -102.552784
## 112 25963 7.425554 150.550812
## 113 5.47% 1135502 47.411631 28.369885
## 114 38964 43.738418 7.424616
## 115 6.01% 2210626 46.862496 103.846656
## 116 14.88% 417765 42.708678 19.374390
## 117 9.02% 22975026 31.791702 -7.092620
## 118 3.24% 11092106 -18.665695 35.529562
## 119 1.58% 16674093 21.916221 95.955974
## 120 20.27% 1273258 -22.957640 18.490410
## 121 NA -0.522778 166.931503
## 122 1.41% 5765513 28.394857 84.124008
## 123 3.20% 15924729 52.132633 5.291266
## 124 4.07% 4258860 -40.900557 174.885971
## 125 6.84% 3846137 12.865416 -85.207229
## 126 0.47% 3850231 17.607789 8.081666
## 127 8.10% 102806948 9.081999 8.675277
## 128 2.74% 15947412 40.339852 127.510093
## 129 NA 41.608635 21.745275
## 130 3.35% 4418218 60.472024 8.468946
## 131 2.67% 4250777 21.473533 55.975413
## 132 4.45% 79927762 30.375321 69.345116
## 133 14491 7.514980 134.582520
## 134 NA 31.952162 35.233154
## 135 3.90% 2890084 8.537981 -80.782127
## 136 2.46% 1162834 -6.314993 143.955550
## 137 4.81% 4359150 -23.442503 -58.443832
## 138 3.31% 25390339 -9.189967 -75.015152
## 139 2.15% 50975903 12.879721 121.774017
## 140 3.47% 22796574 51.919438 19.145136
## 141 6.33% 6753579 39.399872 -8.224454
## 142 0.09% 2809071 25.354826 51.183884
## 143 3.98% 10468793 45.943161 24.966760
## 144 4.59% 107683889 61.524010 105.318756
## 145 1.03% 2186104 -1.940278 29.873888
## 146 16269 17.357822 -62.782998
## 147 20.71% 34280 13.909444 -60.978893
## 148 18.88% 58185 12.984305 -61.287228
## 149 8.36% 35588 -13.759029 -172.104629
## 150 32969 43.942360 12.457777
## 151 13.37% 158277 NA NA
## 152 5.93% 28807838 23.885942 45.079162
## 153 6.60% 7765706 14.497401 -14.452362
## 154 12.69% 3907243 44.016521 21.005859
## 155 55762 -4.679574 55.491977
## 156 4.43% 3319366 8.460555 -11.779889
## 157 4.11% 5703569 1.352083 103.819836
## 158 5.56% 2930419 48.669026 19.699024
## 159 4.20% 1144654 46.151241 14.995463
## 160 0.58% 162164 -9.645710 160.156194
## 161 11.35% 7034861 5.152149 46.199616
## 162 28.18% 39149717 -30.559482 22.937506
## 163 4.15% 42106719 35.907757 127.766922
## 164 12.24% 2201250 6.876992 31.306979
## 165 13.96% 37927409 40.463667 -3.749220
## 166 4.20% 4052088 7.873054 80.771797
## 167 16.53% 14957233 12.862807 30.217636
## 168 7.33% 384258 3.919305 -56.027783
## 169 6.48% 9021165 60.128161 18.643501
## 170 4.58% 6332428 46.818188 8.227512
## 171 8.37% 9358019 34.802075 38.996815
## 172 11.02% 2545477 38.861034 71.276093
## 173 1.98% 20011885 -6.369028 34.888822
## 174 0.75% 35294600 15.870032 100.992541
## 175 4.55% 400182 -8.874217 125.727539
## 176 2.04% 3414638 8.619543 0.824782
## 177 1.12% 24145 -21.178986 -175.198242
## 178 2.69% 741944 10.691803 -61.222503
## 179 16.02% 8099061 33.886917 9.537499
## 180 13.49% 63097818 38.963745 35.243322
## 181 3.91% 3092738 38.969719 59.556278
## 182 7362 -7.109535 177.649330
## 183 1.84% 10784516 1.373333 32.290275
## 184 8.88% 30835699 48.379433 31.165580
## 185 2.35% 8479744 23.424076 53.847818
## 186 3.85% 55908316 55.378051 -3.435973
## 187 14.70% 270663028 37.090240 -95.712891
## 188 8.73% 3303394 -32.522779 -55.765835
## 189 5.92% 16935729 41.377491 64.585262
## 190 4.39% 76152 -15.376706 166.959158
## 191 8.80% 25162368 6.423750 -66.589730
## 192 2.01% 35332140 14.058324 108.277199
## 193 12.91% 10869523 15.552727 48.516388
## 194 11.43% 7871713 -13.133897 27.849332
## 195 4.95% 4717305 -19.015438 29.154857
## Persentase_urban_pop
## 1 25.75400
## 2 61.22901
## 3 73.18900
## 4 87.98450
## 5 66.17700
## 6 24.50627
## 7 91.99100
## 8 63.21900
## 9 84.77933
## 10 58.51500
## 11 56.03100
## 12 83.13195
## 13 97.70077
## 14 36.45156
## 15 31.15791
## 16 79.04400
## 17 98.04100
## 18 45.86592
## 19 47.86100
## 20 43.66914
## 21 69.77300
## 22 48.62599
## 23 68.90139
## 24 86.20726
## 25 77.94200
## 26 75.34701
## 27 29.98000
## 28 13.36600
## 29 51.23900
## 30 75.27046
## 31 23.80500
## 32 56.96800
## 33 82.79763
## 34 41.77000
## 35 23.27900
## 36 87.64300
## 37 60.30800
## 38 81.10400
## 39 29.16395
## 40 67.37301
## 41 80.07600
## 42 57.24199
## 43 77.10900
## 44 66.80500
## 45 73.92100
## 46 45.04600
## 47 87.99401
## 48 77.91497
## 49 70.78598
## 50 81.82800
## 51 63.98600
## 52 42.73000
## 53 72.74601
## 54 72.62700
## 55 18.15325
## 56 68.77966
## 57 NA
## 58 21.22500
## 59 56.74996
## 60 85.44601
## 61 80.70900
## 62 89.74099
## 63 61.93101
## 64 59.03899
## 65 77.37600
## 66 56.01687
## 67 79.38800
## 68 36.39635
## 69 51.43900
## 70 36.50000
## 71 43.77700
## 72 26.68895
## 73 56.19200
## 74 NA
## 75 57.73000
## 76 71.64400
## 77 93.85491
## 78 34.47200
## 79 56.07236
## 80 75.39100
## 81 70.67800
## 82 62.57399
## 83 92.50100
## 84 70.73600
## 85 55.98500
## 86 91.72587
## 87 91.20300
## 88 57.54000
## 89 27.50700
## 90 54.83479
## 91 100.00000
## 92 36.59100
## 93 35.64500
## 94 68.22200
## 95 88.75800
## 96 28.58501
## 97 51.61501
## 98 80.39300
## 99 14.37176
## 100 67.85500
## 101 87.61863
## 102 37.86100
## 103 17.17400
## 104 75.43217
## 105 40.23802
## 106 43.13600
## 107 94.67804
## 108 77.41661
## 109 54.50700
## 110 40.76602
## 111 81.44082
## 112 22.81158
## 113 42.72600
## 114 100.00000
## 115 68.54299
## 116 67.15000
## 117 62.24513
## 118 36.52800
## 119 30.85200
## 120 51.04200
## 121 NA
## 122 20.15300
## 123 91.87600
## 124 87.97480
## 125 58.76000
## 126 16.51700
## 127 51.15700
## 128 62.13400
## 129 NA
## 130 82.61600
## 131 80.71297
## 132 36.90700
## 133 79.47677
## 134 NA
## 135 68.05900
## 136 13.24999
## 137 61.87900
## 138 78.09900
## 139 47.14900
## 140 60.03700
## 141 65.76400
## 142 99.18801
## 143 54.08400
## 144 74.58700
## 145 17.31300
## 146 30.79908
## 147 18.75376
## 148 57.92146
## 149 17.57380
## 150 97.36858
## 151 73.59804
## 152 84.06500
## 153 47.65300
## 154 56.26000
## 155 57.11857
## 156 42.48400
## 157 100.00000
## 158 53.72900
## 159 54.82201
## 160 24.20998
## 161 45.55400
## 162 66.85600
## 163 81.43000
## 164 19.89900
## 165 80.56500
## 166 18.58500
## 167 34.93600
## 168 66.09503
## 169 87.70800
## 170 73.84900
## 171 54.82100
## 172 27.30900
## 173 34.50000
## 174 50.69200
## 175 11.43377
## 176 42.24800
## 177 24.09464
## 178 53.18698
## 179 69.25400
## 180 75.63000
## 181 52.04799
## 182 63.21484
## 183 24.36100
## 184 69.47300
## 185 86.78900
## 186 83.65200
## 187 82.45900
## 188 95.42599
## 189 50.43300
## 190 25.39399
## 191 88.24000
## 192 36.62800
## 193 37.27300
## 194 44.07200
## 195 32.21000karena agak sulit mengakses kolom
barunya Persentase_urban_pop kita akan menyeleksi
kolom-kolom yang berkepentingan
#mengubah tipe data chr menjadi dbl
country_data_cleanup$Density..P.Km2. <- as.numeric(gsub(",", "", country_data$Density..P.Km2.))
country_data_cleanup$Land.Area.Km2. <- as.numeric(gsub(",", "", country_data$Land.Area.Km2.))
country_data_cleanup$Armed.Forces.size <- as.numeric(gsub(",", "", country_data$Armed.Forces.size))
country_data_cleanup$Calling.Code <- as.numeric(gsub(",", "", country_data$Calling.Code))
country_data_cleanup$Co2.Emissions <- as.numeric(gsub(",", "", country_data$Co2.Emissions))
country_data_cleanup$CPI <- as.numeric(gsub(",", "", country_data$CPI))
country_data_cleanup$Fertility.Rate <- as.numeric(gsub(",", "", country_data$Fertility.Rate))
country_data_cleanup %>%
mutate(Persentase_urban_pop= (Urban_population*100) / Population) %>%
select(Country,Persentase_urban_pop,Population)## Country Persentase_urban_pop Population
## 1 Afghanistan 25.75400 38041754
## 2 Albania 61.22901 2854191
## 3 Algeria 73.18900 43053054
## 4 Andorra 87.98450 77142
## 5 Angola 66.17700 31825295
## 6 Antigua and Barbuda 24.50627 97118
## 7 Argentina 91.99100 44938712
## 8 Armenia 63.21900 2957731
## 9 Australia 84.77933 25766605
## 10 Austria 58.51500 8877067
## 11 Azerbaijan 56.03100 10023318
## 12 The Bahamas 83.13195 389482
## 13 Bahrain 97.70077 1501635
## 14 Bangladesh 36.45156 167310838
## 15 Barbados 31.15791 287025
## 16 Belarus 79.04400 9466856
## 17 Belgium 98.04100 11484055
## 18 Belize 45.86592 390353
## 19 Benin 47.86100 11801151
## 20 Bhutan 43.66914 727145
## 21 Bolivia 69.77300 11513100
## 22 Bosnia and Herzegovina 48.62599 3301000
## 23 Botswana 68.90139 2346179
## 24 Brazil 86.20726 212559417
## 25 Brunei 77.94200 433285
## 26 Bulgaria 75.34701 6975761
## 27 Burkina Faso 29.98000 20321378
## 28 Burundi 13.36600 11530580
## 29 Ivory Coast 51.23900 25716544
## 30 Cape Verde 75.27046 483628
## 31 Cambodia 23.80500 16486542
## 32 Cameroon 56.96800 25876380
## 33 Canada 82.79763 36991981
## 34 Central African Republic 41.77000 4745185
## 35 Chad 23.27900 15946876
## 36 Chile 87.64300 18952038
## 37 China 60.30800 1397715000
## 38 Colombia 81.10400 50339443
## 39 Comoros 29.16395 850886
## 40 Republic of the Congo 67.37301 5380508
## 41 Costa Rica 80.07600 5047561
## 42 Croatia 57.24199 4067500
## 43 Cuba 77.10900 11333483
## 44 Cyprus 66.80500 1198575
## 45 Czech Republic 73.92100 10669709
## 46 Democratic Republic of the Congo 45.04600 86790567
## 47 Denmark 87.99401 5818553
## 48 Djibouti 77.91497 973560
## 49 Dominica 70.78598 71808
## 50 Dominican Republic 81.82800 10738958
## 51 Ecuador 63.98600 17373662
## 52 Egypt 42.73000 100388073
## 53 El Salvador 72.74601 6453553
## 54 Equatorial Guinea 72.62700 1355986
## 55 Eritrea 18.15325 6333135
## 56 Estonia 68.77966 1331824
## 57 Eswatini NA 1093238
## 58 Ethiopia 21.22500 112078730
## 59 Fiji 56.74996 889953
## 60 Finland 85.44601 5520314
## 61 France 80.70900 67059887
## 62 Gabon 89.74099 2172579
## 63 The Gambia 61.93101 2347706
## 64 Georgia 59.03899 3720382
## 65 Germany 77.37600 83132799
## 66 Ghana 56.01687 30792608
## 67 Greece 79.38800 10716322
## 68 Grenada 36.39635 112003
## 69 Guatemala 51.43900 16604026
## 70 Guinea 36.50000 12771246
## 71 Guinea-Bissau 43.77700 1920922
## 72 Guyana 26.68895 782766
## 73 Haiti 56.19200 11263077
## 74 Vatican City NA 836
## 75 Honduras 57.73000 9746117
## 76 Hungary 71.64400 9769949
## 77 Iceland 93.85491 361313
## 78 India 34.47200 1366417754
## 79 Indonesia 56.07236 270203917
## 80 Iran 75.39100 82913906
## 81 Iraq 70.67800 39309783
## 82 Republic of Ireland 62.57399 5007069
## 83 Israel 92.50100 9053300
## 84 Italy 70.73600 60297396
## 85 Jamaica 55.98500 2948279
## 86 Japan 91.72587 126226568
## 87 Jordan 91.20300 10101694
## 88 Kazakhstan 57.54000 18513930
## 89 Kenya 27.50700 52573973
## 90 Kiribati 54.83479 117606
## 91 Kuwait 100.00000 4207083
## 92 Kyrgyzstan 36.59100 6456900
## 93 Laos 35.64500 7169455
## 94 Latvia 68.22200 1912789
## 95 Lebanon 88.75800 6855713
## 96 Lesotho 28.58501 2125268
## 97 Liberia 51.61501 4937374
## 98 Libya 80.39300 6777452
## 99 Liechtenstein 14.37176 38019
## 100 Lithuania 67.85500 2786844
## 101 Luxembourg 87.61863 645397
## 102 Madagascar 37.86100 26969307
## 103 Malawi 17.17400 18628747
## 104 Malaysia 75.43217 32447385
## 105 Maldives 40.23802 530953
## 106 Mali 43.13600 19658031
## 107 Malta 94.67804 502653
## 108 Marshall Islands 77.41661 58791
## 109 Mauritania 54.50700 4525696
## 110 Mauritius 40.76602 1265711
## 111 Mexico 81.44082 126014024
## 112 Federated States of Micronesia 22.81158 113815
## 113 Moldova 42.72600 2657637
## 114 Monaco 100.00000 38964
## 115 Mongolia 68.54299 3225167
## 116 Montenegro 67.15000 622137
## 117 Morocco 62.24513 36910560
## 118 Mozambique 36.52800 30366036
## 119 Myanmar 30.85200 54045420
## 120 Namibia 51.04200 2494530
## 121 Nauru NA 10084
## 122 Nepal 20.15300 28608710
## 123 Netherlands 91.87600 17332850
## 124 New Zealand 87.97480 4841000
## 125 Nicaragua 58.76000 6545502
## 126 Niger 16.51700 23310715
## 127 Nigeria 51.15700 200963599
## 128 North Korea 62.13400 25666161
## 129 North Macedonia NA 1836713
## 130 Norway 82.61600 5347896
## 131 Oman 80.71297 5266535
## 132 Pakistan 36.90700 216565318
## 133 Palau 79.47677 18233
## 134 Palestinian National Authority NA NA
## 135 Panama 68.05900 4246439
## 136 Papua New Guinea 13.24999 8776109
## 137 Paraguay 61.87900 7044636
## 138 Peru 78.09900 32510453
## 139 Philippines 47.14900 108116615
## 140 Poland 60.03700 37970874
## 141 Portugal 65.76400 10269417
## 142 Qatar 99.18801 2832067
## 143 Romania 54.08400 19356544
## 144 Russia 74.58700 144373535
## 145 Rwanda 17.31300 12626950
## 146 Saint Kitts and Nevis 30.79908 52823
## 147 Saint Lucia 18.75376 182790
## 148 Saint Vincent and the Grenadines 57.92146 100455
## 149 Samoa 17.57380 202506
## 150 San Marino 97.36858 33860
## 151 S����������� 73.59804 215056
## 152 Saudi Arabia 84.06500 34268528
## 153 Senegal 47.65300 16296364
## 154 Serbia 56.26000 6944975
## 155 Seychelles 57.11857 97625
## 156 Sierra Leone 42.48400 7813215
## 157 Singapore 100.00000 5703569
## 158 Slovakia 53.72900 5454073
## 159 Slovenia 54.82201 2087946
## 160 Solomon Islands 24.20998 669823
## 161 Somalia 45.55400 15442905
## 162 South Africa 66.85600 58558270
## 163 South Korea 81.43000 51709098
## 164 South Sudan 19.89900 11062113
## 165 Spain 80.56500 47076781
## 166 Sri Lanka 18.58500 21803000
## 167 Sudan 34.93600 42813238
## 168 Suriname 66.09503 581372
## 169 Sweden 87.70800 10285453
## 170 Switzerland 73.84900 8574832
## 171 Syria 54.82100 17070135
## 172 Tajikistan 27.30900 9321018
## 173 Tanzania 34.50000 58005463
## 174 Thailand 50.69200 69625582
## 175 East Timor 11.43377 3500000
## 176 Togo 42.24800 8082366
## 177 Tonga 24.09464 100209
## 178 Trinidad and Tobago 53.18698 1394973
## 179 Tunisia 69.25400 11694719
## 180 Turkey 75.63000 83429615
## 181 Turkmenistan 52.04799 5942089
## 182 Tuvalu 63.21484 11646
## 183 Uganda 24.36100 44269594
## 184 Ukraine 69.47300 44385155
## 185 United Arab Emirates 86.78900 9770529
## 186 United Kingdom 83.65200 66834405
## 187 United States 82.45900 328239523
## 188 Uruguay 95.42599 3461734
## 189 Uzbekistan 50.43300 33580650
## 190 Vanuatu 25.39399 299882
## 191 Venezuela 88.24000 28515829
## 192 Vietnam 36.62800 96462106
## 193 Yemen 37.27300 29161922
## 194 Zambia 44.07200 17861030
## 195 Zimbabwe 32.21000 14645468Kemudian kita bisa menghapus kolom dengan mutate dengan
sintaks dibawah ini
country_data_cleanup %>%
mutate(Persentase_urban_pop= (Urban_population*100) / Population) %>%
select(Country,Persentase_urban_pop,Population) %>%
mutate(Country=NULL)## Persentase_urban_pop Population
## 1 25.75400 38041754
## 2 61.22901 2854191
## 3 73.18900 43053054
## 4 87.98450 77142
## 5 66.17700 31825295
## 6 24.50627 97118
## 7 91.99100 44938712
## 8 63.21900 2957731
## 9 84.77933 25766605
## 10 58.51500 8877067
## 11 56.03100 10023318
## 12 83.13195 389482
## 13 97.70077 1501635
## 14 36.45156 167310838
## 15 31.15791 287025
## 16 79.04400 9466856
## 17 98.04100 11484055
## 18 45.86592 390353
## 19 47.86100 11801151
## 20 43.66914 727145
## 21 69.77300 11513100
## 22 48.62599 3301000
## 23 68.90139 2346179
## 24 86.20726 212559417
## 25 77.94200 433285
## 26 75.34701 6975761
## 27 29.98000 20321378
## 28 13.36600 11530580
## 29 51.23900 25716544
## 30 75.27046 483628
## 31 23.80500 16486542
## 32 56.96800 25876380
## 33 82.79763 36991981
## 34 41.77000 4745185
## 35 23.27900 15946876
## 36 87.64300 18952038
## 37 60.30800 1397715000
## 38 81.10400 50339443
## 39 29.16395 850886
## 40 67.37301 5380508
## 41 80.07600 5047561
## 42 57.24199 4067500
## 43 77.10900 11333483
## 44 66.80500 1198575
## 45 73.92100 10669709
## 46 45.04600 86790567
## 47 87.99401 5818553
## 48 77.91497 973560
## 49 70.78598 71808
## 50 81.82800 10738958
## 51 63.98600 17373662
## 52 42.73000 100388073
## 53 72.74601 6453553
## 54 72.62700 1355986
## 55 18.15325 6333135
## 56 68.77966 1331824
## 57 NA 1093238
## 58 21.22500 112078730
## 59 56.74996 889953
## 60 85.44601 5520314
## 61 80.70900 67059887
## 62 89.74099 2172579
## 63 61.93101 2347706
## 64 59.03899 3720382
## 65 77.37600 83132799
## 66 56.01687 30792608
## 67 79.38800 10716322
## 68 36.39635 112003
## 69 51.43900 16604026
## 70 36.50000 12771246
## 71 43.77700 1920922
## 72 26.68895 782766
## 73 56.19200 11263077
## 74 NA 836
## 75 57.73000 9746117
## 76 71.64400 9769949
## 77 93.85491 361313
## 78 34.47200 1366417754
## 79 56.07236 270203917
## 80 75.39100 82913906
## 81 70.67800 39309783
## 82 62.57399 5007069
## 83 92.50100 9053300
## 84 70.73600 60297396
## 85 55.98500 2948279
## 86 91.72587 126226568
## 87 91.20300 10101694
## 88 57.54000 18513930
## 89 27.50700 52573973
## 90 54.83479 117606
## 91 100.00000 4207083
## 92 36.59100 6456900
## 93 35.64500 7169455
## 94 68.22200 1912789
## 95 88.75800 6855713
## 96 28.58501 2125268
## 97 51.61501 4937374
## 98 80.39300 6777452
## 99 14.37176 38019
## 100 67.85500 2786844
## 101 87.61863 645397
## 102 37.86100 26969307
## 103 17.17400 18628747
## 104 75.43217 32447385
## 105 40.23802 530953
## 106 43.13600 19658031
## 107 94.67804 502653
## 108 77.41661 58791
## 109 54.50700 4525696
## 110 40.76602 1265711
## 111 81.44082 126014024
## 112 22.81158 113815
## 113 42.72600 2657637
## 114 100.00000 38964
## 115 68.54299 3225167
## 116 67.15000 622137
## 117 62.24513 36910560
## 118 36.52800 30366036
## 119 30.85200 54045420
## 120 51.04200 2494530
## 121 NA 10084
## 122 20.15300 28608710
## 123 91.87600 17332850
## 124 87.97480 4841000
## 125 58.76000 6545502
## 126 16.51700 23310715
## 127 51.15700 200963599
## 128 62.13400 25666161
## 129 NA 1836713
## 130 82.61600 5347896
## 131 80.71297 5266535
## 132 36.90700 216565318
## 133 79.47677 18233
## 134 NA NA
## 135 68.05900 4246439
## 136 13.24999 8776109
## 137 61.87900 7044636
## 138 78.09900 32510453
## 139 47.14900 108116615
## 140 60.03700 37970874
## 141 65.76400 10269417
## 142 99.18801 2832067
## 143 54.08400 19356544
## 144 74.58700 144373535
## 145 17.31300 12626950
## 146 30.79908 52823
## 147 18.75376 182790
## 148 57.92146 100455
## 149 17.57380 202506
## 150 97.36858 33860
## 151 73.59804 215056
## 152 84.06500 34268528
## 153 47.65300 16296364
## 154 56.26000 6944975
## 155 57.11857 97625
## 156 42.48400 7813215
## 157 100.00000 5703569
## 158 53.72900 5454073
## 159 54.82201 2087946
## 160 24.20998 669823
## 161 45.55400 15442905
## 162 66.85600 58558270
## 163 81.43000 51709098
## 164 19.89900 11062113
## 165 80.56500 47076781
## 166 18.58500 21803000
## 167 34.93600 42813238
## 168 66.09503 581372
## 169 87.70800 10285453
## 170 73.84900 8574832
## 171 54.82100 17070135
## 172 27.30900 9321018
## 173 34.50000 58005463
## 174 50.69200 69625582
## 175 11.43377 3500000
## 176 42.24800 8082366
## 177 24.09464 100209
## 178 53.18698 1394973
## 179 69.25400 11694719
## 180 75.63000 83429615
## 181 52.04799 5942089
## 182 63.21484 11646
## 183 24.36100 44269594
## 184 69.47300 44385155
## 185 86.78900 9770529
## 186 83.65200 66834405
## 187 82.45900 328239523
## 188 95.42599 3461734
## 189 50.43300 33580650
## 190 25.39399 299882
## 191 88.24000 28515829
## 192 36.62800 96462106
## 193 37.27300 29161922
## 194 44.07200 17861030
## 195 32.21000 14645468Ilustrasi selanjutnya adalah kita akan memodifikasi kolom yang sudah
ada. Kolom Country kita modifikasi sedemikian sehingga nama
negaranya jadi huruf kapital semua
country_data_cleanup %>%
mutate(Country=str_to_upper(Country))## Country Density..P.Km2. Abbreviation
## 1 AFGHANISTAN 60 AF
## 2 ALBANIA 105 AL
## 3 ALGERIA 18 DZ
## 4 ANDORRA 164 AD
## 5 ANGOLA 26 AO
## 6 ANTIGUA AND BARBUDA 223 AG
## 7 ARGENTINA 17 AR
## 8 ARMENIA 104 AM
## 9 AUSTRALIA 3 AU
## 10 AUSTRIA 109 AT
## 11 AZERBAIJAN 123 AZ
## 12 THE BAHAMAS 39 BS
## 13 BAHRAIN 2239 BH
## 14 BANGLADESH 1265 BD
## 15 BARBADOS 668 BB
## 16 BELARUS 47 BY
## 17 BELGIUM 383 BE
## 18 BELIZE 17 BZ
## 19 BENIN 108 BJ
## 20 BHUTAN 20 BT
## 21 BOLIVIA 11 BO
## 22 BOSNIA AND HERZEGOVINA 64 BA
## 23 BOTSWANA 4 BW
## 24 BRAZIL 25 BR
## 25 BRUNEI 83 BN
## 26 BULGARIA 64 BG
## 27 BURKINA FASO 76 BF
## 28 BURUNDI 463 BI
## 29 IVORY COAST 83 CI
## 30 CAPE VERDE 138 CV
## 31 CAMBODIA 95 KH
## 32 CAMEROON 56 CM
## 33 CANADA 4 CA
## 34 CENTRAL AFRICAN REPUBLIC 8 CF
## 35 CHAD 13 TD
## 36 CHILE 26 CL
## 37 CHINA 153 CN
## 38 COLOMBIA 46 CO
## 39 COMOROS 467 KM
## 40 REPUBLIC OF THE CONGO 16
## 41 COSTA RICA 100 CR
## 42 CROATIA 73 HR
## 43 CUBA 106 CU
## 44 CYPRUS 131 CY
## 45 CZECH REPUBLIC 139 CZ
## 46 DEMOCRATIC REPUBLIC OF THE CONGO 40 CD
## 47 DENMARK 137 DK
## 48 DJIBOUTI 43 DJ
## 49 DOMINICA 96 DM
## 50 DOMINICAN REPUBLIC 225 DO
## 51 ECUADOR 71 EC
## 52 EGYPT 103 EG
## 53 EL SALVADOR 313 SV
## 54 EQUATORIAL GUINEA 50 GQ
## 55 ERITREA 35 ER
## 56 ESTONIA 31 EE
## 57 ESWATINI 67
## 58 ETHIOPIA 115 ET
## 59 FIJI 49 FJ
## 60 FINLAND 18 FI
## 61 FRANCE 119 FR
## 62 GABON 9 GA
## 63 THE GAMBIA 239 GM
## 64 GEORGIA 57 GE
## 65 GERMANY 240 DE
## 66 GHANA 137 GH
## 67 GREECE 81 GR
## 68 GRENADA 331 GD
## 69 GUATEMALA 167 GT
## 70 GUINEA 53 GN
## 71 GUINEA-BISSAU 70 GW
## 72 GUYANA 4 GY
## 73 HAITI 414 HT
## 74 VATICAN CITY 2003
## 75 HONDURAS 89 HN
## 76 HUNGARY 107 HU
## 77 ICELAND 3 IS
## 78 INDIA 464 IN
## 79 INDONESIA 151 ID
## 80 IRAN 52 IR
## 81 IRAQ 93 IQ
## 82 REPUBLIC OF IRELAND 72
## 83 ISRAEL 400 IL
## 84 ITALY 206 IT
## 85 JAMAICA 273 JM
## 86 JAPAN 347 JP
## 87 JORDAN 115 JO
## 88 KAZAKHSTAN 7 KZ
## 89 KENYA 94 KE
## 90 KIRIBATI 147 KI
## 91 KUWAIT 240 KW
## 92 KYRGYZSTAN 34 KG
## 93 LAOS 32 LA
## 94 LATVIA 30 LV
## 95 LEBANON 667 LB
## 96 LESOTHO 71 LS
## 97 LIBERIA 53 LR
## 98 LIBYA 4 LY
## 99 LIECHTENSTEIN 238 LI
## 100 LITHUANIA 43 LT
## 101 LUXEMBOURG 242 LU
## 102 MADAGASCAR 48 MG
## 103 MALAWI 203 MW
## 104 MALAYSIA 99 MY
## 105 MALDIVES 1802 MV
## 106 MALI 17 ML
## 107 MALTA 1380 MT
## 108 MARSHALL ISLANDS 329 MH
## 109 MAURITANIA 5 MR
## 110 MAURITIUS 626 MU
## 111 MEXICO 66 MX
## 112 FEDERATED STATES OF MICRONESIA 784 FM
## 113 MOLDOVA 123 MD
## 114 MONACO 26337 MC
## 115 MONGOLIA 2 MN
## 116 MONTENEGRO 47 ME
## 117 MOROCCO 83 MA
## 118 MOZAMBIQUE 40 MZ
## 119 MYANMAR 83 MM
## 120 NAMIBIA 3
## 121 NAURU 541 NR
## 122 NEPAL 203 NP
## 123 NETHERLANDS 508 NL
## 124 NEW ZEALAND 18 NZ
## 125 NICARAGUA 55 NI
## 126 NIGER 19 NE
## 127 NIGERIA 226 NG
## 128 NORTH KOREA 214 KP
## 129 NORTH MACEDONIA 83
## 130 NORWAY 15 NO
## 131 OMAN 16 OM
## 132 PAKISTAN 287 PK
## 133 PALAU 39 PW
## 134 PALESTINIAN NATIONAL AUTHORITY 847
## 135 PANAMA 58 PA
## 136 PAPUA NEW GUINEA 20 PG
## 137 PARAGUAY 18 PY
## 138 PERU 26 PE
## 139 PHILIPPINES 368 PH
## 140 POLAND 124 PL
## 141 PORTUGAL 111 PT
## 142 QATAR 248 QA
## 143 ROMANIA 84 RO
## 144 RUSSIA 9 RU
## 145 RWANDA 525 RW
## 146 SAINT KITTS AND NEVIS 205 KN
## 147 SAINT LUCIA 301 LC
## 148 SAINT VINCENT AND THE GRENADINES 284 VC
## 149 SAMOA 70 WS
## 150 SAN MARINO 566 SM
## 151 SÏ¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½Ï¿½ 228 ST
## 152 SAUDI ARABIA 16 SA
## 153 SENEGAL 87 SN
## 154 SERBIA 100 RS
## 155 SEYCHELLES 214 SC
## 156 SIERRA LEONE 111 SL
## 157 SINGAPORE 8358 SG
## 158 SLOVAKIA 114 SK
## 159 SLOVENIA 103 SI
## 160 SOLOMON ISLANDS 25 SB
## 161 SOMALIA 25 SO
## 162 SOUTH AFRICA 49 ZA
## 163 SOUTH KOREA 527 KR
## 164 SOUTH SUDAN 18 SS
## 165 SPAIN 94 ES
## 166 SRI LANKA 341 LK
## 167 SUDAN 25 SD
## 168 SURINAME 4 SR
## 169 SWEDEN 25 SE
## 170 SWITZERLAND 219 CH
## 171 SYRIA 95 SY
## 172 TAJIKISTAN 68 TJ
## 173 TANZANIA 67 TZ
## 174 THAILAND 137 TH
## 175 EAST TIMOR 89 TL
## 176 TOGO 152 TG
## 177 TONGA 147 TO
## 178 TRINIDAD AND TOBAGO 273 TT
## 179 TUNISIA 76 TN
## 180 TURKEY 110 TR
## 181 TURKMENISTAN 13 TM
## 182 TUVALU 393 TV
## 183 UGANDA 229 UG
## 184 UKRAINE 75 UA
## 185 UNITED ARAB EMIRATES 118 AE
## 186 UNITED KINGDOM 281 GB
## 187 UNITED STATES 36 US
## 188 URUGUAY 20 UY
## 189 UZBEKISTAN 79 UZ
## 190 VANUATU 25 VU
## 191 VENEZUELA 32 VE
## 192 VIETNAM 314 VN
## 193 YEMEN 56 YE
## 194 ZAMBIA 25 ZM
## 195 ZIMBABWE 38 ZW
## Agricultural.Land.... Land.Area.Km2. Armed.Forces.size Birth.Rate
## 1 58.10% 652230 323000 32.49
## 2 43.10% 28748 9000 11.78
## 3 17.40% 2381741 317000 24.28
## 4 40.00% 468 NA 7.20
## 5 47.50% 1246700 117000 40.73
## 6 20.50% 443 0 15.33
## 7 54.30% 2780400 105000 17.02
## 8 58.90% 29743 49000 13.99
## 9 48.20% 7741220 58000 12.60
## 10 32.40% 83871 21000 9.70
## 11 57.70% 86600 82000 14.00
## 12 1.40% 13880 1000 13.97
## 13 11.10% 765 19000 13.99
## 14 70.60% 148460 221000 18.18
## 15 23.30% 430 1000 10.65
## 16 42.00% 207600 155000 9.90
## 17 44.60% 30528 32000 10.30
## 18 7.00% 22966 2000 20.79
## 19 33.30% 112622 12000 36.22
## 20 13.60% 38394 6000 17.26
## 21 34.80% 1098581 71000 21.75
## 22 43.10% 51197 11000 8.11
## 23 45.60% 581730 9000 24.82
## 24 33.90% 8515770 730000 13.92
## 25 2.70% 5765 8000 14.90
## 26 46.30% 110879 31000 8.90
## 27 44.20% 274200 11000 37.93
## 28 79.20% 27830 31000 39.01
## 29 64.80% 322463 27000 35.74
## 30 19.60% 4033 1000 19.49
## 31 30.90% 181035 191000 22.46
## 32 20.60% 475440 24000 35.39
## 33 6.90% 9984670 72000 10.10
## 34 8.20% 622984 8000 35.35
## 35 39.70% 1284000 35000 42.17
## 36 21.20% 756096 122000 12.43
## 37 56.20% 9596960 2695000 10.90
## 38 40.30% 1138910 481000 14.88
## 39 71.50% 2235 NA 31.88
## 40 31.10% 342000 12000 32.86
## 41 34.50% 51100 10000 13.97
## 42 27.60% 56594 18000 9.00
## 43 59.90% 110860 76000 10.17
## 44 12.20% 9251 16000 10.46
## 45 45.20% 78867 23000 10.70
## 46 11.60% 2344858 134000 41.18
## 47 62.00% 43094 15000 10.60
## 48 73.40% 23200 13000 21.47
## 49 33.30% 751 NA 12.00
## 50 48.70% 48670 71000 19.51
## 51 22.20% 283561 41000 19.72
## 52 3.80% 1001450 836000 26.38
## 53 76.40% 21041 42000 18.25
## 54 10.10% 28051 1000 33.24
## 55 75.20% 117600 202000 30.30
## 56 23.10% 45228 6000 10.90
## 57 17364 NA NA
## 58 36.30% 1104300 138000 32.34
## 59 23.30% 18274 4000 21.28
## 60 7.50% 338145 25000 8.60
## 61 52.40% 643801 307000 11.30
## 62 20.00% 267667 7000 31.61
## 63 59.80% 11300 1000 38.54
## 64 34.50% 69700 26000 13.47
## 65 47.70% 357022 180000 9.50
## 66 69.00% 238533 16000 29.41
## 67 47.60% 131957 146000 8.10
## 68 23.50% 349 NA 16.47
## 69 36.00% 108889 43000 24.56
## 70 59.00% 245857 13000 36.36
## 71 58.00% 36125 4000 35.13
## 72 8.60% 214969 3000 19.97
## 73 66.80% 27750 0 24.35
## 74 0 NA NA
## 75 28.90% 112090 23000 21.60
## 76 58.40% 93028 40000 9.60
## 77 18.70% 103000 0 12.00
## 78 60.40% 3287263 3031000 17.86
## 79 31.50% 1904569 676000 18.07
## 80 28.20% 1648195 563000 18.78
## 81 21.40% 438317 209000 29.08
## 82 64.50% 70273 9000 12.50
## 83 24.60% 20770 178000 20.80
## 84 43.20% 301340 347000 7.30
## 85 41.00% 10991 4000 16.10
## 86 12.30% 377944 261000 7.40
## 87 12.00% 89342 116000 21.98
## 88 80.40% 2724900 71000 21.77
## 89 48.50% 580367 29000 28.75
## 90 42.00% 811 NA 27.89
## 91 8.40% 17818 25000 13.94
## 92 55.00% 199951 21000 27.10
## 93 10.30% 236800 129000 23.55
## 94 31.10% 64589 6000 10.00
## 95 64.30% 10400 80000 17.55
## 96 77.60% 30355 2000 26.81
## 97 28.00% 111369 2000 33.04
## 98 8.70% 1759540 0 18.83
## 99 32.20% 160 NA 9.90
## 100 47.20% 65300 34000 10.00
## 101 53.70% 2586 2000 10.30
## 102 71.20% 587041 22000 32.66
## 103 61.40% 118484 15000 34.12
## 104 26.30% 329847 136000 16.75
## 105 26.30% 298 5000 14.20
## 106 33.80% 1240192 18000 41.54
## 107 32.40% 316 2000 9.20
## 108 63.90% 181 NA 29.03
## 109 38.50% 1030700 21000 33.69
## 110 42.40% 2040 3000 10.20
## 111 54.60% 1964375 336000 17.60
## 112 31.40% 702 NA 22.82
## 113 74.20% 33851 7000 10.10
## 114 2 NA 5.90
## 115 71.50% 1564116 18000 24.13
## 116 19.00% 13812 12000 11.73
## 117 68.50% 446550 246000 18.94
## 118 63.50% 799380 11000 37.52
## 119 19.50% 676578 513000 17.55
## 120 47.10% 824292 16000 28.64
## 121 21 NA NA
## 122 28.70% 147181 112000 19.89
## 123 53.30% 41543 41000 9.70
## 124 40.50% 268838 9000 11.98
## 125 42.10% 130370 12000 20.64
## 126 36.10% 1267000 10000 46.08
## 127 77.70% 923768 215000 37.91
## 128 21.80% 120538 1469000 13.89
## 129 25713 NA NA
## 130 2.70% 323802 23000 10.40
## 131 4.60% 309500 47000 19.19
## 132 47.80% 796095 936000 28.25
## 133 10.90% 459 NA 14.00
## 134 NA NA NA
## 135 30.40% 75420 26000 18.98
## 136 2.60% 462840 4000 27.07
## 137 55.10% 406752 27000 20.57
## 138 18.50% 1285216 158000 17.95
## 139 41.70% 300000 153000 20.55
## 140 46.90% 312685 191000 10.20
## 141 39.50% 92212 52000 8.50
## 142 5.80% 11586 22000 9.54
## 143 58.80% 238391 126000 9.60
## 144 13.30% 17098240 1454000 11.50
## 145 73.40% 26338 35000 31.70
## 146 23.10% 261 NA 12.60
## 147 17.40% 616 NA 12.00
## 148 25.60% 389 NA 14.24
## 149 12.40% 2831 NA 24.38
## 150 16.70% 61 NA 6.80
## 151 50.70% 964 1000 31.54
## 152 80.80% 2149690 252000 17.80
## 153 46.10% 196722 19000 34.52
## 154 39.30% 77474 32000 9.20
## 155 3.40% 455 0 17.10
## 156 54.70% 71740 9000 33.41
## 157 0.90% 716 81000 8.80
## 158 39.20% 49035 16000 10.60
## 159 30.70% 20273 7000 9.40
## 160 3.90% 28896 NA 32.44
## 161 70.30% 637657 20000 41.75
## 162 79.80% 1219090 80000 20.51
## 163 17.40% 99720 634000 6.40
## 164 644329 185000 35.01
## 165 52.60% 505370 196000 7.90
## 166 43.70% 65610 317000 15.83
## 167 28.70% 1861484 124000 32.18
## 168 0.60% 163820 2000 18.54
## 169 7.40% 450295 30000 11.40
## 170 38.40% 41277 21000 10.00
## 171 75.80% 185180 239000 23.69
## 172 34.10% 144100 17000 30.76
## 173 44.80% 947300 28000 36.70
## 174 43.30% 513120 455000 10.34
## 175 25.60% 14874 2000 29.42
## 176 70.20% 56785 10000 33.11
## 177 45.80% 747 NA 24.30
## 178 10.50% 5128 4000 12.94
## 179 64.80% 163610 48000 17.56
## 180 49.80% 783562 512000 16.03
## 181 72.00% 488100 42000 23.83
## 182 60.00% 26 NA NA
## 183 71.90% 241038 46000 38.14
## 184 71.70% 603550 297000 8.70
## 185 5.50% 83600 63000 10.33
## 186 71.70% 243610 148000 11.00
## 187 44.40% 9833517 1359000 11.60
## 188 82.60% 176215 22000 13.86
## 189 62.90% 447400 68000 23.30
## 190 15.30% 12189 NA 29.60
## 191 24.50% 912050 343000 17.88
## 192 39.30% 331210 522000 16.75
## 193 44.60% 527968 40000 30.45
## 194 32.10% 752618 16000 36.19
## 195 41.90% 390757 51000 30.68
## Calling.Code Capital.Major.City Co2.Emissions CPI CPI.Change....
## 1 93 Kabul 8672 149.90 2.30%
## 2 355 Tirana 4536 119.05 1.40%
## 3 213 Algiers 150006 151.36 2.00%
## 4 376 Andorra la Vella 469 NA
## 5 244 Luanda 34693 261.73 17.10%
## 6 1 St. John's, Saint John 557 113.81 1.20%
## 7 54 Buenos Aires 201348 232.75 53.50%
## 8 374 Yerevan 5156 129.18 1.40%
## 9 61 Canberra 375908 119.80 1.60%
## 10 43 Vienna 61448 118.06 1.50%
## 11 994 Baku 37620 156.32 2.60%
## 12 1 Nassau, Bahamas 1786 116.22 2.50%
## 13 973 Manama 31694 117.59 2.10%
## 14 880 Dhaka 84246 179.68 5.60%
## 15 1 Bridgetown 1276 134.09 4.10%
## 16 375 Minsk 58280 NA 5.60%
## 17 32 City of Brussels 96889 117.11 1.40%
## 18 501 Belmopan 568 105.68 -0.90%
## 19 229 Porto-Novo 6476 110.71 -0.90%
## 20 975 Thimphu 1261 167.18 2.70%
## 21 591 Sucre 21606 148.32 1.80%
## 22 387 Sarajevo 21848 104.90 0.60%
## 23 267 Gaborone 6340 149.75 2.80%
## 24 55 Bras��� 462299 167.40 3.70%
## 25 673 Bandar Seri Begawan 7664 99.03 -0.40%
## 26 359 Sofia 41708 114.42 3.10%
## 27 226 Ouagadougou 3418 106.58 -3.20%
## 28 257 Bujumbura 495 182.11 -0.70%
## 29 225 Yamoussoukro 9674 111.61 -0.90%
## 30 238 Praia 543 110.50 1.10%
## 31 855 Phnom Penh 9919 127.63 2.50%
## 32 237 Yaound� 8291 118.65 2.50%
## 33 1 Ottawa 544894 116.76 1.90%
## 34 236 Bangui 297 186.86 37.10%
## 35 235 N'Djamena 1016 117.70 -1.00%
## 36 56 Santiago 85822 131.91 2.60%
## 37 86 Beijing 9893038 125.08 2.90%
## 38 57 Bogot� 97814 140.95 3.50%
## 39 269 Moroni, Comoros 202 103.62 -4.30%
## 40 242 Brazzaville 3282 124.74 2.20%
## 41 506 San Jos������ 8023 128.85 2.10%
## 42 385 Zagreb 17488 109.82 0.80%
## 43 53 Havana 28284 NA
## 44 357 Nicosia 6626 102.51 0.30%
## 45 420 Prague 102218 116.48 2.80%
## 46 243 Kinshasa 2021 133.85 2.90%
## 47 45 Copenhagen 31786 110.35 0.80%
## 48 253 Djibouti City 620 120.25 3.30%
## 49 1 Roseau 180 103.87 1.00%
## 50 1 Santo Domingo 25258 135.50 1.80%
## 51 593 Quito 41155 124.14 0.30%
## 52 20 Cairo 238560 288.57 9.20%
## 53 503 San Salvador 7169 111.23 0.10%
## 54 240 Malabo 5655 124.35 1.20%
## 55 291 Asmara 711 NA
## 56 372 Tallinn 16590 122.14 2.30%
## 57 268 Mbabane NA NA
## 58 251 Addis Ababa 14870 143.86 15.80%
## 59 679 Suva 2046 132.30 1.80%
## 60 358 Helsinki 45871 112.33 1.00%
## 61 33 Paris 303276 110.05 1.10%
## 62 241 Libreville 5321 122.19 2.10%
## 63 220 Banjul 532 172.73 7.10%
## 64 995 Tbilisi 10128 133.61 4.90%
## 65 49 Berlin 727973 112.85 1.40%
## 66 233 Accra 16670 268.36 7.20%
## 67 30 Athens 62434 101.87 0.20%
## 68 1 St. George's, Grenada 268 107.43 0.80%
## 69 502 Guatemala City 16777 142.92 3.70%
## 70 224 Conakry 2996 262.95 9.50%
## 71 245 Bissau 293 111.65 1.40%
## 72 592 Georgetown, Guyana 2384 116.19 2.10%
## 73 509 Port-au-Prince 2978 179.29 12.50%
## 74 379 Vatican City NA NA
## 75 504 Tegucigalpa 9813 150.34 4.40%
## 76 36 Budapest 45537 121.64 3.30%
## 77 354 Reykjav�� 2065 129.00 3.00%
## 78 91 New Delhi 2407672 180.44 7.70%
## 79 62 Jakarta 563325 151.18 3.00%
## 80 98 Tehran 661710 550.93 39.90%
## 81 964 Baghdad 190061 119.86 0.40%
## 82 353 Dublin 37711 106.58 0.90%
## 83 972 Jerusalem 65166 108.15 0.80%
## 84 39 Rome 320411 110.62 0.60%
## 85 1876 Kingston, Jamaica 8225 162.47 3.90%
## 86 81 Tokyo 1135886 105.48 0.50%
## 87 962 Amman 25108 125.60 0.80%
## 88 7 Astana 247207 182.75 5.20%
## 89 254 Nairobi 17910 180.51 4.70%
## 90 686 South Tarawa 66 99.55 0.60%
## 91 965 Kuwait City 98734 126.60 1.10%
## 92 996 Bishkek 9787 155.68 1.10%
## 93 856 Vientiane 17763 135.87 3.30%
## 94 371 Riga 7004 116.86 2.80%
## 95 961 Beirut 24796 130.02 3.00%
## 96 266 Maseru 2512 155.86 5.20%
## 97 231 Monrovia 1386 223.13 23.60%
## 98 218 50564 125.71 2.60%
## 99 423 Vaduz 51 NA
## 100 370 Vilnius 12963 118.38 2.30%
## 101 352 Luxembourg City 8988 115.09 1.70%
## 102 261 Antananarivo 3905 184.33 5.60%
## 103 265 Lilongwe 1298 418.34 9.40%
## 104 60 Kuala Lumpur 248289 121.46 0.70%
## 105 960 Mal� 1445 99.70 0.20%
## 106 223 Bamako 3179 108.73 -1.70%
## 107 356 Valletta 1342 113.45 1.60%
## 108 692 Majuro 143 NA
## 109 222 Nouakchott 2739 135.02 2.30%
## 110 230 Port Louis 4349 129.91 0.40%
## 111 52 Mexico City 486406 141.54 3.60%
## 112 691 Palikir 143 112.10 0.50%
## 113 373 Chi���� 5115 166.20 4.80%
## 114 377 Monaco City NA NA
## 115 976 Ulaanbaatar 25368 195.76 7.30%
## 116 382 Podgorica 2017 116.32 2.60%
## 117 212 Rabat 61276 111.07 0.20%
## 118 258 Maputo 7943 182.31 2.80%
## 119 95 Naypyidaw 25280 168.18 8.80%
## 120 264 Windhoek 4228 157.97 3.70%
## 121 674 Yaren District NA NA
## 122 977 Kathmandu 9105 188.73 5.60%
## 123 31 Amsterdam 170780 115.91 2.60%
## 124 64 Wellington 34382 114.24 1.60%
## 125 505 Managua 5592 162.74 5.40%
## 126 227 Niamey 2017 109.32 -2.50%
## 127 234 Abuja 120369 267.51 11.40%
## 128 850 Pyongyang 28284 NA
## 129 389 Skopje NA NA
## 130 47 Oslo 41023 120.27 2.20%
## 131 968 Muscat 63457 113.53 0.10%
## 132 92 Islamabad 201150 182.32 10.60%
## 133 680 Ngerulmud 224 118.17 1.30%
## 134 NA NA NA
## 135 507 Panama City 10715 122.07 -0.40%
## 136 675 Port Moresby 7536 155.99 3.60%
## 137 595 Asunci�� 7407 143.82 2.80%
## 138 51 Lima 57414 129.78 2.10%
## 139 63 Manila 122287 129.61 2.50%
## 140 48 Warsaw 299037 114.11 2.20%
## 141 351 Lisbon 48742 110.62 0.30%
## 142 974 Doha 103259 115.38 -0.70%
## 143 40 Bucharest 69259 123.78 3.80%
## 144 7 Moscow 1732027 180.75 4.50%
## 145 250 Kigali 1115 151.09 3.40%
## 146 1 Basseterre 238 104.57 -1.00%
## 147 1 Castries 414 110.13 1.90%
## 148 1 Kingstown 220 109.67 2.30%
## 149 685 Apia 246 117.56 1.00%
## 150 378 City of San Marino NA 110.63 1.00%
## 151 239 S���� 121 185.09 7.90%
## 152 966 Riyadh 563449 118.40 -1.20%
## 153 221 Dakar 10902 109.25 1.80%
## 154 381 Belgrade 45221 144.00 1.80%
## 155 248 Victoria, Seychelles 605 129.96 1.80%
## 156 232 Freetown 1093 234.16 14.80%
## 157 65 37535 114.41 0.60%
## 158 421 Bratislava 32424 115.34 2.70%
## 159 386 Ljubljana 12633 111.05 1.60%
## 160 677 Honiara 169 133.06 1.60%
## 161 252 Mogadishu 645 NA
## 162 27 Pretoria 476644 158.93 4.10%
## 163 82 Seoul 620302 115.16 0.40%
## 164 211 Juba 1727 4583.71 187.90%
## 165 34 Madrid 244002 110.96 0.70%
## 166 94 Colombo 23362 155.53 3.50%
## 167 249 Khartoum 20000 1344.19 51.00%
## 168 597 Paramaribo 1738 294.66 22.00%
## 169 46 Stockholm 43252 110.51 1.80%
## 170 41 Bern 34477 99.55 0.40%
## 171 963 Damascus 28830 143.20 36.70%
## 172 992 Dushanbe 5310 148.57 6.00%
## 173 255 Dodoma 11973 187.43 3.50%
## 174 66 Bangkok 283763 113.27 0.70%
## 175 670 Dili 495 145.38 2.60%
## 176 228 Lom� 3000 113.30 0.70%
## 177 676 Nuku���� 128 121.09 7.40%
## 178 1 Port of Spain 43868 141.75 1.00%
## 179 216 Tunis 29937 155.33 6.70%
## 180 90 Ankara 372725 234.44 15.20%
## 181 993 Ashgabat 70630 NA
## 182 688 Funafuti 11 NA
## 183 256 Kampala 5680 173.87 2.90%
## 184 380 Kyiv 202250 281.66 7.90%
## 185 971 Abu Dhabi 206324 114.52 -1.90%
## 186 44 London 379025 119.62 1.70%
## 187 1 Washington, D.C. 5006302 117.24 7.50%
## 188 598 Montevideo 6766 202.92 7.90%
## 189 998 Tashkent 91811 NA
## 190 678 Port Vila 147 117.13 2.80%
## 191 58 Caracas 164175 2740.27 254.90%
## 192 84 Hanoi 192668 163.52 2.80%
## 193 967 Sanaa 10609 157.58 8.10%
## 194 260 Lusaka 5141 212.31 9.20%
## 195 263 Harare 10983 105.51 0.90%
## Currency.Code Fertility.Rate Forested.Area.... Gasoline.Price
## 1 AFN 4.47 2.10% $0.70
## 2 ALL 1.62 28.10% $1.36
## 3 DZD 3.02 0.80% $0.28
## 4 EUR 1.27 34.00% $1.51
## 5 AOA 5.52 46.30% $0.97
## 6 XCD 1.99 22.30% $0.99
## 7 ARS 2.26 9.80% $1.10
## 8 AMD 1.76 11.70% $0.77
## 9 AUD 1.74 16.30% $0.93
## 10 EUR 1.47 46.90% $1.20
## 11 AZN 1.73 14.10% $0.56
## 12 1.75 51.40% $0.92
## 13 BHD 1.99 0.80% $0.43
## 14 BDT 2.04 11.00% $1.12
## 15 BBD 1.62 14.70% $1.81
## 16 BYN 1.45 42.60% $0.60
## 17 EUR 1.62 22.60% $1.43
## 18 BZD 2.31 59.70% $1.13
## 19 XOF 4.84 37.80% $0.72
## 20 1.98 72.50% $0.98
## 21 BOB 2.73 50.30% $0.71
## 22 BAM 1.27 42.70% $1.05
## 23 BWP 2.87 18.90% $0.71
## 24 BRL 1.73 58.90% $1.02
## 25 BND 1.85 72.10% $0.37
## 26 BGN 1.56 35.40% $1.11
## 27 XOF 5.19 19.30% $0.98
## 28 BIF 5.41 10.90% $1.21
## 29 XOF 4.65 32.70% $0.93
## 30 CVE 2.27 22.50% $1.02
## 31 2.50 52.90% $0.90
## 32 XAF 4.57 39.30% $1.03
## 33 CAD 1.50 38.20% $0.81
## 34 4.72 35.60% $1.41
## 35 XAF 5.75 3.80% $0.78
## 36 CLP 1.65 24.30% $1.03
## 37 CNY 1.69 22.40% $0.96
## 38 COP 1.81 52.70% $0.68
## 39 KMF 4.21 19.70%
## 40 XAF 4.43 65.40% $0.97
## 41 CRC 1.75 54.60% $0.98
## 42 HRK 1.47 34.40% $1.26
## 43 CUP 1.62 31.30% $1.40
## 44 EUR 1.33 18.70% $1.23
## 45 CZK 1.69 34.60% $1.17
## 46 CDF 5.92 67.20% $1.49
## 47 DKK 1.73 14.70% $1.55
## 48 DJF 2.73 0.20% $1.32
## 49 XCD 1.90 57.40%
## 50 DOP 2.35 41.70% $1.07
## 51 USD 2.43 50.20% $0.61
## 52 EGP 3.33 0.10% $0.40
## 53 2.04 12.60% $0.83
## 54 XAF 4.51 55.50%
## 55 ERN 4.06 14.90% $2.00
## 56 EUR 1.59 51.30% $1.14
## 57 NA
## 58 ETB 4.25 12.50% $0.75
## 59 FJD 2.77 55.90% $0.82
## 60 EUR 1.41 73.10% $1.45
## 61 EUR 1.88 31.20% $1.39
## 62 XAF 3.97 90.00% $0.92
## 63 GMD 5.22 48.40% $1.18
## 64 GEL 2.06 40.60% $0.76
## 65 EUR 1.56 32.70% $1.39
## 66 GHS 3.87 41.20% $0.92
## 67 EUR 1.35 31.70% $1.54
## 68 XCD 2.06 50.00% $1.12
## 69 GTQ 2.87 32.70% $0.79
## 70 GNF 4.70 25.80% $0.90
## 71 XOF 4.48 69.80%
## 72 GYD 2.46 83.90% $0.90
## 73 HTG 2.94 3.50% $0.81
## 74 EUR NA
## 75 HNL 2.46 40.00% $0.98
## 76 HUF 1.54 22.90% $1.18
## 77 ISK 1.71 0.50% $1.69
## 78 INR 2.22 23.80% $0.97
## 79 IDR 2.31 49.90% $0.63
## 80 IRR 2.14 6.60% $0.40
## 81 IQD 3.67 1.90% $0.61
## 82 EUR 1.75 11.00% $1.37
## 83 ILS 3.09 7.70% $1.57
## 84 EUR 1.29 31.80% $1.61
## 85 JMD 1.98 30.90% $1.11
## 86 1.42 68.50% $1.06
## 87 JOD 2.76 1.10% $1.10
## 88 KZT 2.84 1.20% $0.42
## 89 KES 3.49 7.80% $0.95
## 90 AUD 3.57 15.00%
## 91 KWD 2.08 0.40% $0.35
## 92 KGS 3.30 3.30% $0.56
## 93 LAK 2.67 82.10% $0.93
## 94 EUR 1.60 54.00% $1.16
## 95 LBP 2.09 13.40% $0.74
## 96 3.14 1.60% $0.70
## 97 4.32 43.10% $0.80
## 98 LYD 2.24 0.10% $0.11
## 99 CHF 1.44 43.10% $1.74
## 100 EUR 1.63 34.80% $1.16
## 101 EUR 1.37 35.70% $1.19
## 102 MGA 4.08 21.40% $1.11
## 103 MWK 4.21 33.20% $1.15
## 104 MYR 2.00 67.60% $0.45
## 105 1.87 3.30% $1.63
## 106 XOF 5.88 3.80% $1.12
## 107 EUR 1.23 1.10% $1.36
## 108 USD 4.05 70.20% $1.44
## 109 MRU 4.56 0.20% $1.13
## 110 MUR 1.41 19.00% $1.12
## 111 MXN 2.13 33.90% $0.73
## 112 USD 3.05 91.90%
## 113 MDL 1.26 12.60% $0.80
## 114 EUR NA $2.00
## 115 MNT 2.90 8.00% $0.72
## 116 EUR 1.75 61.50% $1.16
## 117 MAD 2.42 12.60% $0.99
## 118 MZN 4.85 48.00% $0.65
## 119 MMK 2.15 43.60% $0.54
## 120 3.40 8.30% $0.76
## 121 AUD NA
## 122 NPR 1.92 25.40% $0.91
## 123 1.59 11.20% $1.68
## 124 NZD 1.71 38.60% $1.40
## 125 NIO 2.40 25.90% $0.91
## 126 XOF 6.91 0.90% $0.88
## 127 NGN 5.39 7.20% $0.46
## 128 KPW 1.90 40.70% $0.58
## 129 MKD NA
## 130 NOK 1.56 33.20% $1.78
## 131 OMR 2.89 0.00% $0.45
## 132 PKR 3.51 1.90% $0.79
## 133 USD 2.21 87.60%
## 134 NA
## 135 2.46 61.90% $0.74
## 136 PGK 3.56 74.10% $1.36
## 137 PYG 2.43 37.70% $1.04
## 138 PEN 2.25 57.70% $0.99
## 139 PHP 2.58 27.80% $0.86
## 140 PLN 1.46 30.90% $1.07
## 141 EUR 1.38 34.60% $1.54
## 142 QAR 1.87 0.00% $0.40
## 143 RON 1.71 30.10% $1.16
## 144 RUB 1.57 49.80% $0.59
## 145 RWF 4.04 19.70% $1.17
## 146 XCD 2.11 42.30%
## 147 XCD 1.44 33.20% $1.30
## 148 XCD 1.89 69.20%
## 149 WST 3.88 60.40% $0.91
## 150 EUR 1.26 0.00%
## 151 STN 4.32 55.80%
## 152 SAR 2.32 0.50% $0.24
## 153 XOF 4.63 42.80% $1.14
## 154 RSD 1.49 31.10% $1.16
## 155 SCR 2.41 88.40%
## 156 SLL 4.26 43.10% $1.08
## 157 SGD 1.14 23.10% $1.25
## 158 EUR 1.52 40.40% $1.32
## 159 EUR 1.60 62.00% $1.32
## 160 SBD 4.40 77.90%
## 161 SOS 6.07 10.00% $1.41
## 162 ZAR 2.41 7.60% $0.92
## 163 KRW 0.98 63.40% $1.22
## 164 SSP 4.70 $0.28
## 165 EUR 1.26 36.90% $1.26
## 166 LKR 2.20 32.90% $0.88
## 167 SDG 4.41 8.10% $0.95
## 168 SRD 2.42 98.30% $1.29
## 169 SEK 1.76 68.90% $1.42
## 170 CHF 1.52 31.80% $1.45
## 171 SYP 2.81 2.70% $0.83
## 172 TJS 3.59 3.00% $0.71
## 173 TZS 4.89 51.60% $0.87
## 174 THB 1.53 32.20% $0.71
## 175 USD 4.02 45.40% $1.10
## 176 XOF 4.32 3.10% $0.71
## 177 TOP 3.56 12.50%
## 178 TTD 1.73 46.00% $0.54
## 179 TND 2.20 6.80% $0.73
## 180 TRY 2.07 15.40% $1.42
## 181 TMT 2.79 8.80% $0.29
## 182 AUD NA 33.30%
## 183 UGX 4.96 9.70% $0.94
## 184 UAH 1.30 16.70% $0.83
## 185 AED 1.41 4.60% $0.49
## 186 GBP 1.68 13.10% $1.46
## 187 USD 1.73 33.90% $0.71
## 188 UYU 1.97 10.70% $1.50
## 189 UZS 2.42 7.50% $1.03
## 190 VUV 3.78 36.10% $1.31
## 191 VED 2.27 52.70% $0.00
## 192 VND 2.05 48.10% $0.80
## 193 YER 3.79 1.00% $0.92
## 194 ZMW 4.63 65.20% $1.40
## 195 3.62 35.50% $1.34
## GDP Gross.primary.education.enrollment....
## 1 $19,101,353,833 104.00%
## 2 $15,278,077,447 107.00%
## 3 $169,988,236,398 109.90%
## 4 $3,154,057,987 106.40%
## 5 $94,635,415,870 113.50%
## 6 $1,727,759,259 105.00%
## 7 $449,663,446,954 109.70%
## 8 $13,672,802,158 92.70%
## 9 $1,392,680,589,329 100.30%
## 10 $446,314,739,528 103.10%
## 11 $39,207,000,000 99.70%
## 12 $12,827,000,000 81.40%
## 13 $38,574,069,149 99.40%
## 14 $302,571,254,131 116.50%
## 15 $5,209,000,000 99.40%
## 16 $63,080,457,023 100.50%
## 17 $529,606,710,418 103.90%
## 18 $1,879,613,600 111.70%
## 19 $14,390,709,095 122.00%
## 20 $2,446,674,101 100.10%
## 21 $40,895,322,865 98.20%
## 22 $20,047,848,435
## 23 $18,340,510,789 103.20%
## 24 $1,839,758,040,766 115.40%
## 25 $13,469,422,941 103.20%
## 26 $86,000,000,000 89.30%
## 27 $15,745,810,235 96.10%
## 28 $3,012,334,882 121.40%
## 29 $58,792,205,642 99.80%
## 30 $1,981,845,741 104.00%
## 31 $27,089,389,787 107.40%
## 32 $38,760,467,033 103.40%
## 33 $1,736,425,629,520 100.90%
## 34 $2,220,307,369 102.00%
## 35 $11,314,951,343 86.80%
## 36 $282,318,159,745 101.40%
## 37 $19,910,000,000,000 100.20%
## 38 $323,802,808,108 114.50%
## 39 $1,185,728,677 99.50%
## 40 $10,820,591,131 106.60%
## 41 $61,773,944,174 113.30%
## 42 $60,415,553,039 96.50%
## 43 $100,023,000,000 101.90%
## 44 $24,564,647,935 99.30%
## 45 $246,489,245,495 100.70%
## 46 $47,319,624,204 108.00%
## 47 $348,078,018,464 101.30%
## 48 $3,318,716,359 75.30%
## 49 $596,033,333 114.70%
## 50 $88,941,298,258 105.70%
## 51 $107,435,665,000 103.30%
## 52 $303,175,127,598 106.30%
## 53 $27,022,640,000 94.80%
## 54 $11,026,774,945 61.80%
## 55 $2,065,001,626 68.40%
## 56 $31,386,949,981 97.20%
## 57 $3,791,304,348
## 58 $96,107,662,398 101.00%
## 59 $5,535,548,972 106.40%
## 60 $268,761,201,365 100.20%
## 61 $2,715,518,274,227 102.50%
## 62 $16,657,960,228 139.90%
## 63 $1,763,819,048 98.00%
## 64 $17,743,195,770 98.60%
## 65 $3,845,630,030,824 104.00%
## 66 $66,983,634,224 104.80%
## 67 $209,852,761,469 99.60%
## 68 $1,228,170,370 106.90%
## 69 $76,710,385,880 101.90%
## 70 $13,590,281,809 91.50%
## 71 $1,340,389,411 118.70%
## 72 $4,280,443,645 97.80%
## 73 $8,498,981,821 113.60%
## 74
## 75 $25,095,395,475 91.50%
## 76 $160,967,157,504 100.80%
## 77 $24,188,035,739 100.40%
## 78 $2,611,000,000,000 113.00%
## 79 $1,119,190,780,753 106.40%
## 80 $445,345,282,123 110.70%
## 81 $234,094,042,939 108.70%
## 82 $388,698,711,348 100.90%
## 83 $395,098,666,122 104.90%
## 84 $2,001,244,392,042 101.90%
## 85 $16,458,071,068 91.00%
## 86 $5,081,769,542,380 98.80%
## 87 $43,743,661,972 81.50%
## 88 $180,161,741,180 104.40%
## 89 $95,503,088,538 103.20%
## 90 $194,647,202 101.30%
## 91 $134,761,198,946 92.40%
## 92 $8,454,619,608 107.60%
## 93 $18,173,839,128 102.40%
## 94 $34,117,202,555 99.40%
## 95 $53,367,042,272 95.10%
## 96 $2,460,072,444 120.90%
## 97 $3,070,518,100 85.10%
## 98 $52,076,250,948 109.00%
## 99 $6,552,858,739 104.70%
## 100 $54,219,315,600 103.90%
## 101 $71,104,919,108 102.30%
## 102 $14,083,906,357 142.50%
## 103 $7,666,704,427 142.50%
## 104 $364,701,517,788 105.30%
## 105 $5,729,248,472 97.10%
## 106 $17,510,141,171 75.60%
## 107 $14,786,156,563 105.00%
## 108 $221,278,000 84.70%
## 109 $7,593,752,450 99.90%
## 110 $14,180,444,557 101.10%
## 111 $1,258,286,717,125 105.80%
## 112 $401,932,279 97.20%
## 113 $11,955,435,457 90.60%
## 114 $7,184,844,193
## 115 $13,852,850,259 104.00%
## 116 $5,494,736,901 100.00%
## 117 $118,725,279,596 113.90%
## 118 $14,934,159,926 112.60%
## 119 $76,085,852,617 112.30%
## 120 $12,366,527,719 124.20%
## 121 $133,000,000
## 122 $30,641,380,604 142.10%
## 123 $909,070,395,161 104.20%
## 124 $206,928,765,544 100.00%
## 125 $12,520,915,291 120.60%
## 126 $12,928,145,120 74.70%
## 127 $448,120,428,859 84.70%
## 128 $32,100,000,000 112.80%
## 129 $10,220,781,069
## 130 $403,336,363,636 100.30%
## 131 $76,983,094,928 103.40%
## 132 $304,400,000,000 94.30%
## 133 $283,994,900 112.60%
## 134
## 135 $66,800,800,000 94.40%
## 136 $24,969,611,435 108.50%
## 137 $38,145,288,940 104.40%
## 138 $226,848,050,820 106.90%
## 139 $376,795,508,680 107.50%
## 140 $592,164,400,688 100.00%
## 141 $237,686,075,635 106.20%
## 142 $183,466,208,791 103.80%
## 143 $250,077,444,017 85.20%
## 144 $1,699,876,578,871 102.60%
## 145 $10,122,472,590 133.00%
## 146 $1,050,992,593 108.70%
## 147 $2,122,450,630 102.60%
## 148 $825,385,185 113.40%
## 149 $850,655,017 110.50%
## 150 $1,637,931,034 108.10%
## 151 $429,016,605 106.80%
## 152 $792,966,838,162 99.80%
## 153 $23,578,084,052 81.00%
## 154 $51,409,167,351 100.30%
## 155 $1,698,843,063 100.40%
## 156 $3,941,474,311 112.80%
## 157 $372,062,527,489 100.60%
## 158 $105,422,304,976 98.70%
## 159 $53,742,159,517 100.40%
## 160 $1,425,074,226 106.20%
## 161 $4,720,727,278 23.40%
## 162 $351,431,649,241 100.90%
## 163 $2,029,000,000,000 98.10%
## 164 $11,997,800,751 73.00%
## 165 $1,394,116,310,769 102.70%
## 166 $84,008,783,756 100.20%
## 167 $18,902,284,476 76.80%
## 168 $3,985,250,737 108.80%
## 169 $530,832,908,738 126.60%
## 170 $703,082,435,360 105.20%
## 171 $40,405,006,007 81.70%
## 172 $8,116,626,794 100.90%
## 173 $63,177,068,175 94.20%
## 174 $543,649,976,166 99.80%
## 175 $1,673,540,300 115.30%
## 176 $5,459,979,417 123.80%
## 177 $450,353,314 116.30%
## 178 $24,100,202,834 106.20%
## 179 $38,797,709,924 115.40%
## 180 $754,411,708,203 93.20%
## 181 $40,761,142,857 88.40%
## 182 $47,271,463 86.00%
## 183 $34,387,229,486 102.70%
## 184 $153,781,069,118 99.00%
## 185 $421,142,267,938 108.40%
## 186 $2,827,113,184,696 101.20%
## 187 $21,427,700,000,000 101.80%
## 188 $56,045,912,952 108.50%
## 189 $57,921,286,440 104.20%
## 190 $917,058,851 109.30%
## 191 $482,359,318,768 97.20%
## 192 $261,921,244,843 110.60%
## 193 $26,914,402,224 93.60%
## 194 $23,064,722,446 98.70%
## 195 $21,440,758,800 109.90%
## Gross.tertiary.education.enrollment.... Infant.mortality
## 1 9.70% 47.9
## 2 55.00% 7.8
## 3 51.40% 20.1
## 4 2.7
## 5 9.30% 51.6
## 6 24.80% 5.0
## 7 90.00% 8.8
## 8 54.60% 11.0
## 9 113.10% 3.1
## 10 85.10% 2.9
## 11 27.70% 19.2
## 12 15.10% 8.3
## 13 50.50% 6.1
## 14 20.60% 25.1
## 15 65.40% 11.3
## 16 87.40% 2.6
## 17 79.70% 2.9
## 18 24.70% 11.2
## 19 12.30% 60.5
## 20 15.60% 24.8
## 21 21.8
## 22 23.30% 5.0
## 23 24.90% 30.0
## 24 51.30% 12.8
## 25 31.40% 9.8
## 26 71.00% 5.9
## 27 6.50% 49.0
## 28 6.10% 41.0
## 29 9.30% 59.4
## 30 23.60% 16.7
## 31 13.70% 24.0
## 32 12.80% 50.6
## 33 68.90% 4.3
## 34 3.00% 84.5
## 35 3.30% 71.4
## 36 88.50% 6.2
## 37 50.60% 7.4
## 38 55.30% 12.2
## 39 9.00% 51.3
## 40 12.70% 36.2
## 41 55.20% 7.6
## 42 67.90% 4.0
## 43 41.40% 3.7
## 44 75.90% 1.9
## 45 64.10% 2.7
## 46 6.60% 68.2
## 47 80.60% 3.6
## 48 5.30% 49.8
## 49 7.20% 32.9
## 50 59.90% 24.1
## 51 44.90% 12.2
## 52 35.20% 18.1
## 53 29.40% 11.8
## 54 1.90% 62.6
## 55 3.40% 31.3
## 56 69.60% 2.1
## 57 NA
## 58 8.10% 39.1
## 59 16.10% 21.6
## 60 88.20% 1.4
## 61 65.60% 3.4
## 62 8.30% 32.7
## 63 2.70% 39.0
## 64 63.90% 8.7
## 65 70.20% 3.1
## 66 15.70% 34.9
## 67 136.60% 3.6
## 68 104.60% 13.7
## 69 21.80% 22.1
## 70 11.60% 64.9
## 71 2.60% 54.0
## 72 11.60% 25.1
## 73 1.10% 49.5
## 74 NA
## 75 26.20% 15.1
## 76 48.50% 3.6
## 77 71.80% 1.5
## 78 28.10% 29.9
## 79 36.30% 21.1
## 80 68.10% 12.4
## 81 16.20% 22.5
## 82 77.80% 3.1
## 83 63.40% 3.0
## 84 61.90% 2.6
## 85 27.10% 12.4
## 86 63.20% 1.8
## 87 34.40% 13.9
## 88 61.70% 8.8
## 89 11.50% 30.6
## 90 41.2
## 91 54.40% 6.7
## 92 41.30% 16.9
## 93 15.00% 37.6
## 94 88.10% 3.3
## 95 26.30% 6.4
## 96 10.20% 65.7
## 97 11.90% 53.5
## 98 60.50% 10.2
## 99 35.60% NA
## 100 72.40% 3.3
## 101 19.20% 1.9
## 102 5.40% 38.2
## 103 0.80% 35.3
## 104 45.10% 6.7
## 105 31.20% 7.4
## 106 4.50% 62.0
## 107 54.30% 6.1
## 108 23.70% 27.4
## 109 5.00% 51.5
## 110 40.60% 13.6
## 111 40.20% 11.0
## 112 14.10% 25.6
## 113 39.80% 13.6
## 114 2.6
## 115 65.60% 14.0
## 116 56.10% 2.3
## 117 35.90% 19.2
## 118 7.30% 54.0
## 119 18.80% 36.8
## 120 22.90% 29.0
## 121 NA
## 122 12.40% 26.7
## 123 85.00% 3.3
## 124 82.00% 4.7
## 125 17.40% 15.7
## 126 4.40% 48.0
## 127 10.20% 75.7
## 128 27.00% 13.7
## 129 NA
## 130 82.00% 2.1
## 131 38.00% 9.8
## 132 9.00% 57.2
## 133 54.70% 16.6
## 134 NA
## 135 47.80% 13.1
## 136 1.80% 38.0
## 137 34.60% 17.2
## 138 70.70% 11.1
## 139 35.50% 22.5
## 140 67.80% 3.8
## 141 63.90% 3.1
## 142 17.90% 5.8
## 143 49.40% 6.1
## 144 81.90% 6.1
## 145 6.70% 27.0
## 146 86.70% 9.8
## 147 14.10% 14.9
## 148 23.70% 14.8
## 149 7.60% 13.6
## 150 42.50% 1.7
## 151 13.40% 24.4
## 152 68.00% 6.0
## 153 12.80% 31.8
## 154 67.20% 4.8
## 155 17.10% 12.4
## 156 2.00% 78.5
## 157 84.80% 2.3
## 158 46.60% 4.6
## 159 78.60% 1.7
## 160 17.1
## 161 2.50% 76.6
## 162 22.40% 28.5
## 163 94.30% 2.7
## 164 63.7
## 165 88.90% 2.5
## 166 19.60% 6.4
## 167 16.90% 42.1
## 168 12.60% 16.9
## 169 67.00% 2.2
## 170 59.60% 3.7
## 171 40.10% 14.0
## 172 31.30% 30.4
## 173 4.00% 37.6
## 174 49.30% 7.8
## 175 17.80% 39.3
## 176 14.50% 47.4
## 177 6.40% 13.4
## 178 12.00% 16.4
## 179 31.70% 14.6
## 180 23.90% 9.1
## 181 8.00% 39.3
## 182 20.6
## 183 4.80% 33.8
## 184 82.70% 7.5
## 185 36.80% 6.5
## 186 60.00% 3.6
## 187 88.20% 5.6
## 188 63.10% 6.4
## 189 10.10% 19.1
## 190 4.70% 22.3
## 191 79.30% 21.4
## 192 28.50% 16.5
## 193 10.20% 42.9
## 194 4.10% 40.4
## 195 10.00% 33.9
## Largest.city Life.expectancy Maternal.mortality.ratio
## 1 Kabul 64.5 638
## 2 Tirana 78.5 15
## 3 Algiers 76.7 112
## 4 Andorra la Vella NA NA
## 5 Luanda 60.8 241
## 6 St. John's, Saint John 76.9 42
## 7 Buenos Aires 76.5 39
## 8 Yerevan 74.9 26
## 9 Sydney 82.7 6
## 10 Vienna 81.6 5
## 11 Baku 72.9 26
## 12 Nassau, Bahamas 73.8 70
## 13 Riffa 77.2 14
## 14 Dhaka 72.3 173
## 15 Bridgetown 79.1 27
## 16 Minsk 74.2 2
## 17 Brussels 81.6 5
## 18 Belize City 74.5 36
## 19 Cotonou 61.5 397
## 20 Thimphu 71.5 183
## 21 Santa Cruz de la Sierra 71.2 155
## 22 Tuzla Canton 77.3 10
## 23 Gaborone 69.3 144
## 24 S���� 75.7 60
## 25 75.7 31
## 26 Sofia 74.9 10
## 27 Ouagadougou 61.2 320
## 28 Bujumbura 61.2 548
## 29 Abidjan 57.4 617
## 30 Praia 72.8 58
## 31 Phnom Penh 69.6 160
## 32 Douala 58.9 529
## 33 Toronto 81.9 10
## 34 Bangui 52.8 829
## 35 N'Djamena 54.0 1140
## 36 Santiago 80.0 13
## 37 Shanghai 77.0 29
## 38 Bogot� 77.1 83
## 39 Moroni, Comoros 64.1 273
## 40 Brazzaville 64.3 378
## 41 San Jos������ 80.1 27
## 42 Zagreb 78.1 8
## 43 Havana 78.7 36
## 44 Statos������� 80.8 6
## 45 Prague 79.0 3
## 46 Kinshasa 60.4 473
## 47 Copenhagen 81.0 4
## 48 Djibouti City 66.6 248
## 49 Roseau 76.6 NA
## 50 Santo Domingo 73.9 95
## 51 Quito 76.8 59
## 52 Cairo 71.8 37
## 53 San Salvador 73.1 46
## 54 Malabo 58.4 301
## 55 Asmara 65.9 480
## 56 Tallinn 78.2 9
## 57 Mbabane NA NA
## 58 Addis Ababa 66.2 401
## 59 Suva 67.3 34
## 60 Helsinki 81.7 3
## 61 Paris 82.5 8
## 62 Libreville 66.2 252
## 63 Serekunda 61.7 597
## 64 Tbilisi 73.6 25
## 65 Berlin 80.9 7
## 66 Accra 63.8 308
## 67 Macedonia 81.3 3
## 68 St. George's, Grenada 72.4 25
## 69 Guatemala City 74.1 95
## 70 Kankan 61.2 576
## 71 Bissau 58.0 667
## 72 Georgetown, Guyana 69.8 169
## 73 Port-au-Prince 63.7 480
## 74 NA NA
## 75 Tegucigalpa 75.1 65
## 76 Budapest 75.8 12
## 77 Reykjav�� 82.7 4
## 78 Kurebhar 69.4 145
## 79 Kalimantan 71.5 177
## 80 Tehran 76.5 16
## 81 Baghdad 70.5 79
## 82 Connacht 82.3 5
## 83 Jerusalem 82.8 3
## 84 Rome 82.9 2
## 85 Kingston, Jamaica 74.4 80
## 86 Tokyo 84.2 5
## 87 Amman 74.4 46
## 88 Almaty 73.2 10
## 89 Nairobi 66.3 342
## 90 South Tarawa 68.1 92
## 91 Kuwait City 75.4 12
## 92 Bishkek 71.4 60
## 93 Vientiane 67.6 185
## 94 Riga 74.7 19
## 95 Tripoli, Lebanon 78.9 29
## 96 Maseru 53.7 544
## 97 Monrovia 63.7 661
## 98 72.7 72
## 99 Schaan 83.0 NA
## 100 Vilnius 75.7 8
## 101 Luxembourg City 82.1 5
## 102 Antananarivo 66.7 335
## 103 Lilongwe 63.8 349
## 104 Johor Bahru 76.0 29
## 105 Mal� 78.6 53
## 106 Bamako 58.9 562
## 107 Birkirkara 82.3 6
## 108 Majuro 65.2 NA
## 109 Nouakchott 64.7 766
## 110 Port Louis 74.4 61
## 111 Mexico City 75.0 33
## 112 Palikir 67.8 88
## 113 Chi���� 71.8 19
## 114 Monaco City NA NA
## 115 Ulaanbaatar 69.7 45
## 116 Podgorica 76.8 6
## 117 Casablanca 76.5 70
## 118 Maputo 60.2 289
## 119 Yangon 66.9 250
## 120 Windhoek 63.4 195
## 121 NA NA
## 122 Kathmandu 70.5 186
## 123 Amsterdam 81.8 5
## 124 Auckland 81.9 9
## 125 Managua 74.3 98
## 126 Niamey 62.0 509
## 127 Lagos 54.3 917
## 128 Pyongyang 72.1 89
## 129 Skopje NA NA
## 130 Oslo 82.8 2
## 131 Seeb 77.6 19
## 132 Karachi 67.1 140
## 133 Koror 69.1 NA
## 134 NA NA
## 135 Panama City 78.3 52
## 136 Port Moresby 64.3 145
## 137 Ciudad del Este 74.1 129
## 138 Lima 76.5 88
## 139 Manila 71.1 121
## 140 Warsaw 77.6 2
## 141 Lisbon 81.3 8
## 142 Doha 80.1 9
## 143 Bucharest 75.4 19
## 144 Moscow 72.7 17
## 145 Kigali 68.7 248
## 146 Basseterre 71.3 NA
## 147 Castries 76.1 117
## 148 Calliaqua 72.4 68
## 149 Apia 73.2 43
## 150 City of San Marino 85.4 NA
## 151 S���� 70.2 130
## 152 Riyadh 75.0 17
## 153 Pikine 67.7 315
## 154 Belgrade 75.5 12
## 155 Victoria, Seychelles 72.8 53
## 156 Freetown 54.3 1120
## 157 83.1 8
## 158 Bratislava 77.2 5
## 159 Ljubljana 81.0 7
## 160 Honiara 72.8 104
## 161 Bosaso 57.1 829
## 162 Johannesburg 63.9 119
## 163 Seoul 82.6 11
## 164 Juba 57.6 1150
## 165 Madrid 83.3 4
## 166 Colombo 76.8 36
## 167 Omdurman 65.1 295
## 168 Paramaribo 71.6 120
## 169 S����� 82.5 4
## 170 Z��� 83.6 5
## 171 Damascus 71.8 31
## 172 Dushanbe 70.9 17
## 173 Dar es Salaam 65.0 524
## 174 Bangkok 76.9 37
## 175 Dili 69.3 142
## 176 Lom� 60.8 396
## 177 Nuku���� 70.8 52
## 178 Chaguanas 73.4 67
## 179 Tunis 76.5 43
## 180 Istanbul 77.4 17
## 181 Ashgabat 68.1 7
## 182 Singapore NA NA
## 183 Buganda 63.0 375
## 184 Kyiv 71.6 19
## 185 Dubai 77.8 3
## 186 London 81.3 7
## 187 New York City 78.5 19
## 188 Montevideo 77.8 17
## 189 Tashkent 71.6 29
## 190 Port Vila 70.3 72
## 191 Caracas 72.1 125
## 192 Ho Chi Minh City 75.3 43
## 193 Sanaa 66.1 164
## 194 Lusaka 63.5 213
## 195 Harare 61.2 458
## Minimum.wage Official.language Out.of.pocket.health.expenditure
## 1 $0.43 Pashto 78.40%
## 2 $1.12 Albanian 56.90%
## 3 $0.95 Arabic 28.10%
## 4 $6.63 Catalan 36.40%
## 5 $0.71 Portuguese 33.40%
## 6 $3.04 English 24.30%
## 7 $3.35 Spanish 17.60%
## 8 $0.66 Armenian 81.60%
## 9 $13.59 None 19.60%
## 10 German 17.90%
## 11 $0.47 Azerbaijani language 78.60%
## 12 $5.25 English 27.80%
## 13 Arabic 25.10%
## 14 $0.51 Bengali 71.80%
## 15 $3.13 English 45.20%
## 16 $1.49 Russian 34.50%
## 17 $10.31 French 17.60%
## 18 $1.65 English 22.70%
## 19 $0.39 French 40.50%
## 20 $0.32 Dzongkha 19.80%
## 21 $1.36 Spanish 25.90%
## 22 $1.04 Bosnian 28.60%
## 23 $0.29 English 5.30%
## 24 $1.53 Portuguese 28.30%
## 25 Malay 6.00%
## 26 $1.57 Bulgarian 47.70%
## 27 $0.34 French 36.10%
## 28 Kirundi 19.10%
## 29 $0.36 French 36.00%
## 30 $0.68 Portuguese 23.20%
## 31 Khmer language 59.40%
## 32 $0.35 French 69.70%
## 33 $9.51 French 14.60%
## 34 $0.37 French 39.60%
## 35 $0.60 French 56.40%
## 36 $2.00 Spanish 32.20%
## 37 $0.87 Standard Chinese 32.40%
## 38 $1.23 Spanish 18.30%
## 39 $0.71 French 74.80%
## 40 $0.88 French 43.80%
## 41 $1.84 Spanish 21.50%
## 42 $2.92 Croatian 15.20%
## 43 $0.05 Spanish
## 44 Greek 43.90%
## 45 $3.00 Czech 14.80%
## 46 $0.18 French 37.40%
## 47 Danish 13.70%
## 48 French 20.40%
## 49 $1.48 English 28.40%
## 50 $0.40 Spanish 43.70%
## 51 $2.46 Spanish 43.70%
## 52 Modern Standard Arabic 62.00%
## 53 $0.50 Spanish 27.90%
## 54 $1.05 Spanish 72.00%
## 55 Tigrinya 52.40%
## 56 $3.14 Estonian 22.80%
## 57 English 11.30%
## 58 Amharic 37.80%
## 59 $1.28 Fiji Hindi 21.40%
## 60 Swedish 19.90%
## 61 $11.16 French 6.80%
## 62 $1.46 French 25.90%
## 63 $0.13 English 20.30%
## 64 $0.05 Georgian 57.30%
## 65 $9.99 German 12.50%
## 66 $0.27 English 36.10%
## 67 $4.46 Greek 35.50%
## 68 English 57.00%
## 69 $1.60 Spanish 55.80%
## 70 French 54.50%
## 71 $0.16 Portuguese 37.20%
## 72 $0.98 English 40.50%
## 73 $0.25 French 36.30%
## 74 Italian
## 75 $1.01 Spanish 49.10%
## 76 $2.62 Hungarian 29.00%
## 77 Icelandic 17.00%
## 78 $0.30 Hindi 65.10%
## 79 $0.48 Indonesian 48.30%
## 80 $1.58 Persian 39.70%
## 81 $1.24 Arabic 76.50%
## 82 $10.79 Irish 15.20%
## 83 $7.58 Hebrew 24.40%
## 84 Italian 22.80%
## 85 $1.33 Jamaican English 23.70%
## 86 $6.77 None 13.10%
## 87 $1.49 Arabic 25.10%
## 88 $0.41 Russian 38.80%
## 89 $0.25 Swahili 33.40%
## 90 English 0.20%
## 91 $0.95 Modern Standard Arabic 14.40%
## 92 $0.09 Russian 48.20%
## 93 $0.83 Lao 45.40%
## 94 $2.80 Latvian 41.60%
## 95 $2.15 Arabic 32.10%
## 96 $0.41 English 16.90%
## 97 $0.17 English 19.60%
## 98 $1.88 Arabic 36.70%
## 99 German
## 100 $2.41 Lithuanian 32.10%
## 101 $13.05 Luxembourgish 10.60%
## 102 $0.21 French 21.70%
## 103 $0.12 English 11.00%
## 104 $0.93 Malaysian language 36.70%
## 105 Divehi 16.40%
## 106 $0.23 French 46.30%
## 107 $5.07 Maltese 37.10%
## 108 $2.00 Marshallese 10.00%
## 109 $0.53 Arabic 48.20%
## 110 $0.38 French 50.70%
## 111 $0.49 None 41.40%
## 112 English 2.50%
## 113 $0.31 Romanian 46.20%
## 114 $11.72 French 6.10%
## 115 $0.65 Mongolian 39.30%
## 116 $1.23 Montenegrin language 31.80%
## 117 $1.60 Arabic 53.10%
## 118 $0.27 Portuguese 6.80%
## 119 $0.39 Burmese 73.90%
## 120 English 8.30%
## 121 English
## 122 $0.36 Nepali 60.40%
## 123 $10.29 Dutch 12.30%
## 124 $11.49 English 12.60%
## 125 $0.54 Spanish 36.00%
## 126 $0.29 French 52.30%
## 127 $0.54 English 72.20%
## 128 Korean
## 129 Macedonian 35.60%
## 130 Norwegian 14.30%
## 131 $4.33 Arabic 6.40%
## 132 $0.69 Urdu 66.50%
## 133 $3.00 English 21.80%
## 134 Arabic
## 135 $1.53 Spanish 30.50%
## 136 $1.16 Tok Pisin 5.80%
## 137 $1.55 Spanish 36.50%
## 138 $1.28 Spanish 30.90%
## 139 $1.12 English 53.50%
## 140 $2.93 Polish 23.20%
## 141 $3.78 Portuguese 27.70%
## 142 Arabic 6.20%
## 143 $2.25 Romanian 21.30%
## 144 $0.53 Russian 36.40%
## 145 Swahili 26.00%
## 146 $3.33 English 56.60%
## 147 English 48.40%
## 148 $1.16 English 21.40%
## 149 $0.78 Samoan 11.50%
## 150 Italian 18.30%
## 151 11.70%
## 152 $3.85 Arabic 15.00%
## 153 $0.31 French 44.20%
## 154 $1.57 Serbian 40.60%
## 155 $2.00 French 2.50%
## 156 $0.57 English 38.20%
## 157 Malay 36.70%
## 158 $3.11 Slovak 18.40%
## 159 $5.25 Slovene language 12.50%
## 160 $0.40 English 3.30%
## 161 Arabic
## 162 Afrikaans 7.70%
## 163 $6.49 Korean 36.80%
## 164 English 61.30%
## 165 $5.60 Spanish 24.20%
## 166 $0.35 Tamil 38.40%
## 167 $0.41 Arabic 63.20%
## 168 Dutch 10.10%
## 169 Swedish 15.20%
## 170 German 28.30%
## 171 $1.02 Arabic 53.70%
## 172 $0.23 Persian 63.10%
## 173 $0.09 Swahili 26.10%
## 174 $1.06 Thai 11.80%
## 175 $0.60 Portuguese 10.20%
## 176 $0.34 French 51.00%
## 177 Tongan Language 10.20%
## 178 $2.25 English 37.30%
## 179 $0.47 Arabic 39.80%
## 180 $3.45 Turkish 16.90%
## 181 $0.88 Turkmen 71.10%
## 182 Tuvaluan Language 0.70%
## 183 $0.01 Swahili 40.50%
## 184 $0.84 Ukrainian 47.80%
## 185 Arabic 17.80%
## 186 $10.13 English 14.80%
## 187 $7.25 None 11.10%
## 188 $1.66 Spanish 16.20%
## 189 $0.24 Uzbek 42.70%
## 190 $1.56 French 8.90%
## 191 $0.01 Spanish 45.80%
## 192 $0.73 Vietnamese 43.50%
## 193 Arabic 81.00%
## 194 $0.24 English 27.50%
## 195 Shona 25.80%
## Physicians.per.thousand Population
## 1 0.28 38041754
## 2 1.20 2854191
## 3 1.72 43053054
## 4 3.33 77142
## 5 0.21 31825295
## 6 2.76 97118
## 7 3.96 44938712
## 8 4.40 2957731
## 9 3.68 25766605
## 10 5.17 8877067
## 11 3.45 10023318
## 12 1.94 389482
## 13 0.93 1501635
## 14 0.58 167310838
## 15 2.48 287025
## 16 5.19 9466856
## 17 3.07 11484055
## 18 1.12 390353
## 19 0.08 11801151
## 20 0.42 727145
## 21 1.59 11513100
## 22 2.16 3301000
## 23 0.37 2346179
## 24 2.15 212559417
## 25 1.61 433285
## 26 4.03 6975761
## 27 0.08 20321378
## 28 0.10 11530580
## 29 0.23 25716544
## 30 0.77 483628
## 31 0.17 16486542
## 32 0.09 25876380
## 33 2.61 36991981
## 34 0.06 4745185
## 35 0.04 15946876
## 36 2.59 18952038
## 37 1.98 1397715000
## 38 2.18 50339443
## 39 0.27 850886
## 40 0.12 5380508
## 41 2.89 5047561
## 42 3.00 4067500
## 43 8.42 11333483
## 44 1.95 1198575
## 45 4.12 10669709
## 46 0.07 86790567
## 47 4.01 5818553
## 48 0.22 973560
## 49 1.08 71808
## 50 1.56 10738958
## 51 2.04 17373662
## 52 0.45 100388073
## 53 1.57 6453553
## 54 0.40 1355986
## 55 0.06 6333135
## 56 4.48 1331824
## 57 NA 1093238
## 58 0.08 112078730
## 59 0.84 889953
## 60 3.81 5520314
## 61 3.27 67059887
## 62 0.68 2172579
## 63 0.10 2347706
## 64 7.12 3720382
## 65 4.25 83132799
## 66 0.14 30792608
## 67 5.48 10716322
## 68 1.41 112003
## 69 0.35 16604026
## 70 0.08 12771246
## 71 0.13 1920922
## 72 0.80 782766
## 73 0.23 11263077
## 74 NA 836
## 75 0.31 9746117
## 76 3.41 9769949
## 77 4.08 361313
## 78 0.86 1366417754
## 79 0.43 270203917
## 80 1.58 82913906
## 81 0.71 39309783
## 82 3.31 5007069
## 83 4.62 9053300
## 84 3.98 60297396
## 85 1.31 2948279
## 86 2.41 126226568
## 87 2.32 10101694
## 88 3.25 18513930
## 89 0.16 52573973
## 90 0.20 117606
## 91 2.58 4207083
## 92 1.88 6456900
## 93 0.37 7169455
## 94 3.19 1912789
## 95 2.10 6855713
## 96 0.07 2125268
## 97 0.04 4937374
## 98 2.09 6777452
## 99 NA 38019
## 100 6.35 2786844
## 101 3.01 645397
## 102 0.18 26969307
## 103 0.04 18628747
## 104 1.51 32447385
## 105 4.56 530953
## 106 0.13 19658031
## 107 2.86 502653
## 108 0.42 58791
## 109 0.19 4525696
## 110 2.53 1265711
## 111 2.38 126014024
## 112 0.18 113815
## 113 3.21 2657637
## 114 6.56 38964
## 115 2.86 3225167
## 116 2.76 622137
## 117 0.73 36910560
## 118 0.08 30366036
## 119 0.68 54045420
## 120 0.42 2494530
## 121 NA 10084
## 122 0.75 28608710
## 123 3.61 17332850
## 124 3.59 4841000
## 125 0.98 6545502
## 126 0.04 23310715
## 127 0.38 200963599
## 128 3.67 25666161
## 129 NA 1836713
## 130 2.92 5347896
## 131 2.00 5266535
## 132 0.98 216565318
## 133 1.18 18233
## 134 NA NA
## 135 1.57 4246439
## 136 0.07 8776109
## 137 1.35 7044636
## 138 1.27 32510453
## 139 0.60 108116615
## 140 2.38 37970874
## 141 5.12 10269417
## 142 2.49 2832067
## 143 2.98 19356544
## 144 4.01 144373535
## 145 0.13 12626950
## 146 2.52 52823
## 147 0.64 182790
## 148 0.66 100455
## 149 0.34 202506
## 150 6.11 33860
## 151 0.05 215056
## 152 2.61 34268528
## 153 0.07 16296364
## 154 3.11 6944975
## 155 0.95 97625
## 156 0.03 7813215
## 157 2.29 5703569
## 158 3.42 5454073
## 159 3.09 2087946
## 160 0.19 669823
## 161 0.02 15442905
## 162 0.91 58558270
## 163 2.36 51709098
## 164 NA 11062113
## 165 3.87 47076781
## 166 1.00 21803000
## 167 0.26 42813238
## 168 1.21 581372
## 169 3.98 10285453
## 170 4.30 8574832
## 171 1.22 17070135
## 172 1.70 9321018
## 173 0.01 58005463
## 174 0.81 69625582
## 175 0.72 3500000
## 176 0.08 8082366
## 177 0.52 100209
## 178 4.17 1394973
## 179 1.30 11694719
## 180 1.85 83429615
## 181 2.22 5942089
## 182 0.92 11646
## 183 0.17 44269594
## 184 2.99 44385155
## 185 2.53 9770529
## 186 2.81 66834405
## 187 2.61 328239523
## 188 5.05 3461734
## 189 2.37 33580650
## 190 0.17 299882
## 191 1.92 28515829
## 192 0.82 96462106
## 193 0.31 29161922
## 194 1.19 17861030
## 195 0.21 14645468
## Population..Labor.force.participation.... Tax.revenue.... Total.tax.rate
## 1 48.90% 9.30% 71.40%
## 2 55.70% 18.60% 36.60%
## 3 41.20% 37.20% 66.10%
## 4
## 5 77.50% 9.20% 49.10%
## 6 16.50% 43.00%
## 7 61.30% 10.10% 106.30%
## 8 55.60% 20.90% 22.60%
## 9 65.50% 23.00% 47.40%
## 10 60.70% 25.40% 51.40%
## 11 66.50% 13.00% 40.70%
## 12 74.60% 14.80% 33.80%
## 13 73.40% 4.20% 13.80%
## 14 59.00% 8.80% 33.40%
## 15 65.20% 27.50% 35.60%
## 16 64.10% 14.70% 53.30%
## 17 53.60% 24.00% 55.40%
## 18 65.10% 26.30% 31.10%
## 19 70.90% 10.80% 48.90%
## 20 66.70% 16.00% 35.30%
## 21 71.80% 17.00% 83.70%
## 22 46.40% 20.40% 23.70%
## 23 70.80% 19.50% 25.10%
## 24 63.90% 14.20% 65.10%
## 25 64.70% 8.00%
## 26 55.40% 20.20% 28.30%
## 27 66.40% 15.00% 41.30%
## 28 79.20% 13.60% 41.20%
## 29 57.00% 11.80% 50.10%
## 30 60.50% 20.10% 37.50%
## 31 82.30% 17.10% 23.10%
## 32 76.10% 12.80% 57.70%
## 33 65.10% 12.80% 24.50%
## 34 72.00% 8.60% 73.30%
## 35 70.70% 63.50%
## 36 62.60% 18.20% 34.00%
## 37 68.00% 9.40% 59.20%
## 38 68.80% 14.40% 71.20%
## 39 43.30% 219.60%
## 40 69.40% 9.00% 54.30%
## 41 62.10% 13.60% 58.30%
## 42 51.20% 22.00% 20.50%
## 43 53.60%
## 44 63.10% 24.50% 22.40%
## 45 60.60% 14.90% 46.10%
## 46 63.50% 10.70% 50.70%
## 47 62.20% 32.40% 23.80%
## 48 60.20% 37.90%
## 49 22.10% 32.60%
## 50 64.30% 13.00% 48.80%
## 51 68.00% 34.40%
## 52 46.40% 12.50% 44.40%
## 53 59.10% 18.10% 36.40%
## 54 62.00% 6.10% 79.40%
## 55 78.40% 83.70%
## 56 63.60% 20.90% 47.80%
## 57 28.60%
## 58 79.60% 7.50% 37.70%
## 59 57.60% 24.20% 32.10%
## 60 59.10% 20.80% 36.60%
## 61 55.10% 24.20% 60.70%
## 62 52.90% 10.20% 47.10%
## 63 59.40% 9.40% 48.40%
## 64 68.30% 21.70% 9.90%
## 65 60.80% 11.50% 48.80%
## 66 67.80% 12.60% 55.40%
## 67 51.80% 26.20% 51.90%
## 68 19.40% 47.80%
## 69 62.30% 10.60% 35.20%
## 70 61.50% 10.80% 69.30%
## 71 72.00% 10.30% 45.50%
## 72 56.20% 30.60%
## 73 67.20% 42.70%
## 74
## 75 68.80% 17.30% 39.10%
## 76 56.50% 23.00% 37.90%
## 77 75.00% 23.30% 31.90%
## 78 49.30% 11.20% 49.70%
## 79 67.50% 10.20% 30.10%
## 80 44.70% 7.40% 44.70%
## 81 43.00% 2.00% 30.80%
## 82 62.10% 18.30% 26.10%
## 83 64.00% 23.10% 25.30%
## 84 49.60% 24.30% 59.10%
## 85 66.00% 26.80% 35.10%
## 86 61.70% 11.90% 46.70%
## 87 39.30% 15.10% 28.60%
## 88 68.80% 11.70% 28.40%
## 89 74.70% 15.10% 37.20%
## 90 22.00% 32.70%
## 91 73.50% 1.40% 13.00%
## 92 59.80% 18.00% 29.00%
## 93 78.50% 12.90% 24.10%
## 94 61.40% 22.90% 38.10%
## 95 47.00% 15.30% 32.20%
## 96 67.90% 31.60% 13.60%
## 97 76.30% 12.90% 46.20%
## 98 49.70% 32.60%
## 99 21.60%
## 100 61.60% 16.90% 42.60%
## 101 59.30% 26.50% 20.40%
## 102 86.10% 10.20% 38.30%
## 103 76.70% 17.30% 34.50%
## 104 64.30% 12.00% 38.70%
## 105 69.80% 19.50% 30.20%
## 106 70.80% 11.60% 54.50%
## 107 56.50% 26.20% 44.00%
## 108 17.80% 65.90%
## 109 45.90% 67.00%
## 110 58.30% 19.10% 22.20%
## 111 60.70% 13.10% 55.10%
## 112 25.20% 60.50%
## 113 43.10% 17.70% 38.70%
## 114
## 115 59.70% 16.80% 25.70%
## 116 54.40% 22.20%
## 117 45.30% 21.90% 45.80%
## 118 78.10% 0.00% 36.10%
## 119 61.70% 5.40% 31.20%
## 120 59.50% 27.10% 20.70%
## 121
## 122 83.80% 20.70% 41.80%
## 123 63.60% 23.00% 41.20%
## 124 69.90% 29.00% 34.60%
## 125 66.40% 15.60% 60.60%
## 126 72.00% 11.80% 47.20%
## 127 52.90% 1.50% 34.80%
## 128 80.40%
## 129
## 130 63.80% 23.90% 36.20%
## 131 72.40% 2.50% 27.40%
## 132 52.60% 9.20% 33.90%
## 133 21.30% 76.60%
## 134
## 135 66.60% 37.20%
## 136 47.20% 13.60% 37.10%
## 137 72.10% 10.00% 35.00%
## 138 77.60% 14.30% 36.80%
## 139 59.60% 14.00% 43.10%
## 140 56.70% 17.40% 40.80%
## 141 58.80% 22.80% 39.80%
## 142 86.80% 14.70% 11.30%
## 143 54.70% 14.60% 20.00%
## 144 61.80% 11.40% 46.20%
## 145 83.70% 14.30% 33.20%
## 146 18.50% 49.70%
## 147 67.10% 18.20% 34.70%
## 148 65.90% 25.40% 37.00%
## 149 43.70% 25.50% 19.30%
## 150 18.10% 36.20%
## 151 57.80% 14.60% 37.00%
## 152 55.90% 8.90% 15.70%
## 153 45.70% 16.30% 44.80%
## 154 54.90% 18.60% 36.60%
## 155 34.10% 30.10%
## 156 57.90% 8.60% 30.70%
## 157 70.50% 13.10% 21.00%
## 158 59.50% 18.70% 49.70%
## 159 58.40% 18.60% 31.00%
## 160 83.80% 29.50% 32.00%
## 161 47.40% 0.00%
## 162 56.00% 27.50% 29.20%
## 163 63.00% 15.60% 33.20%
## 164 72.40% 31.40%
## 165 57.50% 14.20% 47.00%
## 166 53.90% 11.90% 55.20%
## 167 48.40% 8.00% 45.40%
## 168 51.10% 19.50% 27.90%
## 169 64.60% 27.90% 49.10%
## 170 68.30% 10.10% 28.80%
## 171 44.10% 14.20% 42.70%
## 172 42.00% 9.80% 67.30%
## 173 83.40% 11.50% 43.80%
## 174 67.30% 14.90% 29.50%
## 175 67.30% 25.00% 17.30%
## 176 77.60% 16.90% 48.20%
## 177 59.80% 22.30% 27.50%
## 178 60.00% 19.50% 40.50%
## 179 46.10% 21.10% 60.70%
## 180 52.80% 17.90% 42.30%
## 181 64.50%
## 182
## 183 70.30% 11.70% 33.70%
## 184 54.20% 20.10% 45.20%
## 185 82.10% 0.10% 15.90%
## 186 62.80% 25.50% 30.60%
## 187 62.00% 9.60% 36.60%
## 188 64.00% 20.10% 41.80%
## 189 65.10% 14.80% 31.60%
## 190 69.90% 17.80% 8.50%
## 191 59.70% 73.30%
## 192 77.40% 19.10% 37.60%
## 193 38.00% 26.60%
## 194 74.60% 16.20% 15.60%
## 195 83.10% 20.70% 31.60%
## Unemployment.rate Urban_population Latitude Longitude
## 1 11.12% 9797273 33.939110 67.709953
## 2 12.33% 1747593 41.153332 20.168331
## 3 11.70% 31510100 28.033886 1.659626
## 4 67873 42.506285 1.521801
## 5 6.89% 21061025 -11.202692 17.873887
## 6 23800 17.060816 -61.796428
## 7 9.79% 41339571 -38.416097 -63.616672
## 8 16.99% 1869848 40.069099 45.038189
## 9 5.27% 21844756 -25.274398 133.775136
## 10 4.67% 5194416 47.516231 14.550072
## 11 5.51% 5616165 40.143105 47.576927
## 12 10.36% 323784 25.034280 -77.396280
## 13 0.71% 1467109 26.066700 50.557700
## 14 4.19% 60987417 23.684994 90.356331
## 15 10.33% 89431 13.193887 -59.543198
## 16 4.59% 7482982 53.709807 27.953389
## 17 5.59% 11259082 50.503887 4.469936
## 18 6.41% 179039 17.189877 -88.497650
## 19 2.23% 5648149 9.307690 2.315834
## 20 2.34% 317538 27.514162 90.433601
## 21 3.50% 8033035 -16.290154 -63.588653
## 22 18.42% 1605144 43.915886 17.679076
## 23 18.19% 1616550 -22.328474 24.684866
## 24 12.08% 183241641 -14.235004 -51.925280
## 25 9.12% 337711 4.535277 114.727669
## 26 4.34% 5256027 42.733883 25.485830
## 27 6.26% 6092349 12.238333 -1.561593
## 28 1.43% 1541177 -3.373056 29.918886
## 29 3.32% 13176900 7.539989 -5.547080
## 30 12.25% 364029 16.538800 -23.041800
## 31 0.68% 3924621 12.565679 104.990963
## 32 3.38% 14741256 7.369722 12.354722
## 33 5.56% 30628482 56.130366 -106.346771
## 34 3.68% 1982064 6.611111 20.939444
## 35 1.89% 3712273 15.454166 18.732207
## 36 7.09% 16610135 -35.675147 -71.542969
## 37 4.32% 842933962 35.861660 104.195397
## 38 9.71% 40827302 4.570868 -74.297333
## 39 4.34% 248152 -11.645500 43.333300
## 40 9.47% 3625010 -0.228021 15.827659
## 41 11.85% 4041885 9.748917 -83.753428
## 42 6.93% 2328318 45.100000 15.200000
## 43 1.64% 8739135 21.521757 -77.781167
## 44 7.27% 800708 35.126413 33.429859
## 45 1.93% 7887156 49.817492 15.472962
## 46 4.24% 39095679 -4.038333 21.758664
## 47 4.91% 5119978 56.263920 9.501785
## 48 10.30% 758549 11.825138 42.590275
## 49 50830 15.414999 -61.370976
## 50 5.84% 8787475 18.735693 -70.162651
## 51 3.97% 11116711 -1.831239 -78.183406
## 52 10.76% 42895824 26.820553 30.802498
## 53 4.11% 4694702 13.794185 -88.896530
## 54 6.43% 984812 1.650801 10.267895
## 55 5.14% 1149670 15.179384 39.782334
## 56 5.11% 916024 58.595272 25.013607
## 57 NA -26.522503 31.465866
## 58 2.08% 23788710 9.145000 40.489673
## 59 4.10% 505048 -17.713371 178.065032
## 60 6.59% 4716888 61.924110 25.748151
## 61 8.43% 54123364 46.227638 2.213749
## 62 20.00% 1949694 -0.803689 11.609444
## 63 9.06% 1453958 13.443182 -15.310139
## 64 14.40% 2196476 42.315407 43.356892
## 65 3.04% 64324835 51.165691 10.451526
## 66 4.33% 17249054 7.946527 -1.023194
## 67 17.24% 8507474 39.074208 21.824312
## 68 40765 12.116500 -61.679000
## 69 2.46% 8540945 15.783471 -90.230759
## 70 4.30% 4661505 9.945587 -9.696645
## 71 2.47% 840922 11.803749 -15.180413
## 72 11.85% 208912 4.860416 -58.930180
## 73 13.78% 6328948 18.971187 -72.285215
## 74 NA 41.902916 12.453389
## 75 5.39% 5626433 15.199999 -86.241905
## 76 3.40% 6999582 47.162494 19.503304
## 77 2.84% 339110 64.963051 -19.020835
## 78 5.36% 471031528 20.593684 78.962880
## 79 4.69% 151509724 -0.789275 113.921327
## 80 11.38% 62509623 32.427908 53.688046
## 81 12.82% 27783368 33.223191 43.679291
## 82 4.93% 3133123 53.412910 -8.243890
## 83 3.86% 8374393 31.046051 34.851612
## 84 9.89% 42651966 41.871940 12.567380
## 85 8.00% 1650594 18.109581 -77.297508
## 86 2.29% 115782416 36.204824 138.252924
## 87 14.72% 9213048 30.585164 36.238414
## 88 4.59% 10652915 48.019573 66.923684
## 89 2.64% 14461523 -0.023559 37.906193
## 90 64489 1.836898 -157.376832
## 91 2.18% 4207083 29.311660 47.481766
## 92 6.33% 2362644 41.204380 74.766098
## 93 0.63% 2555552 19.856270 102.495496
## 94 6.52% 1304943 56.879635 24.603189
## 95 6.23% 6084994 33.854721 35.862285
## 96 23.41% 607508 -29.609988 28.233608
## 97 2.81% 2548426 6.428055 -9.429499
## 98 18.56% 5448597 26.335100 17.228331
## 99 5464 47.141039 9.520935
## 100 6.35% 1891013 55.169438 23.881275
## 101 5.36% 565488 49.815273 6.129583
## 102 1.76% 10210849 -18.766947 46.869107
## 103 5.65% 3199301 -13.254308 34.301525
## 104 3.32% 24475766 4.210484 101.975766
## 105 6.14% 213645 3.202778 73.220680
## 106 7.22% 8479688 17.570692 -3.996166
## 107 3.47% 475902 35.937496 14.375416
## 108 45514 7.131474 171.184478
## 109 9.55% 2466821 21.007890 -10.940835
## 110 6.67% 515980 -20.348404 57.552152
## 111 3.42% 102626859 23.634501 -102.552784
## 112 25963 7.425554 150.550812
## 113 5.47% 1135502 47.411631 28.369885
## 114 38964 43.738418 7.424616
## 115 6.01% 2210626 46.862496 103.846656
## 116 14.88% 417765 42.708678 19.374390
## 117 9.02% 22975026 31.791702 -7.092620
## 118 3.24% 11092106 -18.665695 35.529562
## 119 1.58% 16674093 21.916221 95.955974
## 120 20.27% 1273258 -22.957640 18.490410
## 121 NA -0.522778 166.931503
## 122 1.41% 5765513 28.394857 84.124008
## 123 3.20% 15924729 52.132633 5.291266
## 124 4.07% 4258860 -40.900557 174.885971
## 125 6.84% 3846137 12.865416 -85.207229
## 126 0.47% 3850231 17.607789 8.081666
## 127 8.10% 102806948 9.081999 8.675277
## 128 2.74% 15947412 40.339852 127.510093
## 129 NA 41.608635 21.745275
## 130 3.35% 4418218 60.472024 8.468946
## 131 2.67% 4250777 21.473533 55.975413
## 132 4.45% 79927762 30.375321 69.345116
## 133 14491 7.514980 134.582520
## 134 NA 31.952162 35.233154
## 135 3.90% 2890084 8.537981 -80.782127
## 136 2.46% 1162834 -6.314993 143.955550
## 137 4.81% 4359150 -23.442503 -58.443832
## 138 3.31% 25390339 -9.189967 -75.015152
## 139 2.15% 50975903 12.879721 121.774017
## 140 3.47% 22796574 51.919438 19.145136
## 141 6.33% 6753579 39.399872 -8.224454
## 142 0.09% 2809071 25.354826 51.183884
## 143 3.98% 10468793 45.943161 24.966760
## 144 4.59% 107683889 61.524010 105.318756
## 145 1.03% 2186104 -1.940278 29.873888
## 146 16269 17.357822 -62.782998
## 147 20.71% 34280 13.909444 -60.978893
## 148 18.88% 58185 12.984305 -61.287228
## 149 8.36% 35588 -13.759029 -172.104629
## 150 32969 43.942360 12.457777
## 151 13.37% 158277 NA NA
## 152 5.93% 28807838 23.885942 45.079162
## 153 6.60% 7765706 14.497401 -14.452362
## 154 12.69% 3907243 44.016521 21.005859
## 155 55762 -4.679574 55.491977
## 156 4.43% 3319366 8.460555 -11.779889
## 157 4.11% 5703569 1.352083 103.819836
## 158 5.56% 2930419 48.669026 19.699024
## 159 4.20% 1144654 46.151241 14.995463
## 160 0.58% 162164 -9.645710 160.156194
## 161 11.35% 7034861 5.152149 46.199616
## 162 28.18% 39149717 -30.559482 22.937506
## 163 4.15% 42106719 35.907757 127.766922
## 164 12.24% 2201250 6.876992 31.306979
## 165 13.96% 37927409 40.463667 -3.749220
## 166 4.20% 4052088 7.873054 80.771797
## 167 16.53% 14957233 12.862807 30.217636
## 168 7.33% 384258 3.919305 -56.027783
## 169 6.48% 9021165 60.128161 18.643501
## 170 4.58% 6332428 46.818188 8.227512
## 171 8.37% 9358019 34.802075 38.996815
## 172 11.02% 2545477 38.861034 71.276093
## 173 1.98% 20011885 -6.369028 34.888822
## 174 0.75% 35294600 15.870032 100.992541
## 175 4.55% 400182 -8.874217 125.727539
## 176 2.04% 3414638 8.619543 0.824782
## 177 1.12% 24145 -21.178986 -175.198242
## 178 2.69% 741944 10.691803 -61.222503
## 179 16.02% 8099061 33.886917 9.537499
## 180 13.49% 63097818 38.963745 35.243322
## 181 3.91% 3092738 38.969719 59.556278
## 182 7362 -7.109535 177.649330
## 183 1.84% 10784516 1.373333 32.290275
## 184 8.88% 30835699 48.379433 31.165580
## 185 2.35% 8479744 23.424076 53.847818
## 186 3.85% 55908316 55.378051 -3.435973
## 187 14.70% 270663028 37.090240 -95.712891
## 188 8.73% 3303394 -32.522779 -55.765835
## 189 5.92% 16935729 41.377491 64.585262
## 190 4.39% 76152 -15.376706 166.959158
## 191 8.80% 25162368 6.423750 -66.589730
## 192 2.01% 35332140 14.058324 108.277199
## 193 12.91% 10869523 15.552727 48.516388
## 194 11.43% 7871713 -13.133897 27.849332
## 195 4.95% 4717305 -19.015438 29.154857fungsi str_to_upper berasal dari
package stringr yang berguna untuk konversi semua abjad
menjadi kapital.
Agregasi data dengan group_by dan
summarize
Fungsi summerize digunakan untuk merangkum banyak baris
(amatan) menjadi satu baris, rangkuman ini bisa berupa
mean,
median,variance,sd(standar
deviasi). Berikut illustrasinya
country_data_cleanup %>%
summarize(mean_populasi = mean(Population,na.rm = TRUE),
median_urban_populasi = median(Urban_population,na.rm = TRUE),
sd_populasi = sd(Population,na.rm=TRUE),
q1_urban_populasi = quantile(Urban_population,probs = 0.25,na.rm = TRUE)
)## mean_populasi median_urban_populasi sd_populasi q1_urban_populasi
## 1 39381164 4678104 145092392 1152961Kemudian kita bisa menghitung mean dari setiap kolom
numeric dengan memanfaatkan fungsi across,
dan where. Dalam konteks ini,
Fungsi across digunakan untuk menerapkan
perhitungan mean setiap kolom numeric. Sementara itu,
fungsi where digunakan untuk memastikan bahwa kolom yang
kita terapkan perhitungan sesuai dengan kondisi yang kita inginkan.
country_data_cleanup %>%
summarize( across(where(is.numeric),mean) )## Density..P.Km2. Land.Area.Km2. Armed.Forces.size Birth.Rate Calling.Code
## 1 356.7641 NA NA NA NA
## Co2.Emissions CPI Fertility.Rate Infant.mortality Life.expectancy
## 1 NA NA NA NA NA
## Maternal.mortality.ratio Physicians.per.thousand Population Urban_population
## 1 NA NA NA NA
## Latitude Longitude
## 1 NA NAjika kita ingin mengexclude NA dari perhitungan, perlu
membuat fungsi anonim terlebih dahulu
country_data_cleanup %>%
summarize(across(where(is.numeric),function(x) mean(x,na.rm = TRUE) ))## Density..P.Km2. Land.Area.Km2. Armed.Forces.size Birth.Rate Calling.Code
## 1 356.7641 689624.4 159274.9 20.21497 360.5464
## Co2.Emissions CPI Fertility.Rate Infant.mortality Life.expectancy
## 1 177799.2 190.461 2.698138 21.3328 72.27968
## Maternal.mortality.ratio Physicians.per.thousand Population Urban_population
## 1 160.3923 1.83984 39381164 22304543
## Latitude Longitude
## 1 19.09235 20.23243Selanjutnya, jika kita ingin menambah nama kolom dengan
kata mean, kita bisa memanfaatkan
fungsi rename_with. Penambahan nama kolom ini dapat
menggunakan fungsi str_c
country_data_cleanup %>%
summarize(across(where(is.numeric),function(x) mean(x,na.rm = TRUE) )) %>%
rename_with(.fn = function(x) str_c("mean_",x),.cols = everything())## mean_Density..P.Km2. mean_Land.Area.Km2. mean_Armed.Forces.size
## 1 356.7641 689624.4 159274.9
## mean_Birth.Rate mean_Calling.Code mean_Co2.Emissions mean_CPI
## 1 20.21497 360.5464 177799.2 190.461
## mean_Fertility.Rate mean_Infant.mortality mean_Life.expectancy
## 1 2.698138 21.3328 72.27968
## mean_Maternal.mortality.ratio mean_Physicians.per.thousand mean_Population
## 1 160.3923 1.83984 39381164
## mean_Urban_population mean_Latitude mean_Longitude
## 1 22304543 19.09235 20.23243Fungsi group_by digunakan untuk melakukan manipulasi
atau perhitungan pada dataset berdasarkan grup atau kelompok tertentu.
Grup atau kelompok yang dimaksud biasanya berupa kategori-kategori yang
tersimpan dalam satu kolom. Dalam
penggunaannya, group_by ini dipasangkan dengan
fungsi summarize. Berikut adalah ilustrasinya
country_data_cleanup %>%
group_by(`Official.language`) %>%
summarize(n())## # A tibble: 78 x 2
## Official.language `n()`
## <chr> <int>
## 1 "" 1
## 2 "Afrikaans" 1
## 3 "Albanian" 1
## 4 "Amharic" 1
## 5 "Arabic" 18
## 6 "Armenian" 1
## 7 "Azerbaijani language" 1
## 8 "Bengali" 1
## 9 "Bosnian" 1
## 10 "Bulgarian" 1
## # i 68 more rowsFungsi n() digunakan untuk menghitung frequensi dari
suatu nilai atau kategori.
Sintaks diatas bisa ditulis dengan bentuk lain seperti dibawah ini
country_data_cleanup %>%
count(`Official.language`)## Official.language n
## 1 1
## 2 Afrikaans 1
## 3 Albanian 1
## 4 Amharic 1
## 5 Arabic 18
## 6 Armenian 1
## 7 Azerbaijani language 1
## 8 Bengali 1
## 9 Bosnian 1
## 10 Bulgarian 1
## 11 Burmese 1
## 12 Catalan 1
## 13 Croatian 1
## 14 Czech 1
## 15 Danish 1
## 16 Divehi 1
## 17 Dutch 2
## 18 Dzongkha 1
## 19 English 31
## 20 Estonian 1
## 21 Fiji Hindi 1
## 22 French 25
## 23 Georgian 1
## 24 German 4
## 25 Greek 2
## 26 Hebrew 1
## 27 Hindi 1
## 28 Hungarian 1
## 29 Icelandic 1
## 30 Indonesian 1
## 31 Irish 1
## 32 Italian 3
## 33 Jamaican English 1
## 34 Khmer language 1
## 35 Kirundi 1
## 36 Korean 2
## 37 Lao 1
## 38 Latvian 1
## 39 Lithuanian 1
## 40 Luxembourgish 1
## 41 Macedonian 1
## 42 Malay 2
## 43 Malaysian language 1
## 44 Maltese 1
## 45 Marshallese 1
## 46 Modern Standard Arabic 2
## 47 Mongolian 1
## 48 Montenegrin language 1
## 49 Nepali 1
## 50 None 4
## 51 Norwegian 1
## 52 Pashto 1
## 53 Persian 2
## 54 Polish 1
## 55 Portuguese 7
## 56 Romanian 2
## 57 Russian 4
## 58 Samoan 1
## 59 Serbian 1
## 60 Shona 1
## 61 Slovak 1
## 62 Slovene language 1
## 63 Spanish 19
## 64 Standard Chinese 1
## 65 Swahili 4
## 66 Swedish 2
## 67 Tamil 1
## 68 Thai 1
## 69 Tigrinya 1
## 70 Tok Pisin 1
## 71 Tongan Language 1
## 72 Turkish 1
## 73 Turkmen 1
## 74 Tuvaluan Language 1
## 75 Ukrainian 1
## 76 Urdu 1
## 77 Uzbek 1
## 78 Vietnamese 1country_data_cleanup %>%
mutate(pop_status = case_when(Population < 15000000 ~ "small",
Population >= 15000000 & Population < 39381164 ~ "medium",
Population >= 39381164 ~ "large"
)) %>%
count(pop_status,`Official.language`)## pop_status Official.language n
## 1 large Afrikaans 1
## 2 large Amharic 1
## 3 large Arabic 2
## 4 large Bengali 1
## 5 large Burmese 1
## 6 large English 3
## 7 large French 2
## 8 large German 1
## 9 large Hindi 1
## 10 large Indonesian 1
## 11 large Italian 1
## 12 large Korean 1
## 13 large Modern Standard Arabic 1
## 14 large None 3
## 15 large Persian 1
## 16 large Portuguese 1
## 17 large Russian 1
## 18 large Spanish 3
## 19 large Standard Chinese 1
## 20 large Swahili 3
## 21 large Thai 1
## 22 large Turkish 1
## 23 large Ukrainian 1
## 24 large Urdu 1
## 25 large Vietnamese 1
## 26 medium Arabic 6
## 27 medium Dutch 1
## 28 medium English 3
## 29 medium French 9
## 30 medium Khmer language 1
## 31 medium Korean 1
## 32 medium Malaysian language 1
## 33 medium Nepali 1
## 34 medium None 1
## 35 medium Pashto 1
## 36 medium Polish 1
## 37 medium Portuguese 2
## 38 medium Romanian 1
## 39 medium Russian 1
## 40 medium Spanish 5
## 41 medium Tamil 1
## 42 medium Uzbek 1
## 43 small 1
## 44 small Albanian 1
## 45 small Arabic 9
## 46 small Armenian 1
## 47 small Azerbaijani language 1
## 48 small Bosnian 1
## 49 small Bulgarian 1
## 50 small Catalan 1
## 51 small Croatian 1
## 52 small Czech 1
## 53 small Danish 1
## 54 small Divehi 1
## 55 small Dutch 1
## 56 small Dzongkha 1
## 57 small English 25
## 58 small Estonian 1
## 59 small Fiji Hindi 1
## 60 small French 14
## 61 small Georgian 1
## 62 small German 3
## 63 small Greek 2
## 64 small Hebrew 1
## 65 small Hungarian 1
## 66 small Icelandic 1
## 67 small Irish 1
## 68 small Italian 2
## 69 small Jamaican English 1
## 70 small Kirundi 1
## 71 small Lao 1
## 72 small Latvian 1
## 73 small Lithuanian 1
## 74 small Luxembourgish 1
## 75 small Macedonian 1
## 76 small Malay 2
## 77 small Maltese 1
## 78 small Marshallese 1
## 79 small Modern Standard Arabic 1
## 80 small Mongolian 1
## 81 small Montenegrin language 1
## 82 small Norwegian 1
## 83 small Persian 1
## 84 small Portuguese 4
## 85 small Romanian 1
## 86 small Russian 2
## 87 small Samoan 1
## 88 small Serbian 1
## 89 small Shona 1
## 90 small Slovak 1
## 91 small Slovene language 1
## 92 small Spanish 11
## 93 small Swahili 1
## 94 small Swedish 2
## 95 small Tigrinya 1
## 96 small Tok Pisin 1
## 97 small Tongan Language 1
## 98 small Turkmen 1
## 99 small Tuvaluan Language 1
## 100 <NA> Arabic 1Fungsi pivot_longer dan
pivot_wider
Fungsi pivot_longer digunakan untuk mentransformasi
dataset yang berbentuk wide ke dataset yang berbentuk long. Sebaliknya
pivot_wider digunakan untuk mentransformasi dataset yang
berbentuk long ke dataset yang berbentuk wide. Kedua fungsi ini berasal
dari package tidyr.
## bentuk wide
country_data_cleanup %>%
summarize(across(where(is.numeric),function(x) mean(x,na.rm = TRUE) )) %>%
select(1:10)## Density..P.Km2. Land.Area.Km2. Armed.Forces.size Birth.Rate Calling.Code
## 1 356.7641 689624.4 159274.9 20.21497 360.5464
## Co2.Emissions CPI Fertility.Rate Infant.mortality Life.expectancy
## 1 177799.2 190.461 2.698138 21.3328 72.27968country_data_cleanup %>%
summarize(across(where(is.numeric),function(x) mean(x,na.rm = TRUE) )) %>%
select(1:10) %>%
pivot_longer(cols = everything(),
names_to = "Variable",
values_to = "mean"
)## # A tibble: 10 x 2
## Variable mean
## <chr> <dbl>
## 1 Density..P.Km2. 357.
## 2 Land.Area.Km2. 689624.
## 3 Armed.Forces.size 159275.
## 4 Birth.Rate 20.2
## 5 Calling.Code 361.
## 6 Co2.Emissions 177799.
## 7 CPI 190.
## 8 Fertility.Rate 2.70
## 9 Infant.mortality 21.3
## 10 Life.expectancy 72.3Argument names_to berguna untuk membuat kolom untuk
menaruh nama-nama kolom sebelum transformasi,
sementara values_to berguna untuk membuat kolom untuk
menaruh nilai-nilai data.
# bentuk long
country_data_cleanup %>%
mutate(pop_status = case_when(Population < 15000000 ~ "small",
Population >= 15000000 & Population < 39381164 ~ "medium",
Population >= 39381164 ~ "large"
)) %>%
count(pop_status,`Official.language`)## pop_status Official.language n
## 1 large Afrikaans 1
## 2 large Amharic 1
## 3 large Arabic 2
## 4 large Bengali 1
## 5 large Burmese 1
## 6 large English 3
## 7 large French 2
## 8 large German 1
## 9 large Hindi 1
## 10 large Indonesian 1
## 11 large Italian 1
## 12 large Korean 1
## 13 large Modern Standard Arabic 1
## 14 large None 3
## 15 large Persian 1
## 16 large Portuguese 1
## 17 large Russian 1
## 18 large Spanish 3
## 19 large Standard Chinese 1
## 20 large Swahili 3
## 21 large Thai 1
## 22 large Turkish 1
## 23 large Ukrainian 1
## 24 large Urdu 1
## 25 large Vietnamese 1
## 26 medium Arabic 6
## 27 medium Dutch 1
## 28 medium English 3
## 29 medium French 9
## 30 medium Khmer language 1
## 31 medium Korean 1
## 32 medium Malaysian language 1
## 33 medium Nepali 1
## 34 medium None 1
## 35 medium Pashto 1
## 36 medium Polish 1
## 37 medium Portuguese 2
## 38 medium Romanian 1
## 39 medium Russian 1
## 40 medium Spanish 5
## 41 medium Tamil 1
## 42 medium Uzbek 1
## 43 small 1
## 44 small Albanian 1
## 45 small Arabic 9
## 46 small Armenian 1
## 47 small Azerbaijani language 1
## 48 small Bosnian 1
## 49 small Bulgarian 1
## 50 small Catalan 1
## 51 small Croatian 1
## 52 small Czech 1
## 53 small Danish 1
## 54 small Divehi 1
## 55 small Dutch 1
## 56 small Dzongkha 1
## 57 small English 25
## 58 small Estonian 1
## 59 small Fiji Hindi 1
## 60 small French 14
## 61 small Georgian 1
## 62 small German 3
## 63 small Greek 2
## 64 small Hebrew 1
## 65 small Hungarian 1
## 66 small Icelandic 1
## 67 small Irish 1
## 68 small Italian 2
## 69 small Jamaican English 1
## 70 small Kirundi 1
## 71 small Lao 1
## 72 small Latvian 1
## 73 small Lithuanian 1
## 74 small Luxembourgish 1
## 75 small Macedonian 1
## 76 small Malay 2
## 77 small Maltese 1
## 78 small Marshallese 1
## 79 small Modern Standard Arabic 1
## 80 small Mongolian 1
## 81 small Montenegrin language 1
## 82 small Norwegian 1
## 83 small Persian 1
## 84 small Portuguese 4
## 85 small Romanian 1
## 86 small Russian 2
## 87 small Samoan 1
## 88 small Serbian 1
## 89 small Shona 1
## 90 small Slovak 1
## 91 small Slovene language 1
## 92 small Spanish 11
## 93 small Swahili 1
## 94 small Swedish 2
## 95 small Tigrinya 1
## 96 small Tok Pisin 1
## 97 small Tongan Language 1
## 98 small Turkmen 1
## 99 small Tuvaluan Language 1
## 100 <NA> Arabic 1country_data_cleanup %>%
mutate(pop_status = case_when(Population < 15000000 ~ "small",
Population >= 15000000 & Population < 39381164 ~ "medium",
Population >= 39381164 ~ "large"
)) %>%
count(pop_status,`Official.language`) %>%
pivot_wider(id_cols = `Official.language`,
names_from = pop_status,
values_from = n
)## # A tibble: 78 x 5
## Official.language large medium small `NA`
## <chr> <int> <int> <int> <int>
## 1 Afrikaans 1 NA NA NA
## 2 Amharic 1 NA NA NA
## 3 Arabic 2 6 9 1
## 4 Bengali 1 NA NA NA
## 5 Burmese 1 NA NA NA
## 6 English 3 3 25 NA
## 7 French 2 9 14 NA
## 8 German 1 NA 3 NA
## 9 Hindi 1 NA NA NA
## 10 Indonesian 1 NA NA NA
## # i 68 more rowsArgument names_from berguna untuk membuat kolom-kolom
berdasarkan satu kolom sebelum transformasi,
sementara values_to berguna untuk membuat menaruh
nilai-nilai data. Argument id_cols berguna untuk
medefinisikan kolom-kolom yang kita anggap sebagai id.
Menyimpan hasil manipulasi data dalam objek R
Ilustrasi-ilustrasi sebelumnya tidak menyimpan data hasil manipulasi kita dalam objek R sehingga tidak bisa digunakan secara berulang.
# tidak disimpan
country_data_cleanup %>%
mutate(pop_status = case_when(Population < 15000000 ~ "small",
Population >= 15000000 & Population < 39381164 ~ "medium",
Population >= 39381164 ~ "large"
)) %>%
count(pop_status,`Official.language`) %>%
pivot_wider(id_cols = `Official.language`,
names_from = pop_status,
values_from = n
)## # A tibble: 78 x 5
## Official.language large medium small `NA`
## <chr> <int> <int> <int> <int>
## 1 Afrikaans 1 NA NA NA
## 2 Amharic 1 NA NA NA
## 3 Arabic 2 6 9 1
## 4 Bengali 1 NA NA NA
## 5 Burmese 1 NA NA NA
## 6 English 3 3 25 NA
## 7 French 2 9 14 NA
## 8 German 1 NA 3 NA
## 9 Hindi 1 NA NA NA
## 10 Indonesian 1 NA NA NA
## # i 68 more rowsBerikut ilustrasi menyimpan data hasil manipulasi ke objek R dengan
nama tabel_baru
tabel_baru <- country_data_cleanup %>%
mutate(pop_status = case_when(Population < 15000000 ~ "small",
Population >= 15000000 & Population < 39381164 ~ "medium",
Population >= 39381164 ~ "large"
)) %>%
count(pop_status,`Official.language`) %>%
pivot_wider(id_cols = `Official.language`,
names_from = pop_status,
values_from = n
)
tabel_baru## # A tibble: 78 x 5
## Official.language large medium small `NA`
## <chr> <int> <int> <int> <int>
## 1 Afrikaans 1 NA NA NA
## 2 Amharic 1 NA NA NA
## 3 Arabic 2 6 9 1
## 4 Bengali 1 NA NA NA
## 5 Burmese 1 NA NA NA
## 6 English 3 3 25 NA
## 7 French 2 9 14 NA
## 8 German 1 NA 3 NA
## 9 Hindi 1 NA NA NA
## 10 Indonesian 1 NA NA NA
## # i 68 more rows