Srishti Puri
2023-06-20
Our target COVID-19 wiki page URL is: https://en.wikipedia.org/w/index.php?title=Template:COVID-19_testing_by_country
Define function called “get_wiki_covid19_page” to get the COVID-19 pandemic Wiki page.
# Define a function
get_wiki_covid19_page <- function() {
# Set Wiki page base
wiki_base_url <- "https://en.wikipedia.org/w/index.php"
# Create a List which has an element called `title` to specify the page
wiki_title_list <- list(title = 'Template:COVID-19_testing_by_country')
# Use the `GET` function to get a HTTP response
wiki_response <- GET(wiki_base_url , query = wiki_title_list)
# Use the `return` function to return the response
return(wiki_response)
}## Response [https://en.wikipedia.org/w/index.php?title=Template%3ACOVID-19_testing_by_country]
## Date: 2023-06-21 23:20
## Status: 200
## Content-Type: text/html; charset=UTF-8
## Size: 433 kB
## <!DOCTYPE html>
## <html class="client-nojs vector-feature-language-in-header-enabled vector-fea...
## <head>
## <meta charset="UTF-8">
## <title>Template:COVID-19 testing by country - Wikipedia</title>
## <script>document.documentElement.className="client-js vector-feature-language...
## "wgCanonicalNamespace":"Template","wgCanonicalSpecialPageName":false,"wgNames...
## "CS1 Khmer-language sources (km)","CS1 uses Chinese-language script (zh)","CS...
## "CS1 foreign language sources (ISO 639-2)","CS1 Burmese-language sources (my)...
## },"wgVisualEditor":{"pageLanguageCode":"en","pageLanguageDir":"ltr","pageVari...
## ...Extract all the “tables” in the HTML document.
Select the specific table to work with. In this case, we choose the table at index 2 from the list.
Convert the selected table into a data frame. The extracted data frame has 173 rows and 9 columns.
# Get the root html node from the http response
wiki_covid19_response <- read_html(wiki_covid19_page_response)
# Get the table node from the root html node
wiki_covid19_table_nodes <- html_nodes(wiki_covid19_response, "table")
# Read the specific table from the multiple tables in the table_node using the html_table function
wiki_covid19_table_index <- 2
wiki_covid19_specific_table <- html_table(wiki_covid19_table_nodes[[wiki_covid19_table_index]])
# Convert the table into a dataframe
wiki_covid19_df <- as.data.frame(wiki_covid19_specific_table)
head(wiki_covid19_df)## Country or region Date[a] Tested Units[b] Confirmed(cases)
## 1 Afghanistan 17 Dec 2020 154,767 samples 49,621
## 2 Albania 18 Feb 2021 428,654 samples 96,838
## 3 Algeria 2 Nov 2020 230,553 samples 58,574
## 4 Andorra 23 Feb 2022 300,307 samples 37,958
## 5 Angola 2 Feb 2021 399,228 samples 20,981
## 6 Antigua and Barbuda 6 Mar 2021 15,268 samples 832
## Confirmed /tested,% Tested /population,% Confirmed /population,% Ref.
## 1 32.1 0.40 0.13 [1]
## 2 22.6 15.0 3.4 [2]
## 3 25.4 0.53 0.13 [3][4]
## 4 12.6 387 49.0 [5]
## 5 5.3 1.3 0.067 [6]
## 6 5.4 15.9 0.86 [7]## Country or region Date[a] Tested Units[b]
## Length:173 Length:173 Length:173 Length:173
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Confirmed(cases) Confirmed /tested,% Tested /population,%
## Length:173 Length:173 Length:173
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## Confirmed /population,% Ref.
## Length:173 Length:173
## Class :character Class :character
## Mode :character Mode :characterpreprocess_covid_data_frame <- function(data_frame) {
shape <- dim(data_frame)
# Remove the last row
data_frame <- data_frame[1:172, ]
# We dont need the Units and Ref columns, so can be removed
data_frame["Ref."] <- NULL
data_frame["Units[b]"] <- NULL
# Renaming the columns
names(data_frame) <- c("country", "date", "tested", "confirmed", "confirmed.tested.ratio", "tested.population.ratio", "confirmed.population.ratio")
# Convert column data types
data_frame$country <- as.factor(data_frame$country)
data_frame$date <- as.factor(data_frame$date)
data_frame$tested <- as.numeric(gsub(",","",data_frame$tested))
data_frame$confirmed <- as.numeric(gsub(",","",data_frame$confirmed))
data_frame$'confirmed.tested.ratio' <- as.numeric(gsub(",","",data_frame$`confirmed.tested.ratio`))
data_frame$'tested.population.ratio' <- as.numeric(gsub(",","",data_frame$`tested.population.ratio`))
data_frame$'confirmed.population.ratio' <- as.numeric(gsub(",","",data_frame$`confirmed.population.ratio`))
return(data_frame)
}## country date tested
## Afghanistan : 1 2 Feb 2023 : 6 Min. : 3880
## Albania : 1 1 Feb 2023 : 4 1st Qu.: 512037
## Algeria : 1 31 Jan 2023: 4 Median : 3029859
## Andorra : 1 1 Mar 2021 : 3 Mean : 31376832
## Angola : 1 23 Jul 2021: 3 3rd Qu.: 12386725
## Antigua and Barbuda: 1 29 Jan 2023: 3 Max. :929349291
## (Other) :166 (Other) :149
## confirmed confirmed.tested.ratio tested.population.ratio
## Min. : 0 Min. : 0.00 Min. : 0.006
## 1st Qu.: 37839 1st Qu.: 5.00 1st Qu.: 9.475
## Median : 281196 Median :10.05 Median : 46.950
## Mean : 2508336 Mean :11.25 Mean : 175.504
## 3rd Qu.: 1278105 3rd Qu.:15.25 3rd Qu.: 156.500
## Max. :90749469 Max. :46.80 Max. :3223.000
##
## confirmed.population.ratio
## Min. : 0.000
## 1st Qu.: 0.425
## Median : 6.100
## Mean :12.769
## 3rd Qu.:16.250
## Max. :74.400
## ## X country date tested confirmed confirmed.tested.ratio
## 1 1 Afghanistan 17 Dec 2020 154767 49621 32.1
## 2 2 Albania 18 Feb 2021 428654 96838 22.6
## 3 3 Algeria 2 Nov 2020 230553 58574 25.4
## 4 4 Andorra 23 Feb 2022 300307 37958 12.6
## 5 5 Angola 2 Feb 2021 399228 20981 5.3
## 6 6 Antigua and Barbuda 6 Mar 2021 15268 832 5.4
## tested.population.ratio confirmed.population.ratio
## 1 0.40 0.130
## 2 15.00 3.400
## 3 0.53 0.130
## 4 387.00 49.000
## 5 1.30 0.067
## 6 15.90 0.860To understand the prevalence of COVID19 better, it can help us to look into the ratio of confirmed cases to those tested for each country.
The bubble chart shows ratio of tested cases to the population of the country against each country, filled by the ratio of confirmed cases to the tested cases. Some of the tested ratios are beyond 100% (such as for Faroe Islands) indicating that most likely testing was conducted repetitively on the entire population or its subset.
We observe that Cyprus[d] is conducting testing repetitively as the ratio is greater than 100%. It is likely they are testing the individuals multiple times over. Of the tests reported, 74% are confirmed to be COVID19 positive.
We observe that countries like India and China are known to be heavily populated. We observe that China conducted tests on 11.1% of its population and of those tested 0.16% are confirmed to be COVID19 positive.
The goal is to get the total confirmed and tested cases worldwide, and try to figure the overall positive ratio using confirmed cases / tested cases.
0.08% of the people worldwide are confirmed to be COVID19 positive.
# Get the positive ratio (confirmed / tested)
covid19_positive_ratio <- covid19_confirmed / covid19_tested
covid19_positive_ratio## [1] 0.07994229## country confirmed confirmed.population.ratio
## 166 United States 90749469 27.4## country confirmed confirmed.population.ratio
## 165 United Kingdom 22232377 32.9## [1] "United Kingdom indicates a greater risk of COVID-19 infection"The confirmed to population ratio rate set to less than the threshold of 0.1%
North Korea, Laos and Tanzania show a relatively low prevalence of the COVID19 disease within the population.
## X country date tested confirmed confirmed.tested.ratio
## 89 89 Laos 1 Mar 2021 114030 45 0.039
## 119 119 North Korea 25 Nov 2020 16914 0 0.000
## 156 156 Tanzania 18 Nov 2020 3880 509 13.100
## tested.population.ratio confirmed.population.ratio country_code
## 89 1.6000 0.00063 LAO
## 119 0.0660 0.00000 PRK
## 156 0.0065 0.00085 TZAThe confirmed to population ratio rate set to the threshold of 0.1% to 1%
Burundi and China show a relatively moderate prevalence of the COVID19 disease within the population.
## X country date tested confirmed confirmed.tested.ratio
## 28 28 Burundi 5 Jan 2021 90019 884 0.980
## 34 34 China[c] 31 Jul 2020 160000000 87655 0.055
## tested.population.ratio confirmed.population.ratio country_code
## 28 0.76 0.0074 BDI
## 34 11.10 0.0061 CHNThe confirmed to population ratio rate set to greater than the threshold of 1%
167 countries show a relatively high prevalence of the COVID19 disease within the population.
## X country date tested confirmed
## 1 1 Afghanistan 17 Dec 2020 154767 49621
## 2 2 Albania 18 Feb 2021 428654 96838
## 3 3 Algeria 2 Nov 2020 230553 58574
## 4 4 Andorra 23 Feb 2022 300307 37958
## 5 5 Angola 2 Feb 2021 399228 20981
## 6 6 Antigua and Barbuda 6 Mar 2021 15268 832
## 7 7 Argentina 16 Apr 2022 35716069 9060495
## 8 8 Armenia 29 May 2022 3099602 422963
## 9 9 Australia 9 Sep 2022 78548492 10112229
## 10 10 Austria 1 Feb 2023 205817752 5789991
## 11 11 Azerbaijan 11 May 2022 6838458 792638
## 12 12 Bahamas 28 Nov 2022 259366 37483
## 13 13 Bahrain 3 Dec 2022 10578766 696614
## 14 14 Bangladesh 24 Jul 2021 7417714 1151644
## 15 15 Barbados 14 Oct 2022 770100 103014
## 16 16 Belarus 9 May 2022 13217569 982809
## 17 17 Belgium 24 Jan 2023 36548544 4691499
## 18 18 Belize 8 Jun 2022 572900 60694
## 19 19 Benin 4 May 2021 595112 7884
## 20 20 Bhutan 28 Feb 2022 1736168 12702
## 21 21 Bolivia 5 Jun 2022 4358669 910228
## 22 22 Bosnia and Herzegovina 27 Sep 2022 1872934 399887
## 23 23 Botswana 11 Jan 2022 2026898 232432
## 24 24 Brazil 19 Feb 2021 23561497 10081676
## 25 25 Brunei 2 Aug 2021 153804 338
## 26 26 Bulgaria 2 Feb 2023 10993239 1295524
## 27 27 Burkina Faso 4 Mar 2021 158777 12123
## 29 29 Cambodia 1 Aug 2021 1812706 77914
## 30 30 Cameroon 18 Feb 2021 942685 32681
## 31 31 Canada 26 Nov 2022 66343123 4423053
## 32 32 Chad 2 Mar 2021 99027 4020
## 33 33 Chile 1 Feb 2023 48154268 5123007
## 35 35 Colombia 24 Nov 2022 36875818 6314769
## 36 36 Costa Rica 2 Nov 2021 2575363 561054
## 37 37 Croatia 2 Feb 2023 5481285 1267798
## 38 38 Cuba 2 Feb 2023 14301394 1112470
## 39 39 Cyprus[d] 29 Jan 2023 27820163 644160
## 40 40 Czechia 1 Feb 2023 22544928 4590529
## 41 41 Denmark[e] 31 Jan 2023 67682707 3399947
## 42 42 Djibouti 28 Apr 2022 305941 15631
## 43 43 Dominica 20 Jun 2022 209803 14821
## 44 44 Dominican Republic 22 Jul 2022 3574665 626030
## 45 45 DR Congo 28 Feb 2021 124838 25961
## 46 46 Ecuador 23 Jul 2021 1627189 480720
## 47 47 Egypt 23 Jul 2021 3137519 283947
## 48 48 El Salvador 18 Mar 2022 1847861 161052
## 49 49 Equatorial Guinea 30 Jan 2023 403773 17113
## 50 50 Estonia 31 Jan 2023 3637908 613954
## 51 51 Eswatini 8 Dec 2021 415110 49253
## 52 52 Ethiopia 24 Jun 2021 2981185 278446
## 53 53 Faroe Islands 27 Feb 2022 774000 34237
## 54 54 Fiji 2 Jan 2023 667953 68848
## 55 55 Finland 14 Jan 2022 9042453 371135
## 56 56 France[f][g] 15 May 2022 272417258 29183646
## 57 57 Gabon 23 Jul 2021 958807 25325
## 58 58 Gambia 15 Feb 2021 43217 4469
## 59 59 Georgia[h] 3 Nov 2021 4888787 732965
## 60 60 Germany 7 Jul 2021 65247345 3733519
## 61 61 Ghana 3 Jul 2021 1305749 96708
## 62 62 Greece 18 Dec 2022 101576831 5548487
## 63 63 Greenland 30 Jan 2022 164573 10662
## 64 64 Grenada 11 May 2021 28684 161
## 65 65 Guatemala 6 Jan 2023 6800560 1230098
## 66 66 Guinea 21 Jul 2021 494898 24878
## 67 67 Guinea-Bissau 7 Jul 2022 145231 8400
## 68 68 Guyana 15 Jun 2022 648569 66129
## 69 69 Haiti 26 Nov 2022 223475 33874
## 70 70 Honduras 26 Nov 2021 1133782 377859
## 71 71 Hungary 10 May 2022 11394556 1909948
## 72 72 Iceland 9 Aug 2022 1988652 203162
## 73 73 India 8 Jul 2022 866177937 43585554
## 74 74 Indonesia 20 Jun 2023 75996203 6811330
## 75 75 Iran 31 May 2022 52269202 7232268
## 76 76 Iraq 3 Aug 2022 19090652 2448484
## 77 77 Ireland 31 Jan 2023 12990476 1700817
## 78 78 Israel 17 Jan 2022 41373364 1792137
## 79 79 Italy 16 Mar 2023 269127054 25651205
## 80 80 Ivory Coast 3 Mar 2021 429177 33285
## 81 81 Jamaica 30 Sep 2022 1184973 151931
## 82 82 Japan 1 Mar 2021 8487288 432773
## 83 83 Jordan 6 Jun 2021 7407053 739847
## 84 84 Kazakhstan 28 May 2021 11575012 385144
## 85 85 Kenya 5 Mar 2021 1322806 107729
## 86 86 Kosovo 31 May 2021 611357 107410
## 87 87 Kuwait 9 Mar 2022 7754247 624573
## 88 88 Kyrgyzstan 10 Feb 2021 695415 85253
## 90 90 Latvia 5 Sep 2021 3630095 144518
## 91 91 Lebanon 14 Jun 2021 4599186 542649
## 92 92 Lesotho 30 Mar 2022 431221 32910
## 93 93 Liberia 17 Jul 2021 128246 5396
## 94 94 Libya 14 Apr 2022 2578215 501862
## 95 95 Lithuania 31 Jan 2023 9046584 1170108
## 96 96 Luxembourg[i] 12 May 2022 4248188 244182
## 97 97 Madagascar 19 Feb 2021 119608 19831
## 98 98 Malawi 29 Nov 2022 624784 88086
## 99 99 Malaysia 7 Sep 2021 23705425 1880734
## 100 100 Maldives 13 Mar 2022 2216560 174658
## 101 101 Mali 7 Jul 2021 322504 14449
## 102 102 Malta 8 Sep 2021 1211456 36606
## 103 103 Mauritania 16 Apr 2021 268093 18103
## 104 104 Mauritius 22 Nov 2020 289552 494
## 105 105 Mexico 15 Oct 2021 10503678 3749860
## 106 106 Moldova[j] 20 Apr 2022 3213594 516864
## 107 107 Mongolia 10 Jul 2021 3354200 136053
## 108 108 Montenegro 10 May 2021 394388 98449
## 109 109 Morocco 6 Jan 2023 14217563 1272299
## 110 110 Mozambique 22 Jul 2021 688570 105866
## 111 111 Myanmar 16 Sep 2021 4047680 440741
## 112 112 Namibia 4 Jul 2022 1062663 166229
## 113 113 Nepal 26 Jul 2022 5804358 984475
## 114 114 Netherlands 6 Jul 2021 14526293 1692834
## 115 115 New Caledonia 3 Sep 2021 41962 136
## 116 116 New Zealand 29 Jan 2023 7757935 2136662
## 117 117 Niger 22 Feb 2021 79321 4740
## 118 118 Nigeria 28 Feb 2021 1544008 155657
## 120 120 North Macedonia 1 Jul 2021 881870 155689
## 121 121 Northern Cyprus[k] 12 Jul 2022 7096998 103034
## 122 122 Norway 20 Jan 2022 9811888 554778
## 123 123 Oman 28 Oct 2020 509959 114434
## 124 124 Pakistan 5 Mar 2021 9173593 588728
## 125 125 Palestine 5 Feb 2022 3078533 574105
## 126 126 Panama 28 Jan 2023 7475016 1029701
## 127 127 Papua New Guinea 17 Feb 2021 47490 961
## 128 128 Paraguay 27 Mar 2022 2609819 647950
## 129 129 Peru 17 Nov 2022 36073768 4177786
## 130 130 Philippines 7 Jan 2023 34402980 4073980
## 131 131 Poland 27 Apr 2022 36064311 5993861
## 132 132 Portugal 5 Jan 2022 27515490 1499976
## 133 133 Qatar 11 Nov 2022 4061988 473440
## 134 134 Romania 29 Jan 2021 5405393 724250
## 135 135 Russia 6 Jun 2022 295542733 18358459
## 136 136 Rwanda 6 Oct 2021 2885812 98209
## 137 137 Saint Kitts and Nevis 26 Aug 2021 30231 995
## 138 138 Saint Lucia 7 Oct 2022 212132 29550
## 139 139 Saint Vincent 28 Jan 2023 113504 9585
## 140 140 San Marino 29 Jan 2023 192613 23427
## 141 141 Saudi Arabia 26 Apr 2022 41849069 753632
## 142 142 Senegal 12 Jul 2021 624502 46509
## 143 143 Serbia 2 Feb 2023 12185475 2473599
## 144 144 Singapore 3 Aug 2021 16206203 65315
## 145 145 Slovakia 2 Feb 2023 7391882 1861034
## 146 146 Slovenia 2 Feb 2023 2826117 1322282
## 147 147 South Africa 24 May 2021 11378282 1637848
## 148 148 South Korea 1 Mar 2021 6592010 90029
## 149 149 South Sudan 26 May 2021 164472 10688
## 150 150 Spain 1 Jul 2021 54128524 3821305
## 151 151 Sri Lanka 30 Mar 2021 2384745 93128
## 152 152 Sudan 7 Jan 2021 158804 23316
## 153 153 Sweden 24 May 2021 9996795 1074751
## 154 154 Switzerland[l] 7 Nov 2022 23283909 4276836
## 155 155 Taiwan[m] 3 Feb 2023 30275725 8622129
## 157 157 Thailand 4 Mar 2021 1579597 26162
## 158 158 Togo 6 Jan 2023 807269 39358
## 159 159 Trinidad and Tobago 3 Jan 2022 512730 92997
## 160 160 Tunisia 23 Aug 2021 2893625 703732
## 161 161 Turkey 2 Jul 2021 61236294 5435831
## 162 162 Uganda 11 Feb 2021 852444 39979
## 163 163 Ukraine 24 Nov 2021 15648456 3367461
## 164 164 United Arab Emirates 1 Feb 2023 198685717 1049537
## 165 165 United Kingdom 19 May 2022 522526476 22232377
## 166 166 United States 29 Jul 2022 929349291 90749469
## 167 167 Uruguay 16 Apr 2022 6089116 895592
## 168 168 Uzbekistan 7 Sep 2020 2630000 43975
## 169 169 Venezuela 30 Mar 2021 3179074 159149
## 170 170 Vietnam 28 Aug 2022 45772571 11403302
## 171 171 Zambia 10 Mar 2022 3301860 314850
## 172 172 Zimbabwe 15 Oct 2022 2529087 257893
## confirmed.tested.ratio tested.population.ratio confirmed.population.ratio
## 1 32.10 0.40 0.130
## 2 22.60 15.00 3.400
## 3 25.40 0.53 0.130
## 4 12.60 387.00 49.000
## 5 5.30 1.30 0.067
## 6 5.40 15.90 0.860
## 7 25.40 78.30 20.000
## 8 13.60 105.00 14.300
## 9 12.90 313.00 40.300
## 10 2.80 2312.00 65.000
## 11 11.60 69.10 8.000
## 12 14.50 67.30 9.700
## 13 6.60 674.00 44.400
## 14 15.50 4.50 0.700
## 15 13.40 268.00 35.900
## 16 7.40 139.00 10.400
## 17 12.80 317.00 40.700
## 18 10.60 140.00 14.900
## 19 1.30 5.10 0.067
## 20 0.73 234.00 1.710
## 21 20.90 38.10 8.000
## 22 21.40 54.70 11.700
## 23 11.50 89.90 10.300
## 24 42.80 11.20 4.800
## 25 0.22 33.50 0.074
## 26 11.80 158.00 18.600
## 27 7.60 0.76 0.058
## 29 4.30 11.20 0.480
## 30 3.50 3.60 0.120
## 31 6.70 175.00 11.700
## 32 4.10 0.72 0.029
## 33 10.60 252.00 26.900
## 35 17.10 76.40 13.100
## 36 21.80 51.50 11.200
## 37 23.10 134.00 31.100
## 38 7.80 126.00 9.800
## 39 2.30 3223.00 74.400
## 40 20.40 211.00 42.900
## 41 5.00 1162.00 58.400
## 42 5.10 33.20 1.700
## 43 7.10 293.00 20.700
## 44 17.50 32.90 5.800
## 45 20.80 0.14 0.029
## 46 29.50 9.50 2.800
## 47 9.10 3.10 0.280
## 48 8.70 28.50 2.500
## 49 4.20 30.80 1.300
## 50 16.90 274.00 46.200
## 51 11.90 36.50 4.300
## 52 9.30 2.60 0.240
## 53 4.40 1493.00 65.700
## 54 10.30 74.50 7.700
## 55 4.10 163.00 6.700
## 56 10.70 417.00 44.700
## 57 2.60 3.10 0.082
## 58 10.30 2.00 0.210
## 59 15.00 132.00 19.700
## 60 5.70 77.80 4.500
## 61 7.40 4.20 0.310
## 62 5.50 943.00 51.500
## 63 6.50 293.00 19.000
## 64 0.56 25.70 0.140
## 65 18.10 39.40 7.100
## 66 5.00 3.80 0.190
## 67 5.80 7.70 0.450
## 68 10.20 82.50 8.400
## 69 15.20 2.00 0.300
## 70 33.30 11.80 3.900
## 71 16.80 118.00 19.800
## 72 10.20 546.00 55.800
## 73 5.00 63.00 31.700
## 74 9.00 28.10 2.500
## 75 13.80 62.80 8.700
## 76 12.80 47.50 6.100
## 77 13.10 264.00 34.600
## 78 4.30 451.00 19.500
## 79 9.50 446.00 42.500
## 80 7.80 1.60 0.130
## 81 12.80 43.50 5.600
## 82 5.10 6.70 0.340
## 83 10.00 69.50 6.900
## 84 3.30 62.10 2.100
## 85 8.10 2.80 0.230
## 86 17.60 33.80 5.900
## 87 8.10 181.00 14.600
## 88 12.30 10.70 1.300
## 90 4.00 189.00 7.500
## 91 11.80 67.40 8.000
## 92 7.60 21.50 1.600
## 93 4.20 2.50 0.110
## 94 19.50 37.60 7.300
## 95 12.90 324.00 41.900
## 96 5.70 679.00 39.000
## 97 16.60 0.46 0.076
## 98 14.10 3.30 0.460
## 99 7.90 72.30 5.700
## 100 7.90 398.00 31.300
## 101 4.50 1.60 0.071
## 102 3.00 245.00 7.400
## 103 6.80 6.10 0.410
## 104 0.17 22.90 0.039
## 105 35.70 8.20 2.900
## 106 16.10 122.00 19.600
## 107 4.10 100.00 4.100
## 108 25.00 62.50 15.600
## 109 8.90 38.50 3.400
## 110 15.40 2.20 0.340
## 111 10.90 7.40 0.810
## 112 15.60 38.70 6.100
## 113 17.00 20.70 3.500
## 114 11.70 83.40 9.700
## 115 0.32 15.70 0.050
## 116 27.50 156.00 42.900
## 117 6.00 0.35 0.021
## 118 10.10 0.75 0.076
## 120 17.70 42.50 7.500
## 121 1.50 2177.00 31.600
## 122 5.70 183.00 10.300
## 123 22.40 11.00 2.500
## 124 6.40 4.20 0.270
## 125 18.60 60.90 11.400
## 126 13.80 179.00 24.700
## 127 2.00 0.53 0.011
## 128 24.80 36.60 9.100
## 129 11.60 109.90 12.700
## 130 11.80 34.10 4.000
## 131 16.60 94.00 15.600
## 132 5.50 268.00 14.600
## 133 11.70 141.00 16.400
## 134 13.40 27.90 3.700
## 135 6.20 201.00 12.500
## 136 3.40 22.30 0.760
## 137 3.30 57.60 1.900
## 138 13.90 116.60 16.200
## 139 8.40 103.00 8.700
## 140 12.20 563.00 68.400
## 141 1.80 120.00 2.200
## 142 7.40 3.90 0.290
## 143 20.30 175.00 35.500
## 144 0.40 284.00 1.100
## 145 25.20 135.00 34.100
## 146 46.80 135.00 63.100
## 147 14.40 19.20 2.800
## 148 1.40 12.70 0.170
## 149 6.50 1.30 0.084
## 150 7.10 116.00 8.200
## 151 3.90 10.90 0.430
## 152 14.70 0.36 0.053
## 153 10.80 96.80 10.400
## 154 18.40 270.00 49.700
## 155 28.48 128.30 36.528
## 157 1.70 2.30 0.038
## 158 4.90 9.40 0.460
## 159 18.10 37.60 6.800
## 160 24.30 24.50 6.000
## 161 8.90 73.60 6.500
## 162 4.70 1.90 0.087
## 163 21.50 37.20 8.000
## 164 0.53 2070.00 10.900
## 165 4.30 774.00 32.900
## 166 9.80 281.00 27.400
## 167 14.70 175.00 25.800
## 168 1.70 7.70 0.130
## 169 5.00 11.00 0.550
## 170 24.90 46.40 11.600
## 171 9.50 19.00 1.800
## 172 10.20 17.00 1.700
## country_code
## 1 AFG
## 2 ALB
## 3 DZA
## 4 AND
## 5 AGO
## 6 ATG
## 7 ARG
## 8 ARM
## 9 AUS
## 10 AUT
## 11 AZE
## 12 BHS
## 13 BHR
## 14 BGD
## 15 BRB
## 16 BLR
## 17 BEL
## 18 BLZ
## 19 BEN
## 20 BTN
## 21 BOL
## 22 BIH
## 23 BWA
## 24 BRA
## 25 BRN
## 26 BGR
## 27 BFA
## 29 KHM
## 30 CMR
## 31 CAN
## 32 TCD
## 33 CHL
## 35 COL
## 36 CRI
## 37 HRV
## 38 CUB
## 39 CYP
## 40 CZE
## 41 DNK
## 42 DJI
## 43 DMA
## 44 DOM
## 45 COD
## 46 ECU
## 47 EGY
## 48 SLV
## 49 GNQ
## 50 EST
## 51 SWZ
## 52 ETH
## 53 FRO
## 54 FJI
## 55 FIN
## 56 FRA
## 57 GAB
## 58 GMB
## 59 GEO
## 60 DEU
## 61 GHA
## 62 GRC
## 63 GRL
## 64 GRD
## 65 GTM
## 66 GIN
## 67 GNB
## 68 GUY
## 69 HTI
## 70 HND
## 71 HUN
## 72 ISL
## 73 IND
## 74 IDN
## 75 IRN
## 76 IRQ
## 77 IRL
## 78 ISR
## 79 ITA
## 80 CIV
## 81 JAM
## 82 JPN
## 83 JOR
## 84 KAZ
## 85 KEN
## 86 <NA>
## 87 KWT
## 88 KGZ
## 90 LVA
## 91 LBN
## 92 LSO
## 93 LBR
## 94 LBY
## 95 LTU
## 96 LUX
## 97 MDG
## 98 MWI
## 99 MYS
## 100 MDV
## 101 MLI
## 102 MLT
## 103 MRT
## 104 MUS
## 105 MEX
## 106 MDA
## 107 MNG
## 108 MNE
## 109 MAR
## 110 MOZ
## 111 MMR
## 112 NAM
## 113 NPL
## 114 NLD
## 115 NCL
## 116 NZL
## 117 NER
## 118 NGA
## 120 MKD
## 121 CYP
## 122 NOR
## 123 OMN
## 124 PAK
## 125 PSE
## 126 PAN
## 127 PNG
## 128 PRY
## 129 PER
## 130 PHL
## 131 POL
## 132 PRT
## 133 QAT
## 134 ROU
## 135 RUS
## 136 RWA
## 137 KNA
## 138 LCA
## 139 VCT
## 140 SMR
## 141 SAU
## 142 SEN
## 143 SRB
## 144 SGP
## 145 SVK
## 146 SVN
## 147 ZAF
## 148 KOR
## 149 SSD
## 150 ESP
## 151 LKA
## 152 SDN
## 153 SWE
## 154 CHE
## 155 TWN
## 157 THA
## 158 TGO
## 159 TTO
## 160 TUN
## 161 TUR
## 162 UGA
## 163 UKR
## 164 ARE
## 165 GBR
## 166 USA
## 167 URY
## 168 UZB
## 169 VEN
## 170 VNM
## 171 ZMB
## 172 ZWE