Covid 19 Data Cleanup Save Abandoned
Scripts to cleanup data from https://github.com/CSSEGISandData/COVID-19
Project README
R scripts to process/cleanup data from the repo: https://github.com/CSSEGISandData/COVID-19 into tidy datasets[1]
Last updated on 2021-01-25 21:24:04 UTC
Data source commit reference: 3200a6b24301ff12cf13ac969e3c60c8e245c4ce
Notes
- For the cases, I’ve used the filename to to get the timestamp, because that is more reliable
- 2020-02-14: the original data source has changed its data structure, the timeseries data is empty as of the commit referred below.
- 2020-02-27: changed code to reflect changes in source data files.
-
2020-03-04: added Continents and ISO-3 country codes, using the
countrycodeR package. -
2020-03-05:
- Latitude and longitude information started appearing in cases files in March, used that to add that information the rest of the cases.
- Added code to tidy the WHO situation report timeseries
- 2020-03-12: source data no longer has the classification “Others” for locations not corresponding to countries (i.e. Cruise Ships), so the code has been modified to account for that change.
-
2020-04-01: the new US only timeseries data files from the
source, have different number of columns, the file containing the
deaths has a
Populationcolumn that is not present in the cofirmed cases file. - 2020-04-05: csv files are now gzip compressed to avoid hitting github’s max file size
About the processed data files
Cases
Files
-
covid-19_cases_raw.csv.gz: CSV with processed cases file -
covid-19_cases_raw.RDS: RDS format version
Data structure:
- continent: Geographical continent
- who_region: WHO region
- country_region: Country (or region)
- iso3c: ISO 3166-1 alpha-3 country code
- province_state: Province/State/Subnational division
- confirmed: Cummulative number of confirmed cases
- dead: Cummulative number of deaths
- recovered: Cummulative number of recovered cases
- lat: Latitude
- lon: Longitude
- update: Entry timestamp update in “YYYY-MM-DD hh:mm:ss” format
- data_update: Data file update date in “YYYY-MM-DD” format
- who_region_code: WHO region code
- who_region: WHO region
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Global timeseries data
Files
-
covid-19_ts_combined.csv.gz: CSV with combined timeseries data -
covid-19_ts_combined.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- status: “confirmed”, “deaths”, “recovered”
- number: number of cases
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Files
-
covid-19_ts_confirmed.csv.gz: CSV file with confirmed cases -
covid-19_ts_confirmed.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- confirmed: number of confirmed cases
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Files
-
covid-19_ts_deaths.csv.gz: CSV file with deaths -
covid-19_ts_deaths.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- deaths: number of deaths
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Files
-
covid-19_ts_recovered.csv.gz: CSV file with recovered cases -
covid-19_ts_recovered.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- recovered: number of people who recovered
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
US timeseries data
Files
-
covid-19_ts_us_combined.csv.gz: CSV with combined timeseries data -
covid-19_ts_us_combined.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- status: “confirmed”, “deaths”, “recovered”
- number: number of cases
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Files
-
covid-19_ts_us_confirmed.csv.gz: CSV file with confirmed cases for US -
covid-19_ts_us_confirmed.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- confirmed: number of confirmed cases
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
Files
-
covid-19_ts_us_deaths.csv.gz: CSV file with deaths for US -
covid-19_ts_us_deaths.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- deaths: number of deaths
- lat: Latitude
- lon: Longitude
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
WHO situation report data:
Files:
-
covid-19_who_sitrep_raw.rds: Lightly cleaned WHO situation report in RDS format -
covid-19_ts_who_sitrep.csv.gz: Timeseries from WHO situation reports -
covid-19_ts_who_sitrep.rds: RDS version (tsibble)
Data structure:
- continent: Geographical continent
- iso3c: ISO 3166-1 alpha-3 country code
- country_region: Country (or region)
- province_state: Province/State/Subnational division
- ts: UTC date in “YYYY-MM-DD” format
- cases: number of cases at ts
- who_region: WHO region
- who_region_code: WHO region code
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
WHO metadata
Source: https://apps.who.int/gho/data/node.metadata.COUNTRY?lang=en (CSV Xmart format)
Files:
-
xmart.csv: CSV Xmart format (downloaded on 2020-03-09) -
who_metadata.Rdata: Rdata format version
Data structure:
- continent: Geographical continent
- who_region: WHO region
- country_region: Country (or region)
- iso3c: ISO 3166-1 alpha-3 country code
- province_state: Province/State/Subnational division
- ts: UTC timestamp in “YYYY-MM-DD hh:mm:ss” format
- cases: Number of cases
- who_region_code: Code for WHO region
- world_bank_income_group: World Bank Income Group
- world_bank_income_group_code: World Bank Income Group code
- world_bank_income_group_gni_reference_year: World Bank Income Group GNI reference year
- world_bank_income_group_release_date: World Bank Income Group release year
World Bank population estimate for 2020
Source: https://databank.worldbank.org/source/population-estimates-and-projections
Files: -
Data_Extract_From_Population_estimates_and_projections.zip: data and
metadata from World Bank (dowloaded on 2020-03-14) -
wb_population.Rdata: Rdata format
Data Structure (Rdata file):
- country_name: Country name
- country_code: ISO 3166-1 alpha-3 country code
- series_name: World Bank variable name
- series_code: World Bank variable code
- population_2020: Estimated population for 2020
Plots of confirmed cases
Confirmed cases by country (Worldwide):
Africa
Confirmed cases by country in Africa:
Confirmed cases by country in Africa (per million inhabitants):
Trajectories in Africa
Americas
Confirmed cases by country in the Americas:
Confirmed cases by country in the Americas per million inhabitants:
Trajectories in the Americas
Asia
Confirmed cases by country in Asia:
Confirmed cases by country in Asia per million inhabitants:
Trajectories in Asia
Europe
Confirmed cases by country in Europe:
Confirmed cases by country in Europe per million inhabitants:
Trajectories in Europe
Oceania
Confirmed cases by country in Oceania:
Confirmed cases by country in Oceania per million inhabitants:
Trajectories in Oceania
Confirmed cases (Other locations):
Here are couple of quick tables:
| continent | iso3c | country_region | province_state | confirmed | deaths | recovered | global_confirmed_pct | global_death_pct | global_recovered_pct | who_region_code | who_region | world_bank_income_group | world_bank_income_group_code | world_bank_income_group_gni_reference_year | world_bank_income_group_release_date |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Asia | CHN | China | Hubei | 68150 | 4512 | 63637 | 0.069 | 0.212 | 0.118 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Hong Kong | 10085 | 169 | 9034 | 0.010 | 0.008 | 0.017 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Guangdong | 2106 | 8 | 2055 | 0.002 | 0.000 | 0.004 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Shanghai | 1644 | 7 | 1532 | 0.002 | 0.000 | 0.003 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Heilongjiang | 1394 | 13 | 964 | 0.001 | 0.001 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Zhejiang | 1316 | 1 | 1305 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Henan | 1303 | 22 | 1277 | 0.001 | 0.001 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Hebei | 1294 | 7 | 481 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Beijing | 1028 | 9 | 966 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Hunan | 1027 | 4 | 1017 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Anhui | 994 | 6 | 987 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Xinjiang | 980 | 3 | 977 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Jiangxi | 935 | 1 | 934 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Shandong | 866 | 7 | 856 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Sichuan | 866 | 3 | 852 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Jiangsu | 691 | 0 | 685 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Chongqing | 591 | 6 | 584 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Fujian | 535 | 1 | 519 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Shaanxi | 534 | 3 | 508 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Jilin | 430 | 2 | 155 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Liaoning | 397 | 2 | 379 | 0.000 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Inner Mongolia | 366 | 1 | 351 | 0.000 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Tianjin | 338 | 3 | 309 | 0.000 | 0.000 | 0.001 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Guangxi | 267 | 2 | 262 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Shanxi | 237 | 0 | 225 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Yunnan | 231 | 2 | 228 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Gansu | 183 | 2 | 180 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Hainan | 171 | 6 | 165 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Guizhou | 147 | 2 | 145 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Ningxia | 75 | 0 | 75 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Macau | 47 | 0 | 46 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Qinghai | 18 | 0 | 18 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | CHN | China | Tibet | 1 | 0 | 1 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| continent | iso3c | country_region | province_state | confirmed | deaths | recovered | global_confirmed_pct | global_death_pct | global_recovered_pct | who_region_code | who_region | world_bank_income_group | world_bank_income_group_code | world_bank_income_group_gni_reference_year | world_bank_income_group_release_date |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Americas | USA | US | NA | 25127000 | 419214 | 0 | 25.331 | 19.687 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Asia | IND | India | NA | 10667736 | 153470 | 10330084 | 10.754 | 7.207 | 19.104 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | BRA | Brazil | NA | 8844577 | 217037 | 7821376 | 8.916 | 10.192 | 14.464 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | RUS | Russia | NA | 3679247 | 68397 | 3098505 | 3.709 | 3.212 | 5.730 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | GBR | United Kingdom | NA | 3647463 | 97939 | 0 | 3.677 | 4.599 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | FRA | France | NA | 3044358 | 72668 | 192405 | 3.069 | 3.413 | 0.356 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | ESP | Spain | NA | 2499560 | 55441 | 150376 | 2.520 | 2.604 | 0.278 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | ITA | Italy | NA | 2466813 | 85461 | 1882074 | 2.487 | 4.013 | 3.481 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | TUR | Turkey | NA | 2429605 | 25073 | 2307721 | 2.449 | 1.177 | 4.268 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | DEU | Germany | NA | 2147769 | 52296 | 1840970 | 2.165 | 2.456 | 3.405 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | COL | Colombia | NA | 2015485 | 51374 | 1832168 | 2.032 | 2.413 | 3.388 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | ARG | Argentina | NA | 1867223 | 46827 | 1656149 | 1.882 | 2.199 | 3.063 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Americas | MEX | Mexico | NA | 1763219 | 149614 | 1320448 | 1.778 | 7.026 | 2.442 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | POL | Poland | NA | 1475445 | 35363 | 1230446 | 1.487 | 1.661 | 2.276 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | ZAF | South Africa | NA | 1412986 | 40874 | 1230520 | 1.424 | 1.919 | 2.276 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | IRN | Iran | NA | 1372977 | 57383 | 1164794 | 1.384 | 2.695 | 2.154 | EMR | Eastern Mediterranean | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | UKR | Ukraine | NA | 1231965 | 22926 | 980769 | 1.242 | 1.077 | 1.814 | EUR | Europe | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | PER | Peru | NA | 1093938 | 39608 | 1007748 | 1.103 | 1.860 | 1.864 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | IDN | Indonesia | NA | 989262 | 27835 | 798810 | 0.997 | 1.307 | 1.477 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | NLD | Netherlands | NA | 948933 | 13540 | 0 | 0.957 | 0.636 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | CZE | Czechia | NA | 937617 | 15369 | 814903 | 0.945 | 0.722 | 1.507 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | ROU | Romania | NA | 711010 | 17776 | 653125 | 0.717 | 0.835 | 1.208 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | CHL | Chile | NA | 699110 | 17933 | 654101 | 0.705 | 0.842 | 1.210 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | BEL | Belgium | NA | 693666 | 20779 | 0 | 0.699 | 0.976 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | PRT | Portugal | NA | 636190 | 10469 | 456491 | 0.641 | 0.492 | 0.844 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | IRQ | Iraq | NA | 613763 | 12993 | 581542 | 0.619 | 0.610 | 1.075 | EMR | Eastern Mediterranean | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | ISR | Israel | NA | 597403 | 4419 | 524817 | 0.602 | 0.208 | 0.971 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | SWE | Sweden | NA | 547166 | 11005 | 0 | 0.552 | 0.517 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | PAK | Pakistan | NA | 534041 | 11318 | 488903 | 0.538 | 0.532 | 0.904 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | BGD | Bangladesh | NA | 531799 | 8023 | 476413 | 0.536 | 0.377 | 0.881 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | PHL | Philippines | NA | 513619 | 10242 | 475612 | 0.518 | 0.481 | 0.880 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | CHE | Switzerland | NA | 509279 | 9065 | 317600 | 0.513 | 0.426 | 0.587 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | MAR | Morocco | NA | 466289 | 8150 | 442441 | 0.470 | 0.383 | 0.818 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | AUT | Austria | NA | 404714 | 7418 | 381654 | 0.408 | 0.348 | 0.706 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | SRB | Serbia | NA | 383603 | 3886 | 0 | 0.387 | 0.182 | 0.000 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | SAU | Saudi Arabia | NA | 366371 | 6352 | 357939 | 0.369 | 0.298 | 0.662 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Asia | JPN | Japan | NA | 365899 | 5119 | 290386 | 0.369 | 0.240 | 0.537 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Europe | HUN | Hungary | NA | 359574 | 11968 | 241472 | 0.362 | 0.562 | 0.447 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | JOR | Jordan | NA | 320453 | 4224 | 307707 | 0.323 | 0.198 | 0.569 | EMR | Eastern Mediterranean | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | PAN | Panama | NA | 311244 | 5063 | 259095 | 0.314 | 0.238 | 0.479 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Asia | LBN | Lebanon | NA | 279597 | 2320 | 165729 | 0.282 | 0.109 | 0.306 | EMR | Eastern Mediterranean | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | ARE | United Arab Emirates | NA | 277955 | 792 | 251484 | 0.280 | 0.037 | 0.465 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Asia | NPL | Nepal | NA | 269450 | 2001 | 264137 | 0.272 | 0.094 | 0.488 | SEAR | South-East Asia | Low income | WB_LI | 2017 | 2018 |
| Americas | CAN | Canada | Ontario | 259647 | 5811 | NA | 0.262 | 0.273 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Quebec | 253633 | 9142 | NA | 0.256 | 0.429 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Asia | GEO | Georgia | NA | 253518 | 3055 | 242085 | 0.256 | 0.143 | 0.448 | EUR | Europe | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | ECU | Ecuador | NA | 241292 | 14623 | 204071 | 0.243 | 0.687 | 0.377 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | BLR | Belarus | NA | 237611 | 1649 | 223553 | 0.240 | 0.077 | 0.413 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | SVK | Slovakia | NA | 236476 | 4068 | 189959 | 0.238 | 0.191 | 0.351 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | AZE | Azerbaijan | NA | 228975 | 3082 | 220292 | 0.231 | 0.145 | 0.407 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | HRV | Croatia | NA | 228920 | 4827 | 221097 | 0.231 | 0.227 | 0.409 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | KAZ | Kazakhstan | NA | 225891 | 2958 | 195496 | 0.228 | 0.139 | 0.362 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | BGR | Bulgaria | NA | 214817 | 8820 | 179068 | 0.217 | 0.414 | 0.331 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | DOM | Dominican Republic | NA | 203946 | 2531 | 147886 | 0.206 | 0.119 | 0.273 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | BOL | Bolivia | NA | 201037 | 9985 | 150439 | 0.203 | 0.469 | 0.278 | AMR | Americas | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | TUN | Tunisia | NA | 197373 | 6234 | 144657 | 0.199 | 0.293 | 0.268 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | DNK | Denmark | NA | 194671 | 1983 | 180125 | 0.196 | 0.093 | 0.333 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CRI | Costa Rica | NA | 189308 | 2518 | 146448 | 0.191 | 0.118 | 0.271 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | IRL | Ireland | NA | 187554 | 2970 | 23364 | 0.189 | 0.139 | 0.043 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | MYS | Malaysia | NA | 183801 | 678 | 141446 | 0.185 | 0.032 | 0.262 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | LTU | Lithuania | NA | 176624 | 2649 | 119085 | 0.178 | 0.124 | 0.220 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | ARM | Armenia | NA | 166036 | 3039 | 154708 | 0.167 | 0.143 | 0.286 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | EGY | Egypt | NA | 161817 | 8959 | 126497 | 0.163 | 0.421 | 0.234 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | KWT | Kuwait | NA | 161285 | 952 | 154253 | 0.163 | 0.045 | 0.285 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Europe | SVN | Slovenia | NA | 157830 | 3360 | 135993 | 0.159 | 0.158 | 0.251 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | MDA | Moldova | NA | 156202 | 3361 | 146948 | 0.157 | 0.158 | 0.272 | EUR | Europe | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | PSE | West Bank and Gaza | NA | 155414 | 1791 | 144910 | 0.157 | 0.084 | 0.268 | NA | NA | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | GTM | Guatemala | NA | 154212 | 5465 | 138460 | 0.155 | 0.257 | 0.256 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | GRC | Greece | NA | 151980 | 5646 | 93764 | 0.153 | 0.265 | 0.173 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | QAT | Qatar | NA | 149019 | 248 | 144858 | 0.150 | 0.012 | 0.268 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Americas | HND | Honduras | NA | 140929 | 3447 | 60469 | 0.142 | 0.162 | 0.112 | AMR | Americas | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | MMR | Burma | NA | 137574 | 3062 | 121558 | 0.139 | 0.144 | 0.225 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | ETH | Ethiopia | NA | 133767 | 2066 | 119416 | 0.135 | 0.097 | 0.221 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Asia | OMN | Oman | NA | 133044 | 1521 | 126262 | 0.134 | 0.071 | 0.234 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Americas | PRY | Paraguay | NA | 127652 | 2617 | 103083 | 0.129 | 0.123 | 0.191 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | VEN | Venezuela | NA | 123709 | 1148 | 115426 | 0.125 | 0.054 | 0.213 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | NGA | Nigeria | NA | 121566 | 1504 | 97228 | 0.123 | 0.071 | 0.180 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | CAN | Canada | Alberta | 120793 | 1549 | NA | 0.122 | 0.073 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | BIH | Bosnia and Herzegovina | NA | 119420 | 4555 | 90691 | 0.120 | 0.214 | 0.168 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | LBY | Libya | NA | 113688 | 1763 | 92250 | 0.115 | 0.083 | 0.171 | EMR | Eastern Mediterranean | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | DZA | Algeria | NA | 105596 | 2863 | 71945 | 0.106 | 0.134 | 0.133 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | KEN | Kenya | NA | 99983 | 1744 | 82969 | 0.101 | 0.082 | 0.153 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | BHR | Bahrain | NA | 99817 | 367 | 96532 | 0.101 | 0.017 | 0.179 | EMR | Eastern Mediterranean | High income | WB_HI | 2017 | 2018 |
| Europe | MKD | North Macedonia | NA | 90654 | 2785 | 78154 | 0.091 | 0.131 | 0.145 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Asia | KGZ | Kyrgyzstan | NA | 83900 | 1400 | 79927 | 0.085 | 0.066 | 0.148 | EUR | Europe | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | UZB | Uzbekistan | NA | 78375 | 621 | 76815 | 0.079 | 0.029 | 0.142 | EUR | Europe | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | KOR | Korea, South | NA | 75521 | 1360 | 62956 | 0.076 | 0.064 | 0.116 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Europe | ALB | Albania | NA | 72274 | 1315 | 43890 | 0.073 | 0.062 | 0.081 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | CAN | Canada | British Columbia | 63484 | 1128 | NA | 0.064 | 0.053 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | NOR | Norway | NA | 61082 | 544 | 17998 | 0.062 | 0.026 | 0.033 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | LVA | Latvia | NA | 61008 | 1111 | 48134 | 0.062 | 0.052 | 0.089 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | GHA | Ghana | NA | 60794 | 367 | 57141 | 0.061 | 0.017 | 0.106 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | SGP | Singapore | NA | 59308 | 29 | 59041 | 0.060 | 0.001 | 0.109 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Asia | LKA | Sri Lanka | NA | 58430 | 283 | 49684 | 0.059 | 0.013 | 0.092 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | MNE | Montenegro | NA | 58316 | 768 | 49637 | 0.059 | 0.036 | 0.092 | EUR | Europe | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | UNK | Kosovo | NA | 57317 | 1447 | 50489 | 0.058 | 0.068 | 0.093 | NA | NA | NA | NA | NA | NA |
| Asia | AFG | Afghanistan | NA | 54595 | 2378 | 47298 | 0.055 | 0.112 | 0.087 | EMR | Eastern Mediterranean | Low income | WB_LI | 2017 | 2018 |
| Americas | SLV | El Salvador | NA | 53218 | 1561 | 46845 | 0.054 | 0.073 | 0.087 | AMR | Americas | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | LUX | Luxembourg | NA | 49438 | 562 | 46656 | 0.050 | 0.026 | 0.086 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | ZMB | Zambia | NA | 45337 | 639 | 35960 | 0.046 | 0.030 | 0.067 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | FIN | Finland | NA | 42580 | 644 | 31000 | 0.043 | 0.030 | 0.057 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | EST | Estonia | NA | 40716 | 376 | 30010 | 0.041 | 0.018 | 0.055 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | UGA | Uganda | NA | 39149 | 317 | 13861 | 0.039 | 0.015 | 0.026 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Americas | URY | Uruguay | NA | 37633 | 376 | 29270 | 0.038 | 0.018 | 0.054 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Africa | NAM | Namibia | NA | 32425 | 319 | 30020 | 0.033 | 0.015 | 0.056 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | MOZ | Mozambique | NA | 32418 | 305 | 20558 | 0.033 | 0.014 | 0.038 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | ZWE | Zimbabwe | NA | 31320 | 1005 | 22250 | 0.032 | 0.047 | 0.041 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Asia | CYP | Cyprus | NA | 30017 | 186 | 2057 | 0.030 | 0.009 | 0.004 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | CMR | Cameroon | NA | 29617 | 462 | 28045 | 0.030 | 0.022 | 0.052 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | CAN | Canada | Manitoba | 28697 | 799 | NA | 0.029 | 0.038 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Africa | CIV | Cote d’Ivoire | NA | 26850 | 146 | 24762 | 0.027 | 0.007 | 0.046 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | SDN | Sudan | NA | 26279 | 1603 | 15688 | 0.026 | 0.075 | 0.029 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | SEN | Senegal | NA | 24727 | 575 | 20476 | 0.025 | 0.027 | 0.038 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Americas | CAN | Canada | Saskatchewan | 22177 | 253 | NA | 0.022 | 0.012 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Africa | COD | Congo (Kinshasa) | NA | 21869 | 661 | 14905 | 0.022 | 0.031 | 0.028 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Americas | CUB | Cuba | NA | 21261 | 194 | 16387 | 0.021 | 0.009 | 0.030 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Oceania | AUS | Australia | Victoria | 20442 | 820 | 19591 | 0.021 | 0.039 | 0.036 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Africa | BWA | Botswana | NA | 19654 | 105 | 15911 | 0.020 | 0.005 | 0.029 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | AGO | Angola | NA | 19399 | 459 | 17266 | 0.020 | 0.022 | 0.032 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | MWI | Malawi | NA | 19395 | 508 | 6699 | 0.020 | 0.024 | 0.012 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | MDG | Madagascar | NA | 18743 | 279 | 17930 | 0.019 | 0.013 | 0.033 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | FRA | France | French Polynesia | 17852 | 128 | 4842 | 0.018 | 0.006 | 0.009 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | MLT | Malta | NA | 16658 | 251 | 13801 | 0.017 | 0.012 | 0.026 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | MRT | Mauritania | NA | 16397 | 416 | 15221 | 0.017 | 0.020 | 0.028 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | FRA | France | French Guiana | 15664 | 76 | 9995 | 0.016 | 0.004 | 0.018 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | MDV | Maldives | NA | 14993 | 51 | 13849 | 0.015 | 0.002 | 0.026 | SEAR | South-East Asia | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | JAM | Jamaica | NA | 14879 | 336 | 11870 | 0.015 | 0.016 | 0.022 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | SWZ | Eswatini | NA | 14484 | 479 | 9242 | 0.015 | 0.022 | 0.017 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | GIN | Guinea | NA | 14319 | 81 | 13694 | 0.014 | 0.004 | 0.025 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Asia | SYR | Syria | NA | 13628 | 885 | 7056 | 0.014 | 0.042 | 0.013 | EMR | Eastern Mediterranean | Low income | WB_LI | 2017 | 2018 |
| Asia | THA | Thailand | NA | 13500 | 73 | 10567 | 0.014 | 0.003 | 0.020 | SEAR | South-East Asia | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | CPV | Cabo Verde | NA | 13489 | 127 | 12710 | 0.014 | 0.006 | 0.024 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Asia | TJK | Tajikistan | NA | 13308 | 90 | 13218 | 0.013 | 0.004 | 0.024 | EUR | Europe | Low income | WB_LI | 2017 | 2018 |
| Africa | RWA | Rwanda | NA | 12975 | 174 | 8420 | 0.013 | 0.008 | 0.016 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Americas | BLZ | Belize | NA | 11750 | 290 | 11071 | 0.012 | 0.014 | 0.020 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | HTI | Haiti | NA | 11181 | 243 | 8986 | 0.011 | 0.011 | 0.017 | AMR | Americas | Low income | WB_LI | 2017 | 2018 |
| Africa | GAB | Gabon | NA | 10278 | 67 | 9902 | 0.010 | 0.003 | 0.018 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | BFA | Burkina Faso | NA | 10038 | 112 | 8088 | 0.010 | 0.005 | 0.015 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | FRA | France | Reunion | 9701 | 45 | 9261 | 0.010 | 0.002 | 0.017 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | AND | Andorra | NA | 9549 | 96 | 8652 | 0.010 | 0.005 | 0.016 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | FRA | France | Guadeloupe | 9056 | 157 | 2242 | 0.009 | 0.007 | 0.004 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | BHS | Bahamas | NA | 8101 | 175 | 6720 | 0.008 | 0.008 | 0.012 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Americas | SUR | Suriname | NA | 8057 | 149 | 7246 | 0.008 | 0.007 | 0.013 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | MLI | Mali | NA | 7983 | 323 | 5717 | 0.008 | 0.015 | 0.011 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | COG | Congo (Brazzaville) | NA | 7794 | 117 | 5846 | 0.008 | 0.005 | 0.011 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | LSO | Lesotho | NA | 7656 | 123 | 2168 | 0.008 | 0.006 | 0.004 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | FRA | France | Mayotte | 7544 | 59 | 2964 | 0.008 | 0.003 | 0.005 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | TTO | Trinidad and Tobago | NA | 7473 | 134 | 7015 | 0.008 | 0.006 | 0.013 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Americas | GUY | Guyana | NA | 7298 | 172 | 6370 | 0.007 | 0.008 | 0.012 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | NLD | Netherlands | Aruba | 6723 | 56 | 6240 | 0.007 | 0.003 | 0.012 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | FRA | France | Martinique | 6370 | 44 | 98 | 0.006 | 0.002 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | NIC | Nicaragua | NA | 6204 | 168 | 4225 | 0.006 | 0.008 | 0.008 | AMR | Americas | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | ISL | Iceland | NA | 5981 | 29 | 5868 | 0.006 | 0.001 | 0.011 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | DJI | Djibouti | NA | 5919 | 61 | 5837 | 0.006 | 0.003 | 0.011 | EMR | Eastern Mediterranean | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | GNQ | Equatorial Guinea | NA | 5401 | 86 | 5197 | 0.005 | 0.004 | 0.010 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Oceania | AUS | Australia | New South Wales | 5090 | 54 | 3240 | 0.005 | 0.003 | 0.006 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Africa | CAF | Central African Republic | NA | 4980 | 63 | 4885 | 0.005 | 0.003 | 0.009 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | SOM | Somalia | NA | 4754 | 130 | 3666 | 0.005 | 0.006 | 0.007 | EMR | Eastern Mediterranean | Low income | WB_LI | 2017 | 2018 |
| Africa | TGO | Togo | NA | 4662 | 75 | 3997 | 0.005 | 0.004 | 0.007 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | NLD | Netherlands | Curacao | 4548 | 20 | 4434 | 0.005 | 0.001 | 0.008 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | NER | Niger | NA | 4353 | 151 | 3464 | 0.004 | 0.007 | 0.006 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | GMB | Gambia | NA | 3972 | 128 | 3703 | 0.004 | 0.006 | 0.007 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Gibraltar | 3966 | 63 | 3276 | 0.004 | 0.003 | 0.006 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | SSD | South Sudan | NA | 3788 | 64 | 3542 | 0.004 | 0.003 | 0.007 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | BEN | Benin | NA | 3643 | 48 | 3317 | 0.004 | 0.002 | 0.006 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Channel Islands | 3463 | 79 | 3186 | 0.003 | 0.004 | 0.006 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | TCD | Chad | NA | 3161 | 116 | 2255 | 0.003 | 0.005 | 0.004 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | SLE | Sierra Leone | NA | 3139 | 77 | 2210 | 0.003 | 0.004 | 0.004 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | SMR | San Marino | NA | 2874 | 65 | 2615 | 0.003 | 0.003 | 0.005 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | GNB | Guinea-Bissau | NA | 2532 | 45 | 2421 | 0.003 | 0.002 | 0.004 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | LIE | Liechtenstein | NA | 2441 | 52 | 2322 | 0.002 | 0.002 | 0.004 | NA | NA | High income | WB_HI | 2017 | 2018 |
| Oceania | NZL | New Zealand | NA | 2288 | 25 | 2199 | 0.002 | 0.001 | 0.004 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Africa | COM | Comoros | NA | 2268 | 71 | 1340 | 0.002 | 0.003 | 0.002 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Asia | YEM | Yemen | NA | 2118 | 615 | 1423 | 0.002 | 0.029 | 0.003 | EMR | Eastern Mediterranean | Low income | WB_LI | 2017 | 2018 |
| Africa | ERI | Eritrea | NA | 1940 | 6 | 1304 | 0.002 | 0.000 | 0.002 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Africa | LBR | Liberia | NA | 1914 | 84 | 1714 | 0.002 | 0.004 | 0.003 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Europe | NLD | Netherlands | Sint Maarten | 1753 | 27 | 1574 | 0.002 | 0.001 | 0.003 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | MNG | Mongolia | NA | 1643 | 2 | 1169 | 0.002 | 0.000 | 0.002 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | CAN | Canada | Nova Scotia | 1570 | 65 | NA | 0.002 | 0.003 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Asia | VNM | Vietnam | NA | 1548 | 35 | 1411 | 0.002 | 0.002 | 0.003 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Africa | BDI | Burundi | NA | 1472 | 2 | 773 | 0.001 | 0.000 | 0.001 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Americas | BRB | Barbados | NA | 1387 | 9 | 634 | 0.001 | 0.000 | 0.001 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | MCO | Monaco | NA | 1357 | 9 | 1121 | 0.001 | 0.000 | 0.002 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Oceania | AUS | Australia | Queensland | 1305 | 6 | 1278 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Turks and Caicos Islands | 1287 | 7 | 998 | 0.001 | 0.000 | 0.002 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Africa | STP | Sao Tome and Principe | NA | 1188 | 17 | 997 | 0.001 | 0.001 | 0.002 | AFR | Africa | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | FRA | France | St Martin | 1146 | 12 | 1006 | 0.001 | 0.001 | 0.002 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | New Brunswick | 1124 | 13 | NA | 0.001 | 0.001 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Africa | SYC | Seychelles | NA | 1033 | 3 | 681 | 0.001 | 0.000 | 0.001 | AFR | Africa | High income | WB_HI | 2017 | 2018 |
| Oceania | AUS | Australia | Western Australia | 894 | 9 | 873 | 0.001 | 0.000 | 0.002 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Asia | TWN | Taiwan* | NA | 889 | 7 | 787 | 0.001 | 0.000 | 0.001 | NA | NA | NA | NA | NA | NA |
| Asia | BTN | Bhutan | NA | 855 | 1 | 725 | 0.001 | 0.000 | 0.001 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Oceania | PNG | Papua New Guinea | NA | 835 | 9 | 790 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | LCA | Saint Lucia | NA | 810 | 11 | 424 | 0.001 | 0.001 | 0.001 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | VCT | Saint Vincent and the Grenadines | NA | 738 | 2 | 134 | 0.001 | 0.000 | 0.000 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Diamond Princess | Diamond Princess | Diamond Princess | NA | 712 | 13 | 699 | 0.001 | 0.001 | 0.001 | NA | NA | NA | NA | NA | NA |
| Europe | GBR | United Kingdom | Bermuda | 686 | 12 | 625 | 0.001 | 0.001 | 0.001 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | DNK | Denmark | Faroe Islands | 652 | 1 | 644 | 0.001 | 0.000 | 0.001 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Oceania | AUS | Australia | South Australia | 596 | 4 | 591 | 0.001 | 0.000 | 0.001 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Africa | MUS | Mauritius | NA | 556 | 10 | 518 | 0.001 | 0.000 | 0.001 | AFR | Africa | Upper middle income | WB_UMI | 2017 | 2018 |
| Africa | TZA | Tanzania | NA | 509 | 21 | 183 | 0.001 | 0.001 | 0.000 | AFR | Africa | Low income | WB_LI | 2017 | 2018 |
| Asia | KHM | Cambodia | NA | 458 | 0 | 409 | 0.000 | 0.000 | 0.001 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Isle of Man | 432 | 25 | 364 | 0.000 | 0.001 | 0.001 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Newfoundland and Labrador | 398 | 4 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Cayman Islands | 383 | 2 | 346 | 0.000 | 0.000 | 0.001 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | FRA | France | Saint Barthelemy | 300 | 1 | 204 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Nunavut | 280 | 1 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Oceania | AUS | Australia | Tasmania | 234 | 13 | 221 | 0.000 | 0.001 | 0.000 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Americas | ATG | Antigua and Barbuda | NA | 198 | 6 | 163 | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | NLD | Netherlands | Bonaire, Sint Eustatius and Saba | 196 | 3 | 180 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | BRN | Brunei | NA | 175 | 3 | 169 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Americas | GRD | Grenada | NA | 147 | 1 | 135 | 0.000 | 0.000 | 0.000 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Oceania | AUS | Australia | Australian Capital Territory | 118 | 3 | 115 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | British Virgin Islands | 114 | 1 | 95 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | DMA | Dominica | NA | 113 | 0 | 105 | 0.000 | 0.000 | 0.000 | AMR | Americas | Upper middle income | WB_UMI | 2017 | 2018 |
| Americas | CAN | Canada | Prince Edward Island | 110 | 0 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Oceania | AUS | Australia | Northern Territory | 98 | 0 | 90 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Yukon | 70 | 1 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Asia | TLS | Timor-Leste | NA | 67 | 0 | 50 | 0.000 | 0.000 | 0.000 | SEAR | South-East Asia | Lower middle income | WB_LMI | 2017 | 2018 |
| Oceania | FJI | Fiji | NA | 55 | 2 | 53 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Europe | FRA | France | New Caledonia | 44 | 0 | 43 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Asia | LAO | Laos | NA | 41 | 0 | 41 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | KNA | Saint Kitts and Nevis | NA | 35 | 0 | 33 | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Falkland Islands (Malvinas) | 35 | 0 | 29 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Northwest Territories | 31 | 0 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | DNK | Denmark | Greenland | 30 | 0 | 30 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | VAT | Holy See | NA | 27 | 0 | 15 | 0.000 | 0.000 | 0.000 | NA | NA | NA | NA | NA | NA |
| Oceania | SLB | Solomon Islands | NA | 17 | 0 | 10 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Europe | FRA | France | Saint Pierre and Miquelon | 16 | 0 | 16 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Anguilla | 15 | 0 | 15 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Grand Princess | 13 | 0 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Americas | CAN | Canada | Repatriated Travellers | 13 | 0 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
| Europe | GBR | United Kingdom | Montserrat | 13 | 1 | 12 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| MS Zaandam | MS Zaandam | MS Zaandam | NA | 9 | 2 | 7 | 0.000 | 0.000 | 0.000 | NA | NA | NA | NA | NA | NA |
| Europe | FRA | France | Wallis and Futuna | 4 | 0 | 4 | 0.000 | 0.000 | 0.000 | EUR | Europe | High income | WB_HI | 2017 | 2018 |
| Oceania | MHL | Marshall Islands | NA | 4 | 0 | 4 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Oceania | WSM | Samoa | NA | 2 | 0 | 2 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Upper middle income | WB_UMI | 2017 | 2018 |
| Micronesia | Micronesia | Micronesia | NA | 1 | 0 | 0 | 0.000 | 0.000 | 0.000 | NA | NA | NA | NA | NA | NA |
| Oceania | VUT | Vanuatu | NA | 1 | 0 | 1 | 0.000 | 0.000 | 0.000 | WPR | Western Pacific | Lower middle income | WB_LMI | 2017 | 2018 |
| Americas | CAN | Canada | Diamond Princess | 0 | 1 | NA | 0.000 | 0.000 | 0.000 | AMR | Americas | High income | WB_HI | 2017 | 2018 |
[1] “Tidy Data” H. Wickham, https://www.jstatsoft.org/article/view/v059i10
Open Source Agenda is not affiliated with "Covid 19 Data Cleanup" Project. README Source: jmcastagnetto/covid-19-data-cleanup
Stars
25
Open Issues
0
Last Commit
3 years ago
License
Open Source Agenda Badge
