tidycovid19 Merged Data Frametidycovid19_variable_definitions.RdList variable definitions and data sources for the data frame
created by download_merged_data().
Merge with data(tidycovid19_data_sources) to get full information
for each variable, including description of data source and link to URL.
data(tidycovid19_variable_definitions)
An object of class "data.frame".
vd <- tidycovid19_variable_definitions ds <- tidycovid19_data_sources data_info <- dplyr::left_join(vd, ds, by = c(var_source = "id")) print(data_info)#> var_name var_source #> 1 iso3c <NA> #> 2 country <NA> #> 3 date <NA> #> 4 confirmed jhu_ccse #> 5 deaths jhu_ccse #> 6 recovered jhu_ccse #> 7 ecdc_cases ecdc_covid19 #> 8 ecdc_deaths ecdc_covid19 #> 9 total_tests owid_data #> 10 tests_units owid_data #> 11 positive_rate owid_data #> 12 hosp_patients owid_data #> 13 icu_patients owid_data #> 14 total_vaccinations owid_data #> 15 soc_dist acaps_npi #> 16 mov_rest acaps_npi #> 17 pub_health acaps_npi #> 18 gov_soc_econ acaps_npi #> 19 lockdown acaps_npi #> 20 oxcgrt_stringency_index oxford_npi #> 21 oxcgrt_stringency_legacy_index oxford_npi #> 22 oxcgrt_government_response_index oxford_npi #> 23 oxcgrt_containment_health_index oxford_npi #> 24 apple_mtr_driving apple_mtr #> 25 apple_mtr_walking apple_mtr #> 26 apple_mtr_transit apple_mtr #> 27 gcmr_retail_recreation google_cmr #> 28 gcmr_grocery_pharmacy google_cmr #> 29 gcmr_parks google_cmr #> 30 gcmr_transit_stations google_cmr #> 31 gcmr_workplaces google_cmr #> 32 gcmr_residential google_cmr #> 33 gtrends_score google_trends #> 34 gtrends_country_score google_trends #> 35 region wbank #> 36 income wbank #> 37 population wbank #> 38 land_area_skm wbank #> 39 pop_density wbank #> 40 pop_largest_city wbank #> 41 life_expectancy wbank #> 42 gdp_capita wbank #> 43 timestamp <NA> #> var_def #> 1 ISO3c country code as defined by ISO 3166-1 alpha-3 #> 2 Country name #> 3 Calendar date #> 4 Confirmed Covid-19 cases as reported by JHU CSSE (accumulated) #> 5 Covid-19-related deaths as reported by JHU CSSE (accumulated) #> 6 Covid-19 recoveries as reported by JHU CSSE (accumulated) #> 7 Covid-19 cases as reported by ECDC (accumulated, weekly post 2020-12-14) #> 8 Covid-19-related deaths as reported by ECDC (accumulated, weekly post 2020-12-14) #> 9 Accumulated test counts as reported by Our World in Data #> 10 Definition of what constitutes a 'test' #> 11 The share of COVID-19 tests that are positive, given as a rolling 7-day average #> 12 Number of COVID-19 patients in hospital on a given day #> 13 Number of COVID-19 patients in intensive care units (ICUs) on a given day #> 14 Total number of COVID-19 vaccination doses administered #> 15 Number of social distancing measures reported up to date by ACAPS, net of lifted restrictions #> 16 Number of movement restrictions reported up to date by ACAPS, net of lifted restrictions #> 17 Number of public health measures reported up to date by ACAPS, net of lifted restrictions #> 18 Number of social and economic measures reported up to date by ACAPS, net of lifted restrictions #> 19 Number of lockdown measures reported up to date by ACAPS, net of lifted restrictions #> 20 Stringency index as provided by the Oxford COVID-19 Government Response Tracker #> 21 Legacy stringency index based on old data format (prior April 25, 2020) as provided by the Oxford COVID-19 Government Response Tracker #> 22 Overall government response index as provided by the Oxford COVID-19 Government Response Tracker #> 23 Containment and health index as provided by the Oxford COVID-19 Government Response Tracker #> 24 Apple Maps usage for driving directions, as percentage*100 relative to the baseline of Jan 13, 2020 #> 25 Apple Maps usage for walking directions, as percentage*100 relative to the baseline of Jan 13, 2020 #> 26 Apple Maps usage for public transit directions, as percentage*100 relative to the baseline of Jan 13, 2020 #> 27 Google Community Mobility Reports data for the frequency that people visit retail and recreation places expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 28 Google Community Mobility Reports data for the frequency that people visit grocery stores and pharmacies expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 29 Google Community Mobility Reports data for the frequency that people visit parks expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 30 Google Community Mobility Reports data for the frequency that people visit transit stations expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 31 Google Community Mobility Reports data for the frequency that people visit workplaces expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 32 Google Community Mobility Reports data for the frequency that people visit residential places expressed as a percentage*100 change relative to the baseline period Jan 3 - Feb 6, 2020 #> 33 Google search volume for the term 'coronavirus', relative across time with the country maximum scaled to 100 #> 34 Country-level Google search volume for the term 'coronavirus' over a period starting Jan 1, 2020, relative across countries with the country having the highest search volume scaled to 100 (time-stable) #> 35 Country region as classified by the World Bank (time-stable) #> 36 Country income group as classified by the World Bank (time-stable) #> 37 Country population as reported by the World Bank (original identifier 'SP.POP.TOTL', time-stable) #> 38 Country land mass in square kilometers as reported by the World Bank (original identifier 'AG.LND.TOTL.K2', time-stable) #> 39 Country population density as reported by the World Bank (original identifier 'EN.POP.DNST', time-stable) #> 40 Population in the largest metropolian area of the country as reported by the World Bank (original identifier 'EN.URB.LCTY', time-stable) #> 41 Average life expectancy at birth of country citizens in years as reported by the World Bank (original identifier 'SP.DYN.LE00.IN', time-stable) #> 42 Country gross domestic product per capita, measured in 2010 US-$ as reported by the World Bank (original identifier 'NY.GDP.PCAP.KD', time-stable) #> 43 Date and time where data has been collected from authoritative sources #> function_name #> 1 <NA> #> 2 <NA> #> 3 <NA> #> 4 <NA> #> 5 <NA> #> 6 <NA> #> 7 download_ecdc_covid19_data() #> 8 download_ecdc_covid19_data() #> 9 download_owid_data() #> 10 download_owid_data() #> 11 download_owid_data() #> 12 download_owid_data() #> 13 download_owid_data() #> 14 download_owid_data() #> 15 download_acaps_npi_data() #> 16 download_acaps_npi_data() #> 17 download_acaps_npi_data() #> 18 download_acaps_npi_data() #> 19 download_acaps_npi_data() #> 20 download_oxford_npi_data() #> 21 download_oxford_npi_data() #> 22 download_oxford_npi_data() #> 23 download_oxford_npi_data() #> 24 download_apple_mtr_data() #> 25 download_apple_mtr_data() #> 26 download_apple_mtr_data() #> 27 download_google_cmr_data() #> 28 download_google_cmr_data() #> 29 download_google_cmr_data() #> 30 download_google_cmr_data() #> 31 download_google_cmr_data() #> 32 download_google_cmr_data() #> 33 download_google_trends_data() #> 34 download_google_trends_data() #> 35 download_wbank_data() #> 36 download_wbank_data() #> 37 download_wbank_data() #> 38 download_wbank_data() #> 39 download_wbank_data() #> 40 download_wbank_data() #> 41 download_wbank_data() #> 42 download_wbank_data() #> 43 <NA> #> description #> 1 <NA> #> 2 <NA> #> 3 <NA> #> 4 <NA> #> 5 <NA> #> 6 <NA> #> 7 Country-level weekly data on new cases and deaths provided by the European Centre for Disease Prevention and Control (ECDC). The data was updated daily until 2020-12-14 and contains the latest available public data on the number of new Covid-19 cases reported per week and per country. #> 8 Country-level weekly data on new cases and deaths provided by the European Centre for Disease Prevention and Control (ECDC). The data was updated daily until 2020-12-14 and contains the latest available public data on the number of new Covid-19 cases reported per week and per country. #> 9 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 10 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 11 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 12 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 13 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 14 The Our World in Data team systematically collects data on Covid-19 testing, hospitalizations, and vaccinations from multiple national sources. Data points are collected with varying frequency across countries. The definition on what consitutes a 'test' varies, reflected by the variable 'tests_units' in the data frame. The vaccination data is currently only available based on ad hoc disclosures by a small set of countries. #> 15 The #COVID19 Government Measures Dataset is provided by ACAPS. It puts together measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The data is reported in event structure with an event reflecting a government measure. Measures are characterized as being either imposing/extending measures or lifting them and categorized in five categories with each category being split up in further sub-categories. Please note: ACAPS stopped updating the data on December 10, 2020 #> 16 The #COVID19 Government Measures Dataset is provided by ACAPS. It puts together measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The data is reported in event structure with an event reflecting a government measure. Measures are characterized as being either imposing/extending measures or lifting them and categorized in five categories with each category being split up in further sub-categories. Please note: ACAPS stopped updating the data on December 10, 2020 #> 17 The #COVID19 Government Measures Dataset is provided by ACAPS. It puts together measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The data is reported in event structure with an event reflecting a government measure. Measures are characterized as being either imposing/extending measures or lifting them and categorized in five categories with each category being split up in further sub-categories. Please note: ACAPS stopped updating the data on December 10, 2020 #> 18 The #COVID19 Government Measures Dataset is provided by ACAPS. It puts together measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The data is reported in event structure with an event reflecting a government measure. Measures are characterized as being either imposing/extending measures or lifting them and categorized in five categories with each category being split up in further sub-categories. Please note: ACAPS stopped updating the data on December 10, 2020 #> 19 The #COVID19 Government Measures Dataset is provided by ACAPS. It puts together measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The data is reported in event structure with an event reflecting a government measure. Measures are characterized as being either imposing/extending measures or lifting them and categorized in five categories with each category being split up in further sub-categories. Please note: ACAPS stopped updating the data on December 10, 2020 #> 20 The data on the Oxford Coronavirus Government Response Tracker (OxCGRT) on non-pharmaceutical interventions comes in two data frames that you can select by setting the 'type' parameter. The 'measures' data frame reports data on governmental response measures as reported by the Oxford OxCGRT team. It is tidied by arranging its content by measure. All original country-day observations that are either initial or represent a value (not note) change from the previous day are included. Economic measures (E1-E4) are not included. The 'index' data frame reports the 'Stringency Index' and the 'Legacy Stringency Index' as calculated by the OxCGRT team based on their governance response measures in a country-day structure. Please note: As indicated on the homepage of the project, too a large extend the data is no longer updated after December 31, 2022 while data review processes continue. #> 21 The data on the Oxford Coronavirus Government Response Tracker (OxCGRT) on non-pharmaceutical interventions comes in two data frames that you can select by setting the 'type' parameter. The 'measures' data frame reports data on governmental response measures as reported by the Oxford OxCGRT team. It is tidied by arranging its content by measure. All original country-day observations that are either initial or represent a value (not note) change from the previous day are included. Economic measures (E1-E4) are not included. The 'index' data frame reports the 'Stringency Index' and the 'Legacy Stringency Index' as calculated by the OxCGRT team based on their governance response measures in a country-day structure. Please note: As indicated on the homepage of the project, too a large extend the data is no longer updated after December 31, 2022 while data review processes continue. #> 22 The data on the Oxford Coronavirus Government Response Tracker (OxCGRT) on non-pharmaceutical interventions comes in two data frames that you can select by setting the 'type' parameter. The 'measures' data frame reports data on governmental response measures as reported by the Oxford OxCGRT team. It is tidied by arranging its content by measure. All original country-day observations that are either initial or represent a value (not note) change from the previous day are included. Economic measures (E1-E4) are not included. The 'index' data frame reports the 'Stringency Index' and the 'Legacy Stringency Index' as calculated by the OxCGRT team based on their governance response measures in a country-day structure. Please note: As indicated on the homepage of the project, too a large extend the data is no longer updated after December 31, 2022 while data review processes continue. #> 23 The data on the Oxford Coronavirus Government Response Tracker (OxCGRT) on non-pharmaceutical interventions comes in two data frames that you can select by setting the 'type' parameter. The 'measures' data frame reports data on governmental response measures as reported by the Oxford OxCGRT team. It is tidied by arranging its content by measure. All original country-day observations that are either initial or represent a value (not note) change from the previous day are included. Economic measures (E1-E4) are not included. The 'index' data frame reports the 'Stringency Index' and the 'Legacy Stringency Index' as calculated by the OxCGRT team based on their governance response measures in a country-day structure. Please note: As indicated on the homepage of the project, too a large extend the data is no longer updated after December 31, 2022 while data review processes continue. #> 24 Apple's Mobility Trend Reports reflect requests for directions in Apple Maps. The data frame is organized by country-day and its data are expressed as percentages relative to a baseline volume on January 13th, 2020. The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for regions for which Apple reports regional data. The 'country_city' data frame reports city-level data for cities for which Apple reports this data. Please note: Apple stopped providing this data on April 14, 2022 #> 25 Apple's Mobility Trend Reports reflect requests for directions in Apple Maps. The data frame is organized by country-day and its data are expressed as percentages relative to a baseline volume on January 13th, 2020. The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for regions for which Apple reports regional data. The 'country_city' data frame reports city-level data for cities for which Apple reports this data. Please note: Apple stopped providing this data on April 14, 2022 #> 26 Apple's Mobility Trend Reports reflect requests for directions in Apple Maps. The data frame is organized by country-day and its data are expressed as percentages relative to a baseline volume on January 13th, 2020. The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for regions for which Apple reports regional data. The 'country_city' data frame reports city-level data for cities for which Apple reports this data. Please note: Apple stopped providing this data on April 14, 2022 #> 27 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 28 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 29 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 30 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 31 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 32 Google's Community Mobility Reports chart movement trends over time across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. They show how visits and length of stays at different places change in percentages compared to a baseline (the median value, for the corresponding day of the week, during the 5-week period Jan 3 to Feb 6, 2020). The data comes in three data frames that you can select by the 'type' parameter. The 'country' data frame contains country-day level data. The 'country_region' data frame provides regional data for the countries for which Google reports regional data. The 'us_county' data frame reports daily data for the U.S. at the county level. Please note: Google stopped providing that data on October 15, 2022 #> 33 Data are Google Search Volume (GSV) measures as provided by Google Trends API, with the default search term 'coronavirus'. The data comes in four data frames that you can select by the 'type' parameter and the sample period comprises Jan 1, 2020 up to date. The 'country' data frame lists GSV by country, to assess which country on average uses the search term most often over the sample period. The 'country-day' data frame reports daily search volume data for all countries that show up in the 'country' data frame. Each value is relative within country, meaning that values across countries cannot be compared directly. The 'region' and 'city' data frames list the relative GSV across regions and city within countries when provided by Google Trends. Keep in mind that within each data frame GSV are relative measures with a maximum of 100 indicating the highest search volume. This implies that GSV measures are not comparable across data frames. #> 34 Data are Google Search Volume (GSV) measures as provided by Google Trends API, with the default search term 'coronavirus'. The data comes in four data frames that you can select by the 'type' parameter and the sample period comprises Jan 1, 2020 up to date. The 'country' data frame lists GSV by country, to assess which country on average uses the search term most often over the sample period. The 'country-day' data frame reports daily search volume data for all countries that show up in the 'country' data frame. Each value is relative within country, meaning that values across countries cannot be compared directly. The 'region' and 'city' data frames list the relative GSV across regions and city within countries when provided by Google Trends. Keep in mind that within each data frame GSV are relative measures with a maximum of 100 indicating the highest search volume. This implies that GSV measures are not comparable across data frames. #> 35 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 36 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 37 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 38 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 39 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 40 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 41 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 42 The data frame reports current country-level statistics from the World Bank. The regional and income level classifications are also provided by the World Bank. 'life_expectancy' is measured in years at birth and 'gdp_capita' is measured in 2010 US-$. The original World Bank data items are (in the order how they are represented in the data frame) 'SP.POP.TOTL', 'AG.LND.TOTL.K2', 'EN.POP.DNST', 'EN.URB.LCTY', 'SP.DYN.LE00.IN', 'NY.GDP.PCAP.KD'. When you set the parameter 'var_def' tot 'TRUE'. the data comes in a list containing two data frames. The first contains the actual data, the second contains variable definitions. #> 43 <NA> #> url #> 1 <NA> #> 2 <NA> #> 3 <NA> #> 4 <NA> #> 5 <NA> #> 6 <NA> #> 7 https://www.ecdc.europa.eu/en/covid-19/data #> 8 https://www.ecdc.europa.eu/en/covid-19/data #> 9 https://github.com/owid/covid-19-data/tree/master/public/data #> 10 https://github.com/owid/covid-19-data/tree/master/public/data #> 11 https://github.com/owid/covid-19-data/tree/master/public/data #> 12 https://github.com/owid/covid-19-data/tree/master/public/data #> 13 https://github.com/owid/covid-19-data/tree/master/public/data #> 14 https://github.com/owid/covid-19-data/tree/master/public/data #> 15 https://www.acaps.org/covid19-government-measures-dataset #> 16 https://www.acaps.org/covid19-government-measures-dataset #> 17 https://www.acaps.org/covid19-government-measures-dataset #> 18 https://www.acaps.org/covid19-government-measures-dataset #> 19 https://www.acaps.org/covid19-government-measures-dataset #> 20 https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker #> 21 https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker #> 22 https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker #> 23 https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker #> 24 https://www.apple.com/covid19/mobility #> 25 https://www.apple.com/covid19/mobility #> 26 https://www.apple.com/covid19/mobility #> 27 https://www.google.com/covid19/mobility/ #> 28 https://www.google.com/covid19/mobility/ #> 29 https://www.google.com/covid19/mobility/ #> 30 https://www.google.com/covid19/mobility/ #> 31 https://www.google.com/covid19/mobility/ #> 32 https://www.google.com/covid19/mobility/ #> 33 https://trends.google.com/ #> 34 https://trends.google.com/ #> 35 https://data.worldbank.org #> 36 https://data.worldbank.org #> 37 https://data.worldbank.org #> 38 https://data.worldbank.org #> 39 https://data.worldbank.org #> 40 https://data.worldbank.org #> 41 https://data.worldbank.org #> 42 https://data.worldbank.org #> 43 <NA> #> last_data #> 1 <NA> #> 2 <NA> #> 3 <NA> #> 4 <NA> #> 5 <NA> #> 6 <NA> #> 7 2023-11-27 #> 8 2023-11-27 #> 9 2024-01-27 #> 10 2024-01-27 #> 11 2024-01-27 #> 12 2024-01-27 #> 13 2024-01-27 #> 14 2024-01-27 #> 15 2021-01-04 #> 16 2021-01-04 #> 17 2021-01-04 #> 18 2021-01-04 #> 19 2021-01-04 #> 20 2022-12-31 #> 21 2022-12-31 #> 22 2022-12-31 #> 23 2022-12-31 #> 24 2022-04-12 #> 25 2022-04-12 #> 26 2022-04-12 #> 27 2022-10-15 #> 28 2022-10-15 #> 29 2022-10-15 #> 30 2022-10-15 #> 31 2022-10-15 #> 32 2022-10-15 #> 33 2024-01-21 #> 34 2024-01-21 #> 35 2024-01-28 #> 36 2024-01-28 #> 37 2024-01-28 #> 38 2024-01-28 #> 39 2024-01-28 #> 40 2024-01-28 #> 41 2024-01-28 #> 42 2024-01-28 #> 43 <NA>