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May 7, 2021 14:50
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An overview of the features included in the 0.9.1 release of covidregionaldata. See the release notes for more details: https://github.com/epiforecasts/covidregionaldata/releases/tag/v0.9.1
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| library(covidregionaldata) | |
| # set up a data cache | |
| start_using_memoise() | |
| #> Using a cache at: /tmp/RtmphiSeWY | |
| # check for supported countries | |
| get_available_datasets() | |
| #> # A tibble: 18 x 8 | |
| #> origin class level_1_region level_2_region level_3_region type data_urls | |
| #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> | |
| #> 1 Belgium Belg… region province <NA> regi… https://ep… | |
| #> 2 Brazil Braz… state city <NA> regi… https://gi… | |
| #> 3 Canada Cana… province <NA> <NA> regi… https://he… | |
| #> 4 Colombia Colo… departamento <NA> <NA> regi… https://ra… | |
| #> 5 Cuba Cuba provincia <NA> <NA> regi… https://co… | |
| #> 6 Europea… ECDC country <NA> <NA> nati… https://op… | |
| #> 7 France Fran… region department <NA> regi… https://ww… | |
| #> 8 Germany Germ… bundesland landkreis <NA> regi… https://op… | |
| #> 9 Google Goog… country subregion subregion2 nati… https://st… | |
| #> 10 India India state <NA> <NA> regi… https://ap… | |
| #> 11 Italy Italy regioni <NA> <NA> regi… https://ra… | |
| #> 12 John Ho… JHU country region <NA> nati… https://ra… | |
| #> 13 Lithuan… Lith… county municipality <NA> regi… https://op… | |
| #> 14 Mexico Mexi… estado municipio <NA> regi… Downloads/… | |
| #> 15 South A… Sout… province <NA> <NA> regi… https://ra… | |
| #> 16 United … UK region authority <NA> regi… https://ww… | |
| #> 17 United … USA state county <NA> regi… https://ra… | |
| #> 18 World H… WHO country <NA> <NA> nati… https://co… | |
| #> # … with 1 more variable: source_data_cols <chr> | |
| # get national level data from the WHO | |
| get_national_data(source = "who") | |
| #> Downloading data from https://covid19.who.int/WHO-COVID-19-global-data.csv | |
| #> Rows: 116,255 | |
| #> Columns: 8 | |
| #> Delimiter: "," | |
| #> chr [3]: Country_code, Country, WHO_region | |
| #> dbl [4]: New_cases, Cumulative_cases, New_deaths, Cumulative_deaths | |
| #> date [1]: Date_reported | |
| #> | |
| #> Use `spec()` to retrieve the guessed column specification | |
| #> Pass a specification to the `col_types` argument to quiet this message | |
| #> Cleaning data | |
| #> Processing data | |
| #> # A tibble: 116,366 x 15 | |
| #> date un_region who_region country iso_code cases_new cases_total | |
| #> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> | |
| #> 1 2020-01-03 Asia EMRO Afghanistan AF 0 0 | |
| #> 2 2020-01-03 Europe EURO Albania AL 0 0 | |
| #> 3 2020-01-03 Africa AFRO Algeria DZ 0 0 | |
| #> 4 2020-01-03 Oceania WPRO American Samoa AS 0 0 | |
| #> 5 2020-01-03 Europe EURO Andorra AD 0 0 | |
| #> 6 2020-01-03 Africa AFRO Angola AO 0 0 | |
| #> 7 2020-01-03 Americas AMRO Anguilla AI 0 0 | |
| #> 8 2020-01-03 Americas AMRO Antigua & Bar… AG 0 0 | |
| #> 9 2020-01-03 Americas AMRO Argentina AR 0 0 | |
| #> 10 2020-01-03 Asia EURO Armenia AM 0 0 | |
| #> # … with 116,356 more rows, and 8 more variables: deaths_new <dbl>, | |
| #> # deaths_total <dbl>, recovered_new <dbl>, recovered_total <dbl>, | |
| #> # hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl> | |
| # get national level data from Google | |
| get_national_data(source = "google", verbose = FALSE) | |
| #> # A tibble: 1,586,497 x 21 | |
| #> date country iso_3166_1_alpha… cases_new cases_total deaths_new | |
| #> <date> <chr> <chr> <dbl> <dbl> <dbl> | |
| #> 1 2002-10-09 Afghanistan AFG NA NA NA | |
| #> 2 2002-10-09 Albania ALB NA NA NA | |
| #> 3 2002-10-09 Algeria DZA NA NA NA | |
| #> 4 2002-10-09 American Samoa ASM NA NA NA | |
| #> 5 2002-10-09 Andorra AND NA NA NA | |
| #> 6 2002-10-09 Angola AGO NA NA NA | |
| #> 7 2002-10-09 Anguilla AIA NA NA NA | |
| #> 8 2002-10-09 Antigua and Ba… ATG NA NA NA | |
| #> 9 2002-10-09 Argentina ARG NA NA NA | |
| #> 10 2002-10-09 Armenia ARM NA NA NA | |
| #> # … with 1,586,487 more rows, and 15 more variables: deaths_total <dbl>, | |
| #> # recovered_new <dbl>, recovered_total <dbl>, hosp_new <dbl>, | |
| #> # hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>, | |
| #> # aggregation_level <dbl>, current_hospitalized <dbl>, | |
| #> # new_intensive_care <dbl>, total_intensive_care <dbl>, | |
| #> # current_intensive_care <dbl>, new_ventilator <dbl>, total_ventilator <dbl>, | |
| #> # current_ventilator <dbl> | |
| # get national level data from the JHU | |
| get_national_data(source = "JHU", verbose = FALSE) | |
| #> # A tibble: 106,446 x 16 | |
| #> date country iso_3166_1_alpha… cases_new cases_total deaths_new | |
| #> <date> <chr> <chr> <dbl> <dbl> <dbl> | |
| #> 1 2020-01-22 Afghanistan AFG 0 0 0 | |
| #> 2 2020-01-22 Albania ALB 0 0 0 | |
| #> 3 2020-01-22 Algeria DZA 0 0 0 | |
| #> 4 2020-01-22 Andorra AND 0 0 0 | |
| #> 5 2020-01-22 Angola AGO 0 0 0 | |
| #> 6 2020-01-22 Antigua and Ba… ATG 0 0 0 | |
| #> 7 2020-01-22 Argentina ARG 0 0 0 | |
| #> 8 2020-01-22 Armenia ARM 0 0 0 | |
| #> 9 2020-01-22 Australia AUS 0 0 0 | |
| #> 10 2020-01-22 Austria AUT 0 0 0 | |
| #> # … with 106,436 more rows, and 10 more variables: deaths_total <dbl>, | |
| #> # recovered_new <dbl>, recovered_total <dbl>, hosp_new <dbl>, | |
| #> # hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>, | |
| #> # level_2_region_code <dbl>, Lat <dbl>, Long <dbl> | |
| # get regional level data for the UK with NHS regions data | |
| # here we turn off all optional processing steps | |
| get_regional_data("uk", nhsregions = TRUE, process_fns = c(), verbose = FALSE) | |
| #> # A tibble: 5,093 x 16 | |
| #> date region region_code cases_new cases_total deaths_new deaths_total | |
| #> <date> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> | |
| #> 1 2020-01-30 East of… NA NA NA NA NA | |
| #> 2 2020-01-30 England NA 2 2 NA NA | |
| #> 3 2020-01-30 London NA NA NA NA NA | |
| #> 4 2020-01-30 Midlands NA NA NA NA NA | |
| #> 5 2020-01-30 North E… NA 1 1 NA NA | |
| #> 6 2020-01-30 North W… NA NA NA NA NA | |
| #> 7 2020-01-30 Norther… NA NA NA NA NA | |
| #> 8 2020-01-30 Scotland NA NA NA NA NA | |
| #> 9 2020-01-30 South E… NA NA NA NA NA | |
| #> 10 2020-01-30 South W… NA NA NA NA NA | |
| #> # … with 5,083 more rows, and 9 more variables: recovered_new <dbl>, | |
| #> # recovered_total <dbl>, hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, | |
| #> # tested_total <dbl>, hosp_new_first_admissions <dbl>, hosp_new_blend <dbl>, | |
| #> # release_date <date> | |
| # initialise the Google data source for level 2 data | |
| google <- initialise_dataclass("google", level = 2) | |
| # download the Google source (here uses the cache defined above) | |
| google$download() | |
| #> Downloading data from https://storage.googleapis.com/covid19-open-data/v2/epidemiology.csv | |
| #> Downloading data from https://storage.googleapis.com/covid19-open-data/v2/hospitalizations.csv | |
| #> Downloading data from https://storage.googleapis.com/covid19-open-data/v2/index.csv | |
| # clean the Google data | |
| google$clean() | |
| #> Cleaning data | |
| # show available regions with level 2 data | |
| google$available_regions() | |
| #> [1] "Argentina" "Spain" | |
| #> [3] "Mexico" "France" | |
| #> [5] "Switzerland" "Germany" | |
| #> [7] "Indonesia" "Thailand" | |
| #> [9] "United States of America" "Japan" | |
| #> [11] "South Korea" "Czech Republic" | |
| #> [13] "China" "Ukraine" | |
| #> [15] "Philippines" "Australia" | |
| #> [17] "Canada" "Malaysia" | |
| #> [19] "Taiwan" "United Kingdom" | |
| #> [21] "Sweden" "Estonia" | |
| #> [23] "Italy" "Brazil" | |
| #> [25] "Austria" "Portugal" | |
| #> [27] "Belgium" "Chile" | |
| #> [29] "Colombia" "Peru" | |
| #> [31] "Israel" "Netherlands" | |
| #> [33] "India" "Poland" | |
| #> [35] "Haiti" "Norway" | |
| #> [37] "Afghanistan" "Mozambique" | |
| #> [39] "Russia" "South Africa" | |
| #> [41] "Sierra Leone" "Romania" | |
| #> [43] "Democratic Republic of the Congo" "Venezuela" | |
| #> [45] "Sudan" "Pakistan" | |
| #> [47] "Kenya" "Iraq" | |
| #> [49] "Bangladesh" "Libya" | |
| # filter to UK data available from Google | |
| google$filter("united kingdom") | |
| #> Filtering data to: United Kingdom | |
| # process UK data | |
| google$process() | |
| #> Processing data | |
| # return UK data | |
| google$return() | |
| #> # A tibble: 6,874 x 23 | |
| #> date country iso_3166_1_alph… subregion iso_code cases_new cases_total | |
| #> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> | |
| #> 1 2020-01-01 United … GBR East Mid… UKF NA NA | |
| #> 2 2020-01-01 United … GBR East of … UKH NA NA | |
| #> 3 2020-01-01 United … GBR England ENG NA NA | |
| #> 4 2020-01-01 United … GBR London R… UKI NA NA | |
| #> 5 2020-01-01 United … GBR North Ea… UKC NA NA | |
| #> 6 2020-01-01 United … GBR North We… UKD NA NA | |
| #> 7 2020-01-01 United … GBR Northern… NIR NA NA | |
| #> 8 2020-01-01 United … GBR Scotland SCT NA NA | |
| #> 9 2020-01-01 United … GBR South Ea… UKJ NA NA | |
| #> 10 2020-01-01 United … GBR South We… UKK NA NA | |
| #> # … with 6,864 more rows, and 16 more variables: deaths_new <dbl>, | |
| #> # deaths_total <dbl>, recovered_new <dbl>, recovered_total <dbl>, | |
| #> # hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>, | |
| #> # aggregation_level <dbl>, current_hospitalized <dbl>, | |
| #> # new_intensive_care <dbl>, total_intensive_care <dbl>, | |
| #> # current_intensive_care <dbl>, new_ventilator <dbl>, total_ventilator <dbl>, | |
| #> # current_ventilator <dbl> | |
| #' Created on 2021-05-07 by the reprex package v2.0.0 <https://reprex.tidyverse.org> |
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