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Merge pull request epiforecasts#462 from pitmonticone/master
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Fixed a typo in the README file
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@seabbs
seabbs authored Feb 11, 2022
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2 changes: 1 addition & 1 deletion README.Rmd
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Expand Up @@ -135,7 +135,7 @@ uk_nots %>%
```

See `get_available_datasets()` for supported regions and subregional levels.
To view what datasets we currently have subnationaldata for, along with their current status, check the
To view what datasets we currently have subnational data for, along with their current status, check the
[supported countries](https://epiforecasts.io/covidregionaldata/articles/supported-countries.html) page
or build the [supported countries vignette](vignettes/supported-countries.Rmd).

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88 changes: 38 additions & 50 deletions README.md
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Expand Up @@ -78,7 +78,7 @@ the temporary directory by default),

``` r
start_using_memoise()
#> Using a cache at: /tmp/RtmpPgZXiv
#> Using a cache at: /var/folders/68/22ndk9854tq394wl_n1cxzlr0000gn/T//RtmpylL81U
```

To stop using `memoise` use,
Expand All @@ -103,34 +103,24 @@ the Google COVID-19 open data project), use:
``` r
nots <- get_national_data()
#> Downloading data from https://covid19.who.int/WHO-COVID-19-global-data.csv
#> Rows: 142911 Columns: 8
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): Country_code, Country, WHO_region
#> dbl (4): New_cases, Cumulative_cases, New_deaths, Cumulative_deaths
#> date (1): Date_reported
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Cleaning data
#> Processing data
nots
#> # A tibble: 142,911 × 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 & Barbuda AG 0 0
#> 9 2020-01-03 Americas AMRO Argentina AR 0 0
#> 10 2020-01-03 Asia EURO Armenia AM 0 0
#> # … with 142,901 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>
#> # A tibble: 182,253 × 15
#> date un_region who_region country iso_code cases_new cases_total deaths_new deaths_total recovered_new
#> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-03 Asia EMRO Afghan… AF 0 0 0 0 NA
#> 2 2020-01-03 Europe EURO Albania AL 0 0 0 0 NA
#> 3 2020-01-03 Africa AFRO Algeria DZ 0 0 0 0 NA
#> 4 2020-01-03 Oceania WPRO Americ… AS 0 0 0 0 NA
#> 5 2020-01-03 Europe EURO Andorra AD 0 0 0 0 NA
#> 6 2020-01-03 Africa AFRO Angola AO 0 0 0 0 NA
#> 7 2020-01-03 Americas AMRO Anguil… AI 0 0 0 0 NA
#> 8 2020-01-03 Americas AMRO Antigu… AG 0 0 0 0 NA
#> 9 2020-01-03 Americas AMRO Argent… AR 0 0 0 0 NA
#> 10 2020-01-03 Asia EURO Armenia AM 0 0 0 0 NA
#> # … with 182,243 more rows, and 5 more variables: recovered_total <dbl>, hosp_new <dbl>, hosp_total <dbl>,
#> # tested_new <dbl>, tested_total <dbl>
```

This can also be filtered for a country of interest,
Expand Down Expand Up @@ -169,26 +159,25 @@ for example by level 1 region in the UK, use:
``` r
uk_nots <- get_regional_data(country = "UK", verbose = FALSE)
uk_nots
#> # A tibble: 7,501 × 26
#> date region region_code cases_new cases_total deaths_new deaths_total
#> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-30 East Mi… E12000004 NA NA NA NA
#> 2 2020-01-30 East of… E12000006 NA NA NA NA
#> 3 2020-01-30 England E92000001 2 2 NA NA
#> 4 2020-01-30 London E12000007 NA NA NA NA
#> 5 2020-01-30 North E… E12000001 NA NA NA NA
#> 6 2020-01-30 North W… E12000002 NA NA NA NA
#> 7 2020-01-30 Norther… N92000002 NA NA NA NA
#> 8 2020-01-30 Scotland S92000003 NA NA NA NA
#> 9 2020-01-30 South E… E12000008 NA NA NA NA
#> 10 2020-01-30 South W… E12000009 NA NA NA NA
#> # … with 7,491 more rows, and 19 more variables: recovered_new <dbl>,
#> # recovered_total <dbl>, hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>,
#> # tested_total <dbl>, areaType <chr>, cumCasesByPublishDate <dbl>,
#> # cumCasesBySpecimenDate <dbl>, newCasesByPublishDate <dbl>,
#> # newCasesBySpecimenDate <dbl>, cumDeaths28DaysByDeathDate <dbl>,
#> # cumDeaths28DaysByPublishDate <dbl>, newDeaths28DaysByDeathDate <dbl>,
#> # newDeaths28DaysByPublishDate <dbl>, …
#> # A tibble: 9,893 × 26
#> date region region_code cases_new cases_total deaths_new deaths_total recovered_new recovered_total
#> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-11 North Ea… E12000001 NA NA NA NA NA NA
#> 2 2020-01-11 North We… E12000002 NA NA NA NA NA NA
#> 3 2020-01-11 Yorkshir… E12000003 NA NA NA NA NA NA
#> 4 2020-01-11 East Mid… E12000004 NA NA NA NA NA NA
#> 5 2020-01-11 West Mid… E12000005 NA NA NA NA NA NA
#> 6 2020-01-11 East of … E12000006 NA NA NA NA NA NA
#> 7 2020-01-11 London E12000007 NA NA NA NA NA NA
#> 8 2020-01-11 South Ea… E12000008 NA NA NA NA NA NA
#> 9 2020-01-11 South We… E12000009 NA NA NA NA NA NA
#> 10 2020-01-11 England E92000001 NA NA NA NA NA NA
#> # … with 9,883 more rows, and 17 more variables: hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>,
#> # tested_total <dbl>, areaType <chr>, cumCasesByPublishDate <dbl>, cumCasesBySpecimenDate <dbl>,
#> # newCasesByPublishDate <dbl>, newCasesBySpecimenDate <dbl>, cumDeaths28DaysByDeathDate <dbl>,
#> # cumDeaths28DaysByPublishDate <dbl>, newDeaths28DaysByDeathDate <dbl>, newDeaths28DaysByPublishDate <dbl>,
#> # newPillarFourTestsByPublishDate <lgl>, newPillarOneTestsByPublishDate <dbl>,
#> # newPillarThreeTestsByPublishDate <dbl>, newPillarTwoTestsByPublishDate <dbl>
```

Now we have the data we can create plots, for example the time-series of
Expand All @@ -210,7 +199,7 @@ uk_nots %>%
<img src="man/figures/README-uk_plot-1.png" width="100%" />

See `get_available_datasets()` for supported regions and subregional
levels. To view what datasets we currently have subnationaldata for,
levels. To view what datasets we currently have subnational data for,
along with their current status, check the [supported
countries](https://epiforecasts.io/covidregionaldata/articles/supported-countries.html)
page or build the [supported countries
Expand All @@ -231,9 +220,8 @@ using the following,
#>
#> To cite covidregionaldata in publications use:
#>
#> Joseph Palmer, Katharine Sherratt, Richard Martin-Nielsen, Jonnie
#> Bevan, Hamish Gibbs, Sebastian Funk and Sam Abbott (2021).
#> covidregionaldata: Subnational data for COVID-19 epidemiology, DOI:
#> Joseph Palmer, Katharine Sherratt, Richard Martin-Nielsen, Jonnie Bevan, Hamish Gibbs, Sebastian
#> Funk and Sam Abbott (2021). covidregionaldata: Subnational data for COVID-19 epidemiology, DOI:
#> 10.21105/joss.03290
#>
#> A BibTeX entry for LaTeX users is
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