Package 'covidregionaldata'

Description

Adds extra columns filled with NAs to a dataset. This ensures that all datasets from the covidregionaldata package return datasets of the same underlying structure (i.e. same columns).

Usage

add_extra_na_cols(data)

Arguments

Value

A tibble with relevant NA columns added

See Also

Compulsory processing functions calculate_columns_from_existing_data(), complete_cumulative_columns(), fill_empty_dates_with_na()

Description

Available datasets

Usage

all_country_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 23 rows and 10 columns.

Value

A tibble of available datasets and related information.

Description

Information for downloading, cleaning and processing COVID-19 region level 1 and 2 data for Belgium.

Super class

covidregionaldata::DataClass -> Belgium

Public fields

Methods

Public methods

• Belgium$set_region_codes()

• Belgium$download()

• Belgium$clean_level_1()

• Belgium$clean_level_2()

• Belgium$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Belgium$set_region_codes()

Method download()

Downloads data from source and (for Belgium) applies an initial data patch.

Usage

Belgium$download()

Method clean_level_1()

Region-level Data Cleaning

Usage

Belgium$clean_level_1()

Method clean_level_2()

Province-level Data Cleaning

Usage

Belgium$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

Belgium$clone(deep = FALSE)

Arguments

Source

https://epistat.sciensano.be/Data/COVID19BE_CASES_AGESEX.csv

See Also

Subnational data sources Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Belgium$new(verbose = TRUE, steps = TRUE, get = TRUE, level = "2")
region$return()

## End(Not run)

Description

Information for downloading, cleaning and processing COVID-19 region data for Brazil.

Data available on Github, curated by Wesley Cota: DOI 10.1590/SciELOPreprints.362

Super class

covidregionaldata::DataClass -> Brazil

Public fields

Methods

Public methods

• Brazil$set_region_codes()

• Brazil$clean_common()

• Brazil$clean_level_1()

• Brazil$clean_level_2()

• Brazil$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Brazil$set_region_codes()

Method clean_common()

Common data cleaning for both levels

Usage

Brazil$clean_common()

Method clean_level_1()

State Level Data Cleaning

Usage

Brazil$clean_level_1()

Method clean_level_2()

City Level Data Cleaning

Usage

Brazil$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

Brazil$clone(deep = FALSE)

Arguments

Source

https://github.com/wcota/covid19br

See Also

Subnational data sources Belgium, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Brazil$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Checks which columns are missing (cumulative/daily counts) and if one is present and the other not then calculates the second from the first.

Usage

calculate_columns_from_existing_data(data)

Arguments

Value

A data frame with extra columns if required

See Also

Compulsory processing functions add_extra_na_cols(), complete_cumulative_columns(), fill_empty_dates_with_na()

Description

Information for downloading, cleaning and processing COVID-19 region data for Canada.

Super class

covidregionaldata::DataClass -> Canada

Public fields

Methods

Public methods

• Canada$set_region_codes()

• Canada$clean_common()

• Canada$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Canada$set_region_codes()

Method clean_common()

Provincial Level Data cleaning

Usage

Canada$clean_common()

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Canada$clone(deep = FALSE)

Arguments

Source

https://health-infobase.canada.ca

See Also

Subnational data sources Belgium, Brazil, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Canada$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Checks a given level is supported

Usage

check_level(level, supported_levels)

Arguments

Description

Information for downloading, cleaning and processing COVID-19 region data for Colombia

Super class

covidregionaldata::DataClass -> Colombia

Public fields

Methods

Public methods

• Colombia$set_region_codes()

• Colombia$download()

• Colombia$clean_common()

• Colombia$clean_level_1()

• Colombia$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Colombia$set_region_codes()

Method download()

Colombia specific download using Socrata API This uses the RSocrata package if it is installed or downloads a much larger csv file if that package is not available.

Usage

Colombia$download()

Method clean_common()

Colombia specific data cleaning

Usage

Colombia$clean_common()

Method clean_level_1()

Colombia Specific Department Level Data Cleaning

Aggregates data to the level 1 (department) regional level. Data is provided by the source at the level 2 (municipality) regional level.

Usage

Colombia$clean_level_1()

Method clone()

The objects of this class are cloneable with this method.

Usage

Colombia$clone(deep = FALSE)

Arguments

Source

https://www.datos.gov.co/Salud-y-Protecci-n-Social/Casos-positivos-de-COVID-19-en-Colombia/gt2j-8ykr

See Also

Subnational data sources Belgium, Brazil, Canada, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Colombia$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

The region codes for Colombia

Usage

colombia_codes

Format

An object of class data.frame with 1119 rows and 4 columns.

Value

A tibble of region codes and related information.

Description

If a dataset had a row of NAs added to it (using fill_empty_dates_with_na) then cumulative data columns will have NAs which can cause issues later. This function fills these values with the previous non-NA value.

Usage

complete_cumulative_columns(data)

Arguments

Value

A data tibble with NAs filled in for cumulative data columns.

See Also

Compulsory processing functions add_extra_na_cols(), calculate_columns_from_existing_data(), fill_empty_dates_with_na()

Description

Acts as parent class for national data classes, allowing them to access general methods defined in DataClass() but with additional

Details

On top of the methods documented in DataClass(), this class implements a custom filter function that supports partial matching to English country names using the countrycode package.

Super class

covidregionaldata::DataClass -> CountryDataClass

Public fields

Methods

Public methods

• CountryDataClass$filter()

• CountryDataClass$clone()

Method filter()

Filter method for country level data. Uses countryname to match input countries with known names.

Usage

CountryDataClass$filter(countries, level)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

CountryDataClass$clone(deep = FALSE)

Arguments

See Also

Data interface functions DataClass, get_available_datasets(), get_national_data(), get_regional_data(), initialise_dataclass()

Description

Attributes and methods for COVID-19 data provided by the Covid19 Data Hub

Details

This dataset supports both national and subnational data sources with national level data returned by default. National data is sourced from John Hopkins University and so we recommend using the JHU class included in this package. Subnational data is supported for a subset of countries which can be found after cleaning using the available_regions() method, see the examples for more details. These data sets are minimally cleaned data files hosted by the team at COVID19 Data Hub so please see their source repository for further details (https://github.com/covid19datahub/COVID19/#data-sources) If using for analysis checking the source for further details is strongly advised.

If using this class please cite: "Guidotti et al., (2020). COVID-19 Data Hub Journal of Open Source Software, 5(51), 2376, https://doi.org/10.21105/joss.02376"

Super classes

covidregionaldata::DataClass -> covidregionaldata::CountryDataClass -> Covid19DataHub

Public fields

Methods

Public methods

• Covid19DataHub$clean_common()

• Covid19DataHub$clone()

Method clean_common()

Covid19 Data Hub specific data cleaning. This takes all the raw data, renames some columns and checks types.

Usage

Covid19DataHub$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

Covid19DataHub$clone(deep = FALSE)

Arguments

Source

https://covid19datahub.io/articles/data.html

See Also

Aggregated data sources Google, JHU

National data sources ECDC, Google, JHU, JRC, WHO

Subnational data sources Belgium, Brazil, Canada, Colombia, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

# nolint start
## Not run: 
# set up a data cache
start_using_memoise()

# get all countries data
cv19dh <- Covid19DataHub$new(level = "1", get = TRUE)
cv19dh$return()

# show available regions with data at the second level of interest
cv19dh_level_2 <- Covid19DataHub$new(level = "2")
cv19dh_level_2$download()
cv19dh_level_2$clean()
cv19dh$available_regions()

# get all region data for the uk
cv19dh_level_2$filter("uk")
cv19dh_level_2$process()
cv19dh_level_2$return()

# get all regional data for the UK
uk <- Covid19DataHub$new(regions = "uk", level = "2", get = TRUE)
uk$return()

# get all subregional data for the UK
uk <- Covid19DataHub$new(regions = "uk", level = "3", get = TRUE)
uk$return()

## End(Not run)
# nolint end

Description

Checks for use of memoise and then uses vroom::vroom.

Usage

csv_reader(file, verbose = FALSE, guess_max = 1000, ...)

Arguments

Value

A data table

Description

Information for downloading, cleaning and processing COVID-19 region data for Cuba

Super class

covidregionaldata::DataClass -> Cuba

Public fields

Methods

Public methods

• Cuba$set_region_codes()

• Cuba$clean_common()

• Cuba$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Cuba$set_region_codes()

Method clean_common()

Cuba specific state level data cleaning

Usage

Cuba$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

Cuba$clone(deep = FALSE)

Arguments

Source

https://covid19cubadata.github.io/

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Cuba$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

A parent class containing non-dataset specific methods.

Details

All data sets have shared methods for extracting geographic codes, downloading, processing, and returning data. These functions are contained within this parent class and so are accessible by all data sets which inherit from here. Individual data sets can overwrite any functions or fields providing they define a method with the same name, and can be extended with additional functionality. See the individual method documentaion for further details.

Public fields

Methods

Public methods

• DataClass$set_region_codes()

• DataClass$new()

• DataClass$download()

• DataClass$download_JSON()

• DataClass$clean()

• DataClass$clean_common()

• DataClass$available_regions()

• DataClass$filter()

• DataClass$process()

• DataClass$get()

• DataClass$return()

• DataClass$summary()

• DataClass$test()

• DataClass$clone()

Method set_region_codes()

Place holder for custom country specific function to load region codes.

Usage

DataClass$set_region_codes()

Method new()

Initialize function used by all DataClass objects. Set up the DataClass class with attributes set to input parameters. Should only be called by a DataClass class object.

Usage

DataClass$new(
  level = "1",
  filter_level,
  regions,
  totals = FALSE,
  localise = TRUE,
  verbose = TRUE,
  steps = FALSE,
  get = FALSE,
  process_fns
)

Arguments

Method download()

Download raw data from data_urls, stores a named list of the data_url name and the corresponding raw data table in data$raw

Usage

DataClass$download()

Method download_JSON()

Download raw data from data_urls, stores a named list of the data_url name and the corresponding raw data table in data$raw. Designed as a drop-in replacement for download so it can be used in sub-classes.

Usage

DataClass$download_JSON()

Method clean()

Cleans raw data (corrects format, converts column types, etc). Works on raw data and so should be called after download() Calls the specific class specific cleaning method (clean_common) followed by level specific cleaning methods. clean_level_[1/2]. Cleaned data is stored in data$clean

Usage

DataClass$clean()

Method clean_common()

Cleaning methods that are common across a class. By default this method is empty as if any code is required it should be defined in a child class specific clean_common method.

Usage

DataClass$clean_common()

Method available_regions()

Show regions that are available to be used for filtering operations. Can only be called once clean() has been called. Filtering level is determined by checking the filter_level field.

Usage

DataClass$available_regions(level)

Arguments

Method filter()

Filter cleaned data for a specific region To be called after clean()

Usage

DataClass$filter(regions, level)

Arguments

Method process()

Processes data by adding and calculating absent columns. Called on clean data (after clean()). Some countries may have data as new events (e.g. number of new cases for that day) whilst others have a running total up to that date. Processing calculates these based on what the data comes with via the functions region_dispatch() and process_internal(), which does the following:

• Adds columns not present in the data add_extra_na_cols()

• Ensures there are no negative values set_negative_values_to_zero()

• Removes NA dates fill_empty_dates_with_na()

• Calculates cumulative data complete_cumulative_columns()

• Calculates missing columns from existing ones calculate_columns_from_existing_data()

Usage

DataClass$process(process_fns)

Arguments

Method get()

Get data related to the data class. This runs each distinct step in the workflow in order. Internally calls download(), clean(), filter() and process() download, clean, filter and process methods.

Usage

DataClass$get()

Method return()

Return data. Designed to be called after process() this uses the steps argument to return either a list of all the data preserved at each step or just the processed data. For most datasets a custom method should not be needed.

Usage

DataClass$return()

Method summary()

Create a table of summary information for the data set being processed.

Usage

DataClass$summary()

Returns

Returns a single row summary tibble containing the origin of the data source, class, level 1 and 2 region names, the type of data, the urls of the raw data and the columns present in the raw data.

Method test()

Run tests on a country class instance. Calling test() on a class instance runs tests with the settings in use. For example, if you set level = "1" and localise = FALSE the tests will be run on level 1 data which is not localised. Rather than downloading data for a test users can provide a path to a snapshot file of data to test instead. Tests are run on a clone of the class. This method calls generic tests for all country class objects. It also calls country specific tests which can be defined in an individual country class method called specific_tests(). The snapshots contain the first 1000 rows of data. For more details see the 'testing' vignette: vignette(testing).

Usage

DataClass$test(
  download = FALSE,
  snapshot_dir = paste0(tempdir(), "/snapshots"),
  all = FALSE,
  ...
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

DataClass$clone(deep = FALSE)

Arguments

See Also

Data interface functions CountryDataClass, get_available_datasets(), get_national_data(), get_regional_data(), initialise_dataclass()

Description

Download Excel Documents

Usage

download_excel(url, archive, verbose = FALSE, transpose = TRUE, ...)

Arguments

Value

A data.frame.

Description

Information for downloading, cleaning and processing the European Centre for Disease Prevention and Control COVID-19 data.

Super classes

covidregionaldata::DataClass -> covidregionaldata::CountryDataClass -> ECDC

Public fields

Methods

Public methods

• ECDC$clean_common()

• ECDC$return()

• ECDC$specific_tests()

• ECDC$clone()

Method clean_common()

ECDC specific state level data cleaning

Usage

ECDC$clean_common()

Method return()

Specific return settings for the ECDC dataset.

Usage

ECDC$return()

Method specific_tests()

Run additional tests on ECDC class. Tests ECDC has required additional columns and that there is only one row per country. Designed to be run from test and not run directly.

Usage

ECDC$specific_tests(self_copy, ...)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ECDC$clone(deep = FALSE)

Arguments

Source

https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

See Also

National data sources Covid19DataHub, Google, JHU, JRC, WHO

Examples

## Not run: 
national <- ECDC$new(verbose = TRUE, steps = TRUE, get = TRUE)
national$return()

## End(Not run)

Description

Information for downloading, cleaning and processing COVID-19 region data for Estonia

Super class

covidregionaldata::DataClass -> Estonia

Public fields

Methods

Public methods

• Estonia$set_region_codes()

• Estonia$clean_common()

• Estonia$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Estonia$set_region_codes()

Method clean_common()

Estonia specific state level data cleaning

Usage

Estonia$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

Estonia$clone(deep = FALSE)

Arguments

Source

https://www.terviseamet.ee/et/koroonaviirus/avaandmed

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Estonia$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Checks the date column is an s3 class and that region level column is a character in the cleaned data (data$clean)

Usage

expect_clean_cols(data, level)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_columns_contain_data(), expect_processed_cols(), test_cleaning(), test_download_JSON(), test_download(), test_processing(), test_return()

Description

Checks that cleaned columns cases, deaths, recovered and test (new and total) are not entirely composed of NAs.

Usage

expect_columns_contain_data(DataClass_obj)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_processed_cols(), test_cleaning(), test_download_JSON(), test_download(), test_processing(), test_return()

Description

Checks that processed data columns date, cases_new, cases_total, deaths_new, deaths_total and that region level have the correct types.

Usage

expect_processed_cols(data, level = "1", localised = TRUE)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), test_cleaning(), test_download_JSON(), test_download(), test_processing(), test_return()

Description

There are points, particularly early during data collection, where data was not collected for all regions. This function finds dates which have data for some regions, but not all, and adds rows of NAs for the missing regions. This is mainly for reasons of completeness.

Usage

fill_empty_dates_with_na(data)

Arguments

Value

A tibble with rows of NAs added.

See Also

Compulsory processing functions add_extra_na_cols(), calculate_columns_from_existing_data(), complete_cumulative_columns()

Description

Information for downloading, cleaning and processing COVID-19 region data for France.

Super class

covidregionaldata::DataClass -> France

Public fields

Methods

Public methods

• France$set_region_codes()

• France$clean_level_1()

• France$clean_level_2()

• France$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

France$set_region_codes()

Method clean_level_1()

Region Level Data Cleaning

Usage

France$clean_level_1()

Method clean_level_2()

Department Level Data Cleaning

Usage

France$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

France$clone(deep = FALSE)

Arguments

Source

https://www.data.gouv.fr/fr/datasets/r/406c6a23-e283-4300-9484-54e78c8ae675

https://www.data.gouv.fr/fr/datasets/r/6fadff46-9efd-4c53-942a-54aca783c30c

https://www.data.gouv.fr/fr/datasets/r/001aca18-df6a-45c8-89e6-f82d689e6c01

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- France$new(level = "2", verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

The region codes for France

Usage

france_codes

Format

An object of class data.frame with 104 rows and 5 columns.

Value

A tibble of region codes and related information.

Description

Information for downloading, cleaning and processing COVID-19 region level 1 and 2 data for Germany.

Super class

covidregionaldata::DataClass -> Germany

Public fields

Methods

Public methods

• Germany$set_region_codes()

• Germany$clean_common()

• Germany$clean_level_1()

• Germany$clean_level_2()

• Germany$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Germany$set_region_codes()

Method clean_common()

Common Data Cleaning

Usage

Germany$clean_common()

Method clean_level_1()

Bundesland Level Data Cleaning

Usage

Germany$clean_level_1()

Method clean_level_2()

Landkreis Level Data Cleaning

Usage

Germany$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

Germany$clone(deep = FALSE)

Arguments

Source

https://opendata.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.csv

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Germany$new(verbose = TRUE, steps = TRUE, level = "2", get = TRUE)
region$return()

## End(Not run)

Description

Returns data on what countries are available from the data provided with this package either using a cached dataset or built by searching the target namespace.

Usage

get_available_datasets(type, render = FALSE, namespace = "covidregionaldata")

Arguments

Value

A list of available data sets and the spatial aggregation data is available for.

See Also

Data interface functions CountryDataClass, DataClass, get_national_data(), get_regional_data(), initialise_dataclass()

Examples

# see all available datasets
get_available_datasets()

# see only national level datasets
get_available_datasets("national")

# see only regional level datasets
get_available_datasets("regional")

# render the data
get_available_datasets(render = TRUE)

Description

Provides an interface to source specific classes which support national level data. For simple use cases this allows downloading clean, standardised, national-level COVID-19 data sets. Internally this uses the CountryDataClass() parent class which allows documented downloading, cleaning, and processing. Optionally all steps of data processing can be returned along with the functions used for processing but by default just the finalised processed data is returned. See the examples for some potential use cases and the links to lower level functions for more details and options.

Usage

get_national_data(
  countries,
  source = "who",
  level = "1",
  totals = FALSE,
  steps = FALSE,
  class = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

A tibble with data related to cases, deaths, hospitalisations, recoveries and testing.

See Also

WHO(), ECDC(), JHU(), Google()

Data interface functions CountryDataClass, DataClass, get_available_datasets(), get_regional_data(), initialise_dataclass()

Examples

## Not run: 
# set up a data cache
start_using_memoise()

# download all national data from the WHO
get_national_data(source = "who")

# download data for Canada keeping all processing steps
get_national_data(countries = "canada", source = "ecdc")

# download data for Canada from the JHU and return the full class
jhu <- get_national_data(countries = "canada", source = "jhu", class = TRUE)
jhu

# return the JHU data for canada
jhu$return()

# check which regions the JHU supports national data for
jhu$available_regions()

# filter instead for France (and then reprocess)
jhu$filter("France")
jhu$process()

# explore the structure of the stored JHU data
jhu$data

## End(Not run)

Description

Provides an interface to source specific classes which support regional level data. For simple use cases this allows downloading clean, standardised, regional-level COVID-19 data sets. Internally this uses the DataClass() parent class which allows documented downloading, cleaning, and processing. Optionally all steps of data processing can be returned along with the functions used for processing but by default just the finalised processed data is returned. See the examples for some potential use cases and the links to lower level functions for more details and options.

Usage

get_regional_data(
  country,
  level = "1",
  totals = FALSE,
  localise = TRUE,
  steps = FALSE,
  class = FALSE,
  verbose = TRUE,
  regions,
  ...
)

Arguments

Value

A tibble with data related to cases, deaths, hospitalisations, recoveries and testing stratified by regions within the given country.

See Also

Italy(), UK()

Data interface functions CountryDataClass, DataClass, get_available_datasets(), get_national_data(), initialise_dataclass()

Examples

## Not run: 
# set up a data cache
start_using_memoise()

# download data for Italy
get_regional_data("italy")

# return totals for Italy with no localisation
get_regional_data("italy", localise = FALSE, totals = TRUE)

# download data for the UK but return the class
uk <- get_regional_data("United Kingdom", class = TRUE)
uk

# return UK data from the class object]
uk$return()

## End(Not run)

Description

Glue the spatial level into a variable name

Usage

glue_level(level)

Arguments

Value

A string in the form "level_1_region".

Description

Google specific information for downloading, cleaning and processing covid-19 region data for an example Country. The function works the same as other national data sources, however, data from Google supports three subregions (country, subregion and subregion2) which can be accessed using the 'level' argument. There is also more data available, such as hospitalisations data. The raw data comes as three seperate data sets, "epidemiology" which is comprised of cases, tests and deaths, "index", which holds information about countries linking the other data sets, and "hospitalizations" which holds data about number of people in hospital, ICU, etc.

Super classes

covidregionaldata::DataClass -> covidregionaldata::CountryDataClass -> Google

Public fields

Methods

Public methods

• Google$clean_common()

• Google$clean_level_1()

• Google$clean_level_2()

• Google$new()

• Google$clone()

Method clean_common()

GoogleData specific subregion2 level data cleaning. This takes all the raw data, puts into a single data frame, renames some columns and checks types.

Usage

Google$clean_common()

Method clean_level_1()

Google specific subregion level data cleaning. Takes the data cleaned by clean_common and aggregates it to the country level (level 1).

Usage

Google$clean_level_1()

Method clean_level_2()

Google specific subregion2 level data cleaning. Takes the data cleaned by clean_common and aggregates it to the subregion level (level 2).

Usage

Google$clean_level_2()

Method new()

custom initialize for Google

Usage

Google$new(...)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Google$clone(deep = FALSE)

Arguments

Source

https://github.com/GoogleCloudPlatform/covid-19-open-data

See Also

Aggregated data sources Covid19DataHub, JHU

National data sources Covid19DataHub, ECDC, JHU, JRC, WHO

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

# nolint start
## Not run: 
# set up a data cache
start_using_memoise()

# get all countries
national <- Google$new(level = "1", get = TRUE)
national$return()

# show available regions with data at the second level of interest
google_level_2 <- Google$new(level = "2")
google_level_2$download()
google_level_2$clean()
google$available_regions()

# get all region data for the uk
google_level_2$filter("uk")
google_level_2$process()
google_level_2$return()

# get all regional data for the UK
uk <- Google$new(regions = "uk", level = "2", get = TRUE)
uk$return()

# get all subregional data for the UK
uk <- Google$new(regions = "uk", level = "3", get = TRUE)
uk$return()

## End(Not run)
# nolint end

Description

Information for downloading, cleaning and processing COVID-19 region data for India.

Super class

covidregionaldata::DataClass -> India

Public fields

Methods

Public methods

• India$set_region_codes()

• India$clean_common()

• India$get_desired_status()

• India$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

India$set_region_codes()

Method clean_common()

India state level data cleaning

Usage

India$clean_common()

Method get_desired_status()

Extract data from raw table

Usage

India$get_desired_status(status)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

India$clone(deep = FALSE)

Arguments

Source

https://www.covid19india.org

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- India$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

This function initialises classes based on the DataClass() which allows documented downloading, cleaning, and processing. See the examples for some potential use cases and the DataClass() documentation for more details.

Usage

initialise_dataclass(
  class = character(),
  level = "1",
  totals = FALSE,
  localise = TRUE,
  regions,
  verbose = TRUE,
  steps = FALSE,
  get = FALSE,
  type = c("national", "regional"),
  ...
)

Arguments

Value

An initialised version of the target class if available, e.g. Italy()

See Also

Data interface functions CountryDataClass, DataClass, get_available_datasets(), get_national_data(), get_regional_data()

Examples

## Not run: 
# set up a cache to store data to avoid downloading repeatedly
start_using_memoise()

# check currently available datasets
get_available_datasets()

# initialise a data set in the United Kingdom
# at the UTLA level
utla <- UK$new(level = "2")

# download UTLA data
utla$download()

# clean UTLA data
utla$clean()

# inspect available level 1 regions
utla$available_regions(level = "1")

# filter data to the East of England
utla$filter("East of England")

# process UTLA data
utla$process()

# return processed and filtered data
utla$return()

# inspect all data steps
utla$data

# initialise Italian data, download, clean and process it
italy <- initialise_dataclass("Italy", get = TRUE)
italy$return()

# initialise ECDC data, fully process it, and return totals
ecdc <- initialise_dataclass("ecdc", get = TRUE, totals = TRUE)
ecdc$return()

## End(Not run)

Description

Information for downloading, cleaning and processing COVID-19 region data for Italy.

Super class

covidregionaldata::DataClass -> Italy

Public fields

Methods

Public methods

• Italy$set_region_codes()

• Italy$clean_common()

• Italy$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Italy$set_region_codes()

Method clean_common()

State level data cleaning

Usage

Italy$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

Italy$clone(deep = FALSE)

Arguments

Source

https://github.com/pcm-dpc/COVID-19/

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Italy$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Attributes and methods for COVID-19 data used for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL)

Details

This dataset support both national and subnational data sources with national level data returned by default. Subnational data is supported for a subset of countries which can be found after cleaning using the available_regions() method, see the examples for more details. These data sets are sourced, cleaned, standardised by the JHU team so please see the source repository for further details. Note that unlike many other data sets this means methods applied to this source are not being applied to raw surveillance data but instead to already cleaned data. If using for analysis checking the JHU source for further details is advisable.

If using this data please cite: "Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1"

Super classes

covidregionaldata::DataClass -> covidregionaldata::CountryDataClass -> JHU

Public fields

Methods

Public methods

• JHU$set_region_codes()

• JHU$clean_common()

• JHU$clean_level_1()

• JHU$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

JHU$set_region_codes()

Method clean_common()

JHU specific data cleaning. Joins the raw data sets, checks column types and renames where needed.

Usage

JHU$clean_common()

Method clean_level_1()

JHU specific country level data cleaning. Aggregates the data to the country (level 2) level.

Usage

JHU$clean_level_1()

Method clone()

The objects of this class are cloneable with this method.

Usage

JHU$clone(deep = FALSE)

Arguments

Source

https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data

See Also

Aggregated data sources Covid19DataHub, Google

National data sources Covid19DataHub, ECDC, Google, JRC, WHO

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

# nolint start
## Not run: 
# set up a data cache
start_using_memoise()

# get all countries data
jhu <- JHU$new(level = "1", get = TRUE)
jhu$return()

# show available regions with data at the second level of interest
jhu_level_2 <- JHU$new(level = "2")
jhu_level_2$download()
jhu_level_2$clean()
jhu$available_regions()

# get all region data for the uk
jhu_level_2$filter("uk")
jhu_level_2$process()
jhu_level_2$return()

## End(Not run)
# nolint end

Description

The region codes for JHU

Usage

JHU_codes

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 4193 rows and 2 columns.

Value

A tibble of region codes and related information.

Description

Class for downloading, cleaning and processing COVID-19 region data from the European Commission's Joint Research Centre. Subnational data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available). For more details see https://github.com/ec-jrc/COVID-19

Super classes

covidregionaldata::DataClass -> covidregionaldata::CountryDataClass -> JRC

Public fields

Methods

Public methods

• JRC$clean_common()

• JRC$clean_level_1()

• JRC$clean_level_2()

• JRC$clone()

Method clean_common()

JRC specific data cleaning. The raw source data columns are converted to the correct type and renamed appropriately to match the standard for general processing.

Usage

JRC$clean_common()

Method clean_level_1()

JRC specific country level data cleaning. Selects country level (level 1) columns from the data ready for further processing.

Usage

JRC$clean_level_1()

Method clean_level_2()

JRC specific region level data cleaning. Selects country (level 1) and region (level 2) columns from the data ready for further processing.

Usage

JRC$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

JRC$clone(deep = FALSE)

Arguments

Source

https://github.com/ec-jrc/COVID-19

See Also

National data sources Covid19DataHub, ECDC, Google, JHU, WHO

Examples

## Not run: 
# get country level data
jrc_level_1 <- JRC$new(level = "1", verbose = TRUE, steps = TRUE, get = TRUE)
jrc_level_1$return()

# show available regions with data at the first level of interest (country)
jrc_level_1$available_regions()

# get region level data
jrc_level_2 <- JRC$new(level = "2", verbose = TRUE, steps = TRUE, get = TRUE)
jrc_level_2$return()

# show available regions with data at the second level of interest (region)
jrc_level_2$available_regions()

## End(Not run)

Description

Checks for use of memoise and then uses vroom::vroom.

Usage

json_reader(file, verbose = FALSE, ...)

Arguments

Value

A data table

Description

Information for downloading, cleaning and processing COVID-19 region level 1 and 2 data for Lithuania.

OSP Data fields

The Official Statistics Portal (OSP) provides many data series in their table.

The full range of these vectors can be returned by setting all_osp_fields to TRUE.

The following describes the data provided by the OSP.

* The municipality_code is discarded since it does not correspond to ISO-3166:2 codes used elsewhere in the package.

+ These fields are renamed but returned unmodified.

⁠#⁠ Lithuania offers counts according to three different definitions of whether a death is attributable to COVID-19.

$ This field is not recalculated for counties and is deleted.

Criteria for attributing deaths

Beginning in February 2021 the OSP publishes death counts according to three different criteria, from most to least strictly attributed to COVID-19.

1. of Number of deaths with COVID-19 (coronavirus infection) as the leading cause of death. The indicator is calculated by summing all registered records of medical form E106 (unique persons), in which the main cause of death is IPC disease codes U07.1 or U07.2. Deaths due to external causes are not included (ICD disease codes are V00-Y36, or Y85-Y87, or Y89, or S00-T79, or T89-T98).

2. with Number of deaths with COVID-19 (coronavirus infection) of any cause of death. The indicator is calculated by summing all registered records of the medical form E106 (unique persons), in which the ICD disease codes U07.1, U07.2, U07.3, U07.4, U07.5 are indicated as the main, direct, intermediate cause of death or other important pathological condition, or identified as related to COVID-19 disease (coronavirus infection). Deaths due to external causes are not included (ICD disease codes are V00-Y36, or Y85-Y87, or Y89, or S00-T79, or T89-T98).

3. after Number of deaths from any cause of COVID-19 or COVID-19 deaths due to non-external causes within 28 days. The indicator is calculated by summing all registered records of the medical form E106 (unique persons), in which the ICD disease codes U07.1, U07.2, U07.3, U07.4, U07 are indicated as the main, direct, intermediate cause of death or other important pathological condition, or identified as related to COVID-19 disease (coronavirus infection) and all records of medical form E106 (unique individuals) where the person died within the last 28 days after receiving a positive diagnostic response to the SARS-CoV-2 test or had an entry in medical form E025 with ICD disease code U07.2 or U07.1. Deaths due to external causes are not included (ICD disease codes are V00-Y36, or Y85-Y87, or Y89, or S00-T79, or T89-T98).

The number of deaths reported in the last day is preliminary and increases by about 20-40% in a few days. Such a "delay" in the data is natural: for example, for those who died last night, a death certificate is likely to be issued as soon as this report is published this morning.

De jure and statistical counts

Beginning in February 2021 the OSP makes statistical estimates of the number of recovered and active cases, since review of the data showed that some cases individuals still considered as active cases had recovered, but not documented or registered as such.

These are listed as by the OSP as active_de_jure and recovered_de_jure (officially still considered sick), and active_sttstcl and recovered_sttstcl (an estimate of how many of these are still ill).

Super class

covidregionaldata::DataClass -> Lithuania

Public fields

Methods

Public methods

• Lithuania$set_region_codes()

• Lithuania$clean_common()

• Lithuania$clean_level_1()

• Lithuania$new()

• Lithuania$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Lithuania$set_region_codes()

Method clean_common()

Common data cleaning for both levels

Usage

Lithuania$clean_common()

Method clean_level_1()

Lithuania Specific County Level Data Cleaning

Aggregates data to the level 1 (county) regional level. Data is provided by the source at the level 2 (municipality) regional level.

Usage

Lithuania$clean_level_1()

Method new()

Initialize the country

Usage

Lithuania$new(
  death_definition = "of",
  recovered_definition = "official",
  all_osp_fields = FALSE,
  national_data = FALSE,
  ...
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Lithuania$clone(deep = FALSE)

Arguments

Source

https://hub.arcgis.com/datasets/d49a63c934be4f65a93b6273785a8449_0

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Mexico, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Lithuania$new(verbose = TRUE, steps = TRUE, get = TRUE)

## End(Not run)

Description

The region codes for Lithuania

Usage

lithuania_codes

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 61 rows and 6 columns.

Value

A tibble of region codes and related information, including ISO 3166:2 codes for counties (apskritis) and municipalities (savivaldybe), and noting which municipalities are city municipalities or regional municipalities.

Description

Makes a github workflow yaml file for a given source to be used as an action to check the data as a github action.

Usage

make_github_workflow(
  source,
  workflow_path = paste0(".github/workflows/", source, ".yaml"),
  cron = "36 12 * * *"
)

Arguments

Description

Makes a new regional or national country class with the name provided as the source. This forms a basic template for the user to fill in with the specific field values and cleaning functions required. This also creates a github workflow file for the same country.

Usage

make_new_data_source(
  source,
  type = "subnational",
  newfile_path = paste0("R/", source, ".R")
)

Arguments

Description

A wrapper for message that only prints output when verbose = TRUE.

Usage

message_verbose(verbose = TRUE, ...)

Arguments

Description

Information for downloading, cleaning and processing COVID-19 region data for Mexico.

Notes on region codes:

Level 1 codes = ISO-3166-2, source: https://en.wikipedia.org/wiki/ISO_3166-2:MX

Level 2 codes = INEGI Mexican official statistics geocoding, source: raw data

Level 1 INEGI codes are the first 2 characters of Level 2 INEGI codes

Super class

covidregionaldata::DataClass -> Mexico

Public fields

Methods

Public methods

• Mexico$set_region_codes()

• Mexico$download()

• Mexico$clean_common()

• Mexico$clean_level_1()

• Mexico$clean_level_2()

• Mexico$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Mexico$set_region_codes()

Method download()

Data download() function for Mexico data. This replaces the generic download function in DataClass(). To get the latest data use a PHP script from the website.

Usage

Mexico$download()

Method clean_common()

Common Data Cleaning

Usage

Mexico$clean_common()

Method clean_level_1()

Estados Level Data Cleaning

Usage

Mexico$clean_level_1()

Method clean_level_2()

Municipality Level Data Cleaning

Usage

Mexico$clean_level_2()

Method clone()

The objects of this class are cloneable with this method.

Usage

Mexico$clone(deep = FALSE)

Arguments

Source

https://datos.covid-19.conacyt.mx/

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Netherlands, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Mexico$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Details of the region codes used for the Mexico dataset.

Usage

mexico_codes

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 2489 rows and 4 columns.

Value

A nested tibble of region codes and related information.

Description

Class for downloading, cleaning and processing COVID-19 sub-regional data for the Netherlands, provided by RVIM (English: National Institute for Public Health and the Environment). This data contains number of newly reported cases (that have tested positive), number of newly reported hospital admissions and number of newly reported deaths going back to 27/02/2020. Data is provided at both the province and municipality level.

Super class

covidregionaldata::DataClass -> Netherlands

Public fields

Methods

Public methods

• Netherlands$set_region_codes()

• Netherlands$clean_common()

• Netherlands$clean_level_1()

• Netherlands$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Netherlands$set_region_codes()

Method clean_common()

Common cleaning steps to be applied to raw data, regardless of level (province or municipality) for raw Netherlands data.

Usage

Netherlands$clean_common()

Method clean_level_1()

Netherlands specific province level data cleaning. Takes the data cleaned by clean_common and aggregates it to the Province level (level 1).

Usage

Netherlands$clean_level_1()

Method clone()

The objects of this class are cloneable with this method.

Usage

Netherlands$clone(deep = FALSE)

Arguments

Source

https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/5f6bc429-1596-490e-8618-1ed8fd768427?tab=relations

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, SouthAfrica, Switzerland, UK, USA

Examples

## Not run: 
region <- Netherlands$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Internal shared regional data cleaning designed to be called by process.

Usage

process_internal(
  clean_data,
  level,
  group_vars,
  totals = FALSE,
  localise = TRUE,
  verbose = TRUE,
  process_fns
)

Arguments

See Also

Functions used in the processing pipeline run_default_processing_fns(), run_optional_processing_fns()

Description

Controls the grouping variables used in process_internal based on the supported regions present in the class.

Usage

region_dispatch(level, all_levels, region_names, region_codes)

Arguments

Description

Reset Cache and Update all Local Data

Usage

reset_cache()

Value

Null

Description

Controls data return for get_reigonal_data and get_national_data

Usage

return_data(obj, class = FALSE)

Arguments

Description

The default processing steps to which are always run. Runs on clean data

Usage

run_default_processing_fns(data)

Arguments

See Also

Functions used in the processing pipeline process_internal(), run_optional_processing_fns()

Description

user supplied processing steps which are run after default steps

Usage

run_optional_processing_fns(data, process_fns)

Arguments

See Also

Functions used in the processing pipeline process_internal(), run_default_processing_fns()

Description

Set data values to 0 if they are negative in a dataset. Data in the datasets should always be > 0.

Usage

set_negative_values_to_zero(data)

Arguments

Value

A data frame with all relevant data > 0.

See Also

Optional processing function totalise_data()

Description

Information for downloading, cleaning and processing COVID-19 region data for South Africa.

Super class

covidregionaldata::DataClass -> SouthAfrica

Public fields

Methods

Public methods

• SouthAfrica$set_region_codes()

• SouthAfrica$clean_common()

• SouthAfrica$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

SouthAfrica$set_region_codes()

Method clean_common()

Province level data cleaning

Usage

SouthAfrica$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

SouthAfrica$clone(deep = FALSE)

Arguments

Source

https://github.com/dsfsi/covid19za/

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, Switzerland, UK, USA

Examples

## Not run: 
region <- SouthAfrica$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Adds useMemoise to options meaning memoise is used when reading data in.

Usage

start_using_memoise(path = tempdir(), verbose = TRUE)

Arguments

Description

Sets useMemoise in options to NULL, meaning memoise isn't used when reading data in

Usage

stop_using_memoise()

Description

Information for downloading, cleaning and processing COVID-19 region data for Switzerland

Super class

covidregionaldata::DataClass -> Switzerland

Public fields

Methods

Public methods

• Switzerland$set_region_codes()

• Switzerland$download()

• Switzerland$clean_common()

• Switzerland$clone()

Method set_region_codes()

Set up a table of region codes for clean data

Usage

Switzerland$set_region_codes()

Method download()

Download function to get raw data. Downloads the updated list of CSV files using download_JSON, filters that to identify the required CSV files, then uses the parent method download to download the CSV files.

Usage

Switzerland$download()

Method clean_common()

Switzerland specific state level data cleaning

Usage

Switzerland$clean_common()

Method clone()

The objects of this class are cloneable with this method.

Usage

Switzerland$clone(deep = FALSE)

Arguments

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, UK, USA

Examples

## Not run: 
region <- Switzerland$new(verbose = TRUE, steps = TRUE, get = TRUE)
region$return()

## End(Not run)

Description

Test data can be cleaned properly. The clean method is invoked to generate clean data. This data is checked to ensure it is a data.frame, is not empty, has at least two columns and that columns are clean by calling expect_clean_cols. Also tests that avaliable_regions() are not NA and they are all characters.

Usage

test_cleaning(DataClass_obj)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), expect_processed_cols(), test_download_JSON(), test_download(), test_processing(), test_return()

Description

Test data can be downloaded if download = TRUE, or a requested snapshot file is not found, and store a snap shot in the snapshot_dir. If an existing snapshot file is found then load this data to use in future tests

Usage

test_download(DataClass_obj, download, snapshot_path)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), expect_processed_cols(), test_cleaning(), test_download_JSON(), test_processing(), test_return()

Description

Test data can be downloaded if download = TRUE, or a requested snapshot file is not found, and store a snap shot in the snapshot_dir. If an existing snapshot file is found then load this data to use in future tests

Usage

test_download_JSON(DataClass_obj, download, snapshot_path)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), expect_processed_cols(), test_cleaning(), test_download(), test_processing(), test_return()

Description

Test data can be processed correctly using the process method. process is invoked to generate processed data which is then checked to ensure it is a data.frame, which is not empty, has at least 2 columns and calls expect_processed_columns to check each column types.

Usage

test_processing(DataClass_obj, all = FALSE)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), expect_processed_cols(), test_cleaning(), test_download_JSON(), test_download(), test_return()

Description

Test data can be returned correctly using the return method. return is invoked to generate returned data which is then checked to ensure it is a data.frame, not empty and has at least 2 columns. Each column is then checked to ensure it contains data and is not just composed of NAs.

Usage

test_return(DataClass_obj)

Arguments

See Also

Functions used for testing data is cleaned and processed correctly expect_clean_cols(), expect_columns_contain_data(), expect_processed_cols(), test_cleaning(), test_download_JSON(), test_download(), test_processing()

Description

Get totals data given the time series data.

Usage

totalise_data(data)

Arguments

Value

A data table, totalled up

See Also

Optional processing function set_negative_values_to_zero()

Description

Extracts daily COVID-19 data for the UK, stratified by region and nation. Additional options for this class are: to return subnational English regions using NHS region boundaries instead of PHE boundaries (nhsregions = TRUE), a release date to download from (release_date) and a geographical resolution (resolution).

Super class

covidregionaldata::DataClass -> UK

Public fields

Methods

Public methods

• UK$set_region_codes()

• UK$download()

• UK$clean_level_1()

• UK$clean_level_2()

• UK$new()

• UK$download_filter()

• UK$set_filters()

• UK$download_nhs_regions()

• UK$add_nhs_regions()

• UK$specific_tests()

• UK$clone()

Method set_region_codes()

Specific function for getting region codes for UK .

Usage

UK$set_region_codes()

Method download()

UK specific download() function.

Usage

UK$download()

Method clean_level_1()

Region Level Data Cleaning

Usage

UK$clean_level_1()

Method clean_level_2()

Level 2 Data Cleaning

Usage

UK$clean_level_2()

Method new()

Initalize the UK Class

Usage

UK$new(nhsregions = FALSE, release_date = NULL, resolution = "utla", ...)

Arguments

Examples

\dontrun{
UK$new(
 level = 1, localise = TRUE,
 verbose = True, steps = FALSE,
 nhsregions = FALSE, release_date = NULL,
 resolution = "utla"
)
}

Method download_filter()

Helper function for downloading data API

Usage

UK$download_filter(filter)

Arguments

Method set_filters()

Set filters for UK data api query.

Usage

UK$set_filters()

Method download_nhs_regions()

Download NHS data for level 1 regions Separate NHS data is available for "first" admissions, excluding readmissions. This is available for England + English regions only. Data are available separately for the periods 2020-08-01 to 2021-04-06, and 2021-04-07 - present. See: https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-hospital-activity/ Section 2, "2. Estimated new hospital cases"

Usage

UK$download_nhs_regions()

Returns

nhs data.frame of nhs regions

Method add_nhs_regions()

Add NHS data for level 1 regions Separate NHS data is available for "first" admissions, excluding readmissions. This is available for England + English regions only. See: https://www.england.nhs.uk/statistics/statistical-work-areas/covid-19-hospital-activity/ Section 2, "2. Estimated new hospital cases"

Usage

UK$add_nhs_regions(clean_data, nhs_data)

Arguments

Method specific_tests()

Specific tests for UK data. In addition to generic tests ran by DataClass$test() data for NHS regions are downloaded and ran through the same generic checks (test_cleaning, test_processing, test_return). If download = TRUE or a snapshot file is not found, the nhs data is downloaded and saved to the snapshot location provided. If an existing snapshot file is found then this data is used in the next tests. Tests data can be downloaded, cleaned, processed and returned. Designed to be ran from test and not ran directly.

Usage

UK$specific_tests(
  self_copy,
  download = FALSE,
  all = FALSE,
  snapshot_path = "",
  ...
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

UK$clone(deep = FALSE)

Arguments

Source

https://coronavirus.data.gov.uk/details/download

https://coronavirus.data.gov.uk/details/download

See Also

Subnational data sources Belgium, Brazil, Canada, Colombia, Covid19DataHub, Cuba, Estonia, France, Germany, Google, India, Italy, JHU, Lithuania, Mexico, Netherlands, SouthAfrica, Switzerland, USA