Function reference • covidregionaldata

Get Covid-19 Data

Functions that provide interfaces to download cleaned and processed datasets. Internally many of these functions call the classes documented below and so optional class specific arguments can also be used.

get_available_datasets(): Get supported data sets

get_national_data(): Get national-level data for countries globally from a range of sources

get_regional_data(): Get regional-level data

initialise_dataclass(): Initialise a child class of DataClass if it exists

DataClass: R6 Class containing non-dataset specific methods

CountryDataClass: R6 Class containing national level methods

Dataset Classes

Classes that define individual datasets and contain all the methods needed to download, process, and clean them.

Belgium: Belgium Class for downloading, cleaning and processing notification data

Brazil: Brazil Class for downloading, cleaning and processing notification data

Canada: Canada Class containing origin specific attributes and methods

Colombia: Colombia Class for downloading, cleaning and processing notification data

Covid19DataHub: R6 Class containing specific attributes and methods for Covid19 Data Hub

Cuba: Cuba Class for downloading, cleaning and processing notification data

ECDC: R6 Class containing specific attributes and methods for the European Centre for Disease Prevention and Control dataset

Estonia: Estonia Class for downloading, cleaning and processing notification data

France: France Class containing origin specific attributes and methods

Germany: Germany Class for downloading, cleaning and processing notification data

Google: R6 Class containing specific attributes and methods for Google data

India: India Class for downloading, cleaning and processing notification data

Italy: Italy Class for downloading, cleaning and processing notification data

JHU: R6 Class containing specific attributes and methods for John Hopkins University data

JRC: R6 Class containing specific attributes and methods for European Commission's Joint Research Centre data

Lithuania: Lithuania Class for downloading, cleaning and processing notification data

Mexico: Meixco Class for downloading, cleaning and processing notification data

Netherlands: Netherlands Class for downloading, cleaning and processing notification data

SouthAfrica: SouthAfrica Class for downloading, cleaning and processing notification data

Switzerland: Switzerland Class for downloading, cleaning and processing notification data

UK: United Kingdom Class for downloading, cleaning and processing notification data.

USA: USA Class for downloading, cleaning and processing notification data

WHO: R6 Class containing specific attributes and methods for World Health Organisation data

Dataset processing

Functions used to process data once it has been cleaned into a standardised format. These functions are split into compulsory processing options, which are always used, and optional processing functions which may be added/removed by the user.

Compulsory processing

add_extra_na_cols(): Add extra columns filled with NA values to a dataset.

calculate_columns_from_existing_data(): Cumulative counts from daily counts or daily counts from cumulative, dependent on which columns already exist

complete_cumulative_columns(): Completes cumulative columns if rows were added with NAs.

fill_empty_dates_with_na(): Add rows of NAs for dates where a region does not have any data

Optional processing

set_negative_values_to_zero(): Set negative data to 0

totalise_data(): Get totals data given the time series data.

Utilities

Utility functions that either help manage data, provide additional information, or convert the data into new formats.

all_country_data: Table of available datasets along with level and other information. Rendered from the individual R6 class objects included in this package.

check_level(): Checks a given level is supported

csv_reader(): Custom CSV reading function

download_excel(): Download Excel Documents

glue_level(): Glue the spatial level into a variable name

json_reader(): Custom JSON reading function

make_github_workflow(): Create github action for a given source

make_new_data_source(): Create new country class for a given source

message_verbose(): Wrapper for message

process_internal(): Internal Shared Regional Dataset Processing

region_dispatch(): Control Grouping Variables used in process_internal

reset_cache(): Reset Cache and Update all Local Data

return_data(): Control data return

run_default_processing_fns(): Default processing steps to run

run_optional_processing_fns(): Optional processing steps to run

start_using_memoise(): Add useMemoise to options

stop_using_memoise(): Stop using useMemoise

Tests

Testing functions that check at each step (cleaning, processing, returning) the data structure and types are as expected. Requires testthat to be installed.

expect_clean_cols(): Test clean columns contain the correct data and types

expect_columns_contain_data(): Test that cleaned columns contain data/

expect_processed_cols(): Test that processed columns contain the correct data and types

test_cleaning(): Test clean method works correctly

test_download(): Test download method works correctly

test_download_JSON(): Test download method for JSON files works correctly

test_processing(): Test process method works correctly

test_return(): Test return method works correctly

Datasets

Package datasets

JHU_codes: Region Codes for JHU Dataset. Taken from the region codes provided as part of the WHO dataset.

colombia_codes: Region Codes for Colombia Dataset.

france_codes: Region Codes for France Dataset.

lithuania_codes: Region Codes for Lithuania Dataset.

mexico_codes: Region Codes for Mexico Dataset.

uk_codes: Region Codes for UK Dataset.

vietnam_codes: Region Codes for Vietnam Dataset.