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)

Source

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

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"

See also

Super classes

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

Public fields

origin

name of country to fetch data for

supported_levels

A list of supported levels.

supported_region_names

A list of region names in order of level.

supported_region_codes

A list of region codes in order of level.

common_data_urls

List of named links to raw data. The first, and only entry, is be named main.

source_data_cols

existing columns within the raw data

source_text

Plain text description of the source of the data

source_url

Website address for explanation/introduction of the data

Methods

Public methods

Inherited methods

Method set_region_codes()

Set up a table of region codes for clean data

Usage

JHU$set_region_codes()

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_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 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

deep

Whether to make a deep clone.

Method clone()

The objects of this class are cloneable with this method.

Usage

JHU$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# nolint start
if (FALSE) {
# 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()
}
# nolint end