Data extraction tools for the Covid-19 outbreak

This package is now depreciated with development being moved to covidregionaldata

Note: This package makes extensive use of memoise and writes a .cache to the directory in which its functions are run. This speeds up data retrieval and avoids hitting rate limits but does not follow CRAN best practice. Use with care. The cache can be reset with reset_cache() when updated data is required from the online source.

Installation

Install the development version of the package with:

remotes::install_github("epiforecasts/NCoVUtils")

Usage

Worldwide data

There are two sources of worldwide, country-level data on cases and deaths.

1. Extract total global cases and deaths by country, and specify source, using:

• NCoVUtils::get_total_cases(source = c("WHO", "ECDC"))

2. Extract daily international case and death counts compiled by the WHO using:

• NCoVUtils::get_who_cases(country = NULL, daily = TRUE))

3. Extract daily international case and death counts compiled by ECDC using:

• NCoVUtils::get_ecdc_cases()

A further function for worldwide data extracts non-pharmaceutical interventions by country:

• NCoVUtils::get_interventions_data()

And anonymised international patient linelist data can be imported and cleaned with:

• NCoVUtils::get_linelist()

Sub-national data

We have several functions to extract sub-national level data by country. These are typically at the admin-1 level, the largest regions available. We are also working on joining the data to standard georeferencing codes to allow easy mapping.

Currently we include functions for sub-national data in the following countries:

Europe

• Belgium

• France

• Germany

• Italy

• Spain

• United Kingdom

Americas

• Canada

• United States

Eastern Mediterranean

• Afghanistan

Western Pacific

• Korea

• Japan

South-East Asia

• None currently available

Africa

• None currently available

We are working to improve and expand the package: please see the Issues and feel free to comment. We are keen to standardise geocoding (issues #81 and #84) and include data on priority countries (#72). As our capacity is limited, we would very much appreciate any help on these and welcome new pull requests.

Development

Set up

Set your working directory to the home directory of this project (or use the provided Rstudio project). Install the analysis and all dependencies with:

remotes::install_github("epiforecasts/NCoVUtils", dependencies = TRUE)

Render documentation

Render the documentation with the following:

Rscript inst/scripts/render_output.R

Docker

This package is developed in a docker container based on the tidyverse docker image.

To build the docker image run (from the NCoVUtils directory):

docker build . -t ncovutils

To run the docker image run:

docker run -d -p 8787:8787 --name ncovutils -e USER=ncovutils -e PASSWORD=ncovutils ncovutils

The rstudio client can be found on port :8787 at your local machines ip. The default username:password is ncovutils:ncovutils, set the user with -e USER=username, and the password with - e PASSWORD=newpasswordhere. The default is to save the analysis files into the user directory.

To mount a folder (from your current working directory - here assumed to be tmp) in the docker container to your local system use the following in the above docker run command (as given mounts the whole ncovutils directory to tmp).

--mount type=bind,source=$(pwd)/tmp,target=/home/ncovutils

To access the command line run the following:

docker exec -ti ncovutils bash

Alternatively the package environment can be accessed via binder.