This package compiles a series of publicly available disease outbreak data. Data can be provided as R objects (loaded automatically when loading the package), text files distributed alongside the package, or functions generating a dataset.
The following R datasets are currently available:
| Item | Title |
|---|---|
| dengue_fais_2011 | Dengue on the island of Fais, Micronesia, 2011 |
| dengue_yap_2011 | Dengue on the Yap Main Islands, Micronesia, 2011 |
| ebola_kikwit_1995 | Ebola in Kikwit, Democratic Republic of the Congo, 1995 |
| ebola_sim | Simulated Ebola outbreak |
| ebola_sim_clean | Simulated Ebola outbreak |
| fluH7N9_china_2013 | Influenza A H7N9 in China, 2013 |
| influenza_england_1978_school | Influenza in a boarding school in England, 1978 |
| measles_hagelloch_1861 | Measles in Hagelloch, Germany, 1861 |
| mers_korea_2015 | Middle East respiratory syndrome in South Korea, 2015 |
| norovirus_derbyshire_2001_school | Norovirus in a primary school in Derbyshire, England, 2001 |
| rabies_car_2003 | Dog Rabies in Central African Republic, 2003-2012 |
| s_enteritidis_pt59 | Salmonella Enteritidis PT59 outbreak |
| sars_canada_2003 | Severe Acute Respiratory Syndrome in Canada, 2003 |
| smallpox_abakaliki_1967 | Smallpox in Abakaliki, Nigeria, 1967 |
| zika_girardot_2015 | Zika in Girardot, Colombia, 2015 |
| zika_sanandres_2015 | Zika in San Andres, Colombia, 2015 |
| zika_yap_2007 | Zika on the Yap Main Islands, Micronesia, 2007 |
To install the current stable, CRAN version of the package, type:
To benefit from the latest features and bug fixes, install the development, github version of the package using:
Note that this requires the package devtools installed.
We will try to create a better repository and data submission system at a later stage. The purpose of the current package is only to share examplar datasets during the hackathon. Acceptable forms are: - as a .RData files in the data/ folder (recommended) - as a text file in the inst/ folder - as a function loading/assembling/simulating a dataset
We use the lower case throughout, and snake_case (using underscores) to separate words for the files and dataset names, so that for a RData object, a new dataset woud look like: `my_new_data_RData’. Try using informative names, typically using the disease first. Whenever available, order fields as: 1. disease: mandatory 2. location: optional 3. year: optional 4. sim: mandatory if this is a simulated dataset; otherwise data is assume to be an actual outbreak 5. other: (any other relevant information)
Maintainer: Finlay Campbell (f.campbell15@imperial.ac.uk)