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 | |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
- 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 ([email protected])
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