And then you bit onto them, and learned once again that Cut-me-own-Throat Dibbler could find a use for bits of an animal that the animal didn't know it had got. Dibbler had worked out that with enough fried onions and mustard people would eat anything. [Terry Pratchett, Moving Pictures.]
dibbler provides tools for investigating food-borne outbreaks with (at least partly) known food distribution networks, and genetic information on the cases. This document provides an overview of the package's content.
To install the development version from github:
library(devtools)
install_github("thibautjombart/dibbler")The stable version can be installed from CRAN using:
install.packages("dibbler")Then, to load the package, use:
library("dibbler")Here is a short demonstration of the package using an anonymised Salmonella outbreak dataset, distributed in the outbreaks package as s_enteritidis_pt59. The function make_dibbler will match the structure of the network and the case data, and create a dibbler object:
library("outbreaks")
names(s_enteritidis_pt59$graph)## [1] "from" "to"
head(s_enteritidis_pt59$graph)## from to
## 1 2e7967 dd73b6
## 2 2e7967 c7cd02
## 3 2e7967 2afba4
## 4 2e7967 4df851
## 5 2e7967 48f980
## 6 2e7967 2d3187
dim(s_enteritidis_pt59$graph)## [1] 103 2
s_enteritidis_pt59$cluster## d81c17 064974 3b712b 486c07 6f5824 c77a84 1f4d22 f951d8 44060b 905296
## A A A A A A A A A A
## b4e5d5 0fffca 78e5ba e45c54 ca432a 0b6e5a ef7028 732379 82b4fc 9199be
## A B B B B B B B B B
## cede47 9f5aad acf7bb 24876f 37aad9 1e7431 0b57e4 09f4a0 a6fcaf f59e4b
## B C C C C C C C C C
## 1b55d2 7d3df0 b08945 f80b2e efee6b 6e0643 252679 35a9b6 80afad 1569a5
## C C A A A A A A A A
## 161814 c09e12 38881f
## A A A
## Levels: A B C
case_data <- with(s_enteritidis_pt59,
data.frame(id = names(cluster), cluster = cluster))
head(case_data)## id cluster
## d81c17 d81c17 A
## 064974 064974 A
## 3b712b 3b712b A
## 486c07 486c07 A
## 6f5824 6f5824 A
## c77a84 c77a84 A
x <- make_dibbler(net = s_enteritidis_pt59$graph, nodes_data = case_data)
x##
## /// Foodborne outbreak //
##
## // class: dibbler, epicontacts
## // 43 cases in linelist; 103 edges; directed
##
## // linelist
##
## id cluster
## d81c17 d81c17 A
## 064974 064974 A
## 3b712b 3b712b A
## 486c07 486c07 A
## 6f5824 6f5824 A
## c77a84 c77a84 A
##
## // network
##
## from to
## 1 2e7967 dd73b6
## 2 2e7967 c7cd02
## 3 2e7967 2afba4
## 4 2e7967 4df851
## 5 2e7967 48f980
## 6 2e7967 2d3187
##
## // node types
## 2e7967 cd48bf dd73b6 7ba446 642cb4 c7cd02
## "entry" "internal" "internal" "internal" "internal" "internal"
## 5b44d7 2afba4 fc7f8f 963c41 f60e85 4941c6
## "internal" "internal" "entry" "internal" "internal" "internal"
## 53d0b4 13dc78 9c0e59 6ddfa3 3301f4 874918
## "entry" "internal" "internal" "internal" "entry" "internal"
## f46d1e 0b6e5a c190fa 337cac 03eee3 c1dc98
## "internal" "internal" "entry" "internal" "internal" "internal"
## a7a903 d2d08f 030327 6d4b91 76a432 7fa3b1
## "internal" "entry" "internal" "internal" "internal" "internal"
## f83f2e 881955 e42c72 4df851 d4d75e 7be343
## "internal" "internal" "internal" "internal" "internal" "internal"
## 735807 238842 3e93ce f7fc94 8e02f0 b16cf0
## "internal" "entry" "internal" "internal" "internal" "internal"
## 51ca98 81e071 dc72e8 4e2918 2fabc7 ad208a
## "internal" "internal" "internal" "internal" "internal" "internal"
## 48f980 24dba7 327734 4e8dc8 206e37 958de8
## "internal" "internal" "internal" "internal" "internal" "internal"
## 2d3187 7d21f1 acf7bb 9f5aad 0b57e4 78e5ba
## "internal" "internal" "terminal" "terminal" "terminal" "terminal"
## ca432a e45c54 80afad cede47 82b4fc 732379
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## 9199be 7d3df0 c77a84 6f5824 3b712b 1e7431
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## 37aad9 44060b 905296 f951d8 b4e5d5 1b55d2
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## a6fcaf 24876f 0fffca 09f4a0 38881f d81c17
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## 1569a5 064974 161814 c09e12 b08945 f80b2e
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## 35a9b6 6e0643 efee6b 252679 f59e4b ef7028
## "terminal" "terminal" "terminal" "terminal" "terminal" "terminal"
## 1f4d22 486c07
## "terminal" "terminal"
The resulting object is an extension of epicontact objects; for more information on these objects, and how to handle them, see the epicontacts website.
Here we plot the object, asking to use "cluster" to define colored groups:
plot(x, "cluster")
This is a screenshot of the actual image, which needs to be visualised on a web broswer. Groups are indicated in colors, while different types of nodes are indicated with different symbols:
-
entry nodes: 'origins' of the network, indicated by targets; typically farms
-
internal nodes: nodes located inside the network, indicated as buildings; typically factories or restaurants; note that if the network indicates person-to-person transmission, then internal nodes could be cases as well
-
terminal nodes: nodes located at the periphery of the network, indicated as people; typically cases