library(knitr) opts_chunk$set(message=FALSE, warning=FALSE, eval=TRUE, echo=TRUE)
epicontacts data structure is useful for epidemiological network analysis
of cases and contacts. Data partitioned as line list and contact list
formats can be coerced to the
epicontacts class in order to facilitate
manipulation, visualization and analysis.
Using a simulated ebola outbreak dataset from the outbreaks package, this
vignette will explore how to create an
epicontacts object and use several
generic methods to work with the data.
make_epicontacts() creates the
epicontacts data structure. The function
accepts arguments for:
Before creating an
epicontacts object, it may be helpful to examine the
structure of the line list and contact data. The example that follows uses the
ebola_sim data loaded from the outbreaks package.
library(outbreaks) library(epicontacts) str(ebola_sim)
ebola_sim is a list with two data frames, which contain the line list and
contacts respectively. The line list data frame already has a unique identifier
for cases in the first column, and the contacts data has the individual contacts
represented in the first and second columns. Note that if the input data were
not formatted as such, the id, from and to arguments allow for explicit
definition of the columns that contain these attributes.
Assuming this network of contacts is directed, the following call to
make_epicontacts will generate an
x <- make_epicontacts(linelist = ebola_sim$linelist, contacts = ebola_sim$contacts, directed = TRUE)
class() to confirm that
epicontacts objets are at their core
As with other lists, the named elements of the
epicontacts data structure can
be easily accessed with the
epicontacts data structure enables some convenient implementations of
"generic" functions in R. These functions (
etc.) behave differently depending on the class of the input.
Using the name of an object (or the
print() function explicitly) will invoke
the print method in R. For the
epicontacts data structure, printing is
conveniently trimmed to show how many cases (rows in the line list) and how many
contacts (rows in the contact list), as well as a glimpse of the first 10 rows
of each data frame.
The summary method provides descriptive information regarding the dimensions and relationship between the line list and contact list (i.e. how many ids they share).
With this method, one can reduce the size of the
epicontacts object by
filtering rows based on explicit values in the line list (node) and contact list
(edge) components. For more on how to parameterize the subset, see
nb this function returns an
epicontacts object, which can in turn be
passed to another generic method.
rokupafuneral <- subset(x, node_attribute = list("hospital" = "Rokupa Hospital"), edge_attribute = list("source" = "funeral"))
By default, passing an
epicontacts object into the plot function is
effectively the same as using
vis_epicontacts(), and will generate an
interactive visualiztion of the network of cases and contacts. Note that this
method includes a number of options to customize the plot. For more see
plot(rokupafuneral, y = "outcome")
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