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Create network object from various data types

Source: R/netify.R
netify.Rd

This function takes in various types of network data (dyadic datasets, matrices, arrays, lists, igraph objects, or network objects) and outputs a netify object.

Usage

netify(
  input,
  actor1 = NULL,
  actor2 = NULL,
  time = NULL,
  symmetric = TRUE,
  mode = "unipartite",
  weight = NULL,
  sum_dyads = FALSE,
  actor_time_uniform = TRUE,
  actor_pds = NULL,
  diag_to_NA = TRUE,
  missing_to_zero = TRUE,
  output_format = NULL,
  nodal_vars = NULL,
  dyad_vars = NULL,
  dyad_vars_symmetric = rep(symmetric, length(dyad_vars)),
  input_type = c("auto", "dyad_df", "netify_obj"),
  ...
)

Arguments

input

data object to netify. Can be:

  • A data.frame (or tibble/data.table) with dyadic data

  • A matrix representing an adjacency matrix

  • A 3D array representing longitudinal networks

  • A list of matrices representing longitudinal networks

  • An igraph object

  • A network object (from the network package)

  • A list of igraph or network objects

actor1

character: name of the actor 1 variable in the data (required for data.frame inputs)

actor2

character: name of the actor 2 variable in the data (required for data.frame inputs)

time

character: name of the time variable in the data. Can contain numeric, Date, POSIXct/POSIXlt, or character values. Non-numeric types will be converted to numeric indices while preserving original labels. If no time is provided then a cross-sectional network will be created.

symmetric

logical: whether ties are symmetric, default is TRUE

mode

character: whether the network is unipartite or bipartite, default is unipartite

weight

character: name of the weighted edge variable in the data, default is NULL

sum_dyads

logical: whether to sum up the weight value when there exists repeating dyads

actor_time_uniform

logical: whether to assume actors are the same across the full time series observed in the data TRUE means that actors are the same across the full time series observed in the data and the outputted netify object will be in an array format. FALSE means that actors come in and out of the observed data and their "existence" should be determined by the data, meaning that their first year of existence will be determined by the time point of their first event and their last year of existence by the time point of their last event. Outputted netify object will be in a list format.

actor_pds

a data.frame indicating start and end time point for every actor, this can be created using get_actor_time_info, unless provided this will estimated for the user based on their choice of actor_time_uniform

diag_to_NA

logical: whether diagonals should be set to NA, default is TRUE

missing_to_zero

logical: whether missing values should be set to zero, default is TRUE

output_format

character: "cross_sec", "longit_array", or "longit_list". If not specified and time is NULL then output_format will be "cross_sec" and if time is specified then output_format will default to "longit_list". Only applies to data.frame inputs.

nodal_vars

character vector: names of the nodal variables in the input that should be added as attributes to the netify object (for data.frame inputs)

dyad_vars

character vector: names of the dyadic variables in the input that should be added as attributes to the netify object (for data.frame inputs)

dyad_vars_symmetric

logical vector: whether ties are symmetric, default is to use the same choice as the symmetric argument

input_type

character: force specific input type interpretation. Options are "auto" (default), "dyad_df", or "netify_obj". Use "dyad_df" to force data.frame interpretation or "netify_obj" to force matrix/array/ igraph/network interpretation.

...

additional arguments passed to to_netify when processing network objects

Value

a netify object

Author

Ha Eun Choi, Cassy Dorff, Colin Henry, Shahryar Minhas

Examples


# load example directed event data from ICEWS
# this data comes in the form of a dyadic
# dataframe where all dyad pairs are listed
data(icews)

# From a data.frame: generate a longitudional, directed and weighted network
# where the weights are matlConf
icews_matlConf <- netify(
    input=icews,
    actor1='i', actor2='j', time='year',
    symmetric=FALSE, weight='matlConf')

# From a matrix
adj_matrix <- matrix(rbinom(100, 1, 0.3), 10, 10)
net_from_matrix <- netify(adj_matrix)

# From an igraph object
if (FALSE) { # \dontrun{
library(igraph)
g <- sample_gnp(10, 0.3)
net_from_igraph <- netify(g)
} # }