Create network object from data.frame

This function takes in a dyadic dataset and outputs a netlet object.

Usage

netify(
  dyad_data,
  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 = ifelse(is.null(time), "cross_sec", "longit_list"),
  nodal_vars = NULL,
  dyad_vars = NULL,
  dyad_vars_symmetric = rep(symmetric, length(dyad_vars))
)

Arguments

dyad_data: data object to netify
actor1: character: name of the actor 1 variable in the data
actor2: character: name of the actor 2 variable in the data
time: character: name of the time variable in the data, if no time is provided then it will be assumed
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.R, 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".
nodal_vars: character vector: names of the nodal variables in the dyad_data object that should be added as attributes to the netify object
dyad_vars: character vector: names of the dyadic variables in the dyad_data object that should be added as attributes to the netify object, default is to add all the variables from the extra_dyadic_data data.frame
dyad_vars_symmetric: logical vector: whether ties are symmetric, default is to use the same choice as the symmetric argument

Value

a netlet 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)

# generate a longitudional, directed and weighted network
# where the weights are matlConf and results are organized
# in an array
icews_matlConf <- netify(
    dyad_data=icews,
    actor1='i', actor2='j', time='year',
    symmetric=FALSE, weight='matlConf')

# generate a longitudional, directed and weighted network
# where the weights are matlConf and results are organized
# in an array and we have both dyadic and nodal attributes
icews_matlConf <- netify(
    dyad_data=icews,
    actor1='i', actor2='j', time='year',
    symmetric=FALSE, weight='matlConf',
    nodal_vars=c('i_polity2', 'i_log_gdp', 'i_log_pop'),
    dyad_vars=c('matlCoop', 'verbCoop', 'verbConf'),
    dyad_vars_symmetric=c(FALSE, FALSE, FALSE) )  


# example using cow data
# gathered from the peacesciencer package
library(peacesciencer)
library(dplyr)

# create dyadic data set over time
cow_dyads <- create_dyadyears( 
    subset_years = c(1992:2001)
    ) %>%
    # add mids
    add_cow_mids() %>% 
    # add capital distance
    add_capital_distance() %>%
    # add cow trade
    add_cow_trade() %>%
    # add democracy
    add_democracy()
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> add_cow_mids() IMPORTANT MESSAGE: By default, this function whittles dispute-year data into dyad-year data by first selecting on unique onsets. Thereafter, where duplicates remain, it whittles dispute-year data into dyad-year data in the following order: 1) retaining highest `fatality`, 2) retaining highest `hostlev`, 3) retaining highest estimated `mindur`, 4) retaining highest estimated `maxdur`, 5) retaining reciprocated over non-reciprocated observations, 6) retaining the observation with the lowest start month, and, where duplicates still remained (and they don't), 7) forcibly dropping all duplicates for observations that are otherwise very similar.
#> See: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html
#> Joining with `by = join_by(ccode1, ccode2, year)`

# now lets create a network object in which
# we generate list of networks in which the
# cross-sections represent mid onset
# additionally note that the raw data involves
# country years in which we saw countries go in
# and out of existence so we set actor_time_uniform
# to FALSE
mid_network <- netify(
  cow_dyads,
  actor1='ccode1', actor2='ccode2', time='year',
  # network of interest variable
  weight='cowmidonset', 
  actor_time_uniform=FALSE,
  sum_dyads=FALSE, symmetric=TRUE,
  diag_to_NA=TRUE, missing_to_zero=FALSE,
  # nodal features/covariates
  nodal_vars = c('v2x_polyarchy1', 'v2x_polyarchy2'),
  # dyadic features/covariates
  dyad_vars = c('capdist', 'flow1'),
  dyad_vars_symmetric = c(TRUE, FALSE)
  )
#> ! Warning: Converting `actor1` and/or `actor2` to character vector(s).
mid_network
#> ✔ Hello, you have created network data, yay!
#> • Unipartite
#> • Symmetric
#> • Weights from `cowmidonset`
#> • Longitudinal: 10 Periods
#> • # Unique Actors: 192
#> Network Summary Statistics (averaged across time):
#>              dens miss trans
#> cowmidonset 0.003    0  0.05
#> • Nodal Features: v2x_polyarchy1, v2x_polyarchy2
#> • Dyad Features: capdist, flow1