Adds nodal data to a netify object
add_nodal.Rdadd_nodal takes in a dataframe and outputs a netify object.
Usage
add_nodal(
netlet,
node_data,
actor = NULL,
time = NULL,
node_vars = NULL,
replace_existing = FALSE
)Arguments
- netlet
a netify object
- node_data
a dataframe object
- actor
a character object indicating which variable in node_data uniquely IDs each node
- time
a character object indicating which variable in node_data tracks time
- node_vars
a vector of which variables from node_data should be merged
- replace_existing
a logical indicating whether to replace existing nodal data
Examples
data(icews)
# cross-sectional case
icews_10 <- icews[icews$year==2010,]
verbCoop_net <- netify(
dyad_data=icews_10,
actor1 = 'i', actor2 = 'j',
symmetric=FALSE, weight='verbCoop' )
# nodal data should be a dataframe with one row per actor
# in the cross-sectional case and one row per actor per
# time period in the longitudinal case, e.g.:
nvars = c(
'i_polity2',
'i_gdp', 'i_log_gdp',
'i_pop', 'i_log_pop' )
nodeData <- unique(icews_10[,c('i', nvars)])
head(nodeData)
#> i i_polity2 i_gdp i_log_gdp i_pop i_log_pop
#> 10 Afghanistan NA 16047892927 23.49884 28189672 17.15447
#> 2250 Albania 9 10420206418 23.06701 2913021 14.88470
#> 4490 Algeria 2 140977153156 25.67186 35856344 17.39503
#> 6730 Angola -2 69938841426 24.97089 23364185 16.96671
#> 8970 Argentina 8 552738161802 27.03815 40788453 17.52391
#> 11210 Armenia 5 8513508876 22.86492 2946293 14.89606
verbCoop_net <- add_nodal(
netlet=verbCoop_net, node_data=nodeData,
actor='i', node_vars=nvars )
# nodal data is stored in the nodal_data attribute
# as a data.frame, it can be accessed in the following way:
node_data <- attr(verbCoop_net, 'nodal_data')
head(node_data)
#> actor i_polity2 i_gdp i_log_gdp i_pop i_log_pop
#> 1 Afghanistan NA 16047892927 23.49884 28189672 17.15447
#> 2 Albania 9 10420206418 23.06701 2913021 14.88470
#> 3 Algeria 2 140977153156 25.67186 35856344 17.39503
#> 4 Angola -2 69938841426 24.97089 23364185 16.96671
#> 5 Argentina 8 552738161802 27.03815 40788453 17.52391
#> 6 Armenia 5 8513508876 22.86492 2946293 14.89606
# longitudinal case
verbCoop_longit_net <- netify(
dyad_data=icews,
actor1='i', actor2='j', time='year',
symmetric=FALSE,
weight='verbCoop' )
nodeData <- unique(icews[,c('i', 'year', nvars)])
verbCoop_longit_net <- add_nodal(
netlet=verbCoop_longit_net,
node_data=nodeData,
actor='i', time='year',
node_vars = nvars )
# and in the longitudinal case, it can be accessed
# in the same way
node_data <- attr(verbCoop_longit_net, 'nodal_data')
head(node_data)
#> actor time i_polity2 i_gdp i_log_gdp i_pop i_log_pop
#> 1 Afghanistan 2002 NA 7555185296 22.74550 21000256 16.86005
#> 2 Afghanistan 2003 NA 8222480251 22.83014 22645130 16.93546
#> 3 Afghanistan 2004 NA 8338755823 22.84418 23553551 16.97479
#> 4 Afghanistan 2005 NA 9275174321 22.95061 24411191 17.01055
#> 5 Afghanistan 2006 NA 9772082812 23.00280 25442944 17.05195
#> 6 Afghanistan 2007 NA 11123202208 23.13230 25903301 17.06988