peek provides a
convenient way to inspect portions of network data stored in netify objects.
rather than displaying entire networks (which can be overwhelming for large
datasets), this function allows you to examine specific actors, time periods,
or layers for data exploration, verification, and debugging.
Usage
peek(
netlet,
actors = NULL,
from = 3,
to = 3,
time = 1,
layers = NULL,
drop_dimensions = TRUE
)Arguments
- netlet
a netify object to preview. can be cross-sectional, longitudinal (array or list format), and/or multilayer.
- actors
character vector of actor names or numeric indices to subset. selects these actors as both senders and receivers (extracts subgraph among these actors). overridden by
fromandtoif specified.- from
specifies which actors to display as senders of ties (row actors in the adjacency matrix). can be:
single number: shows the first n actors (e.g.,
from = 5displays actors 1-5)numeric vector: shows specific positions (e.g.,
from = c(1,3,5)displays the 1st, 3rd, and 5th actors)character vector: shows named actors (e.g.,
from = c("usa", "china")displays these specific countries)NULL: shows all row actors (default is 3)
in bipartite networks, from represents actors in the first mode.
- to
specifies which actors to display as receivers of ties (column actors in the adjacency matrix). accepts the same input types as
from. in bipartite networks, columns represent actors in the second mode. default is 3.- time
for longitudinal networks, specifies which time periods to display. can be:
single number: shows the nth time period (e.g.,
time = 1shows the first time period)numeric vector: shows specific time indices (e.g.,
time = c(1,5,10))character vector: shows named time periods (e.g.,
time = c("2002", "2006")displays these specific years)NULL: shows all time periods
default is 1 (first time period only). ignored for cross-sectional networks.
- layers
for multilayer networks, specifies which layer(s) to display. must be a character vector matching layer names in the netify object (e.g.,
layers = c("trade", "alliance")). for single-layer networks, this parameter is ignored. default is NULL (shows all layers).- drop_dimensions
logical. whether to drop singleton array dimensions in the returned preview. default TRUE.
Value
returns a subset of the raw network data (without netify attributes):
- cross-sectional networks
-
single layer: returns a matrix with selected rows and columns
multilayer: returns a 3d array (rows x columns x layers)
- longitudinal networks
-
array format: returns an array with dimensions depending on selection
list format: returns a list of matrices, one per selected time period
all returned objects preserve dimension names (actor names, time labels, layer names) for easy interpretation. single dimensions are automatically dropped.
Details
purpose and design
peek is designed as a lightweight data exploration tool. unlike
subset_netify, which creates new netify objects with all attributes
preserved, peek returns only the raw network data for quick inspection.
this makes it ideal for:
verifying data structure and content
checking specific relationships
debugging data issues
quick visual inspection of network patterns
understanding network directions
in directed networks:
from represents actors sending ties (out-ties)
to represents actors receiving ties (in-ties)
cell
[i,j]contains the tie from actor i to actor j
for example, if cell ["usa", "china"] = 5, this means usa sends a tie of
strength 5 to china.
smart selection behavior
the function includes several convenience features:
single numbers are expanded to ranges (e.g.,
from = 5becomes first 5 actors)out-of-bounds indices are silently ignored (no errors during exploration)
character names are matched to actor labels
dimension reduction: if only one time period or layer is selected, that dimension is dropped from the output
Note
important distinctions:
use
peekfor quick data inspection (returns raw matrices)use
subset_netifyto create new netify objects with attributesuse
get_rawto extract all raw data from a netify object
when multiple layers are present and no layer selection is specified, all layers are returned with a warning message to remind you about the multilayer structure.
Examples
# load example data
data(icews)
# example 1: basic usage with cross-sectional network
icews_10 <- icews[icews$year == 2010, ]
net <- netify(
icews_10,
actor1 = "i", actor2 = "j",
sum_dyads = TRUE,
weight = "verbCoop"
)
# default: see first 3 actors (both sending and receiving)
peek(net)
#> Afghanistan Albania Algeria
#> Afghanistan NA 5 5
#> Albania 5 NA 1
#> Algeria 5 1 NA
# see first 5 senders and first 5 receivers
peek(net, from = 5, to = 5)
#> Afghanistan Albania Algeria Angola Argentina
#> Afghanistan NA 5 5 0 3
#> Albania 5 NA 1 0 0
#> Algeria 5 1 NA 14 0
#> Angola 0 0 14 NA 15
#> Argentina 3 0 0 15 NA
# example 2: specific actors by name
# see ties from us and china to russia, india, and brazil
peek(net,
from = c("United States", "China"),
to = c("Russian Federation", "India", "Brazil")
)
#> Russian Federation India Brazil
#> United States 8645 5788 655
#> China 3715 2433 479
# use actors parameter to see subgraph
peek(net,
actors = c("United States", "China", "Russian Federation")
)
#> United States China Russian Federation
#> United States NA 9178 8645
#> China 9178 NA 3715
#> Russian Federation 8645 3715 NA
# example 3: longitudinal network
net_longit <- netify(
icews,
actor1 = "i", actor2 = "j", time = "year",
sum_dyads = TRUE,
weight = "matlConf"
)
# see first 5 actors in specific years
peek(net_longit,
from = 5, to = 5,
time = c("2002", "2006", "2010")
)
#> $`2002`
#> Afghanistan Albania Algeria Angola Argentina
#> Afghanistan NA 0 0 0 0
#> Albania 0 NA 0 0 0
#> Algeria 0 0 NA 0 0
#> Angola 0 0 0 NA 0
#> Argentina 0 0 0 0 NA
#>
#> $`2006`
#> Afghanistan Albania Algeria Angola Argentina
#> Afghanistan NA 24 0 0 0
#> Albania 24 NA 1 0 0
#> Algeria 0 1 NA 0 0
#> Angola 0 0 0 NA 0
#> Argentina 0 0 0 0 NA
#>
#> $`2010`
#> Afghanistan Albania Algeria Angola Argentina
#> Afghanistan NA 3 1 0 0
#> Albania 3 NA 0 0 0
#> Algeria 1 0 NA 0 0
#> Angola 0 0 0 NA 1
#> Argentina 0 0 0 1 NA
#>
# see all actors in year 2010
peek(net_longit,
from = NULL, to = NULL,
time = "2010"
)
#> $`2010`
#> Afghanistan Albania Algeria Angola
#> Afghanistan NA 3 1 0
#> Albania 3 NA 0 0
#> Algeria 1 0 NA 0
#> Angola 0 0 0 NA
#> Argentina 0 0 0 1
#> Armenia 0 0 0 0
#> Australia 64 0 0 0
#> Austria 1 0 0 0
#> Azerbaijan 0 0 0 0
#> Bahrain 1 0 0 0
#> Bangladesh 8 0 1 0
#> Belarus 0 0 0 0
#> Belgium 0 0 0 0
#> Benin 0 0 0 0
#> Bolivia, Plurinational State Of 0 0 0 0
#> Bosnia And Herzegovina 3 0 2 0
#> Brazil 0 1 0 1
#> Bulgaria 3 0 0 0
#> Burkina Faso 0 0 0 0
#> Burundi 0 0 0 0
#> Cambodia 0 0 0 0
#> Cameroon 0 0 0 0
#> Canada 27 0 0 0
#> Cape Verde 0 0 0 0
#> Central African Republic 0 0 0 0
#> Chad 0 0 0 0
#> Chile 0 0 0 0
#> China 32 0 1 0
#> Colombia 0 0 0 0
#> Comoros 0 0 0 0
#> Congo, Republic Of 0 0 0 14
#> Congo, The Democratic Republic Of 0 0 0 6
#> Costa Rica 0 0 0 0
#> Cote D'ivoire 0 0 0 0
#> Croatia 6 2 0 0
#> Cuba 1 0 2 0
#> Cyprus 0 0 0 0
#> Denmark 22 0 2 0
#> Djibouti 0 0 0 0
#> Dominican Republic 0 0 0 0
#> Ecuador 0 0 0 0
#> Egypt 0 0 3 0
#> El Salvador 0 0 0 0
#> Equatorial Guinea 0 0 0 0
#> Estonia 6 0 0 0
#> Ethiopia 1 0 0 0
#> Fiji 0 0 0 0
#> Finland 4 0 0 0
#> France 85 0 29 3
#> Gabon 0 0 0 0
#> Gambia 0 0 0 0
#> Georgia 18 0 0 0
#> Germany 147 2 1 0
#> Ghana 0 0 0 1
#> Greece 7 39 2 1
#> Guatemala 0 0 0 0
#> Guinea 0 0 0 0
#> Guinea-Bissau 0 0 0 2
#> Guyana 0 0 0 0
#> Haiti 0 0 0 0
#> Honduras 0 0 0 0
#> Hungary 9 0 0 0
#> India 72 1 0 0
#> Indonesia 49 0 1 0
#> Iran, Islamic Republic Of 193 0 0 0
#> Iraq 5 0 4 0
#> Ireland 0 0 0 0
#> Israel 6 0 12 0
#> Italy 62 3 13 0
#> Jamaica 0 0 0 0
#> Japan 28 0 0 0
#> Jordan 16 0 0 0
#> Kazakhstan 7 0 0 0
#> Kenya 0 0 0 0
#> Korea, Democratic People's Republic Of 0 0 0 0
#> Korea, Republic Of 15 0 0 0
#> Kuwait 0 0 0 0
#> Kyrgyzstan 18 0 0 0
#> Lao People's Democratic Republic 0 0 0 0
#> Latvia 0 0 0 0
#> Lebanon 6 0 0 0
#> Liberia 2 0 0 0
#> Libyan Arab Jamahiriya 0 0 4 0
#> Lithuania 2 0 0 0
#> Luxembourg 0 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 5 8 0 0
#> Madagascar 0 0 0 0
#> Malawi 0 0 0 0
#> Malaysia 2 0 1 0
#> Mali 0 0 27 0
#> Mauritania 1 0 22 0
#> Mauritius 0 0 2 0
#> Mexico 0 0 0 0
#> Moldova, Republic Of 2 0 0 0
#> Mongolia 0 0 0 0
#> Morocco 0 0 9 0
#> Mozambique 0 0 0 0
#> Myanmar 0 0 0 0
#> Nepal 2 0 0 0
#> Netherlands 27 1 0 0
#> New Zealand 3 0 0 0
#> Nicaragua 0 0 0 0
#> Niger 0 0 7 0
#> Nigeria 0 0 0 3
#> Norway 7 0 0 0
#> Oman 0 0 0 0
#> Pakistan 1314 0 1 0
#> Panama 0 0 0 0
#> Papua New Guinea 2 0 0 0
#> Paraguay 0 0 0 0
#> Peru 0 0 0 0
#> Philippines 3 0 0 0
#> Poland 35 0 0 0
#> Portugal 2 3 0 0
#> Qatar 3 0 0 0
#> Romania 15 0 0 0
#> Russian Federation 37 0 2 0
#> Rwanda 0 0 0 0
#> Saudi Arabia 7 0 0 0
#> Senegal 0 0 1 0
#> Sierra Leone 0 0 0 0
#> Singapore 2 0 0 0
#> Slovakia 21 1 2 0
#> Slovenia 2 0 2 0
#> Solomon Islands 0 0 0 0
#> Somalia 0 0 2 3
#> South Africa 0 0 1 1
#> Spain 71 0 3 0
#> Sri Lanka 0 0 0 0
#> Sudan 0 0 0 0
#> Suriname 0 0 0 0
#> Sweden 10 0 0 0
#> Switzerland 0 0 0 0
#> Syrian Arab Republic 0 0 0 0
#> Tajikistan 32 0 0 0
#> Tanzania, United Republic Of 0 0 0 0
#> Thailand 0 0 0 0
#> Togo 0 0 2 29
#> Trinidad And Tobago 1 0 0 0
#> Tunisia 0 0 0 0
#> Turkmenistan 0 0 0 0
#> Uganda 0 0 0 0
#> Ukraine 0 0 0 0
#> United Arab Emirates 6 0 0 0
#> United Kingdom 302 5 12 1
#> United States 1641 1 4 0
#> Uruguay 0 0 0 0
#> Uzbekistan 15 0 0 0
#> Vietnam 0 0 0 0
#> Yemen 1 0 0 0
#> Zambia 0 0 0 0
#> Zimbabwe 0 0 0 0
#> Argentina Armenia Australia Austria
#> Afghanistan 0 0 64 1
#> Albania 0 0 0 0
#> Algeria 0 0 0 0
#> Angola 1 0 0 0
#> Argentina NA 1 0 0
#> Armenia 1 NA 0 0
#> Australia 0 0 NA 0
#> Austria 0 0 0 NA
#> Azerbaijan 0 421 0 0
#> Bahrain 0 0 0 0
#> Bangladesh 0 0 0 0
#> Belarus 0 0 0 0
#> Belgium 0 0 0 0
#> Benin 0 0 0 0
#> Bolivia, Plurinational State Of 4 0 1 0
#> Bosnia And Herzegovina 0 0 0 1
#> Brazil 3 0 0 0
#> Bulgaria 0 0 4 0
#> Burkina Faso 0 0 0 0
#> Burundi 0 0 0 0
#> Cambodia 0 0 2 0
#> Cameroon 0 0 0 0
#> Canada 0 0 9 0
#> Cape Verde 0 0 0 0
#> Central African Republic 0 0 0 0
#> Chad 0 0 0 0
#> Chile 11 0 0 0
#> China 22 0 49 0
#> Colombia 13 0 0 0
#> Comoros 1 0 0 0
#> Congo, Republic Of 0 0 0 0
#> Congo, The Democratic Republic Of 0 0 0 0
#> Costa Rica 0 0 0 0
#> Cote D'ivoire 0 0 0 0
#> Croatia 0 0 3 65
#> Cuba 1 0 0 0
#> Cyprus 0 0 1 0
#> Denmark 0 0 1 0
#> Djibouti 0 0 0 0
#> Dominican Republic 1 0 0 0
#> Ecuador 0 0 0 0
#> Egypt 0 0 5 0
#> El Salvador 0 0 0 0
#> Equatorial Guinea 0 0 0 0
#> Estonia 0 0 0 0
#> Ethiopia 0 0 0 0
#> Fiji 0 0 35 0
#> Finland 0 0 0 0
#> France 2 0 9 0
#> Gabon 0 0 0 0
#> Gambia 0 0 0 0
#> Georgia 0 7 0 3
#> Germany 10 1 6 3
#> Ghana 0 0 0 0
#> Greece 2 0 0 4
#> Guatemala 0 0 0 0
#> Guinea 0 0 0 0
#> Guinea-Bissau 0 0 0 0
#> Guyana 0 0 0 0
#> Haiti 1 0 0 0
#> Honduras 2 0 0 0
#> Hungary 0 0 3 3
#> India 0 0 58 5
#> Indonesia 0 0 52 0
#> Iran, Islamic Republic Of 6 4 15 1
#> Iraq 0 0 5 0
#> Ireland 0 0 3 0
#> Israel 1 2 61 2
#> Italy 2 0 2 0
#> Jamaica 0 0 0 0
#> Japan 1 0 14 0
#> Jordan 0 0 1 0
#> Kazakhstan 0 0 0 0
#> Kenya 0 0 5 0
#> Korea, Democratic People's Republic Of 0 0 3 0
#> Korea, Republic Of 0 0 3 0
#> Kuwait 0 0 0 0
#> Kyrgyzstan 0 0 0 0
#> Lao People's Democratic Republic 0 0 0 0
#> Latvia 0 0 0 0
#> Lebanon 2 2 3 0
#> Liberia 0 0 0 0
#> Libyan Arab Jamahiriya 0 0 0 0
#> Lithuania 0 0 0 2
#> Luxembourg 0 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 0 0 0 0
#> Madagascar 0 0 0 0
#> Malawi 0 0 0 0
#> Malaysia 0 0 15 0
#> Mali 0 0 0 0
#> Mauritania 0 0 0 0
#> Mauritius 0 0 0 0
#> Mexico 2 0 3 0
#> Moldova, Republic Of 0 1 0 0
#> Mongolia 0 0 0 0
#> Morocco 0 0 0 0
#> Mozambique 0 0 0 0
#> Myanmar 0 0 5 0
#> Nepal 0 0 0 0
#> Netherlands 0 0 5 0
#> New Zealand 0 0 13 0
#> Nicaragua 0 0 0 0
#> Niger 0 0 0 0
#> Nigeria 0 0 1 0
#> Norway 0 0 1 0
#> Oman 0 0 0 0
#> Pakistan 0 2 11 0
#> Panama 0 0 0 0
#> Papua New Guinea 0 0 6 0
#> Paraguay 3 0 0 0
#> Peru 1 0 1 0
#> Philippines 1 0 20 0
#> Poland 0 0 0 0
#> Portugal 1 0 0 0
#> Qatar 0 0 0 0
#> Romania 0 0 1 0
#> Russian Federation 3 21 0 9
#> Rwanda 0 0 0 0
#> Saudi Arabia 0 0 0 0
#> Senegal 0 0 0 0
#> Sierra Leone 0 0 0 0
#> Singapore 0 0 6 0
#> Slovakia 0 0 0 4
#> Slovenia 0 0 0 0
#> Solomon Islands 0 0 3 0
#> Somalia 0 1 2 0
#> South Africa 6 0 2 0
#> Spain 7 0 0 1
#> Sri Lanka 0 0 7 0
#> Sudan 0 0 4 0
#> Suriname 0 0 0 0
#> Sweden 0 0 117 0
#> Switzerland 0 0 3 0
#> Syrian Arab Republic 0 0 0 0
#> Tajikistan 0 0 0 0
#> Tanzania, United Republic Of 0 0 0 0
#> Thailand 0 0 57 0
#> Togo 0 0 0 0
#> Trinidad And Tobago 0 0 0 0
#> Tunisia 0 0 1 0
#> Turkmenistan 0 0 0 0
#> Uganda 1 0 0 0
#> Ukraine 0 1 0 1
#> United Arab Emirates 0 0 4 0
#> United Kingdom 7 0 109 3
#> United States 11 8 164 7
#> Uruguay 8 0 0 0
#> Uzbekistan 0 0 0 0
#> Vietnam 0 0 10 0
#> Yemen 0 0 26 0
#> Zambia 0 0 0 0
#> Zimbabwe 0 0 7 0
#> Azerbaijan Bahrain Bangladesh
#> Afghanistan 0 1 8
#> Albania 0 0 0
#> Algeria 0 0 1
#> Angola 0 0 0
#> Argentina 0 0 0
#> Armenia 421 0 0
#> Australia 0 0 0
#> Austria 0 0 0
#> Azerbaijan NA 0 2
#> Bahrain 0 NA 0
#> Bangladesh 2 0 NA
#> Belarus 0 0 0
#> Belgium 1 0 0
#> Benin 0 0 0
#> Bolivia, Plurinational State Of 0 0 0
#> Bosnia And Herzegovina 0 0 0
#> Brazil 0 0 0
#> Bulgaria 0 0 0
#> Burkina Faso 0 0 0
#> Burundi 0 0 0
#> Cambodia 0 0 0
#> Cameroon 0 0 0
#> Canada 0 0 0
#> Cape Verde 0 0 0
#> Central African Republic 0 0 0
#> Chad 0 0 0
#> Chile 0 0 0
#> China 6 0 0
#> Colombia 0 0 0
#> Comoros 0 0 0
#> Congo, Republic Of 0 0 0
#> Congo, The Democratic Republic Of 0 0 0
#> Costa Rica 0 0 0
#> Cote D'ivoire 0 0 0
#> Croatia 0 0 0
#> Cuba 0 0 0
#> Cyprus 0 0 0
#> Denmark 0 0 0
#> Djibouti 0 0 0
#> Dominican Republic 0 0 0
#> Ecuador 0 0 0
#> Egypt 0 1 0
#> El Salvador 0 0 0
#> Equatorial Guinea 0 0 0
#> Estonia 0 0 0
#> Ethiopia 0 0 0
#> Fiji 0 0 0
#> Finland 0 0 0
#> France 1 0 0
#> Gabon 0 0 0
#> Gambia 0 0 0
#> Georgia 18 0 0
#> Germany 1 0 0
#> Ghana 0 0 0
#> Greece 0 0 1
#> Guatemala 0 0 0
#> Guinea 0 0 0
#> Guinea-Bissau 0 0 0
#> Guyana 0 0 0
#> Haiti 0 0 0
#> Honduras 0 0 0
#> Hungary 0 0 9
#> India 5 1 132
#> Indonesia 0 0 6
#> Iran, Islamic Republic Of 50 4 0
#> Iraq 2 0 0
#> Ireland 0 0 0
#> Israel 16 1 2
#> Italy 1 0 0
#> Jamaica 0 0 0
#> Japan 0 0 1
#> Jordan 0 0 0
#> Kazakhstan 3 0 0
#> Kenya 0 0 0
#> Korea, Democratic People's Republic Of 0 2 0
#> Korea, Republic Of 3 0 0
#> Kuwait 0 0 0
#> Kyrgyzstan 0 0 4
#> Lao People's Democratic Republic 0 0 0
#> Latvia 1 0 0
#> Lebanon 2 0 0
#> Liberia 0 0 3
#> Libyan Arab Jamahiriya 0 0 0
#> Lithuania 0 0 0
#> Luxembourg 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 0 0 0
#> Madagascar 0 0 0
#> Malawi 0 0 0
#> Malaysia 0 0 1
#> Mali 0 0 0
#> Mauritania 0 0 0
#> Mauritius 0 0 0
#> Mexico 0 0 0
#> Moldova, Republic Of 0 0 0
#> Mongolia 0 0 0
#> Morocco 0 0 0
#> Mozambique 0 0 0
#> Myanmar 0 0 22
#> Nepal 0 0 16
#> Netherlands 0 0 0
#> New Zealand 0 0 0
#> Nicaragua 0 0 0
#> Niger 0 0 0
#> Nigeria 1 0 3
#> Norway 0 0 0
#> Oman 0 0 1
#> Pakistan 4 0 21
#> Panama 0 0 0
#> Papua New Guinea 0 0 0
#> Paraguay 0 0 0
#> Peru 0 0 0
#> Philippines 0 0 0
#> Poland 0 0 0
#> Portugal 0 0 0
#> Qatar 0 28 2
#> Romania 0 0 1
#> Russian Federation 28 0 13
#> Rwanda 0 0 2
#> Saudi Arabia 0 0 1
#> Senegal 0 0 0
#> Sierra Leone 0 0 1
#> Singapore 0 0 1
#> Slovakia 0 0 0
#> Slovenia 0 0 0
#> Solomon Islands 0 0 0
#> Somalia 0 0 0
#> South Africa 0 0 1
#> Spain 0 0 3
#> Sri Lanka 0 1 0
#> Sudan 0 0 0
#> Suriname 0 0 0
#> Sweden 1 0 0
#> Switzerland 0 0 0
#> Syrian Arab Republic 0 2 0
#> Tajikistan 0 0 4
#> Tanzania, United Republic Of 0 0 0
#> Thailand 0 0 0
#> Togo 0 0 0
#> Trinidad And Tobago 0 0 2
#> Tunisia 0 0 0
#> Turkmenistan 0 0 0
#> Uganda 0 0 0
#> Ukraine 1 0 0
#> United Arab Emirates 2 0 2
#> United Kingdom 0 2 19
#> United States 8 0 3
#> Uruguay 0 0 0
#> Uzbekistan 0 0 0
#> Vietnam 0 0 0
#> Yemen 0 0 0
#> Zambia 0 0 0
#> Zimbabwe 0 0 0
#> Belarus Belgium Benin
#> Afghanistan 0 0 0
#> Albania 0 0 0
#> Algeria 0 0 0
#> Angola 0 0 0
#> Argentina 0 0 0
#> Armenia 0 0 0
#> Australia 0 0 0
#> Austria 0 0 0
#> Azerbaijan 0 1 0
#> Bahrain 0 0 0
#> Bangladesh 0 0 0
#> Belarus NA 0 0
#> Belgium 0 NA 0
#> Benin 0 0 NA
#> Bolivia, Plurinational State Of 0 0 0
#> Bosnia And Herzegovina 0 0 0
#> Brazil 0 0 0
#> Bulgaria 0 0 0
#> Burkina Faso 0 0 0
#> Burundi 0 0 0
#> Cambodia 0 0 0
#> Cameroon 0 2 1
#> Canada 0 1 0
#> Cape Verde 0 0 0
#> Central African Republic 0 0 0
#> Chad 0 0 0
#> Chile 0 0 0
#> China 2 3 0
#> Colombia 0 0 0
#> Comoros 0 0 0
#> Congo, Republic Of 0 0 0
#> Congo, The Democratic Republic Of 0 7 0
#> Costa Rica 0 0 0
#> Cote D'ivoire 0 1 0
#> Croatia 0 0 0
#> Cuba 0 2 0
#> Cyprus 0 0 0
#> Denmark 0 1 0
#> Djibouti 0 0 0
#> Dominican Republic 0 0 0
#> Ecuador 0 0 0
#> Egypt 0 7 0
#> El Salvador 0 0 0
#> Equatorial Guinea 0 0 0
#> Estonia 0 1 0
#> Ethiopia 0 0 0
#> Fiji 0 0 0
#> Finland 0 0 0
#> France 1 6 0
#> Gabon 0 0 0
#> Gambia 0 0 0
#> Georgia 17 0 0
#> Germany 4 2 0
#> Ghana 0 0 0
#> Greece 0 2 0
#> Guatemala 0 0 0
#> Guinea 0 0 0
#> Guinea-Bissau 0 0 0
#> Guyana 0 0 0
#> Haiti 0 0 0
#> Honduras 0 0 0
#> Hungary 0 0 0
#> India 2 3 1
#> Indonesia 0 0 0
#> Iran, Islamic Republic Of 4 8 0
#> Iraq 0 2 0
#> Ireland 0 2 0
#> Israel 0 8 0
#> Italy 0 0 0
#> Jamaica 0 0 0
#> Japan 32 0 0
#> Jordan 0 0 0
#> Kazakhstan 0 0 0
#> Kenya 0 0 0
#> Korea, Democratic People's Republic Of 0 0 0
#> Korea, Republic Of 0 0 0
#> Kuwait 0 0 0
#> Kyrgyzstan 19 0 0
#> Lao People's Democratic Republic 0 0 0
#> Latvia 0 0 0
#> Lebanon 0 0 0
#> Liberia 0 0 0
#> Libyan Arab Jamahiriya 0 0 0
#> Lithuania 17 2 0
#> Luxembourg 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 0 2 0
#> Madagascar 0 0 0
#> Malawi 0 0 0
#> Malaysia 0 0 0
#> Mali 0 0 0
#> Mauritania 0 0 0
#> Mauritius 0 0 0
#> Mexico 0 0 0
#> Moldova, Republic Of 0 0 0
#> Mongolia 0 0 0
#> Morocco 0 1 0
#> Mozambique 0 0 0
#> Myanmar 0 0 0
#> Nepal 0 0 0
#> Netherlands 0 3 0
#> New Zealand 0 0 0
#> Nicaragua 0 0 0
#> Niger 0 0 2
#> Nigeria 0 0 14
#> Norway 0 0 0
#> Oman 0 0 0
#> Pakistan 0 0 0
#> Panama 0 0 0
#> Papua New Guinea 0 0 0
#> Paraguay 0 0 0
#> Peru 0 1 0
#> Philippines 0 0 0
#> Poland 14 2 0
#> Portugal 0 0 0
#> Qatar 0 0 0
#> Romania 0 1 0
#> Russian Federation 144 9 0
#> Rwanda 0 2 0
#> Saudi Arabia 0 0 0
#> Senegal 0 0 0
#> Sierra Leone 0 0 0
#> Singapore 0 4 0
#> Slovakia 0 0 0
#> Slovenia 0 0 0
#> Solomon Islands 0 0 0
#> Somalia 0 0 0
#> South Africa 1 1 0
#> Spain 0 15 0
#> Sri Lanka 0 0 0
#> Sudan 0 1 0
#> Suriname 0 0 0
#> Sweden 0 0 0
#> Switzerland 0 0 0
#> Syrian Arab Republic 0 0 0
#> Tajikistan 0 0 0
#> Tanzania, United Republic Of 0 0 0
#> Thailand 0 1 0
#> Togo 0 0 0
#> Trinidad And Tobago 0 0 0
#> Tunisia 0 0 0
#> Turkmenistan 0 0 0
#> Uganda 0 0 0
#> Ukraine 7 0 0
#> United Arab Emirates 0 0 0
#> United Kingdom 0 0 0
#> United States 18 10 3
#> Uruguay 0 0 0
#> Uzbekistan 0 0 0
#> Vietnam 0 0 0
#> Yemen 0 0 0
#> Zambia 0 0 0
#> Zimbabwe 0 1 0
#> Bolivia, Plurinational State Of
#> Afghanistan 0
#> Albania 0
#> Algeria 0
#> Angola 0
#> Argentina 4
#> Armenia 0
#> Australia 1
#> Austria 0
#> Azerbaijan 0
#> Bahrain 0
#> Bangladesh 0
#> Belarus 0
#> Belgium 0
#> Benin 0
#> Bolivia, Plurinational State Of NA
#> Bosnia And Herzegovina 0
#> Brazil 10
#> Bulgaria 0
#> Burkina Faso 0
#> Burundi 0
#> Cambodia 0
#> Cameroon 0
#> Canada 0
#> Cape Verde 0
#> Central African Republic 0
#> Chad 0
#> Chile 4
#> China 0
#> Colombia 10
#> Comoros 0
#> Congo, Republic Of 0
#> Congo, The Democratic Republic Of 0
#> Costa Rica 0
#> Cote D'ivoire 0
#> Croatia 0
#> Cuba 0
#> Cyprus 0
#> Denmark 1
#> Djibouti 0
#> Dominican Republic 0
#> Ecuador 0
#> Egypt 0
#> El Salvador 0
#> Equatorial Guinea 0
#> Estonia 0
#> Ethiopia 0
#> Fiji 0
#> Finland 0
#> France 1
#> Gabon 0
#> Gambia 0
#> Georgia 0
#> Germany 1
#> Ghana 0
#> Greece 0
#> Guatemala 0
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#> Saudi Arabia 0 90 0 0
#> Senegal 0 0 0 0
#> Sierra Leone 0 0 0 0
#> Singapore 2 0 0 0
#> Slovakia 2 0 0 0
#> Slovenia 0 0 0 0
#> Solomon Islands 0 0 0 0
#> Somalia 0 80 8 1
#> South Africa 1 0 2 35
#> Spain 0 1 0 0
#> Sri Lanka 0 0 0 0
#> Sudan 0 8 1 0
#> Suriname 0 0 0 0
#> Sweden 0 7 0 0
#> Switzerland 0 1 0 4
#> Syrian Arab Republic 0 4 0 0
#> Tajikistan 0 0 0 0
#> Tanzania, United Republic Of 0 0 1 0
#> Thailand 19 1 0 0
#> Togo 0 0 0 0
#> Trinidad And Tobago 0 0 0 4
#> Tunisia 0 0 0 0
#> Turkmenistan 0 0 0 0
#> Uganda 0 0 0 9
#> Ukraine 0 1 0 0
#> United Arab Emirates 0 3 0 0
#> United Kingdom 2 46 4 21
#> United States 17 158 2 38
#> Uruguay 0 0 0 0
#> Uzbekistan 0 0 0 0
#> Vietnam NA 0 0 0
#> Yemen 0 NA 0 0
#> Zambia 0 0 NA 1
#> Zimbabwe 0 0 1 NA
#>
# example 4: using numeric indices
# see specific positions in the network
peek(net,
from = c(1, 3, 5, 7), # 1st, 3rd, 5th, 7th senders
to = 1:10
) # first 10 receivers
#> Afghanistan Albania Algeria Angola Argentina Armenia Australia
#> Afghanistan NA 5 5 0 3 17 325
#> Algeria 5 1 NA 14 0 0 3
#> Argentina 3 0 0 15 NA 23 1
#> Australia 325 0 3 0 1 0 NA
#> Austria Azerbaijan Bahrain
#> Afghanistan 3 20 5
#> Algeria 4 8 0
#> Argentina 4 16 2
#> Australia 2 1 9
# example 5: quick inspection patterns
# see who usa interacts with
peek(net, from = "united states", to = 10) # usa's ties to first 10 countries
#> Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria
#> Azerbaijan Bahrain
peek(net, from = 10, to = "united states") # first 10 countries' ties to usa
#>
#> Afghanistan
#> Albania
#> Algeria
#> Angola
#> Argentina
#> Armenia
#> Australia
#> Austria
#> Azerbaijan
#> Bahrain