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.
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).
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 × columns × 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',
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 2
#> Albania 5 NA 1 0 0
#> Algeria 5 1 NA 6 0
#> Angola 0 0 6 NA 8
#> Argentina 2 0 0 8 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('Russia', 'India', 'Brazil'))
#> India Brazil
#> United States 3324 369
#> China 1209 257
# Use actors parameter to see subgraph
peek(net,
actors = c('United States', 'China', 'Russia'))
#> United States China
#> United States NA 4937
#> China 4937 NA
# Example 3: Longitudinal network
net_longit <- netify(
icews,
actor1 = 'i', actor2 = 'j', time = 'year',
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 3 0 0 0
#> Albania 3 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 1 1 0 0
#> Albania 1 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 1 1 0
#> Albania 1 NA 0 0
#> Algeria 1 0 NA 0
#> Angola 0 0 0 NA
#> Argentina 0 0 0 1
#> Armenia 0 0 0 0
#> Australia 40 0 0 0
#> Austria 1 0 0 0
#> Azerbaijan 0 0 0 0
#> Bahrain 1 0 0 0
#> Bangladesh 3 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 2 0 2 0
#> Brazil 0 1 0 1
#> Bulgaria 2 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 19 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 3 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 3
#> Costa Rica 0 0 0 0
#> Cote D'ivoire 0 0 0 0
#> Croatia 6 2 0 0
#> Cuba 1 0 1 0
#> Cyprus 0 0 0 0
#> Denmark 17 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 48 0 11 2
#> Gabon 0 0 0 0
#> Gambia 0 0 0 0
#> Georgia 14 0 0 0
#> Germany 80 2 1 0
#> Ghana 0 0 0 1
#> Greece 4 36 2 1
#> Guatemala 0 0 0 0
#> Guinea 0 0 0 0
#> Guinea-Bissau 0 0 0 1
#> Guyana 0 0 0 0
#> Haiti 0 0 0 0
#> Honduras 0 0 0 0
#> Hungary 4 0 0 0
#> India 17 1 0 0
#> Indonesia 49 0 1 0
#> Iran, Islamic Republic Of 141 0 0 0
#> Iraq 4 0 2 0
#> Ireland 0 0 0 0
#> Israel 5 0 6 0
#> Italy 13 1 13 0
#> Jamaica 0 0 0 0
#> Japan 2 0 0 0
#> Jordan 12 0 0 0
#> Kazakhstan 4 0 0 0
#> Kenya 0 0 0 0
#> Korea, Democratic People's Republic Of 0 0 0 0
#> Korea, Republic Of 6 0 0 0
#> Kuwait 0 0 0 0
#> Kyrgyzstan 12 0 0 0
#> Lao People's Democratic Republic 0 0 0 0
#> Latvia 0 0 0 0
#> Lebanon 4 0 0 0
#> Liberia 2 0 0 0
#> Libyan Arab Jamahiriya 0 0 1 0
#> Lithuania 2 0 0 0
#> Luxembourg 0 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 5 4 0 0
#> Madagascar 0 0 0 0
#> Malawi 0 0 0 0
#> Malaysia 1 0 1 0
#> Mali 0 0 4 0
#> Mauritania 1 0 15 0
#> Mauritius 0 0 1 0
#> Mexico 0 0 0 0
#> Moldova, Republic Of 2 0 0 0
#> Mongolia 0 0 0 0
#> Morocco 0 0 1 0
#> Mozambique 0 0 0 0
#> Myanmar 0 0 0 0
#> Nepal 2 0 0 0
#> Netherlands 21 1 0 0
#> New Zealand 2 0 0 0
#> Nicaragua 0 0 0 0
#> Niger 0 0 6 0
#> Nigeria 0 0 0 3
#> Norway 2 0 0 0
#> Oman 0 0 0 0
#> Pakistan 691 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 23 0 0 0
#> Portugal 2 2 0 0
#> Qatar 1 0 0 0
#> Romania 15 0 0 0
#> Russian Federation 27 0 2 0
#> Rwanda 0 0 0 0
#> Saudi Arabia 4 0 0 0
#> Senegal 0 0 1 0
#> Sierra Leone 0 0 0 0
#> Singapore 2 0 0 0
#> Slovakia 18 1 2 0
#> Slovenia 2 0 1 0
#> Solomon Islands 0 0 0 0
#> Somalia 0 0 2 3
#> South Africa 0 0 1 1
#> Spain 26 0 2 0
#> Sri Lanka 0 0 0 0
#> Sudan 0 0 0 0
#> Suriname 0 0 0 0
#> Sweden 3 0 0 0
#> Switzerland 0 0 0 0
#> Syrian Arab Republic 0 0 0 0
#> Tajikistan 18 0 0 0
#> Tanzania, United Republic Of 0 0 0 0
#> Thailand 0 0 0 0
#> Togo 0 0 2 2
#> 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 4 0 0 0
#> United Kingdom 116 2 7 1
#> United States 1018 1 4 0
#> Uruguay 0 0 0 0
#> Uzbekistan 10 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 40 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 50 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 1 0 0 0
#> Bulgaria 0 0 3 0
#> Burkina Faso 0 0 0 0
#> Burundi 0 0 0 0
#> Cambodia 0 0 1 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 7 0 0 0
#> China 3 0 34 0
#> Colombia 1 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 2 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 25 0
#> Finland 0 0 0 0
#> France 1 0 4 0
#> Gabon 0 0 0 0
#> Gambia 0 0 0 0
#> Georgia 0 5 0 3
#> Germany 8 1 1 2
#> 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 21 5
#> Indonesia 0 0 43 0
#> Iran, Islamic Republic Of 1 4 2 1
#> Iraq 0 0 2 0
#> Ireland 0 0 2 0
#> Israel 1 2 21 1
#> Italy 1 0 2 0
#> Jamaica 0 0 0 0
#> Japan 1 0 11 0
#> Jordan 0 0 1 0
#> Kazakhstan 0 0 0 0
#> Kenya 0 0 4 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 1 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 7 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 3 0
#> New Zealand 0 0 4 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 2 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 12 0
#> Poland 0 0 0 0
#> Portugal 1 0 0 0
#> Qatar 0 0 0 0
#> Romania 0 0 1 0
#> Russian Federation 2 13 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 6 0 0 1
#> Sri Lanka 0 0 3 0
#> Sudan 0 0 3 0
#> Suriname 0 0 0 0
#> Sweden 0 0 116 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 50 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 3 0
#> United Kingdom 4 0 103 3
#> United States 7 6 142 2
#> Uruguay 2 0 0 0
#> Uzbekistan 0 0 0 0
#> Vietnam 0 0 9 0
#> Yemen 0 0 25 0
#> Zambia 0 0 0 0
#> Zimbabwe 0 0 7 0
#> Azerbaijan Bahrain Bangladesh
#> Afghanistan 0 1 3
#> Albania 0 0 0
#> Algeria 0 0 1
#> Angola 0 0 0
#> Argentina 0 0 0
#> Armenia 50 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 1 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 11 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 4
#> India 5 1 65
#> Indonesia 0 0 3
#> Iran, Islamic Republic Of 19 2 0
#> Iraq 2 0 0
#> Ireland 0 0 0
#> Israel 13 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 3
#> 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 1 0 6
#> 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 24 2
#> Romania 0 0 1
#> Russian Federation 22 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 2
#> 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 1
#> United States 3 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 1 2 0
#> Colombia 0 0 0
#> Comoros 0 0 0
#> Congo, Republic Of 0 0 0
#> Congo, The Democratic Republic Of 0 3 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 1 0
#> Gabon 0 0 0
#> Gambia 0 0 0
#> Georgia 4 0 0
#> Germany 4 1 0
#> Ghana 0 0 0
#> Greece 0 1 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 1 2 1
#> Indonesia 0 0 0
#> Iran, Islamic Republic Of 4 4 0
#> Iraq 0 2 0
#> Ireland 0 2 0
#> Israel 0 8 0
#> Italy 0 0 0
#> Jamaica 0 0 0
#> Japan 26 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 1 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 2 2 0
#> Luxembourg 0 0 0
#> Macedonia, The Former Yugoslav Republic Of 0 1 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 6
#> 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 11 2 0
#> Portugal 0 0 0
#> Qatar 0 0 0
#> Romania 0 1 0
#> Russian Federation 76 9 0
#> Rwanda 0 2 0
#> Saudi Arabia 0 0 0
#> Senegal 0 0 0
#> Sierra Leone 0 0 0
#> Singapore 0 2 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 6 0 0
#> United Arab Emirates 0 0 0
#> United Kingdom 0 0 0
#> United States 13 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 3
#> Bulgaria 0
#> Burkina Faso 0
#> Burundi 0
#> Cambodia 0
#> Cameroon 0
#> Canada 0
#> Cape Verde 0
#> Central African Republic 0
#> Chad 0
#> Chile 1
#> 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
#> Guinea 0
#> Guinea-Bissau 0
#> Guyana 0
#> Haiti 0
#> Honduras 1
#> Hungary 4
#> India 1
#> Indonesia 0
#> Iran, Islamic Republic Of 0
#> Iraq 0
#> Ireland 0
#> Israel 1
#> Italy 0
#> Jamaica 0
#> Japan 0
#> Jordan 0
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#> Sudan 0 4 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 3
#> Tunisia 0 0 0 0
#> Turkmenistan 0 0 0 0
#> Uganda 0 0 0 9
#> Ukraine 0 1 0 0
#> United Arab Emirates 0 2 0 0
#> United Kingdom 2 13 3 6
#> United States 6 68 2 6
#> 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 2 10 200
#> Algeria 5 1 NA 6 0 0 1
#> Argentina 2 0 0 8 NA 12 1
#> Australia 200 0 1 0 1 0 NA
#> Austria Azerbaijan Bahrain
#> Afghanistan 2 13 2
#> Algeria 2 4 0
#> Argentina 2 7 2
#> Australia 2 1 4
# 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
#> United States 3718 42 76 68 164 288 927
#> Austria Azerbaijan Bahrain
#> United States 16 457 38
peek(net, from = 10, to = "United States") # First 10 countries' ties to USA
#> United States
#> Afghanistan 3718
#> Albania 42
#> Algeria 76
#> Angola 68
#> Argentina 164
#> Armenia 288
#> Australia 927
#> Austria 16
#> Azerbaijan 457
#> Bahrain 38