README.md
In NPSCORELAB/COREnets: Network Datasets for Research and Education
COREnets
Installation
First, if you haven’t done so, install {remotes}:
install.packages("remotes")
Proceed to install {COREnets} from Github:
remotes::install_github("NPSCORELAB/COREnets")
Using the Package to Access Data
{COREnets} contains a series of network data sets that can be
accessed using the get_data function:
library(COREnets)
drugnet <- COREnets::get_data("drugnet")
drugnet
#> PROTO_NET drugnet Nodes:293 Edges:337
#> Acquaintanceship
#> +-D-- E:337 N:212 NC:1
#>
#> Edge list:
#> from from_class to to_class edge_class
#> 1 person 2 person Acquaintanceship
#> 1 person 10 person Acquaintanceship
#> 2 person 1 person Acquaintanceship
#> 334 entries not printed.
#> + Edge attributes:
#>
#> Node list:
#> + Node attributes: name<chr> node_class<chr> Gender<chr> Ethnicity<chr> HasTie<chr> hr_ethnicity<chr> hr_gender<chr> hr_has_tie<chr>
In order to look up the available data sets use the list_data_sources
function:
COREnets::list_data_sources()
#> [1] "anabaptists"
#> [2] "australian_embassy_bombing_2004"
#> [3] "bali_bombings_2002"
#> [4] "bali_bombings_2005"
#> [5] "christmas_eve_bombings_2000"
#> [6] "ciel"
#> [7] "cocaine_smuggling_acero"
#> [8] "cocaine_smuggling_jake"
#> [9] "cocaine_smuggling_juanes"
#> [10] "cocaine_smuggling_mambo"
#> [11] "drugnet"
#> [12] "fifa"
#> [13] "harry_potter_death_eaters"
#> [14] "harry_potter_dumbledores_army"
#> [15] "london_gang"
#> [16] "madrid_train_bombings_2004"
#> [17] "montreal_street_gangs"
#> [18] "noordin_139"
#> [19] "november17"
#> [20] "paul_revere"
#> [21] "siren"
#> [22] "southeast_asian_aggregate_attack_series_2005"
#> [23] "vivace_bombing_cell_2005"
#> [24] "zegota"
Get a brief description of the data set:
COREnets::get_description("drugnet")
#> [1] "These data represent a network of drug users in Hartford. Ties are directed and represent acquaintanceship. The network is a result of two years of ethnographic observations of people's drug habits. "
Each data object contains two main lists of information, the reference
and network lists:
names(drugnet)
#> [1] "reference" "network"
class(drugnet$reference)
#> [1] "list"
class(drugnet$network)
#> [1] "list"
reference
The reference list contains the following fields of information on the
data set:
Field
Type
Definition
title
character
A formal title for the dataset as presented by other databases or the
author.
name
character
An informal dataset label for internal use.
tags
character
An internal classification assinged to the dataset.
description
character
A brief definition of the dataset to include the type of data,
collection, etc.
abstract
character
A brief summary of the data and network context.
codebook
data.frame
A data table used for gathering and storing relationships and their
definitions.
bibtex
character
The citation for the dataset in bibtex format. Some datasets may have
mupltiple entries.
paper\_link
character
Hyperlink(s) to publications linked to the dataset.
network
The network list contains all the relevant data to generate a
sociogram and conduct the analysis. However, because each data set is
slightly different, this list is segmented into three entries:
metadata: A list of lists each containing information on the
different edge types contained in the edge list. The following list
are included as individual nested items for each edge type
sub-graph, each contain a variety of fields:
| Field | Type | Definition |
| :------------ | :-------- | :--------------------------------------------------------------------------------- |
| edge_class | character | A string matching the name of an edge class in the codebook. |
| is_bimodal | logical | A logial denoting wheter or not the edge type yields a bipartite graph. |
| is_directed | logical | A logical denoting whether the network edges are directed or not. |
| is_dynamic | logical | A logical denoting whether the edges are dynamic or not. |
| is_weighted | logical | A logical denoting whether or not the edges are weighted. |
| has_isolates | logical | A logical which defines if the graph contains isolates or not. |
| has_loops | logical | A logical defining the presence or absence of self-loops. |
| edge_count | double | A number corresponding to the number of edges. |
| node_count | double | A number corresponding to the number of nodes. |
| node_classes | double | A number corresponding to the number of node classes included for each edge class. |
edges_table: A data.frame that contains a minimum of two
columns, one column of nodes acting as a vector source or starting
point (from) and another column of nodes that are the target of
the connection (to). In addition to the from and to variables
the data include a class variable for each (from_class and
to_class).
drugnet$network$edges_table %>%
glimpse()
#> Observations: 337
#> Variables: 5
#> $ from <chr> "1", "1", "2", "2", "3", "4", "4", "5", "6", "7", "7", "7"…
#> $ to <chr> "2", "10", "1", "10", "7", "7", "211", "134", "152", "3", …
#> $ from_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ to_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ edge_class <chr> "Acquaintanceship", "Acquaintanceship", "Acquaintanceship"…
nodes_table: A data.frame contain node non-relational
characteristics. A unique identifier for each node in the
edge_table should be present in the name variable. In addition,
a node_class observation is included for each node.
drugnet$network$nodes_table %>%
glimpse()
#> Observations: 293
#> Variables: 8
#> $ name <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11",…
#> $ node_class <chr> "people", "people", "people", "people", "people", "peopl…
#> $ Gender <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,…
#> $ Ethnicity <dbl> 1, 1, 1, 1, 3, 3, 1, 3, 1, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3,…
#> $ HasTie <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ hr_ethnicity <chr> "White/Other", "White/Other", "White/Other", "White/Othe…
#> $ hr_gender <chr> "Male", "Male", "Male", "Female", "Male", "Male", "Male"…
#> $ hr_has_tie <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
Generating Graph Objects
Each network in the package contains the necessary edges and nodes
tables to generate network objects with {igraph} or
{network}. For instance:
core_as_igraph(drugnet)
#> IGRAPH 5c4819f DN-- 293 337 --
#> + attr: name (v/c), node_class (v/c), Gender (v/n), Ethnicity (v/n),
#> | HasTie (v/n), hr_ethnicity (v/c), hr_gender (v/c), hr_has_tie (v/l),
#> | from_class (e/c), to_class (e/c), edge_class (e/c)
#> + edges from 5c4819f (vertex names):
#> [1] 1 ->2 1 ->10 2 ->1 2 ->10 3 ->7 4 ->7 4 ->211 5 ->134 6 ->152
#> [10] 7 ->3 7 ->4 7 ->9 8 ->107 8 ->117 9 ->1 9 ->2 9 ->7 10->1
#> [19] 10->2 11->135 11->220 12->89 13->216 14->24 14->52 16->10 16->19
#> [28] 17->64 17->79 18->55 18->104 18->165 19->18 20->64 20->182 21->16
#> [37] 21->22 22->21 22->64 22->107 23->20 23->22 23->64 24->14 24->31
#> [46] 24->124 27->117 28->29 29->28 30->19 31->24 31->32 31->124 31->149
#> + ... omitted several edges
core_as_network(drugnet)
#> Network attributes:
#> vertices = 293
#> directed = TRUE
#> hyper = FALSE
#> loops = FALSE
#> multiple = FALSE
#> bipartite = FALSE
#> total edges= 337
#> missing edges= 0
#> non-missing edges= 337
#>
#> Vertex attribute names:
#> Ethnicity Gender HasTie hr_ethnicity hr_gender hr_has_tie node_class vertex.names
#>
#> Edge attribute names:
#> edge_class from_class to_class
NPSCORELAB/COREnets documentation built on March 28, 2020, 7:57 a.m.
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COREnets
Installation
First, if you haven’t done so, install {remotes}:
install.packages("remotes")
Proceed to install {COREnets} from Github:
remotes::install_github("NPSCORELAB/COREnets")
Using the Package to Access Data
{COREnets} contains a series of network data sets that can be
accessed using the get_data function:
library(COREnets)
drugnet <- COREnets::get_data("drugnet")
drugnet
#> PROTO_NET drugnet Nodes:293 Edges:337
#> Acquaintanceship
#> +-D-- E:337 N:212 NC:1
#>
#> Edge list:
#> from from_class to to_class edge_class
#> 1 person 2 person Acquaintanceship
#> 1 person 10 person Acquaintanceship
#> 2 person 1 person Acquaintanceship
#> 334 entries not printed.
#> + Edge attributes:
#>
#> Node list:
#> + Node attributes: name<chr> node_class<chr> Gender<chr> Ethnicity<chr> HasTie<chr> hr_ethnicity<chr> hr_gender<chr> hr_has_tie<chr>
In order to look up the available data sets use the list_data_sources
function:
COREnets::list_data_sources()
#> [1] "anabaptists"
#> [2] "australian_embassy_bombing_2004"
#> [3] "bali_bombings_2002"
#> [4] "bali_bombings_2005"
#> [5] "christmas_eve_bombings_2000"
#> [6] "ciel"
#> [7] "cocaine_smuggling_acero"
#> [8] "cocaine_smuggling_jake"
#> [9] "cocaine_smuggling_juanes"
#> [10] "cocaine_smuggling_mambo"
#> [11] "drugnet"
#> [12] "fifa"
#> [13] "harry_potter_death_eaters"
#> [14] "harry_potter_dumbledores_army"
#> [15] "london_gang"
#> [16] "madrid_train_bombings_2004"
#> [17] "montreal_street_gangs"
#> [18] "noordin_139"
#> [19] "november17"
#> [20] "paul_revere"
#> [21] "siren"
#> [22] "southeast_asian_aggregate_attack_series_2005"
#> [23] "vivace_bombing_cell_2005"
#> [24] "zegota"
Get a brief description of the data set:
COREnets::get_description("drugnet")
#> [1] "These data represent a network of drug users in Hartford. Ties are directed and represent acquaintanceship. The network is a result of two years of ethnographic observations of people's drug habits. "
Each data object contains two main lists of information, the reference
and network lists:
names(drugnet)
#> [1] "reference" "network"
class(drugnet$reference)
#> [1] "list"
class(drugnet$network)
#> [1] "list"
reference
The reference list contains the following fields of information on the
data set:
network
The network list contains all the relevant data to generate a
sociogram and conduct the analysis. However, because each data set is
slightly different, this list is segmented into three entries:
metadata: A list of lists each containing information on the different edge types contained in the edge list. The following list are included as individual nested items for each edge type sub-graph, each contain a variety of fields:
| Field | Type | Definition | | :------------ | :-------- | :--------------------------------------------------------------------------------- | | edge_class | character | A string matching the name of an edge class in the codebook. | | is_bimodal | logical | A logial denoting wheter or not the edge type yields a bipartite graph. | | is_directed | logical | A logical denoting whether the network edges are directed or not. | | is_dynamic | logical | A logical denoting whether the edges are dynamic or not. | | is_weighted | logical | A logical denoting whether or not the edges are weighted. | | has_isolates | logical | A logical which defines if the graph contains isolates or not. | | has_loops | logical | A logical defining the presence or absence of self-loops. | | edge_count | double | A number corresponding to the number of edges. | | node_count | double | A number corresponding to the number of nodes. | | node_classes | double | A number corresponding to the number of node classes included for each edge class. |
edges_table: Adata.framethat contains a minimum of two columns, one column of nodes acting as a vector source or starting point (from) and another column of nodes that are the target of the connection (to). In addition to thefromandtovariables the data include a class variable for each (from_classandto_class).
drugnet$network$edges_table %>%
glimpse()
#> Observations: 337
#> Variables: 5
#> $ from <chr> "1", "1", "2", "2", "3", "4", "4", "5", "6", "7", "7", "7"…
#> $ to <chr> "2", "10", "1", "10", "7", "7", "211", "134", "152", "3", …
#> $ from_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ to_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ edge_class <chr> "Acquaintanceship", "Acquaintanceship", "Acquaintanceship"…
nodes_table: Adata.framecontain node non-relational characteristics. A unique identifier for each node in theedge_tableshould be present in thenamevariable. In addition, anode_classobservation is included for each node.
drugnet$network$nodes_table %>%
glimpse()
#> Observations: 293
#> Variables: 8
#> $ name <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11",…
#> $ node_class <chr> "people", "people", "people", "people", "people", "peopl…
#> $ Gender <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,…
#> $ Ethnicity <dbl> 1, 1, 1, 1, 3, 3, 1, 3, 1, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3,…
#> $ HasTie <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ hr_ethnicity <chr> "White/Other", "White/Other", "White/Other", "White/Othe…
#> $ hr_gender <chr> "Male", "Male", "Male", "Female", "Male", "Male", "Male"…
#> $ hr_has_tie <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
Generating Graph Objects
Each network in the package contains the necessary edges and nodes
tables to generate network objects with {igraph} or
{network}. For instance:
core_as_igraph(drugnet)
#> IGRAPH 5c4819f DN-- 293 337 --
#> + attr: name (v/c), node_class (v/c), Gender (v/n), Ethnicity (v/n),
#> | HasTie (v/n), hr_ethnicity (v/c), hr_gender (v/c), hr_has_tie (v/l),
#> | from_class (e/c), to_class (e/c), edge_class (e/c)
#> + edges from 5c4819f (vertex names):
#> [1] 1 ->2 1 ->10 2 ->1 2 ->10 3 ->7 4 ->7 4 ->211 5 ->134 6 ->152
#> [10] 7 ->3 7 ->4 7 ->9 8 ->107 8 ->117 9 ->1 9 ->2 9 ->7 10->1
#> [19] 10->2 11->135 11->220 12->89 13->216 14->24 14->52 16->10 16->19
#> [28] 17->64 17->79 18->55 18->104 18->165 19->18 20->64 20->182 21->16
#> [37] 21->22 22->21 22->64 22->107 23->20 23->22 23->64 24->14 24->31
#> [46] 24->124 27->117 28->29 29->28 30->19 31->24 31->32 31->124 31->149
#> + ... omitted several edges
core_as_network(drugnet)
#> Network attributes:
#> vertices = 293
#> directed = TRUE
#> hyper = FALSE
#> loops = FALSE
#> multiple = FALSE
#> bipartite = FALSE
#> total edges= 337
#> missing edges= 0
#> non-missing edges= 337
#>
#> Vertex attribute names:
#> Ethnicity Gender HasTie hr_ethnicity hr_gender hr_has_tie node_class vertex.names
#>
#> Edge attribute names:
#> edge_class from_class to_class
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