Type:	Package
Title:	Integrating Data Exchange and Analysis for Networks ('ideanet')
Version:	1.1.1
Date:	2025-06-02
Description:	A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.
Maintainer:	Tom Wolff <tom.wolff@northwestern.edu>
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
Imports:	CliquePercolation, cluster, colorspace, concorR, cowplot, data.table, dplyr, forcats, ggplot2, ggthemes, grDevices, gridGraphics, igraph (≥ 2.1.0), intergraph, jsonlite, magrittr, Matrix, methods, moments, network, readxl, reshape2, rlang, RSpectra, shiny, sna, stringr, tibble, tidyr, tidyselect
Suggests:	DT, devtools, egor, ergm, shinythemes, shinyWidgets, knitr, rmarkdown, shinycssloaders, visNetwork
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0), igraphdata
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-03 16:26:32 UTC; wms1212
Author:	Tom Wolff [aut, cre], Jonathan Howard Morgan [aut], Gabriel Varela [aut], Kieran Lele [aut], Ethan Bhojani [aut], Emily Heraty [aut], Dana Pasquale [aut], Peter Mucha [aut], James Moody [aut]
Repository:	CRAN
Date/Publication:	2025-07-03 16:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Find the Convex Hull of Admissible Modularity Partitions (CHAMP)

Description

The Convex Hull of Admissible Modularity Partitions (CHAMP) method post-processes an input set of partitions as collected by get_partitions (or as formatted similarly from some other source of selected partitions) to identify the partitions that are somewhere optimal in the resolution parameter and the associated domains of (generalized) modularity optimization. That is, given the input set of partitions of nodes in a network into communities, CHAMP identifies which input partition is optimal at each value of the resolution parameter, gamma. Importantly, CHAMP is deterministic and polynomial in time given a specified input set of partitions; that is, all of the computational complexity and pseudo-stochastic heuristic nature of community detection is in identifying a good input set in get_partitions.

The CHAMP method was developed and studied in Weir, William H., Scott Emmons, Ryan Gibson, Dane Taylor, and Peter J. Mucha. “Post-Processing Partitions to Identify Domains of Modularity Optimization.” Algorithms 10, no. 3 (August 19, 2017): 93. doi:10.3390/a10030093.

See also https://github.com/wweir827/CHAMP and https://github.com/ragibson/ModularityPruning.

Usage

CHAMP(network, partitions, plottitle = NULL)

Arguments

Value

CHAMP returns the input list of partitions with a $CHAMPsummary about which partitions are somewhere optimal (in the sense of modularity Q with a resolution parameter gamma) and their domains of optimality, along with the generated $CHAMPfigure plot of the upper envelope of Q(gamma). The returned list object also contains the original list entered into the partitions argument.

Author(s)

Peter J. Mucha (peter.j.mucha@dartmouth.edu), Alex Craig, Rachel Matthew, Sydney Rosenbaum and Ava Scharfstein

Examples

# Use get_partitions and CHAMP to generate multiple partitions of the
# Zachary karate club and identify the domains of optimality in the
# resolution parameter for different partitions
data(karate, package = "igraphdata")
partitions <- get_partitions(karate, n_runs = 500)
partitions <- CHAMP(karate, partitions, plottitle = "Weighted Karate Club")

Community Detection Across Multiple Routines (comm_detect)

Description

The comm_detect function runs a set of several commonly-used community detection routines on a network and provides community assignments from these routines. Need to mention that only supports undirected nets and that for some routines the median community value is used.

Usage

comm_detect(g, modres = 1, slow_routines = FALSE, shiny = FALSE)

Arguments

Value

comm_detect returns a list contianing three data frames. comm_members indicates each node's assigned community membership from each community detection routine. comm_summaries indicates the number of communities inferred from each routine as well as the modularity score arising from community assignments. comp_scores contains a matrix indicating the similarity of community assignments between each pair of community detection routines, measured using adjusted rand scores. A fourth element in the list, plots, contains a series of network visualizations in which nodes are colored by their assigned community memberships from each routine. If shiny == FALSE, this function will display these visualizations in the user's plot window.

Examples

# Run netwrite
nw_fauxmesa <- netwrite(nodelist = fauxmesa_nodes,
                      node_id = "id",
                      i_elements = fauxmesa_edges$from,
                      j_elements = fauxmesa_edges$to,
                      directed = TRUE,
                      net_name = "faux_mesa",
                      output = "graph")

# Run comm_detect
faux_communities <- comm_detect(g = nw_fauxmesa$faux_mesa)

Measuring Homophily in Ego Networks (ego_homophily)

Description

The ego_homophily function identifies how similar ego is from their alters on a given attribute.

Usage

ego_homophily(
  ego_id,
  ego_measure,
  alter_ego,
  alter_measure,
  prefix = NULL,
  suffix = NULL,
  prop = FALSE
)

Arguments

Value

ego_homophily returns a dataframe of vectors that include the ego identifier and the number or proportion of alters with the same selected attribute

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


# Homophily as a Count
race_homophily_count <- ego_homophily(ego_id = ngq_nw$egos$ego_id,
                                     ego_measure = ngq_nw$egos$race,
                                     alter_ego = ngq_nw$alters$ego_id,
                                     alter_measure = ngq_nw$alters$race,
                                     suffix = "race")

race_homophily_count

# Homophily as a Proportion
race_homophily_prop <- ego_homophily(ego_id = ngq_nw$egos$ego_id,
                                    ego_measure = ngq_nw$egos$race,
                                    alter_ego = ngq_nw$alters$ego_id,
                                    alter_measure = ngq_nw$alters$race,
                                    prop = TRUE,
                                    suffix = "race")
race_homophily_prop

Ego Network Cleaning and Measure Calculation (ego_netwrite)

Description

The ego_netwrite function reads in data pertaining to ego networks and processes them into a set of standardized outputs, including measures commonly calculated for ego networks.

Usage

ego_netwrite(
  egos,
  ego_id,
  alters = NULL,
  alter_id = NULL,
  alter_ego = NULL,
  alter_types = NULL,
  max_alters = Inf,
  alter_alter = NULL,
  aa_ego = NULL,
  i_elements = NULL,
  j_elements = NULL,
  directed = FALSE,
  aa_type = NULL,
  missing_code = 99999,
  na.rm = FALSE,
  egor = FALSE,
  egor_design = NULL
)

Arguments

Value

ego_netwrite returns a list containing several output objects. Users may find it easier to access and work with outputs by applying list2env to this list, which will separate outputs and store them in the R Global Environment. Note, however, that this risks overwriting existing objects in the Global Environment should those objects share names with objects in netwrite's output. Outputs include a data frame containing measures of ego attributes, another data frame containing measures of alter attributes and network position, a third containing the alter-alter edgelist (when applicable), a fourth containing summary measures for each individual ego network, and a fifth providing summary measures for the overall dataset. Additionally, ego_netwrite returns a list of igraph objects constructed for each individual ego network, as well as an egor object for the overall dataset if desired.

Examples

# Simple Processing, Ignoring Ego-Alter or Alter-Alter Relation Types
ngq_nw <- ego_netwrite(egos = ngq_egos,
                       ego_id = ngq_egos$ego_id,

                       alters = ngq_alters,
                       alter_id = ngq_alters$alter_id,
                       alter_ego = ngq_alters$ego_id,

                       max_alters = 10,
                       alter_alter = ngq_aa,
                       aa_ego = ngq_aa$ego_id,
                       i_elements = ngq_aa$alter1,
                       j_elements = ngq_aa$alter2,
                       directed = FALSE)


# View summaries of individual ego networks
head(ngq_nw$summaries)

# View summary of overall dataset
head(ngq_nw$overall_summary)

# View sociogram of fourth ego network
plot(ngq_nw$igraph_objects[[4]]$igraph_ego)


# For advanced applications involving multiple relationship types
# and `egor` object creation, please consult the `ego_netwrite` vignette
vignette("ego_netwrite", package = "ideanet")

Reshaping Egocentric Data (ego_reshape)

Description

The ego_reshape function reshapes egocentric network data stored in a single wide dataset into three dataframes optimized for use with ego_netwrite.

Usage

ego_reshape(
  data,
  ego_id,
  ego_vars,
  alters,
  alter_vars,
  alter_alter,
  aa_vars = NULL,
  directed = NULL,
  loops = NULL,
  missing_code = 99999,
  output_name = "ego_long"
)

Arguments

Value

A list containing three data frames: an ego list, an ego-alter edgelist, and an alter-alter edgelist. These dataframes are optimized for use with ego_netwrite.

Krackhardt and Stern’s E-I Index (ei_index)

Description

Linear transformation of the proportion homophilous measure (Krackhardt and Stern 1988; Perry et al. 2018)

Usage

ei_index(
  ego_id,
  ego_measure,
  alter_ego,
  alter_measure,
  prefix = NULL,
  suffix = NULL
)

Arguments

Value

ei_index returns a dataframe of vectors that include the ego identifier and the ei-index value for the selected attribute

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


# Calculate E-I Index for Race
race_ei <- ei_index(ego_id = ngq_nw$egos$ego_id, ego_measure = ngq_nw$egos$race,
                   alter_ego = ngq_nw$alters$ego_id, alter_measure = ngq_nw$alters$race,
                   prefix = "race")

race_ei

Euclidean Distance (euclidean_distance)

Description

Typical difference between between ego and their alters for a given continuous attribute (Perry et al. 2018)

Usage

euclidean_distance(
  ego_id,
  ego_measure,
  alter_ego,
  alter_measure,
  prefix = NULL,
  suffix = NULL
)

Arguments

Value

euclidean_distance returns a dataframe of vectors that include the ego identifier and euclidean distance for the desired continuous attribute

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


# Calculate Euclidean Distance
pol_euc <- euclidean_distance(ego_id = ngq_nw$egos$ego_id, ego_measure = ngq_nw$egos$pol,
                             alter_ego = ngq_nw$alters$ego_id, alter_measure = ngq_nw$alters$pol,
                             prefix = "pol")
pol_euc

Goodreau's Faux Mesa High School (Edgelist)

Description

This data set (originally found in as a network object in the ergm package) represents a simulation of an in-school friendship network. The network is named "Faux Mesa High" because the school community on which it is based is in the rural western US, with a student body that is largely Hispanic and Native American.

Usage

fauxmesa_edges

Format

A data frame with 203 rows and 2 columns:

from

Outgoing node

to

Receiving node

...

Source

The data set is based upon a model fit to data from one school community from the AddHealth Study, Wave I (Resnick et al., 1997). It was constructed as follows:

A vector representing the sex of each student in the school was randomly re-ordered. The same was done with the students' response to questions on race and grade. These three attribute vectors were permuted independently. Missing values for each were randomly assigned with weights determined by the size of the attribute classes in the school.

The following ergm formula was used to fit a model to the original data:

 ~ edges + nodefactor("Grade") + nodefactor("Race") +
nodefactor("Sex") + nodematch("Grade",diff=TRUE) +
nodematch("Race",diff=TRUE) + nodematch("Sex",diff=FALSE) +
gwdegree(1.0,fixed=TRUE) + gwesp(1.0,fixed=TRUE) + gwdsp(1.0,fixed=TRUE) 

The resulting model fit was then applied to a network with actors possessing the permuted attributes and with the same number of edges as in the original data.

The processes for handling missing data and defining the race attribute are described in Hunter, Goodreau & Handcock (2008).

Goodreau's Faux Mesa High School (Nodelist)

Description

This data set (originally found in as a network object in the ergm package) represents a simulation of an in-school friendship network. The network is named "Faux Mesa High" because the school community on which it is based is in the rural western US, with a student body that is largely Hispanic and Native American.

Usage

fauxmesa_nodes

Format

A data frame with 205 rows and 4 columns:

id

Node ID

grade

Student grade year

race

Student race

sex

Student sex

...

Source

The data set is based upon a model fit to data from one school community from the AddHealth Study, Wave I (Resnick et al., 1997). It was constructed as follows:

A vector representing the sex of each student in the school was randomly re-ordered. The same was done with the students' response to questions on race and grade. These three attribute vectors were permuted independently. Missing values for each were randomly assigned with weights determined by the size of the attribute classes in the school.

The following ergm formula was used to fit a model to the original data:

 ~ edges + nodefactor("Grade") + nodefactor("Race") +
nodefactor("Sex") + nodematch("Grade",diff=TRUE) +
nodematch("Race",diff=TRUE) + nodematch("Sex",diff=FALSE) +
gwdegree(1.0,fixed=TRUE) + gwesp(1.0,fixed=TRUE) + gwdsp(1.0,fixed=TRUE) 

The resulting model fit was then applied to a network with actors possessing the permuted attributes and with the same number of edges as in the original data.

The processes for handling missing data and defining the race attribute are described in Hunter, Goodreau & Handcock (2008).

Edgelist of marriage alliances and business relationships between Florentine families during the Italian Renaissance

Description

Breiger & Pattison (1986), in their discussion of local role analysis, use a subset of data on the social relations among Renaissance Florentine families collected by John Padgett from historical documents. The two relations are business ties (recorded financial ties such as loans, credits and joint partnerships) and marriage alliances. This dataset has since become a standard for illustrating role analysis methods and working with networks featuring multiple types of relations.

Usage

florentine_edges

Format

A data frame with 35 rows and 4 columns:

source

Outgoing node

target

Receiving node

weight

A placeholder variable for tie/edge weights, set to 1

type

Relation type

...

Source

John Padgett

References

Ronald L. Breiger and Philippa E Pattison. 1986. "Cumulated social roles: The duality of persons and their algebras." Social Networks 8(13):215-256.

Nodelist of marriage alliances and business relationships between Florentine families during the Italian Renaissance

Description

Breiger & Pattison (1986), in their discussion of local role analysis, use a subset of data on the social relations among Renaissance Florentine families collected by John Padgett from historical documents. The two relations are business ties (recorded financial ties such as loans, credits and joint partnerships) and marriage alliances. This dataset has since become a standard for illustrating role analysis methods and working with networks featuring multiple types of relations.

Usage

florentine_nodes

Format

A data frame with 16 rows and 2 columns:

id

Unique node ID number

family

Name of family corresponding to node

...

Source

John Padgett

References

Ronald L. Breiger and Philippa E Pattison. 1986. "Cumulated social roles: The duality of persons and their algebras." Social Networks 8(13):215-256.

American College Football

Description

Network of American football games between Division IA colleges during regular season Fall 2000.

Usage

football

Format

An 'igraph' object containing 613 edges between 115 vertices (nodes). Vertices contain three attributes:

id

Unique identification number.

label

Name of college team represented by vertex.

value

A numberic indicator of football conference affiliation.

...

Source

Included by permission of M. Girvan and M. E. J. Newman (Website)

References

M. Girvan and M. E. J. Newman. 2002. "Community structure in social and biological networks." Proc. Natl. Acad. Sci. USA 99:7821-7826.

Find the SBM-equivalence iterative map on the CHAMP set of somewhere optimal partitions

Description

get_CHAMP_map calculates the iterative map defined by Newman's equivalence between modularity optimization and inference on the degree-corrected planted partition stochastic block model on a CHAMP set of partitions. That is, given an input set of partitions of nodes in a network into communities, calculated by get_partitions or by other means and coerced into that format, CHAMP identifies which input partition is optimal at each value of the resolution parameter, gamma, and then get_CHAMP_map calculates the iterative map of this set onto itself. Importantly, a fixed point of this map, where a partition points to itself, indicates that partition is self-consistent in the sense of this equivalence between modularity and planted partition models. As with CHAMP, the get_CHAMP_map code is deterministic and fast given a specified input set of partitions; that is, all of the computational complexity and pseudo-stochastic heuristic nature of community detection is in identifying a good input set in get_partitions.

The CHAMP method was developed and studied in Weir, William H., Scott Emmons, Ryan Gibson, Dane Taylor, and Peter J. Mucha. “Post-Processing Partitions to Identify Domains of Modularity Optimization.” Algorithms 10, no. 3 (August 19, 2017): 93. doi:10.3390/a10030093.

The equivalence between modularity optimization and planted partition inference was derived by M. E. J. Newman in “Equivalence between Modularity Optimization and Maximum Likelihood Methods for Community Detection.” Physical Review E 94, no. 5 (November 22, 2016): 052315. doi:10.1103/PhysRevE.94.052315.

The iterative map on the CHAMP set was developed and studied in Gibson, Ryan A., and Peter J. Mucha. “Finite-State Parameter Space Maps for Pruning Partitions in Modularity-Based Community Detection.” Scientific Reports 12, no. 1 (September 23, 2022): 15928. doi:10.1038/s41598-022-20142-6.

See also https://github.com/wweir827/CHAMP and https://github.com/ragibson/ModularityPruning.

Usage

get_CHAMP_map(network, partitions, plotlabel = NULL, shiny = FALSE)

Arguments

Value

get_CHAMP_map returns the input list of partitions with the $CHAMPsummary updated to indicate the iterative map, that is, information about the next partition that each partition points to in the map, along with the generated $CHAMPmap plot of the partitions in the CHAMP set (by their numbers of communities) versus gamma. If shiny = TRUE, the returned list also includes a data frame entitled shiny_partitions that is used for visualizations in ideanetViz.

Author(s)

Peter J. Mucha (peter.j.mucha@dartmouth.edu), Alex Craig, Rachel Matthew, Sydney Rosenbaum and Ava Scharfstein

Examples

# Use get_partitions, CHAMP, and get_CHAMP_map to generate
# multiple partitions of the Zachary karate club and identify
# the domains of optimality in the resolution parameter for
# different partitions
data(karate, package = "igraphdata")
partitions <- get_partitions(karate, n_runs = 2500)
partitions <- CHAMP(karate, partitions, plottitle = "Weighted Karate Club")
partitions <- get_CHAMP_map(karate, partitions, plotlabel = "Weighted Karate Club")

Selecting Individual Networks from ego_netwrite Output (get_egograph)

Description

The get_egograph function extracts one or more specific ego networks from a list object created by ego_netwrite. Specifically, it extracts the igraph objects associated with the ego networks selected by the user. This can be useful for close inspection and/or comparison of individual ego networks in your data.

Usage

get_egograph(egonw = NULL, ego_id = NULL)

Arguments

Value

get_egograph returns a list containing the contents of the igraph_objects list found in egonw corresponding to the values specified in ego_id.

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                       ego_id = ngq_egos$ego_id,

                       alters = ngq_alters,
                       alter_id = ngq_alters$alter_id,
                       alter_ego = ngq_alters$ego_id,

                       max_alters = 10,
                       alter_alter = ngq_aa,
                       aa_ego = ngq_aa$ego_id,
                       i_elements = ngq_aa$alter1,
                       j_elements = ngq_aa$alter2,
                       directed = FALSE)


# Select `igraph` objects associated with `ego_id` 3.
ego3 <- get_egograph(ngq_nw, 3)

Gather a collection of community detection partitions (get_partitions)

Description

The get_partitions function is a wrapper to gather a collection of community detection partitions using igraph's cluster_leiden for maximizing modularity at various resolution parameter values, along with the routines called by the comm_detect function, to gather different partitions for subsequent input to the CHAMP code for post-processing partitions to identify domains of modularity optimization.

Usage

get_partitions(
  network,
  gamma_range = c(0, 3),
  n_runs = 100,
  n_iterations = 2,
  seed = NULL,
  add_comm_detect = TRUE
)

Arguments

Value

get_partitions returns a list of unique partitions appropriate for subsequent input to CHAMP.

Author(s)

Peter J. Mucha (peter.j.mucha@dartmouth.edu), Alex Craig, Rachel Matthew, Sydney Rosenbaum and Ava Scharfstein

Examples

# Use get_partitions to generate multiple partitions of the
# Zachary karate club at different resolution parameters
data(karate, package = "igraphdata")
partitions <- get_partitions(karate, n_runs = 2500)

H-Index (h_index)

Description

Measure of ego network diversity for categorical attributes (Perry et al. 2018)

Usage

h_index(ego_id, measure, prefix = NULL, suffix = NULL)

Arguments

Value

h_index returns a dataframe of vectors that include the ego identifier and h-index of diversity for the desired categorical attribute.

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


# Get H-index for race
race_hindex <- h_index(ego_id = ngq_nw$alters$ego_id,
                      measure = ngq_nw$alters$race,
                      prefix = "race")

race_hindex

Multiplex Network of Relationships Between Managers of a High-Tech Company

Description

A network of a small hi-tech computer firm that sold, installed, and maintained computer systems, represented as an edgelist. Relationships in the network can take on three modes: 1 represents advice relationships, 2 represents friendship relationships, and 3 represents chain of command (e.g., "reporting-to").

Usage

hightech

Format

A data frame with 312 rows and 4 columns:

node

Outgoing node

target

Receiving node

weight

A placeholder variable for tie/edge weights, set to 1

layer

Relation type

...

Source

Carnegie Mellon University

References

David Krackhardt. 1987. "Cognitive social structures". Social Networks 9(2):104-134. https://doi.org/10.1016/0378-8733(87)90009-8

Interactive GUI for Working with Sociocentric Networks (ideanetViz)

Description

ideanetViz is a Shiny app that presents the output of ideanet's workflow for sociocentric data (i.e. netwrite) in a clear and accessible GUI. This GUI is convenient for users with limited R experience and is useful for classrooms, workshops, and other educational spaces. It is also useful for experienced users interested in quick exploration of network data. Moreover, ideanetViz streamlines customization of network visualizations and provides quick access into ideanet's more advanced analytic tools for sociocentric networks.

ideanetViz's design is centered around a series of tabs lining the top of the app, which are ordered according to a typical workflow for acquiring, processing, exploring, and modeling data.

Usage

ideanetViz()

Value

Launches an external window in which users can interact with the ideanetViz GUI. At different points in working with the GUI, users have the option to export generated data as CSV files and visualizations as image files.

Agresti's Index of Qualitative Variation (iqv)

Description

A normalized value of the h-index for measuring the diversity of an ego's network for categorical attributes (Perry et al. 2018)

Usage

iqv(ego_id, measure, prefix = NULL, suffix = NULL)

Arguments

Value

iqv returns a dataframe of vectors that include the ego identifier and iqv value of diversity for the desired categorical attribute.

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


# Get IQV for sex
sex_iqv <- iqv(ego_id = ngq_nw$alters$ego_id,
              measure = ngq_nw$alters$sex,
              prefix = "sex")

sex_iqv

Character Relations in Marvel Comics

Description

A network, represented as edgelist, containing weighted edges between Marvel Comics characters. Edge weights were calculated based on how many times two characters' names appeared within 15 words of one another in a comic.

Usage

marvel

Format

A data frame with 9891 rows and 3 columns:

Source

Outgoing node

Target

Receiving node

Weight

Edge weight

...

Source

Melanie Walsh (Github), adapted from data originally compiled by Cesc Rosselló, Ricardo Alberich, and Joe Miro from Russ Chappell (Website)

Merging Network Canvas CSV Files (nc_merge)

Description

The nc_merge function combines CSV files exported from Network Canvas, a popular tool for egocentric data capture. It is designed to address issues that may be encountered by nc_read when Network Canvas exports separate CSV files for individual responses.

Usage

nc_merge(path, export_path)

Arguments

Value

nc_merge always writes two CSV files to the directory specified in export_path: an ego list and an alters list. If CSV files containing alter-alter ties are detected, it also writes a third merged CSV of these ties.

Reading and Reshaping Network Canvas Data (nc_read)

Description

The nc_read function reads in and processes CSV files produced by Network Canvas, a popular tool for egocentric data capture. nc_read produces three dataframes optimized for use with ego_netwrite.

Usage

nc_read(path, protocol = NULL, cat.to.factor = TRUE)

Arguments

Value

nc_read returns a list containing three items: an ego list, an ego-alter edgelist, and an alter-alter edgelist. If multiple edge types exist for ego-alter and/or alter-alter ties, edgelists for each type of tie will be stored as individual data frames as elements in a list. All data frames are optimized for use with ego_netwrite.

Note that in the alters data frame(s), column node_type reflects the "node type" assigned to a given alter as specified in a Network Canvas protocol. Values in node_type are not necessarily those which should be fed into the alter_types argument in ego_netwrite.

Reading Network Data Files and Initial Cleaning (netread)

Description

The netread function reads in various files storing relational data converts them into edgelists that ensure their compatibility with other ideanet functions.

Usage

netread(
  path = NULL,
  filetype = NULL,
  sheet = NULL,
  nodelist = NULL,
  node_sheet = NULL,
  object = NULL,
  col_names = TRUE,
  row_names = FALSE,
  format = NULL,
  net_name = "network",
  missing_code = 99999,
  i_elements = NULL,
  j_elements = NULL
)

Arguments

Value

A list containing an edgelist and a nodelist, both of which are formatted to be compatible with the netwrite function.

Network Cleaning and Variable Calculation (netwrite)

Description

The netwrite function reads in relational data of several formats and processes them into a set of standardized outputs. These outputs include sets of commonly calculated measures at the individual node and network-wide levels.

Usage

netwrite(
  data_type = c("edgelist"),
  adjacency_matrix = FALSE,
  adjacency_list = FALSE,
  nodelist = FALSE,
  node_id = NULL,
  node_netid = NULL,
  edgelist = FALSE,
  i_elements = FALSE,
  j_elements = FALSE,
  edge_netid = NULL,
  fix_nodelist = TRUE,
  weights = NULL,
  type = NULL,
  remove_loops = FALSE,
  missing_code = 99999,
  weight_type = "frequency",
  directed = FALSE,
  net_name = "network",
  shiny = FALSE,
  output = c("graph", "largest_bi_component", "largest_component", "node_measure_plot",
    "nodelist", "edgelist", "system_level_measures", "system_measure_plot"),
  message = TRUE
)

Arguments

Value

netwrite returns a list containing several output objects. Users may find it easier to access and work with outputs by applying list2env to this list, which will separate outputs and store them in the R Global Environment. Note, however, that this risks overwriting existing objects in the Global Environment should those objects share names with objects in netwrite's output. Depending on the values assigned to the output argument, netwrite will produce any or all of the following:

If output contains graph, netwrite will return an igraph object of the network represented in the original data. If a vector is entered into the type argument, netwrite also produces a list containing igraph objects for each unique relation type as well as the overall network. These output objects are named according to the value specified in the net_name argument.

If output contains "nodelist", netwrite will return a dataframe containing individual-level information for each node in the network. This dataframe contains a set of frequently used node-level measures for each node in the network. If a vector is entered into the type argument, netwrite will produce these node-level measures for each unique relation type.

If output contains "edgelist", netwrite will return a formatted edgelist for the network represented in the original data. If a vector is entered into the type argument, netwrite also produces a list containing edgelists for each unique relation type as well as the overall network.

If output contains "system_level_measures", netwrite will return a data frame providing network-level summary information.

If output contains "node_measure_plot", netwrite will return a plot summarizing the distribution of frequently used node-level measures across all nodes in the network. If a vector is entered into the type argument, netwrite also produces a list containing node-level summary plots for each unique relation type as well as the overall network.

If output contains "system_measure_plot", netwrite will return a plot summarizing the distribution of frequently used network-level measures. If a vector is entered into the type argument, netwrite also produces a list containing network-level summary plots for each unique relation type as well as the overall network.

If output contains "largest_bi_component", netwrite will return an igraph object of the largest bicomponent in the network represented in the original data. If a vector is entered into the type argument, netwrite also produces a list containing the largest bicomponent for each unique relation type as well as the overall network.

If output contains "largest_bi_component", netwrite will return an igraph object of the largest main component in the network represented in the original data. If a vector is entered into the type argument, netwrite also produces a list containing the largest main component for each unique relation type as well as the overall network.

If users are working with data containing multiple independent networks, netwrite will return a list containing the above outputs for each network in their data, provided that users have passed a vector of network identifiers to the edge_netid argument. Each network's output will be labeled according to its corresponding value in edge_netid.

Examples

# Use netwrite on an edgelist
nw_fauxmesa <- netwrite(nodelist = fauxmesa_nodes,
                      node_id = "id",
                      i_elements = fauxmesa_edges$from,
                      j_elements = fauxmesa_edges$to,
                      directed = TRUE,
                      net_name = "faux_mesa")

### Inspect updated edgelist
head(nw_fauxmesa$edgelist)

### Inspect data frame of node-level measures
head(nw_fauxmesa$node_measures)

### Inspect system-level summary
head(nw_fauxmesa$system_level_measures)

### Plot sociogram of network
plot(nw_fauxmesa$faux_mesa)

### View node-level summary visualization
nw_fauxmesa$node_measure_plot

### View system-level summary visualization
nw_fauxmesa$system_measure_plot



# Run netwrite on an adjacency matrix

nw_triad <- netwrite(data_type = "adjacency_matrix",
                     adjacency_matrix = triad,
                     directed = TRUE,
                     net_name = "triad_igraph")

Ego Networks Elicited from the "Important Matters" Name Generator Question (Alter-Alter Edgelist)

Description

This dataset contains a simplified subset of 20 ego networks elicited using the "important matters" name generator question (NGQ), which is frequently used to capture an individual's close personal ties. These networks were collected as part of an experiment illustrating how networks generated by this question may vary depending on the topics covered in preceding survey items. Networks were collected using an online survey deployed via Amazon Mechanical Turk.

Usage

ngq_aa

Format

A data frame with 123 rows and 5 columns:

ego_id

Unique identifier for ego providing network

alter1

Within-network unique identifier for Alter 1 in alter-alter edgelist.

alter2

Within-network unique identifier for Alter 2 in alter-alter edgelist.

type

A character indicating the type of relationship that Alter 1 and Alter 2 have with one another. Note that each dyad-type combination has its own unique row in this dataset, so more than one row may correspond to a single dyad if the dyad involves multiple types of relationships.

freqtalk

A numeric indicating how frequently ego believes Alter 1 and Alter 2 talk with one another. 1 indicates "Never," 2 "Less than once a month," 3 "1-3 times a month," 4 "1-3 times a week," 5 "Daily or almost daily."

...

Source

Original Data, Collected by Danielle Montagne, Joseph Quinn, Liann Tucker, and Tom Wolff.

Ego Networks Elicited from the "Important Matters" Name Generator Question (Alter List)

Description

This dataset contains a simplifed subset of 20 ego networks elicited using the "important matters" name generator question (NGQ), which is frequently used to capture an individual's close personal ties. These networks were collected as part of an experiment illustrating how networks generated by this question may vary depending on the topics covered in preceding survey items. Networks were collected using an online survey deployed via Amazon Mechanical Turk.

Usage

ngq_alters

Format

A data frame with 67 rows and 14 columns:

ego_id

Unique identifier for ego providing network

alter_id

Within-network unique identifier for person nominated by ego (alter).

sex

A numeric indicating alter's sex as reported by ego. 1 indicates male, 2 female.

race

A character indicating a simplified characterization of alter's race/ethnicity as reported by ego. Values include "White", "Black", and "Other".

black

A logical indicating ego's perception of alter as "Black" or "African-American."

white

A logical indicating ego's perception of alter as "White."

other_race

A logical indicating ego's perception of alter as belonging to a racial/ethnic group other than "Black," "African-American," or "White."

pol

A numeric indicating political orientation on a seven-point scale, as perceived by ego. 1 indicates "Extremely Liberal," 4 "Moderate," and 7 "Extremely Conservative."

family

A logical indicating alter as ego's family member.

friend

A logical indicating alter as ego's friend.

other_rel

A logical indicating alter as have a relationship to ego other than one of the types of relationships listed above.

face

A numeric indicating how frequently ego and alter interact in person. 1 indicates "Never," 2 "Less than once a month," 3 "1-3 times a month," 4 "1-3 times a week," 5 "Daily or almost daily."

phone

A numeric indicating how frequently ego and alter talk on the phone or via video chat. 1 indicates "Never," 2 "Less than once a month," 3 "1-3 times a month," 4 "1-3 times a week," 5 "Daily or almost daily."

text

A numeric indicating how frequently ego and alter interact via electronic messaging (e.g. texting, email, social media). 1 indicates "Never," 2 "Less than once a month," 3 "1-3 times a month," 4 "1-3 times a week," 5 "Daily or almost daily."

...

Source

Original Data, Collected by Danielle Montagne, Joseph Quinn, Liann Tucker, and Tom Wolff.

Ego Networks Elicited from the "Important Matters" Name Generator Question (Nodelist)

Description

This dataset contains a simplified subset of 20 ego networks elicited using the "important matters" name generator question (NGQ), which is frequently used to capture an individual's close personal ties. These networks were collected as part of an experiment illustrating how networks generated by this question may vary depending on the topics covered in preceding survey items. Networks were collected using an online survey deployed via Amazon Mechanical Turk.

Usage

ngq_egos

Format

A data frame with 20 rows and 9 columns:

ego_id

Unique identifier for ego providing network

age

A numeric indicating ego's self-reported age

sex

A numeric indicating ego's self-reported sex. 1 indicates male, 2 female.

race

A character indicating a simplification of ego's self-reported race/ethnicity. Values include "White", "Black", and "Other".

black

A logical indicating ego's self-identification as "Black" or "African-American."

white

A logical indicating ego's self-identification as "White."

other_race

A logical indicating ego's self-identification with a race or ethnicity other than "Black," "African-American," or "White."

edu

A numeric indicating ego's highest level of educational attainment. 1 indicates less than a high school diploma, 4 indicates a high school diploma or GED, 5 some college, 6 a college degree, and 7 a graduate or professional degree.

pol

A numeric indicating ego's self-identified political orientation on a seven-point scale. 1 indicates "Extremely Liberal," 4 "Moderate," and 7 "Extremely Conservative."

...

Source

Original Data, Collected by Danielle Montagne, Joseph Quinn, Liann Tucker, and Tom Wolff.

Pearson's Phi (pearson_phi)

Description

The pearson_phi function identifies the underlying homophilous preference of ego based on the distribution of alter attributes in the population (Perry et al. 2018)

Usage

pearson_phi(
  ego_id,
  ego_measure,
  alter_ego,
  alter_measure,
  prefix = NULL,
  suffix = NULL
)

Arguments

Value

pearson_phi returns a dataframe of vectors that include the ego identifier and phi value of homophilous preference.

Examples

# Run `ego_netwrite`
ngq_nw <- ego_netwrite(egos = ngq_egos,
                      ego_id = ngq_egos$ego_id,

                      alters = ngq_alters,
                      alter_id = ngq_alters$alter_id,
                      alter_ego = ngq_alters$ego_id,

                      max_alters = 10,
                      alter_alter = ngq_aa,
                      aa_ego = ngq_aa$ego_id,
                      i_elements = ngq_aa$alter1,
                      j_elements = ngq_aa$alter2,
                      directed = FALSE)


race_pphi <- pearson_phi(ego_id = ngq_nw$egos$ego_id, ego_measure = ngq_nw$egos$race,
                        alter_ego = ngq_nw$alters$ego_id, alter_measure = ngq_nw$alters$race,
                        suffix = "race")

race_pphi

Quadratic Assignment Procedure (qap_run).

Description

The qap_run function is a wrapper around sna's Quadratic Assignment Procedure models sna::netlm and sna::netlogit. It expects a networks objects containing dependent and independent variables of interest. It is required to use the output from qap_setup.

Usage

qap_run(
  net,
  dependent = NULL,
  variables,
  directed = FALSE,
  family = "linear",
  reps = 500
)

Arguments

Value

'qap_run' returns a list of elements that include:

- covs_df, a data frame containing term labels, estimates, standard errors and p-values

- mods_df, a data frame containing model-level information including the number of observations, AIC and BIC statistics.

Examples

flor <- netwrite(nodelist = florentine_nodes,
                 node_id = "id",
                 i_elements = florentine_edges$source,
                 j_elements = florentine_edges$target,
                 type = florentine_edges$type,
                 directed = FALSE,
                 net_name = "florentine_graph")

flor_setup <- qap_setup(flor$florentine_graph,
                        variables = c("total_degree"),
                        methods = c("difference"))

flor_qap <- qap_run(flor_setup$graph,
                    variables = c("diff_total_degree"))

# Inspect results
flor_qap$covs_df

Individual to Dyadic variable transformation (qap_setup).

Description

The qap_setup function transform an individual level attributes into dyadic comparisons following a set of methods. Output can be used to compute QAP measurements using sister functions in ideanet.

Usage

qap_setup(
  net,
  variables = NULL,
  methods = NULL,
  directed = FALSE,
  additional_vars = NULL
)

Arguments

Value

qap_setup returns a list of elements that include:

- graph, an updated igraph object containing the newly constructed dyadic variables and additional individual-level variables.

- nodes, a nodelist reflecting additional variables if included.

- edges, a nodelist reflecting new dyadic variables.

Examples

flor <- netwrite(nodelist = florentine_nodes,
                 node_id = "id",
                 i_elements = florentine_edges$source,
                 j_elements = florentine_edges$target,
                 type = florentine_edges$type,
                 directed = FALSE,
                 net_name = "florentine_graph")

flor_setup <- qap_setup(flor$florentine_graph,
                        variables = c("total_degree"),
                        methods = c("difference"))

Positional (Role) Analysis in Networks (role_analysis)

Description

The role_analysis function takes networks processed by the netwrite function and performs positional analysis on them. Positional analysis methods allows users to infer distinct "roles" in networks from patterns in network activity. role_analysis currently supports the identification of roles using two methods: hierarchical clustering (cite) and convergence of correlations (CONCOR, Breiger 1975).

Usage

role_analysis(
  graph,
  nodes,
  directed = NA,
  method = "cluster",
  min_partitions = NA,
  max_partitions = NA,
  min_partition_size = NA,
  backbone = 0.9,
  viz = FALSE,
  fast_triad = NULL,
  retain_variables = FALSE,
  cluster_summaries = FALSE,
  dendro_names = FALSE,
  self_ties = FALSE,
  cutoff = 0.999,
  max_iter = 50
)

Arguments

Value

The role_analysis returns a list of outputs that users can access to help interpret results. This contents of this list varies somewhat depending on the method being used for positional analysis.

When hierarchical clustering is used, the list contains the following: cluster_assignments is a data frame indicating each node's membership within inferred clusters at each level of partitioning. cluster_sociogram contains a visualization of the network wherein nodes are colored by their membership within clusters at the optimal level of partitioning. cluster_dendrogram is a visualization of the dendrogram produced from clustering nodes. Red boxes on the visualization indicate nodes' cluster memberships at the optimal level of partitioning. cluster_modularity is a visualization of the modularity scores of the matrix of similarity scores between nodes for each level of partitioning. This visualization helps identify the optimal level of partitioning inferred by the role_analysis function. cluster_summaries_cent contains one or more visualization representing how clusters inferred at the optimal level of partitioning differ from one another on several important node-level measures. cluster_summaries_triad contains one or more visualization representing how clusters inferred at the optimal level of partitioning differ from one another on in terms of their positions within certain kinds of triads in the network. cluster_relations_heatmaps is a list object containing several heatmap visualizations representing the extent to which nodes in one inferred cluster are connected to nodes in another cluster. cluster_relations_sociogram contains a network visualization representing the extent to which nodes in clusters inferred at the optimal level of partitioning are tied to one another. Nodes in this visualization represent inferred clusters in the aggregate.

When CONCOR is used, this list contains the following: concor_assignments is a data frame indicating each node's membership within inferred blocks at each level of partitioning. concor_sociogram contains a visualization of the network wherein nodes are colored by their membership within blocks at the optimal level of partitioning. concor_block_tree is a visualization representing how smaller blocks are derived from larger blocks at each level of partitioning using CONCOR. concor_modularity is a visualization of the modularity scores of the matrix of similarity scores between nodes for each level of partitioning. This visualization helps identify the optimal level of partitioning inferred by the role_analysis function. concor_relations_heatmaps is a list object containing several heatmap visualizations representing the extent to which nodes in one inferred block are connected to nodes in another block. concor_relations_sociogram contains a network visualization representing the extent to which nodes in blocks inferred at the optimal level of partitioning are tied to one another. Nodes in this visualization represent inferred blocks in the aggregate.

Examples

flor <- netwrite(nodelist = florentine_nodes,
                 node_id = "id",
                 i_elements = florentine_edges$source,
                 j_elements = florentine_edges$target,
                 type = florentine_edges$type,
                 directed = FALSE,
                 net_name = "florentine")

# Clustering method
flor_cluster <- role_analysis(graph = flor$igraph_list,
                              nodes = flor$node_measures,
                              directed = FALSE,
                              method = "cluster",
                              min_partitions = 2,
                              max_partitions = 8,
                              viz = TRUE)

### View cluster dendrogram
flor_cluster$cluster_dendrogram

### View modularity summary plot
flor_cluster$cluster_modularity

### View cluster assignments
head(flor_cluster$cluster_assignments)

### View centrality summary plot for aggregate network
flor_cluster$cluster_summaries_cent$summary_graph
### View cenrality summary plot for network of relation `business`
flor_cluster$cluster_summaries_cent$business

### View triad position summary plot for network of relation `marriage`
flor_cluster$cluster_summaries_triad$marriage


# CONCOR method
flor_concor <- role_analysis(graph = flor$igraph_list,
                             nodes = flor$node_measures,
                             directed = FALSE,
                             method = "concor",
                             min_partitions = 1,
                             max_partitions = 4,
                             viz = TRUE)

### View CONCOR tree
flor_concor$concor_block_tree

### View modularity summary plot
flor_concor$concor_modularity

### View cluster assignments
head(flor_concor$concor_assignments)

### View chi-squared heatmaps of relations between blocks
flor_concor$concor_relations_heatmaps$chisq

A Small Network Containing all Triads and Motifs

Description

An adjacency matrix representing a network of 9 nodes, the ties between which form all possible triads and 3-node motifs that can appear in a directed network.

Usage

triad

Format

A matrix with 9 rows and 9 columns