netlit Vignette

Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on longstanding assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature.

netlit provides functions to generate network statistics from a literature review. Specifically, it processes a dataset where each row is a proposed relationship (“edge”) between two concepts or variables (“nodes”). The aim is to offer easy tools to begin using the power of network analysis in R for literature reviews. Using netlit simply requires researchers to enter relationships they observe in prior studies into a simple spreadsheet.

Using the `netlit` R Package

The netlit package provides functions to generate network statistics from a literature review. Specifically, netlit provides a wrapper for igraph functions to facilitate using network analysis in literature reviews.

Install this package with

devtools::install_github("judgelord/netlit")

To install netlit from CRAN, run the following:

install.packages("netlit")

Basic Usage

The review() function takes in a dataframe, data, that includes from and to columns (a directed graph structure).

In the example below, we use example data from this project on redistricting. These data are a set of related concepts (from and to) in the redistricting literature and citations for these relationships (cites and cites_empirical). See vignette("netlit") for more details on this example.

library(netlit)

data("literature")

literature %>% kable()

from	to	cites	cites_empirical
computers	detect gerrymandering	Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018	Wang 2016
computers	public participation	Altman & McDonald 2010; Altman & McDonald 2011	NA
number of competitive districts	preserve communities of interest	Gimpel & Harbridge-Yong 2020	Gimpel & Harbridge-Yong 2020
partisan advantage	proportionality	Caughey et al. 2017; Tamas 2019	NA
partisan gerrymandering	efficiency gap	Chen 2017	Chen 2017
preserve communities of interest	constitutional test	Stephanopoulos 2012	NA
mean-median vote comparison	detect gerrymandering	McDonald & Best 2015; Wang 2016; Wang 2016b	Wang 2016b
mean-median vote comparison	constitutional test	McDonald & Best 2015, Best et al. 2018	NA
partisan gerrymandering	constitutional test	Kang 2017	NA
partisan gerrymandering	instability	Yoshinaka & Murhpy 2011	Yoshinaka & Murhpy 2011
partisan gerrymandering	elite polarization	Masket et al. 2012	Masket et al. 2012
majority minority districts	number of minority representatives	Atsusaka 2021	Atsusaka 2021
majority minority districts	partisan advantage	Cox & Holden 2011; Sabouni & Shelton 2021	Sabouni & Shelton 2021
majority minority districts	voter turnout	Fraga 2016	Fraga 2016
redistricting commission	partisan advantage	Cain 2011	NA
partisan gerrymandering	partisan donor advantage	Kirkland 2013	Kirkland 2013
change in constituency boundaries	legislator voting	Bertelli & Carson 2011, Hayes et al. 2010	Bertelli & Carson 2011, Hayes et al. 2010
change in constituency boundaries	legislative outcomes	Bertelli & Carson 2011, Gul & Pesendorfer 2010	Bertelli & Carson 2011
competitiveness	voter turnout	Moskowitz & Schneer 2019; Hunt 2018	Moskowitz & Schneer 2019; Hunt 2018
sorting	elite polarization	Krasa & Polborn 2018	Krasa & Polborn 2018
contiguity	partisan advantage	Chen & Rodden 2013	Chen & Rodden 2013
electorate composition change	incumbent vote share	Hood & McKee 2013; Ansolabehere & Snyder 2012	Hood & McKee 2013; Ansolabehere & Snyder 2012
electorate composition change	personal vote	Carsey et al. 2017, Bertelli & Carson 2011	Carsey et al. 2017
house-senate delegation alignment	pork spending	Chen 2010	NA
stability in voters’ fellow constituents	voter sense of place	Hayes & McKee 2011	Hayes & McKee 2011
change in constituency boundaries	voter information about their district	Winburn & Wagner 2010	NA
voter information about their district	rolloff	Winburn & Wagner 2010	Winburn & Wagner 2010
voter information about their district	voter turnout	Winburn & Wagner 2010	Winburn & Wagner 2010
voter information about their district	split ticket voting	Winburn & Wagner 2010	Winburn & Wagner 2010
electorate composition change	campaign resource allocation	Limbocker & You 2020	Limbocker & You 2020
geographic partisan distribution	partisan advantage	Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020	Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020
preserve communities of interest	stability in voters’ fellow constituents	Winburn & Wagner 2010	NA
preserve communities of interest	voter information about their district	Winburn & Wagner 2010	NA
preserve communities of interest	rolloff	Hayes & McKee 2011; Winburn & Wagner 2010	Hayes & McKee 2011
number of competitive districts	elite polarization	Grainger 2010	Grainger 2010
partisan advantage	floor votes align with district preferences	Caughey et al. 2017	NA
partisan advantage	floor votes align with state preferences	Caughey et al. 2017	NA
partisan advantage	partisan advantage	Arrington 2016; Campisi et al. 2019; Katz, King & Rosenblatt 2020	NA
partisan advantage	legislator voting	Lo 2013	Lo 2013
partisan advantage	elite polarization	Lo 2013	NA
partisan advantage	efficiency gap	Chen & Cottrell 2016	Chen & Cottrell 2016
partisan advantage	number of competitive districts	Goedert 2017; Goedert 2014	Goedert 2017, Yoshinaka & Murhpy 2011; Goedert 2014
proportionality	house-senate delegation alignment	Chen 2010	NA
compactness	minority representation	Webster 2013	NA
compactness	compactness	Barnes & Solomon 2020; Gatesman & Unwin 2021; Magleby & Mosesson 2018; De Assis et al. 2014; Altman & McDonald 2011; Liu et al. 2016, Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020	Barnes & Solomon 2020; Magleby & Mosesson 2018; De Assis et al. 2014; Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020
efficiency gap	efficiency gap	Stephanopoulos & McGhee 2015, McGhee 2014	McGhee 2014
equal population	equal population	Gatesman & Unwin 2021; Magleby & Mosesson 2018	Magleby & Mosesson 2018
redistricting commission	majority representation	Matsusaka 2010	Matsusaka 2010
redistricting commission	elite ideological moderation	McGhee & Shor 2017	McGhee & Shor 2017
redistricting commission	competitiveness	Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014	Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014
redistricting by courts	competitiveness	Carson et al. 2014	Carson et al. 2014
upcoming redistricting	legislative majority-seeking behavior	Makse 2014	Makse 2014
partisan dislocation	partisan dislocation	Deford, Eubank & Rodden 2020	NA
compactness	voter turnout	Ladewig 2018	Ladewig 2018
preserve communities of interest	preserve communities of interest	Makse 2012	Makse 2012
partisan gerrymandering	partisan advantage	Wang 2016; Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020	Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020
preserve communities of interest	partisan gerrymandering	Sabouni & Shelton 2021	Sabouni & Shelton 2021
redistricting commission	candidate quality	Williamson 2019	Williamson 2019
efficiency gap	efficiency principle	Veomett 2018	NA
wasted votes	efficiency gap	McGhee 2017	NA
efficiency gap	ideological representation	Caughey et al. 2017b	Caughey et al. 2017b
identification with governing party	support for redistricting process	Fougere et al. 2010	Fougere et al. 2010
redistricting commission	support for redistricting process	Fougere et al. 2010	Fougere et al. 2010
efficiency gap	proportionality	Warrington 2018	NA
change in constituency boundaries	issue salience	Gardner 2012	NA
change in constituency boundaries	degree of political conflict	Gardner 2012	NA
partisan gerrymandering	majority representation	Goedert 2014; Nagle 2019	NA
detect gerrymandering	inequality of opportunity vs outcome	Wang et al. 2018	NA
change in constituency boundaries	electorate composition change	Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012	Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012

netlit offers four main functions: make_edgelist(), make_nodelist(), augment_nodelist(), and review().

review() is the primary function. The others are helper functions that perform the individual steps that review() does all at once. review() takes in a dataframe with at least two columns representing linked concepts (e.g., a cause and an effect) and returns data augmented with network statistics. Users must either specify “from” nodes and “to” nodes with the from and to arguments or include columns named from and to in the supplied data object.

review() returns a list of three objects:

an augmented edgelist (a list of relationships with edge_betweenness calculated),
an augmented nodelist (a list of concepts with degree and betweenness calculated), and
a graph object suitable for use in other igraph functions or other network visualization packages.

Users may wish to include edge attributes (e.g., information about the relationship between the two concepts) or node attributes (information about each concept). We show how to do so below. But first, consider the basic use of review():

lit <- review(literature, from = "from", to = "to")

lit

## A netlit_review object with the following components:
## 
## $edgelist
##  - 69 edges
##  - edge attributes: edge_betweenness
## $nodelist
##  - 56 nodes
##  - node attributes: degree_in, degree_out, degree_total, betweenness
## $graph
##    an igraph object

edges <- lit$edgelist

edges %>%  kable()

from	to	edge_betweenness
computers	detect gerrymandering	2.0
computers	public participation	1.0
number of competitive districts	preserve communities of interest	86.0
partisan advantage	proportionality	19.5
partisan gerrymandering	efficiency gap	13.5
preserve communities of interest	constitutional test	7.0
mean-median vote comparison	detect gerrymandering	2.0
mean-median vote comparison	constitutional test	1.0
partisan gerrymandering	constitutional test	1.0
partisan gerrymandering	instability	8.0
partisan gerrymandering	elite polarization	2.0
majority minority districts	number of minority representatives	1.0
majority minority districts	partisan advantage	23.0
majority minority districts	voter turnout	1.0
redistricting commission	partisan advantage	22.0
partisan gerrymandering	partisan donor advantage	8.0
change in constituency boundaries	legislator voting	1.0
change in constituency boundaries	legislative outcomes	1.0
competitiveness	voter turnout	3.0
sorting	elite polarization	1.0
contiguity	partisan advantage	24.0
electorate composition change	incumbent vote share	2.0
electorate composition change	personal vote	2.0
house-senate delegation alignment	pork spending	12.0
stability in voters’ fellow constituents	voter sense of place	9.0
change in constituency boundaries	voter information about their district	4.0
voter information about their district	rolloff	2.0
voter information about their district	voter turnout	8.0
voter information about their district	split ticket voting	10.0
electorate composition change	campaign resource allocation	2.0
geographic partisan distribution	partisan advantage	24.0
preserve communities of interest	stability in voters’ fellow constituents	16.0
preserve communities of interest	voter information about their district	22.0
preserve communities of interest	rolloff	8.0
number of competitive districts	elite polarization	1.0
partisan advantage	floor votes align with district preferences	8.0
partisan advantage	floor votes align with state preferences	8.0
partisan advantage	partisan advantage	0.0
partisan advantage	legislator voting	8.0
partisan advantage	elite polarization	5.0
partisan advantage	efficiency gap	15.0
partisan advantage	number of competitive districts	71.0
proportionality	house-senate delegation alignment	22.0
compactness	minority representation	1.0
compactness	compactness	0.0
efficiency gap	efficiency gap	0.0
equal population	equal population	0.0
redistricting commission	majority representation	1.0
redistricting commission	elite ideological moderation	1.0
redistricting commission	competitiveness	2.0
redistricting by courts	competitiveness	2.0
upcoming redistricting	legislative majority-seeking behavior	1.0
partisan dislocation	partisan dislocation	0.0
compactness	voter turnout	1.0
preserve communities of interest	preserve communities of interest	0.0
partisan gerrymandering	partisan advantage	25.5
preserve communities of interest	partisan gerrymandering	49.0
redistricting commission	candidate quality	1.0
efficiency gap	efficiency principle	10.0
wasted votes	efficiency gap	6.0
efficiency gap	ideological representation	10.0
identification with governing party	support for redistricting process	1.0
redistricting commission	support for redistricting process	1.0
efficiency gap	proportionality	10.5
change in constituency boundaries	issue salience	1.0
change in constituency boundaries	degree of political conflict	1.0
partisan gerrymandering	majority representation	7.0
detect gerrymandering	inequality of opportunity vs outcome	3.0
change in constituency boundaries	electorate composition change	4.0

nodes <- lit$nodelist

nodes %>%  kable()

node	degree_in	degree_out	degree_total	betweenness
computers	0	2	2	0.0
number of competitive districts	1	2	3	64.0
partisan advantage	6	8	14	111.5
partisan gerrymandering	1	7	8	42.0
preserve communities of interest	2	6	8	79.0
mean-median vote comparison	0	2	2	0.0
majority minority districts	0	3	3	0.0
redistricting commission	0	6	6	0.0
change in constituency boundaries	0	6	6	0.0
competitiveness	2	1	3	2.0
sorting	0	1	1	0.0
contiguity	0	1	1	0.0
electorate composition change	1	3	4	3.0
house-senate delegation alignment	1	1	2	11.0
stability in voters’ fellow constituents	1	1	2	8.0
voter information about their district	2	3	5	17.0
geographic partisan distribution	0	1	1	0.0
proportionality	2	1	3	20.0
compactness	1	3	4	0.0
efficiency gap	4	4	8	25.5
equal population	1	1	2	0.0
redistricting by courts	0	1	1	0.0
upcoming redistricting	0	1	1	0.0
partisan dislocation	1	1	2	0.0
wasted votes	0	1	1	0.0
identification with governing party	0	1	1	0.0
detect gerrymandering	2	1	3	2.0
public participation	1	0	1	0.0
constitutional test	3	0	3	0.0
instability	1	0	1	0.0
elite polarization	4	0	4	0.0
number of minority representatives	1	0	1	0.0
voter turnout	4	0	4	0.0
partisan donor advantage	1	0	1	0.0
legislator voting	2	0	2	0.0
legislative outcomes	1	0	1	0.0
incumbent vote share	1	0	1	0.0
personal vote	1	0	1	0.0
pork spending	1	0	1	0.0
voter sense of place	1	0	1	0.0
rolloff	2	0	2	0.0
split ticket voting	1	0	1	0.0
campaign resource allocation	1	0	1	0.0
floor votes align with district preferences	1	0	1	0.0
floor votes align with state preferences	1	0	1	0.0
minority representation	1	0	1	0.0
majority representation	2	0	2	0.0
elite ideological moderation	1	0	1	0.0
legislative majority-seeking behavior	1	0	1	0.0
candidate quality	1	0	1	0.0
efficiency principle	1	0	1	0.0
ideological representation	1	0	1	0.0
support for redistricting process	2	0	2	0.0
issue salience	1	0	1	0.0
degree of political conflict	1	0	1	0.0
inequality of opportunity vs outcome	1	0	1	0.0

Edge and node attributes can be added using the edge_attributes and node_attributes arguments. edge_attributes is a vector that identifies columns in the supplied data frame that the user would like to retain. node_attributes is a separate dataframe that contains attributes for each node in the primary data set. The example node_attributes data include one column type indicating a type for each each node/variable/concept.

data("node_attributes")

node_attributes %>% kable()

node	type
campaign resource allocation	effect
candidate quality	effect
change in constituency boundaries	condition
preserve communities of interest	goal
competitiveness	goal
computers	condition
constitutional test	metric
contiguity	condition
sorting	condition
detect gerrymandering	goal
electorate composition change	condition
elite ideological moderation	effect
equal population	metric
floor votes align with district preferences	effect
house-senate delegation alignment	effect
incumbent vote share	effect
instability	effect
legislative majority-seeking behavior	effect
legislative outcomes	effect
legislator voting	effect
majority minority districts	policy
mean-median vote comparison	metric
minority representation	effect
number of competitive districts	goal
number of minority representatives	effect
partisan advantage	goal
partisan gerrymandering	condition
personal vote	effect
pork spending	policy
proportionality	goal
public participation	goal
redistricting by courts	policy
redistricting commission	policy
rolloff	effect
split ticket voting	effect
stability in voters’ fellow constituents	effect
upcoming redistricting	condition
voter information about their district	effect
voter sense of place	effect
voter turnout	effect
elite polarization	effect
geographic partisan distribution	condition
compactness	value
efficiency gap	metric
partisan dislocation	metric
wasted votes	metric
identification with governing party	condition
partisan donor advantage	effect
floor votes align with state preferences	effect
majority representation	effect
efficiency principle	metric
ideological representation	effect
support for redistricting process	effect
issue salience	effect
degree of political conflict	effect
inequality of opportunity vs outcome	effect

lit <- review(literature,
              edge_attributes = c("cites", "cites_empirical"),
              node_attributes = node_attributes)

lit

## A netlit_review object with the following components:
## 
## $edgelist
##  - 69 edges
##  - edge attributes: cites, cites_empirical, edge_betweenness
## $nodelist
##  - 56 nodes
##  - node attributes: type, degree_in, degree_out, degree_total, betweenness
## $graph
##    an igraph object

Tip: to retain all variables from literature, use edge_attributes = names(literature).

More Advanced Uses: larger networks, visualizing your network, network descriptives

library(tidyverse)
library(magrittr)
library(ggraph)

We separated multiple cites to a theorized relationship with semicolons. Let’s count the total number of citations and the number of citations to empirical work by splitting out each cite and measuring the length of that vector.

# count cites 
literature %<>% 
  group_by(to, from) %>% 
  mutate(cite_weight = str_split(cites, ";")[[1]]  %>% length(),
         cite_weight_empirical = str_split(cites_empirical, ";",)[[1]] %>% length(),
         cite_weight_empirical = ifelse(is.na(cites_empirical), 0, cite_weight_empirical)) %>% 
  ungroup() 

# subsets 
literature %<>% mutate(communities_node = str_c(to, from) %>% str_detect("Commun"),
                       confound = case_when(
      from == "Preserve\nCommunities\nOf Interest" & to == "Rolloff" ~ T,
      from == "Voter\nInformation\nAbout Their\nDistrict" & to == "Rolloff" ~ T,
      from == "Preserve\nCommunities\nOf Interest" 
            & to == "Voter\nInformation\nAbout Their\nDistrict" ~ T,
      T ~ F),
              empirical = ifelse(!is.na(cites_empirical),
                                 "Empirical work", 
                                 "No empirical work"))

Now we use review() on this expanded edgelist, including all variables in the literature data with edge_attributes = names(literature).

# now with all node and edge attributes 
lit <- review(literature,
              edge_attributes = names(literature),
              node_attributes = node_attributes
              )

edges <- lit$edgelist

edges %>% kable()

from	to	cites	cites_empirical	cite_weight	cite_weight_empirical	communities_node	confound	empirical	edge_betweenness
Computers	Detect Gerrymandering	Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018	Wang 2016	4	1	FALSE	FALSE	Empirical work	2.0
Computers	Public Participation	Altman & McDonald 2010; Altman & McDonald 2011	NA	2	0	FALSE	FALSE	No empirical work	1.0
Number Of Competitive Districts	Preserve Communities Of Interest	Gimpel & Harbridge-Yong 2020	Gimpel & Harbridge-Yong 2020	1	1	TRUE	FALSE	Empirical work	86.0
Partisan Advantage	Proportionality	Caughey et al. 2017; Tamas 2019	NA	2	0	FALSE	FALSE	No empirical work	19.5
Partisan Gerrymandering	Efficiency Gap	Chen 2017	Chen 2017	1	1	FALSE	FALSE	Empirical work	13.5
Preserve Communities Of Interest	Constitutional Test	Stephanopoulos 2012	NA	1	0	TRUE	FALSE	No empirical work	7.0
Mean-Median Vote Comparison	Detect Gerrymandering	McDonald & Best 2015; Wang 2016; Wang 2016b	Wang 2016b	3	1	FALSE	FALSE	Empirical work	2.0
Mean-Median Vote Comparison	Constitutional Test	McDonald & Best 2015, Best et al. 2018	NA	1	0	FALSE	FALSE	No empirical work	1.0
Partisan Gerrymandering	Constitutional Test	Kang 2017	NA	1	0	FALSE	FALSE	No empirical work	1.0
Partisan Gerrymandering	Instability	Yoshinaka & Murhpy 2011	Yoshinaka & Murhpy 2011	1	1	FALSE	FALSE	Empirical work	8.0
Partisan Gerrymandering	Elite Polarization	Masket et al. 2012	Masket et al. 2012	1	1	FALSE	FALSE	Empirical work	2.0
Majority Minority Districts	Number Of Minority Representatives	Atsusaka 2021	Atsusaka 2021	1	1	FALSE	FALSE	Empirical work	1.0
Majority Minority Districts	Partisan Advantage	Cox & Holden 2011; Sabouni & Shelton 2021	Sabouni & Shelton 2021	2	1	FALSE	FALSE	Empirical work	23.0
Majority Minority Districts	Voter Turnout	Fraga 2016	Fraga 2016	1	1	FALSE	FALSE	Empirical work	1.0
Redistricting Commission	Partisan Advantage	Cain 2011	NA	1	0	FALSE	FALSE	No empirical work	22.0
Partisan Gerrymandering	Partisan Donor Advantage	Kirkland 2013	Kirkland 2013	1	1	FALSE	FALSE	Empirical work	8.0
Change In Constituency Boundaries	Legislator Voting	Bertelli & Carson 2011, Hayes et al. 2010	Bertelli & Carson 2011, Hayes et al. 2010	1	1	FALSE	FALSE	Empirical work	1.0
Change In Constituency Boundaries	Legislative Outcomes	Bertelli & Carson 2011, Gul & Pesendorfer 2010	Bertelli & Carson 2011	1	1	FALSE	FALSE	Empirical work	1.0
Competitiveness	Voter Turnout	Moskowitz & Schneer 2019; Hunt 2018	Moskowitz & Schneer 2019; Hunt 2018	2	2	FALSE	FALSE	Empirical work	3.0
Sorting	Elite Polarization	Krasa & Polborn 2018	Krasa & Polborn 2018	1	1	FALSE	FALSE	Empirical work	1.0
Contiguity	Partisan Advantage	Chen & Rodden 2013	Chen & Rodden 2013	1	1	FALSE	FALSE	Empirical work	24.0
Electorate Composition Change	Incumbent Vote Share	Hood & McKee 2013; Ansolabehere & Snyder 2012	Hood & McKee 2013; Ansolabehere & Snyder 2012	2	2	FALSE	FALSE	Empirical work	2.0
Electorate Composition Change	Personal Vote	Carsey et al. 2017, Bertelli & Carson 2011	Carsey et al. 2017	1	1	FALSE	FALSE	Empirical work	2.0
House-Senate Delegation Alignment	Pork Spending	Chen 2010	NA	1	0	FALSE	FALSE	No empirical work	12.0
Stability In Voters’ Fellow Constituents	Voter Sense Of Place	Hayes & McKee 2011	Hayes & McKee 2011	1	1	FALSE	FALSE	Empirical work	9.0
Change In Constituency Boundaries	Voter Information About Their District	Winburn & Wagner 2010	NA	1	0	FALSE	FALSE	No empirical work	4.0
Voter Information About Their District	Rolloff	Winburn & Wagner 2010	Winburn & Wagner 2010	1	1	FALSE	TRUE	Empirical work	2.0
Voter Information About Their District	Voter Turnout	Winburn & Wagner 2010	Winburn & Wagner 2010	1	1	FALSE	FALSE	Empirical work	8.0
Voter Information About Their District	Split Ticket Voting	Winburn & Wagner 2010	Winburn & Wagner 2010	1	1	FALSE	FALSE	Empirical work	10.0
Electorate Composition Change	Campaign Resource Allocation	Limbocker & You 2020	Limbocker & You 2020	1	1	FALSE	FALSE	Empirical work	2.0
Geographic Partisan Distribution	Partisan Advantage	Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020	Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020	3	3	FALSE	FALSE	Empirical work	24.0
Preserve Communities Of Interest	Stability In Voters’ Fellow Constituents	Winburn & Wagner 2010	NA	1	0	TRUE	FALSE	No empirical work	16.0
Preserve Communities Of Interest	Voter Information About Their District	Winburn & Wagner 2010	NA	1	0	TRUE	TRUE	No empirical work	22.0
Preserve Communities Of Interest	Rolloff	Hayes & McKee 2011; Winburn & Wagner 2010	Hayes & McKee 2011	2	1	TRUE	TRUE	Empirical work	8.0
Number Of Competitive Districts	Elite Polarization	Grainger 2010	Grainger 2010	1	1	FALSE	FALSE	Empirical work	1.0
Partisan Advantage	Floor Votes Align With District Preferences	Caughey et al. 2017	NA	1	0	FALSE	FALSE	No empirical work	8.0
Partisan Advantage	Floor Votes Align With State Preferences	Caughey et al. 2017	NA	1	0	FALSE	FALSE	No empirical work	8.0
Partisan Advantage	Partisan Advantage	Arrington 2016; Campisi et al. 2019; Katz, King & Rosenblatt 2020	NA	3	0	FALSE	FALSE	No empirical work	0.0
Partisan Advantage	Legislator Voting	Lo 2013	Lo 2013	1	1	FALSE	FALSE	Empirical work	8.0
Partisan Advantage	Elite Polarization	Lo 2013	NA	1	0	FALSE	FALSE	No empirical work	5.0
Partisan Advantage	Efficiency Gap	Chen & Cottrell 2016	Chen & Cottrell 2016	1	1	FALSE	FALSE	Empirical work	15.0
Partisan Advantage	Number Of Competitive Districts	Goedert 2017; Goedert 2014	Goedert 2017, Yoshinaka & Murhpy 2011; Goedert 2014	2	2	FALSE	FALSE	Empirical work	71.0
Proportionality	House-Senate Delegation Alignment	Chen 2010	NA	1	0	FALSE	FALSE	No empirical work	22.0
Compactness	Minority Representation	Webster 2013	NA	1	0	FALSE	FALSE	No empirical work	1.0
Compactness	Compactness	Barnes & Solomon 2020; Gatesman & Unwin 2021; Magleby & Mosesson 2018; De Assis et al. 2014; Altman & McDonald 2011; Liu et al. 2016, Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020	Barnes & Solomon 2020; Magleby & Mosesson 2018; De Assis et al. 2014; Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020	6	4	FALSE	FALSE	Empirical work	0.0
Efficiency Gap	Efficiency Gap	Stephanopoulos & McGhee 2015, McGhee 2014	McGhee 2014	1	1	FALSE	FALSE	Empirical work	0.0
Equal Population	Equal Population	Gatesman & Unwin 2021; Magleby & Mosesson 2018	Magleby & Mosesson 2018	2	1	FALSE	FALSE	Empirical work	0.0
Redistricting Commission	Majority Representation	Matsusaka 2010	Matsusaka 2010	1	1	FALSE	FALSE	Empirical work	1.0
Redistricting Commission	Elite Ideological Moderation	McGhee & Shor 2017	McGhee & Shor 2017	1	1	FALSE	FALSE	Empirical work	1.0
Redistricting Commission	Competitiveness	Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014	Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014	2	2	FALSE	FALSE	Empirical work	2.0
Redistricting By Courts	Competitiveness	Carson et al. 2014	Carson et al. 2014	1	1	FALSE	FALSE	Empirical work	2.0
Upcoming Redistricting	Legislative Majority-Seeking Behavior	Makse 2014	Makse 2014	1	1	FALSE	FALSE	Empirical work	1.0
Partisan Dislocation	Partisan Dislocation	Deford, Eubank & Rodden 2020	NA	1	0	FALSE	FALSE	No empirical work	0.0
Compactness	Voter Turnout	Ladewig 2018	Ladewig 2018	1	1	FALSE	FALSE	Empirical work	1.0
Preserve Communities Of Interest	Preserve Communities Of Interest	Makse 2012	Makse 2012	1	1	TRUE	FALSE	Empirical work	0.0
Partisan Gerrymandering	Partisan Advantage	Wang 2016; Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020	Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020	4	3	FALSE	FALSE	Empirical work	25.5
Preserve Communities Of Interest	Partisan Gerrymandering	Sabouni & Shelton 2021	Sabouni & Shelton 2021	1	1	TRUE	FALSE	Empirical work	49.0
Redistricting Commission	Candidate Quality	Williamson 2019	Williamson 2019	1	1	FALSE	FALSE	Empirical work	1.0
Efficiency Gap	Efficiency Principle	Veomett 2018	NA	1	0	FALSE	FALSE	No empirical work	10.0
Wasted Votes	Efficiency Gap	McGhee 2017	NA	1	0	FALSE	FALSE	No empirical work	6.0
Efficiency Gap	Ideological Representation	Caughey et al. 2017b	Caughey et al. 2017b	1	1	FALSE	FALSE	Empirical work	10.0
Identification With Governing Party	Support For Redistricting Process	Fougere et al. 2010	Fougere et al. 2010	1	1	FALSE	FALSE	Empirical work	1.0
Redistricting Commission	Support For Redistricting Process	Fougere et al. 2010	Fougere et al. 2010	1	1	FALSE	FALSE	Empirical work	1.0
Efficiency Gap	Proportionality	Warrington 2018	NA	1	0	FALSE	FALSE	No empirical work	10.5
Change In Constituency Boundaries	Issue Salience	Gardner 2012	NA	1	0	FALSE	FALSE	No empirical work	1.0
Change In Constituency Boundaries	Degree Of Political Conflict	Gardner 2012	NA	1	0	FALSE	FALSE	No empirical work	1.0
Partisan Gerrymandering	Majority Representation	Goedert 2014; Nagle 2019	NA	2	0	FALSE	FALSE	No empirical work	7.0
Detect Gerrymandering	Inequality Of Opportunity Vs Outcome	Wang et al. 2018	NA	1	0	FALSE	FALSE	No empirical work	3.0
Change In Constituency Boundaries	Electorate Composition Change	Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012	Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012	3	3	FALSE	FALSE	Empirical work	4.0

nodes <- lit$nodelist

nodes %>%  kable()

node	type	degree_in	degree_out	degree_total	betweenness
Computers	condition	0	2	2	0.0
Number Of Competitive Districts	goal	1	2	3	64.0
Partisan Advantage	goal	6	8	14	111.5
Partisan Gerrymandering	condition	1	7	8	42.0
Preserve Communities Of Interest	goal	2	6	8	79.0
Mean-Median Vote Comparison	metric	0	2	2	0.0
Majority Minority Districts	policy	0	3	3	0.0
Redistricting Commission	policy	0	6	6	0.0
Change In Constituency Boundaries	condition	0	6	6	0.0
Competitiveness	goal	2	1	3	2.0
Sorting	condition	0	1	1	0.0
Contiguity	condition	0	1	1	0.0
Electorate Composition Change	condition	1	3	4	3.0
House-Senate Delegation Alignment	effect	1	1	2	11.0
Stability In Voters’ Fellow Constituents	effect	1	1	2	8.0
Voter Information About Their District	effect	2	3	5	17.0
Geographic Partisan Distribution	condition	0	1	1	0.0
Proportionality	goal	2	1	3	20.0
Compactness	value	1	3	4	0.0
Efficiency Gap	metric	4	4	8	25.5
Equal Population	metric	1	1	2	0.0
Redistricting By Courts	policy	0	1	1	0.0
Upcoming Redistricting	condition	0	1	1	0.0
Partisan Dislocation	metric	1	1	2	0.0
Wasted Votes	metric	0	1	1	0.0
Identification With Governing Party	condition	0	1	1	0.0
Detect Gerrymandering	goal	2	1	3	2.0
Public Participation	goal	1	0	1	0.0
Constitutional Test	metric	3	0	3	0.0
Instability	effect	1	0	1	0.0
Elite Polarization	effect	4	0	4	0.0
Number Of Minority Representatives	effect	1	0	1	0.0
Voter Turnout	effect	4	0	4	0.0
Partisan Donor Advantage	effect	1	0	1	0.0
Legislator Voting	effect	2	0	2	0.0
Legislative Outcomes	effect	1	0	1	0.0
Incumbent Vote Share	effect	1	0	1	0.0
Personal Vote	effect	1	0	1	0.0
Pork Spending	policy	1	0	1	0.0
Voter Sense Of Place	effect	1	0	1	0.0
Rolloff	effect	2	0	2	0.0
Split Ticket Voting	effect	1	0	1	0.0
Campaign Resource Allocation	effect	1	0	1	0.0
Floor Votes Align With District Preferences	effect	1	0	1	0.0
Floor Votes Align With State Preferences	effect	1	0	1	0.0
Minority Representation	effect	1	0	1	0.0
Majority Representation	effect	2	0	2	0.0
Elite Ideological Moderation	effect	1	0	1	0.0
Legislative Majority-Seeking Behavior	effect	1	0	1	0.0
Candidate Quality	effect	1	0	1	0.0
Efficiency Principle	metric	1	0	1	0.0
Ideological Representation	effect	1	0	1	0.0
Support For Redistricting Process	effect	2	0	2	0.0
Issue Salience	effect	1	0	1	0.0
Degree Of Political Conflict	effect	1	0	1	0.0
Inequality Of Opportunity Vs Outcome	effect	1	0	1	0.0

The `igraph` object

# define igraph object as g
g <- lit$graph

g

## IGRAPH ebb15e1 DN-B 56 69 -- 
## + attr: name (v/c), type (v/c), degree_in (v/n), degree_out (v/n),
## | degree_total (v/n), betweenness (v/n), cites (e/c), cites_empirical
## | (e/c), cite_weight (e/n), cite_weight_empirical (e/n),
## | communities_node (e/l), confound (e/l), empirical (e/c),
## | edge_betweenness (e/n)
## + edges from ebb15e1 (vertex names):
## [1] Computers                        ->Detect\nGerrymandering            
## [2] Computers                        ->Public\nParticipation             
## [3] Number Of\nCompetitive\nDistricts->Preserve\nCommunities\nOf Interest
## [4] Partisan\nAdvantage              ->Proportionality                   
## + ... omitted several edges

What does it mean?

D means directed
N means named graph
W means weighted graph
name (v/c) means name is a node attribute and it’s a character
cite_weight (e/n) means cite_weight is an edge attribute and it’s numeric

With `ggraph`

We can also plot using the package ggraph package to plot the igraph object.

This package allows us to plot self-ties, but it is slightly more difficult to use ggplot features (e.g. colors and legend labels) compared to ggnetwork.

set.seed(5)

p <- ggraph(g, layout = 'fr') + 
  geom_node_point(
    aes(color = degree_total %>% as.factor() ),
    size = 6, 
    alpha = .7
    ) + 
  geom_edge_arc2(
    start_cap = circle(3,'mm'),
    end_cap = circle(6, 'mm'),
    aes(
      color = cite_weight ,
      linetype = empirical
      ),
    curvature = 0,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_edge_loop(
      start_cap = circle(5, 'mm'),
      end_cap = circle(2, 'mm'),
      aes( color = cite_weight ,
           linetype = empirical
      ),
      n = 300,
      strength = .6,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_node_text( aes(label = name), size = 2.3) + 
  ggplot2::theme_void() + 
  theme(legend.position="bottom") + 
  labs(edge_color = "Number of\nPublications",
       color = "Total Degree\nCentrality",
       edge_linetype = "") + 
  scale_edge_colour_viridis(
                        discrete = FALSE,
                        option = "plasma",
                        begin = 0,
                        end = .9,
                        direction = -1,
                        guide = "legend",
                        aesthetics = "edge_colour") +
  scale_color_viridis_d(option = "mako", 
                        begin = 1, 
                        end = .5)

p

Subgraphs

p + facet_wrap("communities_node")

p + facet_wrap("confound")

Betweenness

Edge Betweenness

ggraph(g, layout = 'fr') + 
  geom_node_point(size = 10, 
                  alpha = .1) + 
  theme_void() + 
  theme(legend.position="bottom"
        ) + 
  scale_color_viridis_c(begin = .5, 
                        end = 1, 
                        direction = -1, 
                        option = "cividis") + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         guide = "legend",
                         option = "cividis")  +    
  geom_edge_arc2(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    aes(
      color = edge_betweenness,
      linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness))  +
  geom_node_text(aes(label = name), 
                 size = 2.3) + 
  labs(edge_color = "Edge Betweenness",
       color = "Node Betweenness",
       edge_linetype = "")

Node Betweenness

p <- ggraph(g, layout = 'fr') + 
  geom_node_point(
    aes(color = betweenness),
    size = 6, 
    alpha = .7
    ) + 
  geom_edge_arc2(
      start_cap = circle(3, 'mm'),
      end_cap = circle(6, 'mm'),
    aes(
      color = cite_weight,
      linetype = empirical
      ),
    curvature = 0,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_edge_loop(
      start_cap = circle(5, 'mm'),
      end_cap = circle(2, 'mm'),
      aes( color = cite_weight,
      linetype = empirical
      ),
      n = 300,
      strength = .6,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_node_text(aes(label = name), 
                  size = 2.3) + 
  theme_void() + 
  theme(legend.position="bottom") + 
  labs(edge_color = "Number of\nPublications",
       color = "Betweeneness",
       edge_linetype = "") + 
scale_edge_color_viridis(option = "plasma", 
                         begin = 0, 
                         end = .9, 
                         direction = -1,
                         guide = "legend") +
  scale_color_gradient2()

p

ggraph(g, layout = 'fr') + 
  geom_node_point(aes(color = betweenness),
                  size = 10, 
                  alpha = 1) + 
  theme_void() + 
  theme(legend.position="bottom") + 
 scale_color_viridis_c(begin = .5, 
                       end = 1, 
                       direction = -1, 
                       option = "cividis") + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         option = "cividis",
    guide = "legend")  +    
  geom_edge_arc2(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    aes(
    color = edge_betweenness,
    linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness)) +
  labs(edge_color = "Edge Betweenness",
       color = "Node Betweenness",
       edge_linetype = "") + 
  geom_node_text(aes(label = name), 
                 size = 2.3)

Degree

ggraph(g, layout = 'fr') + 
  geom_node_point(aes(color = degree_total),
                  size = 10, 
                  alpha = 1) + 
  theme_void() + 
  theme(legend.position="bottom"        ) + 
  scale_color_gradient2() + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         option = "cividis",
                         guide = "legend")  +    
  geom_edge_arc2(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    aes(
    color = edge_betweenness,
    linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness)) +
  labs(edge_color = "Edge Betweenness",
       color = "Total Degree",
       edge_linetype = "") + 
  geom_node_text(aes(label = name), 
                 size = 2.3)

About the example data

Articles were chosen according to specific selection criteria. We first identified articles published since 2010 that either 1) were published in one of eight high-ranking journals or 2) gained at least 50 citations according to Google Scholar. We then chose articles that contained four possible key terms in the title or abstract.

# Journal articles in example data
data("literature_metadata")

literature_metadata %>% kable()

ID	Author	Year	Publication	Title	Citations	Outside U.S.
Hayes & McKee 2011	Hayes & McKee	2011	AJPS	The Intersection of Redistricting, Race, and Participation	41	NA
Katz, King & Rosenblatt 2020	Katz, King & Rosenblatt	2020	APSR	Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies	20	NA
Lo 2013	Lo	2013	QJPS	Legislative Responsiveness to Gerrymandering: Evidence from the 2003 Texas Redistricting	20	NA
Chen & Rodden 2013	Chen & Rodden	2013	QJPS	Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures	361	NA
Matsusaka 2010	Matsusaka	2010	QJPS	Popular Control of Public Policy: A Quantitative Approach	138	NA
Moskowitz & Schneer 2019	Moskowitz & Schneer	2019	QJPS	Reevaluating Competition and Turnout in US House Elections	10	NA
McGhee & Shor 2017	McGhee & Shor	2017	Perspectives on Politics	Has the Top Two Primary Elected More Moderates?	20	NA
Wildgen & Engstrom 1980	Wildgen & Engstrom	1980	Legislative Studies Quarterly	Spatial Distribution of Partisan Support and the Seats/Votes Relationship	30	NA
Cain 1985	Cain	1985	APSR	Assessing the Partisan Effects of Redistricting	243	NA
Buchler 2005	Buchler	2005	Journal of Theoretical Politics	Competition, Representation and Redistricting: The Case Against Competitive Congressional Districts	81	NA
Lublin & McDonald 2006	Lublin & McDonald	2006	Election Law Journal	Is It Time to Draw the Line?: The Impact of Redistricting on Competition in State House Elections	44	NA
Caughey et al. 2017	Caughey et al.	2017	JOP	Incremental Democracy: The Policy Effects of Partisan Control of State Government	92	NA
Glazer et al. 1987	Glazer et al.	1987	AJPS	Partisan and Incumbency Effects of 1970s Congressional Redistricting	102	NA
Jacobson 2005	Jacobson	2005	Political Science Quarterly	Polarized Politics and the 2004 Congressional and Presidential Elections	134	NA
Abramowitz et al. 2006	Abramowitz et al.	2006	JOP	Incumbency, Redistricting, and the Decline of Competition in US House Elections	461	NA
Cain et al. 2005	Cain et al.	2005	Brookings Institution	From Equality to Fairness: The Path of Political Reform since Baker v. Carr	56	NA
Griffin & Newman 2007	Griffin & Newman	2007	JOP	The Unequal Representation of Latinos and Whites	145	NA
Grofman et al. 2000	Grofman et al.	2000	NCL Review	Drawing Effective Minority Districts: A Conceptual Framework and Some Empirical Evidence	165	NA
McDonald 2006	McDonald	2006	PS: Political Science & Politics	Drawing the Line on District Competition	76	NA
Desposato & Petrocik 2003	Desposato & Petrocik	2003	AJPS	The Variable Incumbency Advantage: New Voters, Redistricting, and the Personal Vote	186	NA
Ashworth & Bueno de Mesquita 2006	Ashworth & Bueno de Mesquita	2006	JOP	Delivering the Goods: Legislative Particularism in Different Electoral and Institutional Settings	201	NA
Hayes & McKee 2008	Hayes & McKee	2008	American Politics Research	Toward a One-Party South?	65	NA
Winburn & Wagner 2010	Winburn & Wagner	2010	Political Research Quarterly	Carving Voters Out: Redistricting’s Influence on Political Information, Turnout, and Voting Behavior	46	NA
Hayes & McKee 2009	Hayes & McKee	2009	AJPS	The Participatory Effects of Redistricting	85	NA
Chen 2010	Chen	2010	AJPS	The Effect of Electoral Geography on Pork Barreling in Bicameral Legislatures	38	NA
Cameron et al. 1996	Cameron et al.	1996	APSR	Do Majority-Minority Districts Maximize Substantive Black Representation in Congress?	709	NA
Canon 1999	Canon	1999	Legislative Studies Quarterly	Electoral Systems and the Representation of Minority Interests in Legislatures	77	NA
Gay 2007	Gay	2007	JOP	Legislating Without Constraints: The Effect of Minority Districting on Legislators’ Responsiveness to Constituency Preferences	47	NA
Bratton & Haynie 1999	Bratton & Haynie	1999	JOP	Agenda Setting and Legislative Success in State Legislatures: The Effects of Gender and Race	740	NA
Wyrick 1991	Wyrick	1991	American Politics Quarterly	Management of Political Influence: Gerrymandering in the 1980s	10	NA
Barabas & Jerit 2004	Barabas & Jerit	2004	State Politics & Policy Quaterly	Redistricting Principles and Racial Representation	44	NA
Shotts 2003	Shotts	2003	JOP	Does Racial Redistricting Cause Conservative Policy Outcomes? Policy Preferences of Southern Representatives in the 1980s and 1990s	76	NA
Bullock 1995	Bullock	1995	American Politics Quarterly	The Impact of Changing the Racial Composition of Congressional Districts on Legislators’ Roll Call Behavior	63	NA
Overby & Cosgrove 1996	Overby & Cosgrove	1996	JOP	Unintended Consequences? Racial Redistricting and the Representation of Minority Interests	164	NA
Sharpe & Garand 2001	Sharpe & Garand	2001	Political Research Quarterly	Race, Roll Calls, and Redistricting: The Impact of Race-Based Redistricting on Congressional Roll-Call	48	NA
LeVeaux & Garand 2003	LeVeaux & Garand	2003	Social Science Quarterly	Race‐Based Redistricting, Core Constituencies, and Legislative Responsiveness to Constituency Change*	13	NA
Lyons & Galderisi 1995	Lyons & Galderisi	1995	Political Research Quarterly	Incumbency, Reapportionment, and US House Redistricting	43	NA
Hirsch 2003	Hirsch	2003	Election Law Journal	The United States House of Unrepresentatives: What Went Wrong in the Latest Round of Congressional Redistricting	159	NA
Grofman 1982	Grofman	1982	Political Geography Quarterly	Reformers, Politicians, and the Courts: A Preliminary Look at US Redistricting in the 1980s	12	NA
Forgette & Winkle 2006	Forgette & Winkle	2006	Social Science Quarterly	Partisan Gerrymandering and the Voting Rights Act	16	NA
Hetherington et al. 2003	Hetherington et al.	2003	JOP	The Redistricting Cycle and Strategic Candidate Decisions in US House Races	91	NA
Carson et al. 2006	Carson et al.	2006	AJPS	The Electoral Costs of Party Loyalty in Congress	342	NA
Lublin 1999	Lublin	1999	APSR	Racial Redistricting and African-American Representation: A Critique of “Do Majority-Minority Districts Maximize Substantive Black Representation in Congress?”	216	NA
Forgette & Platt 2005	Forgette & Platt	2005	Political Geography	Redistricting Principles and Incumbency Protection in the US Congress	33	NA
Carson & Crespin 2004	Carson & Crespin	2004	State Politics & Policy Quaterly	The Effect of State Redistricting Methods on Electoral Competition in United States House of Representatives Races	100	NA
Katz et al. 2020	Katz et al.	2020	APSR	Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies	20	NA
Bertelli & Carson 2011	Bertelli & Carson	2011	Electoral Studies	Small Changes, Big Results: Legislative Voting Behavior in the Presence of New Voters	12	NA
Chen & Cottrell 2016	Chen & Cottrell	2016	Electoral Studies	Evaluating Partisan Gains from Congressional Gerrymandering: Using Computer Simulations to Estimate the Effect of Gerrymandering in the U.S. House	62	NA
Hunt 2018	Hunt	2018	Electoral Studies	When Does Redistricting Matter? Changing Conditions and Their Effects on Voter Turnout	8	NA
Sauger & Grofman 2016	Sauger & Grofman	2016	Electoral Studies	Partisan Bias and Redistricting in France	15	1
Wong 2019	Wong	2019	BJPS	Gerrymandering in Electoral Autocracies: Evidence from Hong Kong	16	1
Incerti 2018	Incerti	2018	Electoral Studies	The Optimal Allocation of Campaign Funds in US House Elections	5	NA
Limbocker & You 2020	Limbocker & You	2020	Electoral Studies	Campaign Styles: Persistency in Campaign Resource Allocation	2	NA
Carson et al. 2014	Carson et al.	2014	State Politics & Policy Quaterly	Reevaluating the Effects of Redistricting on Electoral Competition, 1972–2012	38	NA
Makse 2014	Makse	2014	State Politics & Policy Quaterly	The Redistricting Cycle, Partisan Tides, and Party Strategy in State Legislative Elections	12	NA
Hood & McKee 2013	Hood & McKee	2013	State Politics & Policy Quaterly	Unwelcome Constituents: Redistricting and Countervailing Partisan Tides	7	NA
Goedert 2017	Goedert	2017	State Politics & Policy Quaterly	The Pseudoparadox of Partisan Mapmaking and Congressional Competition	6	NA
Kirkland 2013	Kirkland	2013	State Politics & Policy Quaterly	Wallet-Based Redistricting: Evidence for the Concentration of Wealth in Majority Party Districts	6	NA
Carsey et al. 2017	Carsey et al.	2017	State Politics & Policy Quaterly	Rethinking the Normal Vote, the Personal Vote, and the Impact of Legislative Professionalism in U.S. State Legislative Elections	6	NA
Stephanopoulos & McGhee 2015	Stephanopoulos & McGhee	2015	University of Chicago Law Review	Partisan Gerrymandering and the Efficiency Gap	345	NA
Chen & Rodden 2015	Chen & Rodden	2015	Election Law Journal	Cutting Through the Thicket: Redistricting Simulations and the Detection of Partisan Gerrymanders	87	NA
Barnes & Solomon 2020	Barnes & Solomon	2020	Political Analysis	Gerrymandering and Compactness: Implementation Flexibility and Abuse	13	NA
Atsusaka 2021	Atsusaka	2021	APSR	A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases	0	NA
Gatesman & Unwin 2021	Gatesman & Unwin	2021	Political Analysis	Lattice Studies of Gerrymandering Strategies	0	NA
Magleby & Mosesson 2018	Magleby & Mosesson	2018	Political Analysis	A New Approach for Developing Neutral Redistricting Plans	23	NA
Deford, Eubank & Rodden 2020	Deford, Eubank & Rodden	2020	Political Analysis	Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering	0	NA
Krasa & Polborn 2018	Krasa & Polborn	2018	APSR	Political Competition in Legislative Elections	41	NA
Saxon 2020	Saxon	2020	Political Analysis	Reviving Legislative Avenues for Gerrymandering Reform with a Flexible, Automated Tool	3	NA
Kang 2017	Kang	2017	Michigan Law Review	Gerrymandering and the Constitutional Norm Against Government Partisanship	61	NA
Stephanopoulos 2012	Stephanopoulos	2012	University of Pennsylvania Law Review	Redistricting and the Territorial Community	61	NA
Altman & McDonald 2010	Altman & McDonald	2010	Duke Journal of Constitutional Law and Public Policy	The Promise and Perils of Computers in Redistricting	87	NA
McDonald & Best 2015	McDonald & Best	2015	Election Law Journal	Unfair Partisan Gerrymanders in Politics and Law: A Diagnostic Applied to Six Cases	73	NA
Tam Cho & Liu 2016	Tam Cho & Liu	2016	Election Law Journal	Toward a Talismanic Redistricting Tool: A Computational Method for Identifying Extreme Redistricting Plans	76	NA
McGhee 2014	McGhee	2014	Legislative Studies Quarterly	Measuring Partisan Bias in Single-Member District Electoral Systems	97	NA
Wang 2016	Wang	2016	Stanford Law Review	Three Tests for Practical Evaluation of Partisan Gerrymandering	97	NA
Cox & Holden 2011	Cox & Holden	2011	University of Chicago Law Review	Reconsidering Racial and Partisan Gerrymandering	76	NA
Stewart et al. 2019	Stewart et al.	2019	Nature	Information Gerrymandering and Undemocratic Decisions	81	NA
Siegel-Hawley 2013	Siegel-Hawley	2013	Harvard Educational Review	Educational Gerrymandering? Race and Attendance Boundaries in a Demographically Changing Suburb	58	NA
Richards 2014	Richards	2014	American Educational Research Journal	The Gerrymandering of School Attendance Zones and the Segregation of Public Schools	91	NA
Fraga 2016	Fraga	2016	JOP	Redistricting and the Causal Impact of Race on Voter Turnout	67	NA
De Assis et al. 2014	De Assis et al.	2014	Computers & Operations Research	A Redistricting Problem Applied to Meter Reading in Power Distribution Networks	52	NA
Hayes et al. 2010	Hayes	2010	Legislative Studies Quarterly	Redistricting, Responsiveness, and Issue Attention	52	NA
Liu et al. 2016	Liu et al.	2016	Swarm and Evolutionary Computation	PEAR: A Massively Parallel Evolutionary Computation Approach for Political Redistricting Optimization and Analysis	62	NA
Yoshinaka & Murphy 2011	Yoshinaka & Murphy	2011	Political Research Quarterly	The Paradox of Redistricting: How Partisan Mapmakers Foster Competition but Disrupt Representation	53	NA
Webster 2013	Webster	2013	Political Geography	Reflections on Current Criteria to Evaluate Redistricting Plans	53	NA
Gentry et al. 2013	Gentry et al.	2013	American Journal of Transplantation	Addressing Geographic Disparities in Liver Transplantation Through Redistricting	137	NA
Grainger 2010	Grainger	2010	The Journal of Law and Economics	Redistricting and Polarization: Who Draws the Lines in California?	50	NA
Masket et al. 2012	Masket et al.	2012	PS: Political Science & Politics	The Gerrymanderers are Coming! Legislative Redistricting Won’t Affect Competition or Polarization Much, No Matter Who Does It	57	NA
Altman & McDonald 2011	Altman & McDonald	2011	Journal of Statistical Software	BARD: Better Automated Redistricting	84	NA
Gul & Pesendorfer 2010	Gul & Pesendorfer	2010	American Economic Review	Strategic Redistricting	67	NA
Cain 2011	Cain	2011	Yale Law Journal	Redistricting Commissions: A Better Political Buffer	128	NA
Arrington 2016	Arrington	2016	Election Law Journal	A Practical Procedure for Detecting a Partisan Gerrymander	5	NA
Ladewig 2018	Ladewig	2018	Election Law Journal	‘‘Appearances Do Matter’’: Congressional District Compactness and Electoral Turnout	0	NA
Campisi et al. 2019	Campisi et al.	2019	Election Law Journal	Declination as a Metric to Detect Partisan Gerrymandering	5	NA
Makse 2012	Makse	2012	Election Law Journal	Defining Communities of Interest in Redistricting Through Initiative Voting	17	NA
Gimpel & Harbridge-Yong 2020	Gimpel & Harbridge-Yong	2020	Election Law Journal	Conflicting Goals of Redistricting: Do Districts That Maximize Competition Reckon with Communities of Interest?	1	NA
Chen 2017	Chen	2017	Election Law Journal	The Impact of Political Geography on Wisconsin Redistricting: An Analysis of Wisconsin’s Act 43 Assembly Districting Plan	22	NA
Ansolabehere & Snyder 2012	Ansolabehere & Snyder	2012	Election Law Journal	The Effects of Redistricting on Incumbents	26	NA
Sabouni & Shelton 2021	Sabouni & Shelton	2021	Election Law Journal	State Legislative Redistricting: The Effectiveness of Traditional Districting Principles in the 2010 Wave	0	NA
Williamson 2019	Williamson	2019	Election Law Journal	Examining the Effects of Partisan Redistricting on Candidate Entry Decisions	2	NA
Veomett 2018	Veomett	2018	Election Law Journal	Efficiency Gap, Voter Turnout, and the Efficiency Principle	23	NA
Tamas 2019	Tamas	2019	Election Law Journal	American Disproportionality: A Historical Analysis of Partisan Bias in Elections to the U.S. House of Representatives	5	NA
Duchin et al. 2019	Duchin et al.	2019	Election Law Journal	Locating the Representational Baseline: Republicans in Massachusetts	25	NA
McGhee 2017	McGhee	2017	Election Law Journal	Measuring Efficiency in Redistricting	29	NA
Wang et al. 2018	Wang et al.	2018	Election Law Journal	An Antidote for Gobbledygook: Organizing the Judge’s Partisan Gerrymandering Toolkit into Tests of Opportunity and Outcome	5	NA
Caughey et al. 2017b	Caughey et al.	2017	Election Law Journal	Partisan Gerrymandering and the Political Process: Effects on Roll-Call Voting and State Policies	31	NA
Powell et al. 2020	Powell et al.	2020	Election Law Journal	Partisan Gerrymandering, Clustering, or Both? A New Approach to a Persistent Question	2	NA
Fougere et al. 2010	Fougere et al.	2010	Election Law Journal	Partisanship, Public Opinion, and Redistricting	33	NA
Best et al. 2018	Best et al.	2018	Election Law Journal	Considering the Prospects for Establishing a Packing Gerrymandering Standard	32	NA
Warrington 2018	Warrington	2018	Election Law Journal	Quantifying Gerrymandering Using the Vote Distribution	34	NA
Gardner 2012	Gardner	2012	Election Law Journal	How to Do Things with Boundaries: Redistricting and the Construction of Politics	14	NA
Goedert 2014	Goedert	2014	Election Law Journal	Redistricting, Risk, and Representation: How Five State Gerrymanders Weathered the Tides of the 2000s	8	NA
Wang 2016b	Wang	2016	Election Law Journal	Three Practical Tests for Gerrymandering: Application to Maryland and Wisconsin	38	NA
Ramachandran & Gold 2018	Ramachandran & Gold	2018	Election Law Journal	Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions	9	NA
Nagle 2019	Nagle	2019	Election Law Journal	What Criteria Should Be Used for Redistricting Reform?	16	NA

# count publications per journal
pub_table <- literature_metadata %>% 
  filter(str_detect(paste(literature$cites, collapse = "|"), Author)) %>% 
  count(Publication, name = "Articles") %>%
  mutate(Publication = case_when(
    Publication == "AJPS" ~ "American Journal of Political Science",
    Publication == "APSR" ~ "American Political Science Review",
    Publication == "BJPS" ~ "British Journal of Political Science",
    Publication == "JOP" ~ "The Journal of Politics",
    Publication == "NCL Review" ~ "North Carolina Law Review",
    Publication == "QJPS" ~ "Quarterly Journal of Political Science",
    TRUE ~ Publication
  )) 

pub_table %>% kable()

Publication	Articles
American Journal of Political Science	4
American Economic Review	1
American Politics Research	1
American Political Science Review	4
Computers & Operations Research	1
Duke Journal of Constitutional Law and Public Policy	1
Election Law Journal	27
Electoral Studies	4
The Journal of Politics	2
Journal of Statistical Software	1
Legislative Studies Quarterly	2
Michigan Law Review	1
Perspectives on Politics	1
Political Analysis	5
Political Geography	1
Political Research Quarterly	1
PS: Political Science & Politics	2
Quarterly Journal of Political Science	4
Stanford Law Review	1
State Politics & Policy Quaterly	6
Swarm and Evolutionary Computation	1
The Journal of Law and Economics	1
University of Chicago Law Review	2
University of Pennsylvania Law Review	1
Yale Law Journal	1

Redistricting Literature

Devin Judge-Lord, Adeline Lo & Kyler Hudson

Using the `netlit` R Package

Basic Usage

More Advanced Uses: larger networks, visualizing your network, network descriptives

The `igraph` object

With `ggraph`

Subgraphs

Betweenness

Degree

About the example data

netlit Vignette

Redistricting Literature

Devin Judge-Lord, Adeline Lo & Kyler Hudson

Using the netlit R Package

Basic Usage

More Advanced Uses: larger networks, visualizing your network, network descriptives

The igraph object

With ggraph

Subgraphs

Betweenness

Degree

About the example data

Using the `netlit` R Package

The `igraph` object

With `ggraph`