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.
To install netlit from CRAN, run the following:
install.packages("netlit")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 the main netlit
vignette for
more details on this example.
library(netlit)
data("literature")
head(literature)#> to from cites cites_empirical
#> 1 detect gerrymandering computers Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018 Wang 2016
#> 2 public participation computers Altman & McDonald 2010; Altman & McDonald 2011 <NA>
#> 3 preserve communities of interest number of competitive districts Gimpel & Harbridge-Yong 2020 Gimpel & Harbridge-Yong 2020
#> 4 proportionality partisan advantage Caughey et al. 2017; Tamas 2019 <NA>
#> 5 efficiency gap partisan gerrymandering Chen 2017 Chen 2017
#> 6 constitutional test preserve communities of interest Stephanopoulos 2012 <NA>
netlit offers four functions: make_edgelist(), make_nodelist(),
augment_nodelist(), and review().
review() is the primary function (and probably the only one you need).
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 withedge_betweennesscalculated), - an augmented
nodelist(a list of concepts withdegreeandbetweennesscalculated), and - a
graphobject suitable for use in otherigraphfunctions 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
#> $graph
#> an igraph object
head(lit$edgelist)#> from to edge_betweenness
#> 1 computers detect gerrymandering 2.0
#> 2 computers public participation 1.0
#> 3 number of competitive districts preserve communities of interest 86.0
#> 4 partisan advantage proportionality 19.5
#> 5 partisan gerrymandering efficiency gap 13.5
#> 6 preserve communities of interest constitutional test 7.0
head(lit$nodelist)#> node degree_in degree_out degree_total betweenness
#> 1 computers 0 2 2 0.0
#> 2 number of competitive districts 1 2 3 64.0
#> 3 partisan advantage 6 8 14 111.5
#> 4 partisan gerrymandering 1 7 8 42.0
#> 5 preserve communities of interest 2 6 8 79.0
#> 6 mean-median vote comparison 0 2 2 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. (To retain all variables
from literature, use edge_attributes = names(literature).)
node_attributes is a separate dataframe that contains attributes for
each node in the primary data set. node_attributes must be a dataframe
with a column node with values matching the to or from columns of
the data argument.
The example node_attributes data include one column type indicating
a type for each each node/variable/concept.
data("node_attributes")
head(node_attributes)#> node type
#> 1 campaign resource allocation effect
#> 2 candidate quality effect
#> 3 change in constituency boundaries condition
#> 4 preserve communities of interest goal
#> 5 competitiveness goal
#> 6 computers condition
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
#> $graph
#> an igraph object
head(lit$edgelist)#> from to cites cites_empirical edge_betweenness
#> 1 computers detect gerrymandering Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018 Wang 2016 2.0
#> 2 computers public participation Altman & McDonald 2010; Altman & McDonald 2011 <NA> 1.0
#> 3 number of competitive districts preserve communities of interest Gimpel & Harbridge-Yong 2020 Gimpel & Harbridge-Yong 2020 86.0
#> 4 partisan advantage proportionality Caughey et al. 2017; Tamas 2019 <NA> 19.5
#> 5 partisan gerrymandering efficiency gap Chen 2017 Chen 2017 13.5
#> 6 preserve communities of interest constitutional test Stephanopoulos 2012 <NA> 7.0
head(lit$nodelist)#> node type degree_in degree_out degree_total betweenness
#> 1 computers condition 0 2 2 0.0
#> 2 number of competitive districts goal 1 2 3 64.0
#> 3 partisan advantage goal 6 8 14 111.5
#> 4 partisan gerrymandering condition 1 7 8 42.0
#> 5 preserve communities of interest goal 2 6 8 79.0
#> 6 mean-median vote comparison metric 0 2 2 0.0
Below is a plot of redistricting literature network from the main
netlit
vignette using
the graph object returned by the netlit::review() function as the
input to network graphing functions from packages like ggnetwork. The
nodelist and edgelist objects also provide required inputs for other
network visualization packages, e.g. ggraph or visNetwork (vignettes
on how to make similar plots in ggraph and visNetwork will be posted
shortly).
Nodes represent theoretical concepts, shaded by total degree centrality. Arrows connect concepts theorized as directional relationships in works, colored by number of works. Solid edges indicate empirically studied connections; dashed are relationships that have been theorized but not studied empirically.
