couplingMap: Coupling Analysis
In massimoaria/bibliometrix: Comprehensive Science Mapping Analysis

couplingMap

R Documentation

Coupling Analysis

Description

It performs a coupling network analysis and plots community detection results on a bi-dimensional map (Coupling Map).

Usage

couplingMap(
  M,
  analysis = "documents",
  field = "CR",
  n = 500,
  label.term = NULL,
  ngrams = 1,
  impact.measure = "local",
  minfreq = 5,
  community.repulsion = 0.1,
  stemming = FALSE,
  size = 0.5,
  n.labels = 1,
  repel = TRUE,
  cluster = "walktrap"
)

Arguments

`M`	is a bibliographic dataframe.
`analysis`	is the textual attribute used to select the unit of analysis. It can be `analysis = c("documents", "authors", "sources")`.
`field`	is the textual attribute used to measure the coupling strength. It can be `field = c("CR", "ID","DE", "TI", "AB")`.
`n`	is an integer. It indicates the number of units to include in the analysis.
`label.term`	is a character. It indicates which content metadata have to use for cluster labeling. It can be `label.term = c("ID","DE","TI","AB")`. If `label.term = NULL` cluster items will be use for labeling.
`ngrams`	is an integer between 1 and 4. It indicates the type of n-gram to extract from texts. An n-gram is a contiguous sequence of n terms. The function can extract n-grams composed by 1, 2, 3 or 4 terms. Default value is `ngrams=1`.
`impact.measure`	is a character. It indicates the impact measure used to rank cluster elements (documents, authors or sources). It can be `impact.measure = c("local", "global")`.\ With `impact.measure = "local"`, couplingMap calculates elements impact using the Normalized Local Citation Score while using `impact.measure = "global"`, the function uses the Normalized Global Citation Score to measure elements impact.
`minfreq`	is a integer. It indicates the minimum frequency (per thousand) of a cluster. It is a number in the range (0,1000).
`community.repulsion`	is a real. It indicates the repulsion force among network communities. It is a real number between 0 and 1. Default is `community.repulsion = 0.1`.
`stemming`	is logical. If it is TRUE the word (from titles or abstracts) will be stemmed (using the Porter's algorithm).
`size`	is numerical. It indicates the size of the cluster circles and is a number in the range (0.01,1).
`n.labels`	is integer. It indicates how many labels associate to each cluster. Default is `n.labels = 1`.
`repel`	is logical. If it is TRUE ggplot uses geom_label_repel instead of geom_label.
`cluster`	is a character. It indicates the type of cluster to perform among ("optimal", "louvain","leiden", "infomap","edge_betweenness","walktrap", "spinglass", "leading_eigen", "fast_greedy").

Details

The analysis can be performed on three different units: documents, authors or sources and the coupling strength can be measured using the classical approach (coupled by references) or a novel approach based on unit contents (keywords or terms from titles and abstracts)

The x-axis measures the cluster centrality (by Callon's Centrality index) while the y-axis measures the cluster impact by Mean Normalized Local Citation Score (MNLCS). The Normalized Local Citation Score (NLCS) of a document is calculated by dividing the actual count of local citing items by the expected citation rate for documents with the same year of publication.

Value

a list containing:

`map`		The coupling map as ggplot2 object
`clusters`		Centrality and Density values for each cluster.
`data`		A list of units following in each cluster
`nclust`		The number of clusters
`NCS`		The Normalized Citation Score dataframe
`net`		A list containing the network output (as provided from the networkPlot function)

Examples


## Not run: 
data(management, package = "bibliometrixData")
res <- couplingMap(management, analysis = "authors", field = "CR", n = 250, impact.measure="local", 
                   minfreq = 3, size = 0.5, repel = TRUE)
plot(res$map)

## End(Not run)

massimoaria/bibliometrix documentation built on July 5, 2024, 4:18 a.m.