Package 'bibliometrix'

Comprehensive Science Mapping Analysis

Description

Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' (<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>), 'Lens' (<https://lens.org>), and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

Details

INSTALLATION

- Stable version from CRAN:

install.packages("bibliometrix")

- Or development version from GitHub:

install.packages("devtools") devtools::install_github("massimoaria/bibliometrix")

- Load "bibliometrix"

library('bibliometrix')

DATA LOADING AND CONVERTING

The export file can be imported and converted by R using the function *convert2df*:

file <- ("https://www.bibliometrix.org/datasets/savedrecs.txt")

M <- convert2df(file, dbsource = "wos", format = "bibtex")

*convert2df* creates a bibliographic data frame with cases corresponding to manuscripts and variables to Field Tag in the original export file. Each manuscript contains several elements, such as authors' names, title, keywords and other information. All these elements constitute the bibliographic attributes of a document, also called metadata. Data frame columns are named using the standard Clarivate Analytics WoS Field Tag codify.

BIBLIOMETRIC ANALYSIS

The first step is to perform a descriptive analysis of the bibliographic data frame. The function *biblioAnalysis* calculates main bibliometric measures using this syntax:

results <- biblioAnalysis(M, sep = ";")

The function *biblioAnalysis* returns an object of class "bibliometrix".

To summarize main results of the bibliometric analysis, use the generic function *summary*. It displays main information about the bibliographic data frame and several tables, such as annual scientific production, top manuscripts per number of citations, most productive authors, most productive countries, total citation per country, most relevant sources (journals) and most relevant keywords. *summary* accepts two additional arguments. *k* is a formatting value that indicates the number of rows of each table. *pause* is a logical value (TRUE or FALSE) used to allow (or not) pause in screen scrolling. Choosing k=10 you decide to see the first 10 Authors, the first 10 sources, etc.

S <- summary(object = results, k = 10, pause = FALSE)

Some basic plots can be drawn using the generic function plot:

plot(x = results, k = 10, pause = FALSE)

BIBLIOGRAPHIC NETWORK MATRICES

Manuscript's attributes are connected to each other through the manuscript itself: author(s) to journal, keywords to publication date, etc. These connections of different attributes generate bipartite networks that can be represented as rectangular matrices (Manuscripts x Attributes). Furthermore, scientific publications regularly contain references to other scientific works. This generates a further network, namely, co-citation or coupling network. These networks are analyzed in order to capture meaningful properties of the underlying research system, and in particular to determine the influence of bibliometric units such as scholars and journals.

*biblioNetwork* function

The function *biblioNetwork* calculates, starting from a bibliographic data frame, the most frequently used networks: Coupling, Co-citation, Co-occurrences, and Collaboration. *biblioNetwork* uses two arguments to define the network to compute: - *analysis* argument can be "co-citation", "coupling", "collaboration", or "co-occurrences". - *network* argument can be "authors", "references", "sources", "countries", "universities", "keywords", "author_keywords", "titles" and "abstracts".

i.e. the following code calculates a classical co-citation network:

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")

VISUALIZING BIBLIOGRAPHIC NETWORKS

All bibliographic networks can be graphically visualized or modeled. Using the function *networkPlot*, you can plot a network created by *biblioNetwork* using R routines.

The main argument of *networkPlot* is type. It indicates the network map layout: circle, kamada-kawai, mds, etc.

In the following, we propose some examples.

### Country Scientific Collaboration

# Create a country collaboration network

M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")

NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")

# Plot the network

net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.8)

### Co-Citation Network

# Create a co-citation network

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")

# Plot the network

net=networkPlot(NetMatrix, n = 30, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5)

### Keyword co-occurrences

# Create keyword co-occurrences network

NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")

# Plot the network

net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)

CO-WORD ANALYSIS: THE CONCEPTUAL STRUCTURE OF A FIELD

The aim of the co-word analysis is to map the conceptual structure of a framework using the word co-occurrences in a bibliographic collection. The analysis can be performed through dimensionality reduction techniques such as Multidimensional Scaling (MDS), Correspondence Analysis (CA) or Multiple Correspondence Analysis (MCA). Here, we show an example using the function *conceptualStructure* that performs a CA or MCA to draw a conceptual structure of the field and K-means clustering to identify clusters of documents which express common concepts. Results are plotted on a two-dimensional map. *conceptualStructure* includes natural language processing (NLP) routines (see the function *termExtraction*) to extract terms from titles and abstracts. In addition, it implements the Porter's stemming algorithm to reduce inflected (or sometimes derived) words to their word stem, base or root form.

# Conceptual Structure using keywords (method="MCA")

CS <- conceptualStructure(M,field="ID", method="MCA", minDegree=4, clust=4 ,k.max=8, stemming=FALSE, labelsize=10, documents=10)

HISTORICAL DIRECT CITATION NETWORK

The historiographic map is a graph proposed by E. Garfield to represent a chronological network map of most relevant direct citations resulting from a bibliographic collection. The function histNetwork generates a chronological direct citation network matrix which can be plotted using *histPlot*:

# Create a historical citation network

histResults <- histNetwork(M, sep = ";")

# Plot a historical co-citation network

net <- histPlot(histResults, size = 10)

Author(s)

Massimo Aria [cre, aut, cph] (<https://orcid.org/0000-0002-8517-9411>), Corrado Cuccurullo [aut] (<https://orcid.org/0000-0002-7401-8575>)

Maintainer: Massimo Aria <[email protected]>

References

Aria, M. & Cuccurullo, C. (2017). *bibliometrix*: An R-tool for comprehensive science mapping analysis, *Journal of Informetrics*, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007 (https://doi.org/10.1016/j.joi.2017.08.007).

Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains, *Scientometrics*, DOI: 10.1007/s11192-016-1948-8 (https://doi.org/10.1007/s11192-016-1948-8).

Cuccurullo, C., Aria, M., & Sarto, F. (2015). Twenty years of research on performance management in business and public administration domains. Presentation at the *Correspondence Analysis and Related Methods conference (CARME 2015)* in September 2015 (https://www.bibliometrix.org/documents/2015Carme_cuccurulloetal.pdf).

Sarto, F., Cuccurullo, C., & Aria, M. (2014). Exploring healthcare governance literature: systematic review and paths for future research. *Mecosan* (https://www.francoangeli.it/Riviste/Scheda_Rivista.aspx?IDarticolo=52780&lingua=en).

Cuccurullo, C., Aria, M., & Sarto, F. (2013). Twenty years of research on performance management in business and public administration domains. In *Academy of Management Proceedings* (Vol. 2013, No. 1, p. 14270). Academy of Management (https://doi.org/10.5465/AMBPP.2013.14270abstract).

---

Top-Authors' Productivity over Time

Description

It calculates and plots the author production (in terms of number of publications) over the time.

Usage

authorProdOverTime(M, k = 10, graph = TRUE)

Arguments

M	is a bibliographic data frame obtained by convert2df function.
k	is a integer. It is the number of top authors to analyze and plot. Default is k = 10.
graph	is logical. If TRUE the function plots the author production over time graph. Default is graph = TRUE.

Value

The function authorProdOverTime returns a list containing two objects:

dfAU		is a data frame
dfpapersAU		is a data frame
graph		a ggplot object

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

data(scientometrics, package = "bibliometrixData")
res <- authorProdOverTime(scientometrics, k=10)
print(res$dfAU)
plot(res$graph)

---

Bibliometric Analysis

Description

It performs a bibliometric analysis of a dataset imported from SCOPUS and Clarivate Analytics Web of Science databases.

Usage

biblioAnalysis(M, sep = ";")

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics Web of Science file.
sep	is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".

Value

biblioAnalysis returns an object of class "bibliometrix".

The functions summary and plot are used to obtain or print a summary and some useful plots of the results.

An object of class "bibliometrix" is a list containing the following components:

Articles		the total number of manuscripts
Authors		the authors' frequency distribution
AuthorsFrac		the authors' frequency distribution (fractionalized)
FirstAuthors		corresponding author of each manuscript
nAUperPaper		the number of authors per manuscript
Appearances		the number of author appearances
nAuthors		the number of authors
AuMultiAuthoredArt		the number of authors of multi-authored articles
MostCitedPapers		the list of manuscripts sorted by citations
Years		publication year of each manuscript
FirstAffiliation		the affiliation of the first author
Affiliations		the frequency distribution of affiliations (of all co-authors for each paper)
Aff_frac		the fractionalized frequency distribution of affiliations (of all co-authors for each paper)
CO		the affiliation country of the first author
Countries		the affiliation countries' frequency distribution
CountryCollaboration		Intra-country (SCP) and intercountry (MCP) collaboration indices
TotalCitation		the number of times each manuscript has been cited
TCperYear		the yearly average number of times each manuscript has been cited
Sources		the frequency distribution of sources (journals, books, etc.)
DE		the frequency distribution of authors' keywords
ID		the frequency distribution of keywords associated to the manuscript by SCOPUS and Clarivate Analytics Web of Science database

See Also

convert2df to import and convert an WoS or SCOPUS Export file in a bibliographic data frame.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

## Not run: 
data(management, package = "bibliometrixData")

results <- biblioAnalysis(management)

summary(results, k = 10, pause = FALSE)

## End(Not run)

---

Creating Bibliographic networks

Description

biblioNetwork creates different bibliographic networks from a bibliographic data frame.

Usage

biblioNetwork(
  M,
  analysis = "coupling",
  network = "authors",
  n = NULL,
  sep = ";",
  short = FALSE,
  shortlabel = TRUE,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
analysis	is a character object. It indicates the type of analysis can be performed. analysis argument can be "collaboration", "coupling", "co-occurrences" or "co-citation". Default is analysis = "coupling".
network	is a character object. It indicates the network typology. The network argument can be "authors", "references", "sources", "countries","keywords", "author_keywords", "titles", or "abstracts". Default is network = "authors".
n	is an integer. It indicates the number of items to select. If N = NULL, all items are selected.
sep	is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".
short	is a logical. If TRUE all items with frequency<2 are deleted to reduce the matrix size.
shortlabel	is logical. IF TRUE, reference labels are stored in a short format. Default is shortlabel=TRUE.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

Details

The function biblioNetwork can create a collection of bibliographic networks following the approach proposed by Batagelj & Cerinsek (2013) and Aria & cuccurullo (2017).

Typical networks output of biblioNetwork are:

#### Collaboration Networks ############
– Authors collaboration (analysis = "collaboration", network = "authors")
– University collaboration (analysis = "collaboration", network = universities")
– Country collaboration (analysis = "collaboration", network = "countries")

#### Co-citation Networks ##############
– Authors co-citation (analysis = "co-citation", network = "authors")
– Reference co-citation (analysis = "co-citation", network = "references")
– Source co-citation (analysis = "co-citation", network = "sources")

#### Coupling Networks ################
– Manuscript coupling (analysis = "coupling", network = "references")
– Authors coupling (analysis = "coupling", network = "authors")
– Source coupling (analysis = "coupling", network = "sources")
– Country coupling (analysis = "coupling", network = "countries")

#### Co-occurrences Networks ################
– Authors co-occurrences (analysis = "co-occurrences", network = "authors")
– Source co-occurrences (analysis = "co-occurrences", network = "sources")
– Keyword co-occurrences (analysis = "co-occurrences", network = "keywords")
– Author-Keyword co-occurrences (analysis = "co-occurrences", network = "author_keywords")
– Title content co-occurrences (analysis = "co-occurrences", network = "titles")
– Abstract content co-occurrences (analysis = "co-occurrences", network = "abstracts")

References:
Batagelj, V., & Cerinsek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845-864.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.

Value

It is a squared network matrix. It is an object of class dgMatrix of the package Matrix.

See Also

convert2df to import and convert a SCOPUS and Thomson Reuters' ISI Web of Knowledge export file in a data frame.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE 1: Authors collaboration network

# data(scientometrics, package = "bibliometrixData")

# NetMatrix <- biblioNetwork(scientometrics, analysis = "collaboration", 
# network = "authors", sep = ";")

# net <- networkPlot(NetMatrix, n = 30, type = "kamada", Title = "Collaboration",labelsize=0.5) 


# EXAMPLE 2: Co-citation network

data(scientometrics, package = "bibliometrixData")

NetMatrix <- biblioNetwork(scientometrics, analysis = "co-citation", 
network = "references", sep = ";")

net <- networkPlot(NetMatrix, n = 30, type = "kamada", Title = "Co-Citation",labelsize=0.5)

---

Shiny UI for bibliometrix package

Description

biblioshiny performs science mapping analysis using the main functions of the bibliometrix package.

Usage

biblioshiny(
  host = "127.0.0.1",
  port = NULL,
  launch.browser = TRUE,
  maxUploadSize = 200
)

Arguments

host	The IPv4 address that the application should listen on. Defaults to the shiny.host option, if set, or "127.0.0.1" if not.
port	is the TCP port that the application should listen on. If the port is not specified, and the shiny.port option is set (with options(shiny.port = XX)), then that port will be used. Otherwise, use a random port.
launch.browser	If true, the system's default web browser will be launched automatically after the app is started. Defaults to true in interactive sessions only. This value of this parameter can also be a function to call with the application's URL.
maxUploadSize	is a integer. The max upload file size argument. Default value is 200 (megabyte)

Examples

#biblioshiny()

---

Tag list and bibtex fields.

Description

Data frame containing a list of tags and corresponding: WoS, SCOPUS and generic bibtex fields; and Dimensions.ai csv and xlsx fields.

Format

A data frame with 44 rows and 6 variables:

TAG

Tag Fields

SCOPUS

Scopus bibtex fields

ISI

WOS/ISI bibtex fields

GENERIC

Generic bibtex fields

DIMENSIONS_OLD

DIMENSIONS cvs/xlsx old fields

DIMENSIONS

DIMENSIONS cvs/xlsx fields

---

Bradford's law

Description

It estimates and draws the Bradford's law source distribution.

Usage

bradford(M)

Arguments

M	is a bibliographic dataframe.

Details

Bradford's law is a pattern first described by (Samuel C. Bradford, 1934) that estimates the exponentially diminishing returns of searching for references in science journals.

One formulation is that if journals in a field are sorted by number of articles into three groups, each with about one-third of all articles, then the number of journals in each group will be proportional to 1:n:n2.

Reference:
Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85-86.

Value

The function bradford returns a list containing the following objects:

table		a dataframe with the source distribution partitioned in the three zones
graph		the source distribution plot in ggplot2 format

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

## Not run: 
data(management, package = "bibliometrixData")

BR <- bradford(management)

## End(Not run)

---

Citation frequency distribution

Description

It calculates frequency distribution of citations.

Usage

citations(M, field = "article", sep = ";")

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics Web of Science file.
field	is a character. It can be "article" or "author" to obtain frequency distribution of cited citations or cited authors (only first authors for WoS database) respectively. The default is field = "article".
sep	is the field separator character. This character separates citations in each string of CR column of the bibliographic data frame. The default is sep = ";".

Value

an object of class "list" containing the following components:

Cited		the most frequent cited manuscripts or authors
Year		the publication year (only for cited article analysis)
Source		the journal (only for cited article analysis)

See Also

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

## EXAMPLE 1: Cited articles

data(scientometrics,package = "bibliometrixData")

CR <- citations(scientometrics, field = "article", sep = ";")

CR$Cited[1:10]
CR$Year[1:10]
CR$Source[1:10]

## EXAMPLE 2: Cited first authors

data(scientometrics)

CR <- citations(scientometrics, field = "author", sep = ";")

CR$Cited[1:10]

---

Bibliographic bipartite network matrices

Description

cocMatrix computes occurrences between elements of a Tag Field from a bibliographic data frame. Manuscript is the unit of analysis.

Usage

cocMatrix(
  M,
  Field = "AU",
  type = "sparse",
  n = NULL,
  sep = ";",
  binary = TRUE,
  short = FALSE,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original WoS or SCOPUS file.
Field	is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify. Field can be equal to one of these tags: AU Authors SO Publication Name (or Source) JI ISO Source Abbreviation DE Author Keywords ID Keywords associated by WoS or SCOPUS database CR Cited References for a complete list of filed tags see: Field Tags used in bibliometrix
type	indicates the output format of co-occurrences: type = "matrix" produces an object of class matrix type = "sparse" produces an object of class dgMatrix of the package Matrix. "sparse" argument generates a compact representation of the matrix.
n	is an integer. It indicates the number of items to select. If N = NULL, all items are selected.
sep	is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".
binary	is a logical. If TRUE each cell contains a 0/1. if FALSE each cell contains the frequency.
short	is a logical. If TRUE all items with frequency<2 are deleted to reduce the matrix size.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

Details

This occurrence matrix represents a bipartite network which can be transformed into a collection of bibliographic networks such as coupling, co-citation, etc..

The function follows the approach proposed by Batagelj & Cerinsek (2013) and Aria & cuccurullo (2017).

References:
Batagelj, V., & Cerinsek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845-864.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.

Value

a bipartite network matrix with cases corresponding to manuscripts and variables to the objects extracted from the Tag Field.

See Also

convert2df to import and convert an ISI or SCOPUS Export file in a data frame.

biblioAnalysis to perform a bibliometric analysis.

biblioNetwork to compute a bibliographic network.

Examples

# EXAMPLE 1: Articles x Authors occurrence matrix

data(scientometrics, package = "bibliometrixData")
WA <- cocMatrix(scientometrics, Field = "AU", type = "sparse", sep = ";")

# EXAMPLE 2: Articles x Cited References occurrence matrix

# data(scientometrics, package = "bibliometrixData")

# WCR <- cocMatrix(scientometrics, Field = "CR", type = "sparse", sep = ";")

# EXAMPLE 3: Articles x Cited First Authors occurrence matrix

# data(scientometrics, package = "bibliometrixData")
# scientometrics <- metaTagExtraction(scientometrics, Field = "CR_AU", sep = ";")
# WCR <- cocMatrix(scientometrics, Field = "CR_AU", type = "sparse", sep = ";")

---

Country Collaboration Networks by Region

Description

A function to create and plot country collaboration networks by Region

Usage

collabByRegionPlot(
  NetMatrix,
  normalize = NULL,
  n = NULL,
  degree = NULL,
  type = "auto",
  label = TRUE,
  labelsize = 1,
  label.cex = FALSE,
  label.color = FALSE,
  label.n = Inf,
  halo = FALSE,
  cluster = "walktrap",
  community.repulsion = 0,
  vos.path = NULL,
  size = 3,
  size.cex = FALSE,
  curved = FALSE,
  noloops = TRUE,
  remove.multiple = TRUE,
  remove.isolates = FALSE,
  weighted = NULL,
  edgesize = 1,
  edges.min = 0,
  alpha = 0.5,
  verbose = TRUE
)

Arguments

NetMatrix	is a country collaboration matrix obtained by the function biblioNetwork.
normalize	is a character. It can be "association", "jaccard", "inclusion","salton" or "equivalence" to obtain Association Strength, Jaccard, Inclusion, Salton or Equivalence similarity index respectively. The default is type = NULL.
n	is an integer. It indicates the number of vertices to plot.
degree	is an integer. It indicates the min frequency of a vertex. If degree is not NULL, n is ignored.
type	is a character object. It indicates the network map layout: type="auto" Automatic layout selection type="circle" Circle layout type="sphere" Sphere layout type="mds" Multidimensional Scaling layout type="fruchterman" Fruchterman-Reingold layout type="kamada" Kamada-Kawai layout
label	is logical. If TRUE vertex labels are plotted.
labelsize	is an integer. It indicates the label size in the plot. Default is labelsize=1
label.cex	is logical. If TRUE the label size of each vertex is proportional to its degree.
label.color	is logical. If TRUE, for each vertex, the label color is the same as its cluster.
label.n	is an integer. It indicates the number of vertex labels to draw.
halo	is logical. If TRUE communities are plotted using different colors. Default is halo=FALSE
cluster	is a character. It indicates the type of cluster to perform among ("none", "optimal", "louvain","leiden", "infomap","edge_betweenness","walktrap", "spinglass", "leading_eigen", "fast_greedy").
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.
vos.path	is a character indicating the full path where VOSviewer.jar is located.
size	is integer. It defines the size of each vertex. Default is size=3.
size.cex	is logical. If TRUE the size of each vertex is proportional to its degree.
curved	is a logical or a number. If TRUE edges are plotted with an optimal curvature. Default is curved=FALSE. Curved values are any numbers from 0 to 1.
noloops	is logical. If TRUE loops in the network are deleted.
remove.multiple	is logical. If TRUE multiple links are plotted using just one edge.
remove.isolates	is logical. If TRUE isolates vertices are not plotted.
weighted	This argument specifies whether to create a weighted graph from an adjacency matrix. If it is NULL then an unweighted graph is created and the elements of the adjacency matrix gives the number of edges between the vertices. If it is a character constant then for every non-zero matrix entry an edge is created and the value of the entry is added as an edge attribute named by the weighted argument. If it is TRUE then a weighted graph is created and the name of the edge attribute will be weight.
edgesize	is an integer. It indicates the network edge size.
edges.min	is an integer. It indicates the min frequency of edges between two vertices. If edge.min=0, all edges are plotted.
alpha	is a number. Legal alpha values are any numbers from 0 (transparent) to 1 (opaque). The default alpha value usually is 0.5.
verbose	is a logical. If TRUE, network will be plotted. Default is verbose = TRUE.

Value

It is a list containing the following elements:

graph		a network object of the class igraph
cluster_obj		a communities object of the package igraph
cluster_res		a data frame with main results of clustering procedure.

Examples

## Not run: 
data(management, package="bibliometrixData")

management <- metaTagExtraction(management, Field = "AU_CO")

NetMatrix <- biblioNetwork(management, analysis = "collaboration", network = "countries") 

net <- collabByRegionPlot(NetMatrix, edgesize = 4, label.cex = TRUE, labelsize=2.5, 
                          weighted = TRUE, size=0.5, size.cex=TRUE, community.repulsion = 0, 
                         verbose=FALSE)

cbind(names(net))

plot(net[[4]]$graph)

## End(Not run)

---

Creating and plotting conceptual structure map of a scientific field

Description

The function conceptualStructure creates a conceptual structure map of a scientific field performing Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA) or Metric Multidimensional Scaling (MDS) and Clustering of a bipartite network of terms extracted from keyword, title or abstract fields.

Usage

conceptualStructure(
  M,
  field = "ID",
  ngrams = 1,
  method = "MCA",
  quali.supp = NULL,
  quanti.supp = NULL,
  minDegree = 2,
  clust = "auto",
  k.max = 5,
  stemming = FALSE,
  labelsize = 10,
  documents = 2,
  graph = TRUE,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original ISI or SCOPUS file.
field	is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify. field can be equal to one of these tags: ID Keywords Plus associated by ISI or SCOPUS database DE Author's keywords ID_TM Keywords Plus stemmed through the Porter's stemming algorithm DE_TM Author's Keywords stemmed through the Porter's stemming algorithm TI Terms extracted from titles AB Terms extracted from abstracts
ngrams	is an integer between 1 and 3. 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.
method	is a character object. It indicates the factorial method used to create the factorial map. Use method="CA" for Correspondence Analysis, method="MCA" for Multiple Correspondence Analysis or method="MDS" for Metric Multidimensional Scaling. The default is method="MCA"
quali.supp	is a vector indicating the indexes of the categorical supplementary variables. It is used only for CA and MCA.
quanti.supp	is a vector indicating the indexes of the quantitative supplementary variables. It is used only for CA and MCA.
minDegree	is an integer. It indicates the minimum occurrences of terms to analyze and plot. The default value is 2.
clust	is an integer or a character. If clust="auto", the number of cluster is chosen automatically, otherwise clust can be an integer between 2 and 8.
k.max	is an integer. It indicates the maximum number of cluster to keep. The default value is 5. The max value is 20.
stemming	is logical. If TRUE the Porter's Stemming algorithm is applied to all extracted terms. The default is stemming = FALSE.
labelsize	is an integer. It indicates the label size in the plot. Default is labelsize=10
documents	is an integer. It indicates the number of documents per cluster to plot in the factorial map. The default value is 2. It is used only for CA and MCA.
graph	is logical. If TRUE the function plots the maps otherwise they are saved in the output object. Default value is TRUE
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

Value

It is an object of the class list containing the following components:

net		bipartite network
res		Results of CA, MCA or MDS method
km.res		Results of cluster analysis
graph_terms		Conceptual structure map (class "ggplot2")
graph_documents_Contrib		Factorial map of the documents with the highest contributes (class "ggplot2")
graph_docuemnts_TC		Factorial map of the most cited documents (class "ggplot2")

See Also

termExtraction to extract terms from a textual field (abstract, title, author's keywords, etc.) of a bibliographic data frame.

biblioNetwork to compute a bibliographic network.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE Conceptual Structure using Keywords Plus

data(scientometrics, package = "bibliometrixData")

CS <- conceptualStructure(scientometrics, field="ID", method="CA", 
             stemming=FALSE, minDegree=3, k.max = 5)

---

Import and Convert bibliographic export files and API objects.

Description

It converts a SCOPUS, Clarivate Analytics WoS, Dimensions, Lens.org, PubMed and COCHRANE Database export files or pubmedR and dimensionsR JSON/XML objects into a data frame, with cases corresponding to articles and variables to Field Tags as used in WoS.

Usage

convert2df(
  file,
  dbsource = "wos",
  format = "plaintext",
  remove.duplicates = TRUE
)

Arguments

file	a character array containing a sequence of filenames coming from WoS, Scopus, Dimensions, Lens.org, and Pubmed. Alternatively, file can be an object resulting from an API query fetched from Dimensions, PubMed or OpenAlex databases: a) 'wos' Clarivate Analytics WoS (in plaintext '.txt', Endnote Desktop '.ciw', or bibtex formats '.bib'); b) 'scopus' SCOPUS (exclusively in bibtex format '.bib'); c) 'dimensions' Digital Science Dimensions (in csv '.csv' or excel '.xlsx' formats); d) 'lens' Lens.org (in csv '.csv'); e) 'pubmed' an object of the class pubmedR (package pubmedR) containing a collection obtained from a query performed with pubmedR package; f) 'dimensions' an object of the class dimensionsR (package dimensionsR) containing a collection obtained from a query performed with dimensionsR package; g) 'openalex' OpenAlex .csv file; h) 'openalex_api' a data frame object returned by openalexR package, containing a collection of works resulting from a query fetched from OpenAlex database.
dbsource	is a character indicating the bibliographic database. dbsource can be dbsource = c('cochrane','dimensions','generic','isi','openalex', 'pubmed','scopus','wos', 'lens') . Default is dbsource = "isi".
format	is a character indicating the SCOPUS, Clarivate Analytics WoS, and other databases export file format. format can be c('api', 'bibtex', 'csv', 'endnote','excel','plaintext', 'pubmed'). Default is format = "plaintext".
remove.duplicates	is logical. If TRUE, the function will remove duplicated items checking by DOI and database ID.

Value

a data frame with cases corresponding to articles and variables to Field Tags in the original export file.

I.e We have three files download from Web of Science in plaintext format, file will be:

file <- c("filename1.txt", "filename2.txt", "filename3.txt")

data frame columns are named using the standard Clarivate Analytics WoS Field Tag codify. The main field tags are:

AU		Authors
TI		Document Title
SO		Publication Name (or Source)
JI		ISO Source Abbreviation
DT		Document Type
DE		Authors' Keywords
ID		Keywords associated by SCOPUS or WoS database
AB		Abstract
C1		Author Address
RP		Reprint Address
CR		Cited References
TC		Times Cited
PY		Year
SC		Subject Category
UT		Unique Article Identifier
DB		Database

for a complete list of field tags see: Field Tags used in bibliometrix

Examples

# Example:
# Import and convert a Web of Science collection form an export file in plaintext format:

## Not run: 
files <- 'https://www.bibliometrix.org/datasets/wos_plaintext.txt'

M <- convert2df(file = files, dbsource = 'wos', format = "plaintext")

## End(Not run)

---

Index of Countries.

Description

Data frame containing a normalized index of countries.
Data are used by biblioAnalysis function to extract Country Field of Cited References and Authors.

Format

A data frame with 199 rows and 4 variables:

countries

country names

continent

continent names

Longitude

country centroid longitude

Latitude

country centroid latitude

---

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)

See Also

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

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)

---

Custom Theme variables for Biblioshiny.

Description

List containing a set of custom theme variables for Biblioshiny.

Format

A list with 3 elements:

name

object name

attribs

attributes

children

CSS style

---

Authors' dominance ranking

Description

It calculates the authors' dominance ranking from an object of the class 'bibliometrix' as proposed by Kumar & Kumar, 2008.

Usage

dominance(results, k = 10)

Arguments

results	is an object of the class 'bibliometrix' for which the analysis of the authors' dominance ranking is desired.
k	is an integer, used for table formatting (number of authors). Default value is 10.

Value

The function dominance returns a data frame with cases corresponding to the first k most productive authors and variables to typical field of a dominance analysis.

the data frame variables are:

Author		Author's name
Dominance Factor		Dominance Factor (DF = FAA / MAA)
Tot Articles		N. of Authored Articles (TAA)
Single Authored		N. of Single-Authored Articles (SAA)
Multi Authored		N. of Multi-Authored Articles (MAA=TAA-SAA)
First Authored		N. of First Authored Articles (FAA)
Rank by Articles		Author Ranking by N. of Articles
Rank by DF		Author Ranking by Dominance Factor

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

data(scientometrics, package = "bibliometrixData")
results <- biblioAnalysis(scientometrics)
DF=dominance(results)
DF

---

Searching of duplicated records in a bibliographic database

Description

Search duplicated records in a dataframe.

Usage

duplicatedMatching(M, Field = "TI", exact = FALSE, tol = 0.95)

Arguments

M	is the bibliographic data frame.
Field	is a character object. It indicates one of the field tags used to identify duplicated records. Field can be equal to one of these tags: TI (title), AB (abstract), UT (manuscript ID).
exact	is logical. If exact = TRUE the function searches duplicates using exact matching. If exact=FALSE, the function uses the restricted Damerau-Levenshtein distance to find duplicated documents.
tol	is a numeric value giving the minimum relative similarity to match two manuscripts. Default value is tol = 0.95. To use the restricted Damerau-Levenshtein distance, exact argument has to be set as FALSE.

Details

A bibliographic data frame is obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file. The function identifies duplicated records in a bibliographic data frame and deletes them. Duplicate entries are identified through the restricted Damerau-Levenshtein distance. Two manuscripts that have a relative similarity measure greater than tol argument are stored in the output data frame only once.

Value

the value returned from duplicatedMatching is a data frame without duplicated records.

See Also

convert2df to import and convert an WoS or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

data(scientometrics, package = "bibliometrixData")

M=rbind(scientometrics[1:20,],scientometrics[10:30,])

newM <- duplicatedMatching(M, Field = "TI", exact=FALSE, tol = 0.95)

dim(newM)

---

Field Tag distribution by Year

Description

It calculates the median year for each item of a field tag.

Usage

fieldByYear(
  M,
  field = "ID",
  timespan = NULL,
  min.freq = 2,
  n.items = 5,
  labelsize = NULL,
  remove.terms = NULL,
  synonyms = NULL,
  dynamic.plot = FALSE,
  graph = TRUE
)

Arguments

M	is a bibliographic data frame obtained by convert2df function.
field	is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify.
timespan	is a vector with the min and max year. If it is = NULL, the analysis is performed on the entire period. Default is timespan = NULL.
min.freq	is an integer. It indicates the min frequency of the items to include in the analysis
n.items	is an integer. I indicates the maximum number of items per year to include in the plot.
labelsize	is deprecated argument. It will be removed in the next update.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
dynamic.plot	is a logical. If TRUE plot aesthetics are optimized for plotly package.
graph	is logical. If TRUE the function plots Filed Tag distribution by Year graph. Default is graph = TRUE.

Value

The function fieldByYear returns a list containing threeobjects:

df		is a data frame
df_graph		is a data frame with data used to build the graph
graph		a ggplot object

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

data(management, package = "bibliometrixData")
timespan=c(2005,2015)
res <- fieldByYear(management, field = "ID", timespan = timespan, 
                    min.freq = 5, n.items = 5, graph = TRUE)

---

h-index calculation

Description

It calculates the authors' h-index and its variants.

Usage

Hindex(M, field = "author", elements = NULL, sep = ";", years = Inf)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
field	is character. It can be equal to c("author", "source"). field indicates if H-index have to be calculated for a list of authors or for a list of sources. Default value is field = "author".
elements	is a character vector. It contains the authors' names list or the source list for which you want to calculate the H-index. When the field is "author", the argument has the form C("SURNAME1 N","SURNAME2 N",...), in other words, for each author: surname and initials separated by one blank space. If elements=NULL, the function calculates impact indices for all elements contained in the data frame. i.e for the authors SEMPRONIO TIZIO CAIO and ARIA MASSIMO elements argument is elements = c("SEMPRONIO TC", "ARIA M").
sep	is the field separator character. This character separates authors in each string of AU column of the bibliographic data frame. The default is sep = ";".
years	is a integer. It indicates the number of years to consider for Hindex calculation. Default is Inf.

Value

an object of class "list". It contains two elements: H is a data frame with h-index, g-index and m-index for each author; CitationList is a list with the bibliographic collection for each author.

See Also

convert2df to import and convert an WoS or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

### EXAMPLE 1: ###
 
data(scientometrics, package = "bibliometrixData")

authors <- c("SMALL H", "CHEN DZ")

Hindex(scientometrics, field = "author", elements = authors, sep = ";")$H

Hindex(scientometrics, field = "source", elements = "SCIENTOMETRICS", sep = ";")$H

### EXAMPLE 2: Garfield h-index###
 
data(garfield, package = "bibliometrixData")

indices=Hindex(garfield, field = "author", elements = "GARFIELD E", years=Inf, sep = ";")

# h-index, g-index and m-index of Eugene Garfield
indices$H

# Papers and total citations
head(indices$CitationList[[1]])

---

Historical co-citation network

Description

histNetwork creates a historical citation network from a bibliographic data frame.

Usage

histNetwork(M, min.citations, sep = ";", network = TRUE, verbose = TRUE)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS, OpenAlex, Lens.org and Clarivate Analytics Web of Science file.
min.citations	DEPRECATED. New algorithm does not use this parameters. It will be remove in the next version of bibliometrix.
sep	is the field separator character. This character separates strings in CR column of the data frame. The default is sep = ";".
network	is logical. If TRUE, function calculates and returns also the direct citation network. If FALSE, the function returns only the local citation table.
verbose	is logical. If TRUE, results are printed on screen.

Value

histNetwork returns an object of class "list" containing the following components:

NetMatrix		the historical co-citation network matrix
histData		the set of n most cited references
M		the bibliographic data frame

See Also

convert2df to import and convert a supported export file in a bibliographic data frame.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

biblioNetwork to compute a bibliographic network.

Examples

## Not run: 
data(management, package = "bibliometrixData")

histResults <- histNetwork(management, sep = ";")

## End(Not run)

---

Plotting historical co-citation network

Description

histPlot plots a historical co-citation network.

Usage

histPlot(
  histResults,
  n = 20,
  size = 5,
  labelsize = 5,
  remove.isolates = TRUE,
  title_as_label = FALSE,
  label = "short",
  verbose = TRUE
)

Arguments

histResults	is an object of class "list" containing the following components: NetMatrix the historical citation network matrix Degree the min degree of the network histData the set of n most cited references M the bibliographic data frame is a network matrix obtained by the function histNetwork.
n	is integer. It defines the number of vertices to plot.
size	is an integer. It defines the point size of the vertices. Default value is 5.
labelsize	is an integer. It indicates the label size in the plot. Default is labelsize=5.
remove.isolates	is logical. If TRUE isolates vertices are not plotted.
title_as_label	is a logical. DEPRECATED
label	is a character. It indicates which label type to use as node id in the historiograph. It can be label=c("short", "title", "keywords", "keywordsplus"). Default is label = "short".
verbose	is logical. If TRUE, results and plots are printed on screen.

Details

The function histPlot can plot a historical co-citation network previously created by histNetwork.

Value

It is list containing: a network object of the class igraph and a plot object of the class ggraph.

See Also

histNetwork to compute a historical co-citation network.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE Citation network
## Not run: 
data(management, package = "bibliometrixData")

histResults <- histNetwork(management, sep = ";")

net <- histPlot(histResults, n=20, labelsize = 5)

## End(Not run)

---

Get Complete Author Information and ID from Scopus

Description

Uses SCOPUS API author search to identify author identification information.

Usage

idByAuthor(df, api_key)

Arguments

df	is a dataframe composed of three columns: lastname author's last name firstname author's first name affiliation Part of the affiliation name (university name, city, etc.) i.e. df[1,1:3]<-c("aria","massimo","naples") When affiliation is not specified, the field df$affiliation have to be NA. i.e. df[2,1:3]<-c("cuccurullo","corrado", NA)
api_key	is a character. It contains the Elsevier API key. Information about how to obtain an API Key Elsevier API website

Value

a data frame with cases corresponding to authors and variables to author's information and ID got from SCOPUS.

See Also

retrievalByAuthorID for downloading the complete author bibliographic collection from SCOPUS

Examples

## Request a personal API Key to Elsevier web page https://dev.elsevier.com/sc_apis.html
#
# api_key="your api key"

## create a data frame with the list of authors to get information and IDs
# i.e. df[1,1:3]<-c("aria","massimo","naples")
#      df[2,1:3]<-c("cuccurullo","corrado", NA)

## run idByAuthor function
#
# authorsID <- idByAuthor(df, api_key)

---

ID and DE keyword associations

Description

It associates authors' keywords to keywords plus.

Usage

keywordAssoc(M, sep = ";", n = 10, excludeKW = NA)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
sep	is the field separator character. This character separates keywords in each string of ID and DE columns of the bibliographic data frame. The default is sep = ";".
n	is a integer. It indicates the number of authors' keywords to associate to each keyword plus. The default is n = 10.
excludeKW	is character vector. It contains authors' keywords to exclude from the analysis.

Value

an object of class "list".

See Also

convert2df to import and convert a WoS or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

data(scientometrics, package = "bibliometrixData")

KWlist <- keywordAssoc(scientometrics, sep = ";",n = 10, excludeKW = NA)

# list of first 10 Keywords plus
names(KWlist)

# list of first 10 authors' keywords associated to the first Keyword plus
KWlist[[1]][1:10]

---

Yearly occurrences of top keywords/terms

Description

It calculates yearly occurrences of top keywords/terms.

Usage

KeywordGrowth(
  M,
  Tag = "ID",
  sep = ";",
  top = 10,
  cdf = TRUE,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original WoS or SCOPUS file.
Tag	is a character object. It indicates one of the keyword field tags of the standard ISI WoS Field Tag codify (ID or DE) or a field tag created by termExtraction function (TI_TM, AB_TM, etc.).
sep	is the field separator character. This character separates strings in each keyword column of the data frame. The default is sep = ";".
top	is a numeric. It indicates the number of top keywords to analyze. The default value is 10.
cdf	is a logical. If TRUE, the function calculates the cumulative occurrences distribution.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

Value

an object of class data.frame

Examples

data(scientometrics, package = "bibliometrixData")
topKW=KeywordGrowth(scientometrics, Tag = "ID", sep = ";", top=5, cdf=TRUE)
topKW

# Plotting results
## Not run: 
install.packages("reshape2")
library(reshape2)
library(ggplot2)
DF=melt(topKW, id='Year')
ggplot(DF,aes(Year,value, group=variable, color=variable))+geom_line

## End(Not run)

---

Author local citations

Description

It calculates local citations (LCS) of authors and documents of a bibliographic collection.

Usage

localCitations(M, fast.search = FALSE, sep = ";", verbose = FALSE)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
fast.search	is logical. If true, the function calculates local citations only for 25 percent top cited documents.
sep	is the field separator character. This character separates citations in each string of CR column of the bibliographic data frame. The default is sep = ";".
verbose	is a logical. If TRUE, results are printed on screen.

Details

Local citations measure how many times an author (or a document) included in this collection have been cited by the documents also included in the collection.

Value

an object of class "list" containing author local citations and document local citations.

See Also

citations function for citation frequency distribution.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

data(scientometrics, package = "bibliometrixData")

CR <- localCitations(scientometrics, sep = ";")

CR$Authors[1:10,]
CR$Papers[1:10,]

---

Bibliometrix logo.

Description

The matrix contains the rgb format of the bibliometrix official logo.

Format

A matrix with 927 rows and 800 columns.

---

Lotka's law coefficient estimation

Description

It estimates Lotka's law coefficients for scientific productivity (Lotka A.J., 1926).

Usage

lotka(results)

Arguments

results

is an object of the class 'bibliometrix' for which the analysis of the authors' dominance ranking is desired.

Details

Reference: Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington academy of sciences, 16(12), 317-323.

Value

The function lotka returns a list of summary statistics of the Lotka's law estimation of an object of class bibliometrix.

the list contains the following objects:

Beta		Beta coefficient
C		Constant coefficient
R2		Goodness of Fit
fitted		Fitted Values
p.value		Pvalue of two-sample Kolmogorov-Smirnov test between the empirical and the theoretical Lotka's Law distribution (with Beta=2)
AuthorProd		Authors' Productivity frequency table

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

data(scientometrics, package = "bibliometrixData")
results <- biblioAnalysis(scientometrics)
L=lotka(results)
L

---

Merge bibliographic data frames from supported bibliogtraphic DBs

Description

Merge bibliographic data frames from different databases (WoS,SCOPUS, Lens, Openalex, etc-) into a single one.

Usage

mergeDbSources(..., remove.duplicated = TRUE, verbose = TRUE)

Arguments

...	are the bibliographic data frames to merge.
remove.duplicated	is logical. If TRUE duplicated documents will be deleted from the bibliographic collection.
verbose	is logical. If TRUE, information on duplicate documents is printed on the screen.

Details

bibliographic data frames are obtained by the converting function convert2df. The function merges data frames identifying common tag fields and duplicated records.

Value

the value returned from mergeDbSources is a bibliographic data frame.

See Also

convert2df to import and convert an ISI or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

data(isiCollection, package = "bibliometrixData")

data(scopusCollection, package = "bibliometrixData")

M <- mergeDbSources(isiCollection, scopusCollection, remove.duplicated=TRUE)

dim(M)

---

Meta-Field Tag Extraction

Description

It extracts other field tags, different from the standard WoS/SCOPUS codify.

Usage

metaTagExtraction(M, Field = "CR_AU", sep = ";", aff.disamb = TRUE)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original WoS or SCOPUS file.
Field	is a character object. New tag extracted from aggregated data is specified by this string. Field can be equal to one of these tags: "CR_AU" First Author of each cited reference "CR_SO" Source of each cited reference "AU_CO" Country of affiliation for co-authors "AU1_CO" Country of affiliation for the first author "AU_UN" University of affiliation for each co-author and the corresponding author (AU1_UN) "SR" Short tag of the document (as used in reference lists)
sep	is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".
aff.disamb	is a logical. If TRUE and Field="AU_UN", then a disambiguation algorithm is used to identify and match scientific affiliations (univ, research centers, etc.). The default is aff.disamb=TRUE.

Value

the bibliometric data frame with a new column containing data about new field tag indicated in the argument Field.

See Also

convert2df for importing and converting bibliographic files into a data frame.

biblioAnalysis function for bibliometric analysis

Examples

# Example 1: First Authors for each cited reference

data(scientometrics, package = "bibliometrixData")
scientometrics <- metaTagExtraction(scientometrics, Field = "CR_AU", sep = ";")
unlist(strsplit(scientometrics$CR_AU[1], ";"))


#Example 2: Source for each cited reference

data(scientometrics)
scientometrics <- metaTagExtraction(scientometrics, Field = "CR_SO", sep = ";")
unlist(strsplit(scientometrics$CR_SO[1], ";"))

#Example 3: Affiliation country for co-authors

data(scientometrics)
scientometrics <- metaTagExtraction(scientometrics, Field = "AU_CO", sep = ";")
scientometrics$AU_CO[1:10]

---

Completeness of bibliographic metadata

Description

It calculates the percentage of missing data in the metadata of a bibliographic data frame.

Usage

missingData(M)

Arguments

M	is a bibliographic data frame obtained by convert2df function.

Details

Each metadata is assigned a status c("Excellent," "Good," "Acceptable", "Poor", "Critical," "Completely missing") depending on the percentage of missing data. In particular, the column *status* classifies the percentage of missing value in 5 categories: "Excellent" (0 "Poor" (from 20.01

The results of the function allow us to understand which analyses can be performed with bibliometrix and which cannot based on the completeness (or status) of different metadata.

Value

The function missingData returns a list containing two objects:

allTags		is a data frame including results for all original metadata tags from the collection
mandatoryTags		is a data frame that included only the tags needed for analysis with bibliometrix and biblioshiny.

Examples

data(scientometrics, package = "bibliometrixData")
res <- missingData(scientometrics)
print(res$mandatoryTags)

---

Save a network graph object as Pajek files

Description

The function net2Pajek save a bibliographic network previously created by networkPlot as pajek files.

Usage

net2Pajek(net, filename = "my_pajek_network", path = NULL)

Arguments

net	is a network graph object returned by the function networkPlot.
filename	is a character. It indicates the filename for Pajek export files.
path	is a character. It indicates the path where the files will be saved. When path="NULL, the files will be saved in the current folder. Default is NULL.

Value

The function returns no object but will save three Pajek files in the folder given in the "path" argument with the name "filename.clu," "filename.vec," and "filename.net."

See Also

net2VOSviewer to export and plot the network with VOSviewer software.

Examples

## Not run: 
data(management, package = "bibliometrixData")

NetMatrix <- biblioNetwork(management, analysis = "co-occurrences", 
network = "keywords", sep = ";")

net <- networkPlot(NetMatrix, n = 30, type = "auto", Title = "Co-occurrence Network",labelsize=1) 

net2Pajek(net, filename="pajekfiles", path=NULL)

## End(Not run)

---

Open a bibliometrix network in VosViewer

Description

net2VOSviewer plots a network created with networkPlot using VOSviewer by Nees Jan van Eck and Ludo Waltman.

Usage

net2VOSviewer(net, vos.path = NULL)

Arguments

net	is an object created by networkPlot function.
vos.path	is a character indicating the full path where VOSviewer.jar is located.

Details

The function networkPlot can plot a bibliographic network previously created by biblioNetwork. The network map can be plotted using internal R routines or using VOSviewer by Nees Jan van Eck and Ludo Waltman.

Value

It write a .net file that can be open in VOSviewer

See Also

biblioNetwork to compute a bibliographic network.

networkPlot to create and plot a network object

Examples

# EXAMPLE 

# VOSviewer.jar have to be present in the working folder

# data(scientometrics, package = "bibliometrixData")

# NetMatrix <- biblioNetwork(scientometrics, analysis = "co-citation", 
# network = "references", sep = ";")

# net <- networkPlot(NetMatrix, n = 30, type = "kamada", Title = "Co-Citation",labelsize=0.5) 

# net2VOSviewer(net)

---

Plotting Bibliographic networks

Description

networkPlot plots a bibliographic network.

Usage

networkPlot(
  NetMatrix,
  normalize = NULL,
  n = NULL,
  degree = NULL,
  Title = "Plot",
  type = "auto",
  label = TRUE,
  labelsize = 1,
  label.cex = FALSE,
  label.color = FALSE,
  label.n = NULL,
  halo = FALSE,
  cluster = "walktrap",
  community.repulsion = 0.1,
  vos.path = NULL,
  size = 3,
  size.cex = FALSE,
  curved = FALSE,
  noloops = TRUE,
  remove.multiple = TRUE,
  remove.isolates = FALSE,
  weighted = NULL,
  edgesize = 1,
  edges.min = 0,
  alpha = 0.5,
  verbose = TRUE
)

Arguments

NetMatrix	is a network matrix obtained by the function biblioNetwork.
normalize	is a character. It can be "association", "jaccard", "inclusion","salton" or "equivalence" to obtain Association Strength, Jaccard, Inclusion, Salton or Equivalence similarity index respectively. The default is type = NULL.
n	is an integer. It indicates the number of vertices to plot.
degree	is an integer. It indicates the min frequency of a vertex. If degree is not NULL, n is ignored.
Title	is a character indicating the plot title.
type	is a character object. It indicates the network map layout: type="auto" Automatic layout selection type="circle" Circle layout type="sphere" Sphere layout type="mds" Multidimensional Scaling layout type="fruchterman" Fruchterman-Reingold layout type="kamada" Kamada-Kawai layout
label	is logical. If TRUE vertex labels are plotted.
labelsize	is an integer. It indicates the label size in the plot. Default is labelsize=1
label.cex	is logical. If TRUE the label size of each vertex is proportional to its degree.
label.color	is logical. If TRUE, for each vertex, the label color is the same as its cluster.
label.n	is an integer. It indicates the number of vertex labels to draw.
halo	is logical. If TRUE communities are plotted using different colors. Default is halo=FALSE
cluster	is a character. It indicates the type of cluster to perform among ("none", "optimal", "louvain","leiden", "infomap","edge_betweenness","walktrap", "spinglass", "leading_eigen", "fast_greedy").
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.
vos.path	is a character indicating the full path where VOSviewer.jar is located.
size	is integer. It defines the size of each vertex. Default is size=3.
size.cex	is logical. If TRUE the size of each vertex is proportional to its degree.
curved	is a logical or a number. If TRUE edges are plotted with an optimal curvature. Default is curved=FALSE. Curved values are any numbers from 0 to 1.
noloops	is logical. If TRUE loops in the network are deleted.
remove.multiple	is logical. If TRUE multiple links are plotted using just one edge.
remove.isolates	is logical. If TRUE isolates vertices are not plotted.
weighted	This argument specifies whether to create a weighted graph from an adjacency matrix. If it is NULL then an unweighted graph is created and the elements of the adjacency matrix gives the number of edges between the vertices. If it is a character constant then for every non-zero matrix entry an edge is created and the value of the entry is added as an edge attribute named by the weighted argument. If it is TRUE then a weighted graph is created and the name of the edge attribute will be weight.
edgesize	is an integer. It indicates the network edge size.
edges.min	is an integer. It indicates the min frequency of edges between two vertices. If edge.min=0, all edges are plotted.
alpha	is a number. Legal alpha values are any numbers from 0 (transparent) to 1 (opaque). The default alpha value usually is 0.5.
verbose	is a logical. If TRUE, network will be plotted. Default is verbose = TRUE.

Details

The function networkPlot can plot a bibliographic network previously created by biblioNetwork.

Value

It is a list containing the following elements:

graph		a network object of the class igraph
cluster_obj		a communities object of the package igraph
cluster_res		a data frame with main results of clustering procedure.

See Also

biblioNetwork to compute a bibliographic network.

net2VOSviewer to export and plot the network with VOSviewer software.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE Keywordd co-occurrence network

data(management, package = "bibliometrixData")

NetMatrix <- biblioNetwork(management, analysis = "co-occurrences", 
network = "keywords", sep = ";")

net <- networkPlot(NetMatrix, n = 30, type = "auto", Title = "Co-occurrence Network",labelsize=1)

---

Calculating network summary statistics

Description

networkStat calculates main network statistics.

Usage

networkStat(object, stat = "network", type = "degree")

Arguments

object	is a network matrix obtained by the function biblioNetwork or an graph object of the class igraph.
stat	is a character. It indicates which statistics are to be calculated. stat = "network" calculates the statistics related to the network; stat = "all" calculates the statistics related to the network and the individual nodes that compose it. Default value is stat = "network".
type	is a character. It indicates which centrality index is calculated. type values can be c("degree", "closeness", "betweenness","eigenvector","pagerank","hub","authority", "all"). Default is "degree".

Details

The function networkStat can calculate the main network statistics from a bibliographic network previously created by biblioNetwork.

Value

It is a list containing the following elements:

graph		a network object of the class igraph
network		a communities a list with the main statistics of the network
vertex		a data frame with the main measures of centrality and prestige of vertices.

See Also

biblioNetwork to compute a bibliographic network.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE Co-citation network

# to run the example, please remove # from the beginning of the following lines
# data(scientometrics, package = "bibliometrixData")

# NetMatrix <- biblioNetwork(scientometrics, analysis = "co-citation", 
#      network = "references", sep = ";")

# netstat <- networkStat(NetMatrix, stat = "all", type = "degree")

---

Calculate the normalized citation score metric

Description

It calculates the normalized citation score for documents, authors and sources using both global and local citations.

Usage

normalizeCitationScore(M, field = "documents", impact.measure = "local")

Arguments

M	is a bibliographic data frame obtained by convert2df function.
field	is a character. It indicates the unit of analysis on which calculate the NCS. It can be equal to field = c("documents", "authors", "sources"). Default is field = "documents".
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", normalizeCitationScore 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.

Details

The document Normalized Citation Score (NCS) of a document is calculated by dividing the actual count of citing items by the expected citation rate for documents with the same year of publication.

The MNCS of a set of documents, for example the collected works of an individual, or published on a journal, is the average of the NCS values for all the documents in the set.

The NGCS is the NCS calculated using the global citations (total citations that a document received considering the whole bibliographic database).

The NLCS is the NCS calculated using the local citations (total citations that a document received from a set of documents included in the same collection).

Value

a dataframe.

Examples

## Not run: 
data(management, package = "bibliometrixData")
NCS <- normalizeCitationScore(management, field = "authors", impact.measure = "local")

## End(Not run)

---

Calculate similarity indices

Description

It calculates a relative measure of bibliographic co-occurrences.

Usage

normalizeSimilarity(NetMatrix, type = "association")

Arguments

NetMatrix	is a coupling matrix obtained by the network functions biblioNetwork or cocMatrix.
type	is a character. It can be "association", "jaccard", "inclusion","salton" or "equivalence" to obtain Association Strength, Jaccard, Inclusion, Salton or Equivalence similarity index respectively. The default is type = "association".

Details

couplingSimilarity calculates Association strength, Inclusion, Jaccard or Salton similarity from a co-occurrence bibliographic matrix.

The association strength is used by Van Eck and Waltman (2007) and Van Eck et al. (2006). Several works refer to the measure as the proximity index, while Leydesdorff (2008)and Zitt et al. (2000) refer to it as the probabilistic affinity (or activity) index.

The inclusion index, also called Simpson coefficient, is an overlap measure used in information retrieval.

The Jaccard index (or Jaccard similarity coefficient) gives us a relative measure of the overlap of two sets. It is calculated as the ratio between the intersection and the union of the reference lists (of two manuscripts).

The Salton index, instead, relates the intersection of the two lists to the geometric mean of the size of both sets. The square of Salton index is also called Equivalence index.

The indices are equal to zero if the intersection of the reference lists is empty.

References

Leydesdorff, L. (2008). On the normalization and visualization of author Cocitation data: Salton's cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77– 85.
Van Eck, N.J., Waltman, L., Van den Berg, J., & Kaymak, U. (2006). Visualizing the computational intelligence field. IEEE Computational Intelligence Magazine, 1(4), 6– 10.
Van Eck, N.J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(5), 625– 645
. Van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American society for information science and technology, 60(8), 1635-1651.
Zitt, M., Bassecoulard, E., & Okubo, Y. (2000). Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science. Scientometrics, 47(3), 627– 657.

Value

a similarity matrix.

See Also

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

Examples

data(scientometrics, package = "bibliometrixData")
NetMatrix <- biblioNetwork(scientometrics, analysis = "co-occurrences", 
              network = "keywords", sep = ";")
S=normalizeSimilarity(NetMatrix, type = "association")

---

Plotting dendrogram resulting from Conceptual Structure Analysis

Description

plot method for class 'bibliodendrogram'

Usage

## S3 method for class 'bibliodendrogram'
plot(x, ...)

Arguments

x	is the object for which plots are desired.
...	is a generic param for plot functions.

Value

The function plot draws a dendrogram.

---

Plotting bibliometric analysis results

Description

plot method for class 'bibliometrix'

Usage

## S3 method for class 'bibliometrix'
plot(x, ...)

Arguments

x	is the object for which plots are desired.
...	can accept two arguments: k is an integer, used for plot formatting (number of objects). Default value is 10. pause is a logical, used to allow pause in screen scrolling of results. Default value is pause = FALSE.

Value

The function plot returns a list of plots of class ggplot2.

See Also

The bibliometric analysis function biblioAnalysis.

summary to compute a list of summary statistics of the object of class bibliometrix.

Examples

data(scientometrics, package = "bibliometrixData")

results <- biblioAnalysis(scientometrics)

plot(results, k = 10, pause = FALSE)

---

Plot a Thematic Evolution Analysis

Description

It plot a Thematic Evolution Analysis performed using the thematicEvolution function.

Usage

plotThematicEvolution(Nodes, Edges, measure = "inclusion", min.flow = 0)

Arguments

Nodes	is a list of nodes obtained by thematicEvolution function.
Edges	is a list of edges obtained by thematicEvolution function.
measure	is a character. It can be measure=("inclusion","stability", "weighted").
min.flow	is numerical. It indicates the minimum value of measure to plot a flow.

Value

a sankeyPlot

See Also

thematicMap function to create a thematic map based on co-word network analysis and clustering.

thematicMap function to perform a thematic evolution analysis.

networkPlot to plot a bibliographic network.

Examples

## Not run: 
data(managemeent, package = "bibliometrixData")
years=c(2004,2015)

nexus <- thematicEvolution(management,field="ID",years=years,n=100,minFreq=2)

plotThematicEvolution(nexus$Nodes,nexus$Edges)

## End(Not run)

---

DEPRECATED: Load a sequence of ISI or SCOPUS Export files into a large character object

Description

The function readFiled is deprecated. You can import and convert your export files directly using the function convert2df.

Usage

readFiles(...)

Arguments

...	is a sequence of names of files downloaded from WOS.(in plain text or bibtex format) or SCOPUS Export file (exclusively in bibtex format).

Value

a character vector of length the number of lines read.

See Also

convert2df for converting SCOPUS of ISI Export file into a dataframe

Examples

# WoS or SCOPUS Export files can be read using \code{\link{readFiles}} function:

# largechar <- readFiles('filename1.txt','filename2.txt','filename3.txt')

# filename1.txt, filename2.txt and filename3.txt are ISI or SCOPUS Export file 
# in plain text or bibtex format.

# D <- readFiles('https://www.bibliometrix.org/datasets/bibliometrics_articles.txt')

---

Get Author Content on SCOPUS by ID

Description

Uses SCOPUS API search to get information about documents on a set of authors using SCOPUS ID.

Usage

retrievalByAuthorID(id, api_key, remove.duplicated = TRUE, country = TRUE)

Arguments

id	is a vector of characters containing the author's SCOPUS IDs. SCOPUS IDs con be obtained using the function idByAuthor.
api_key	is a character. It contains the Elsvier API key. Information about how to obtain an API Key Elsevier API website
remove.duplicated	is logical. If TRUE duplicated documents will be deleted from the bibliographic collection.
country	is logical. If TRUE authors' country information will be downloaded from SCOPUS.

Value

a list containing two objects: (i) M which is a data frame with cases corresponding to articles and variables to main Field Tags named using the standard ISI WoS Field Tag codify. M includes the entire bibliographic collection downloaded from SCOPUS. The main field tags are:

AU		Authors
TI		Document Title
SO		Publication Name (or Source)
DT		Document Type
DE		Authors' Keywords
ID		Keywords associated by SCOPUS or ISI database
AB		Abstract
C1		Author Address
RP		Reprint Address
TC		Times Cited
PY		Year
UT		Unique Article Identifier
DB		Database

(ii) authorDocuments which is a list containing a bibliographic data frame for each author.

LIMITATIONS: Currently, SCOPUS API does not allow to download document references. As consequence, it is not possible to perform co-citation analysis (the field CR is empty).

See Also

idByAuthor for downloading author information and SCOPUS ID.

Examples

## Request a personal API Key to Elsevier web page https://dev.elsevier.com/sc_apis.html

## api_key="your api key"

## create a data frame with the list of authors to get information and IDs
# i.e. df[1,1:3] <- c("aria","massimo","naples")
#      df[2,1:3] <- c("cuccurullo","corrado", "naples")

## run idByAuthor function
#
# authorsID <- idByAuthor(df, api_key)
#

## extract the IDs
# 
# id <- authorsID[,3]
#

## create the bibliographic collection
# 
# res <- retrievalByAuthorID(id, api_key)
#
# M <- res$M  # the entire bibliographic data frame
# M <- res$authorDocuments # the list containing a bibliographic data frame for each author

---

Reference Publication Year Spectroscopy

Description

rpys computes a Reference Publication Year Spectroscopy for detecting the Historical Roots of Research Fields. The method was introduced by Marx et al., 2014.

Usage

rpys(M, sep = ";", timespan = NULL, graph = T)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original ISI or SCOPUS file.
sep	is the cited-references separator character. This character separates cited-references in the CR column of the data frame. The default is sep = ";".
timespan	is a numeric vector c(min year,max year). The default value is NULL (the entire timespan is considered).
graph	is a logical. If TRUE the function plot the spectroscopy otherwise the plot is created but not drawn down.

Details

Reference:
Marx, W., Bornmann, L., Barth, A., & Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65(4), 751-764.

Value

a list containing the spectroscopy (class ggplot2) and three dataframes with the number of citations per year, the list of the cited references for each year, and the reference list with citations recorded year by year, respectively.

See Also

convert2df to import and convert an ISI or SCOPUS Export file in a data frame.

biblioAnalysis to perform a bibliometric analysis.

biblioNetwork to compute a bibliographic network.

Examples

data(scientometrics, package = "bibliometrixData")
res <- rpys(scientometrics, sep=";", graph = TRUE)

---

Number of documents published annually per Top Sources

Description

It calculates yearly published documents of the top sources.

Usage

sourceGrowth(M, top = 5, cdf = TRUE)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original ISI or SCOPUS file.
top	is a numeric. It indicates the number of top sources to analyze. The default value is 5.
cdf	is a logical. If TRUE, the function calculates the cumulative occurrences distribution.

Value

an object of class data.frame

Examples

data(scientometrics, package = "bibliometrixData")
topSO=sourceGrowth(scientometrics, top=1, cdf=TRUE)
topSO

# Plotting results
## Not run: 
install.packages("reshape2")
library(reshape2)
library(ggplot2)
DF=melt(topSO, id='Year')
ggplot(DF,aes(Year,value, group=variable, color=variable))+geom_line()

## End(Not run)

---

Splitting Network communities

Description

networkPlot Create a network plot with separated communities.

Usage

splitCommunities(graph, n = NULL)

Arguments

graph	is a network plot obtained by the function networkPlot.
n	is an integer. It indicates the number of vertices to plot for each community.

Details

The function splitCommunities splits communities in separated subnetworks from a bibliographic network plot previously created by networkPlot.

Value

It is a network object of the class igraph

See Also

biblioNetwork to compute a bibliographic network.

networkPlot to plot a bibliographic network.

net2VOSviewer to export and plot the network with VOSviewer software.

cocMatrix to compute a co-occurrence matrix.

biblioAnalysis to perform a bibliometric analysis.

Examples

# EXAMPLE Keywordd co-occurrence network

data(management, package = "bibliometrixData")

NetMatrix <- biblioNetwork(management, analysis = "co-occurrences", 
network = "keywords", sep = ";")

net <- networkPlot(NetMatrix, n = 30, type = "auto", 
               Title = "Co-occurrence Network",labelsize=1, verbose=FALSE) 

graph <- splitCommunities(net$graph, n = 30)

---

List of English stopwords.

Description

A character vector containing a complete list of English stopwords
Data are used by biblioAnalysis function to extract Country Field of Cited References and Authors.

Format

A character vector with 665 rows.

---

Summarizing bibliometric analysis results

Description

summary method for class 'bibliometrix'

Usage

## S3 method for class 'bibliometrix'
summary(object, ...)

Arguments

object	is the object for which a summary is desired.
...	can accept two arguments: k integer, used for table formatting (number of rows). Default value is 10. pause logical, used to allow pause in screen scrolling of results. Default value is pause = FALSE. width integer, used to define screen output width. Default value is width = 120. verbose logical, used to allow screen output. Default is TRUE.

Value

The function summary computes and returns a list of summary statistics of the object of class bibliometrics.

the list contains the following objects:

MainInformation		Main Information about Data
AnnualProduction		Annual Scientific Production
AnnualGrowthRate		Annual Percentage Growth Rate
MostProdAuthors		Most Productive Authors
MostCitedPapers		Top manuscripts per number of citations
MostProdCountries		Corresponding Author's Countries
TCperCountries		Total Citation per Countries
MostRelSources		Most Relevant Sources
MostRelKeywords		Most Relevant Keywords

See Also

biblioAnalysis function for bibliometric analysis

plot to draw some useful plots of the results.

Examples

data(scientometrics, package = "bibliometrixData")

results <- biblioAnalysis(scientometrics)

summary(results)

---

Summarizing network analysis results

Description

summary method for class 'bibliometrix_netstat'

Usage

## S3 method for class 'bibliometrix_netstat'
summary(object, ...)

Arguments

object	is the object for which a summary is desired.
...	can accept two arguments: k integer, used for table formatting (number of rows). Default value is 10.

Value

The function summary computes and returns on display several statistics both at network and vertex level.

Examples

# to run the example, please remove # from the beginning of the following lines
#data(scientometrics, package = "bibliometrixData")

#NetMatrix <- biblioNetwork(scientometrics, analysis = "collaboration", 
#                   network = "authors", sep = ";")
#netstat <- networkStat(NetMatrix, stat = "all", type = "degree")
#summary(netstat)

---

Tabulate elements from a Tag Field column

Description

It tabulates elements from a Tag Field column of a bibliographic data frame.

Usage

tableTag(
  M,
  Tag = "CR",
  sep = ";",
  ngrams = 1,
  remove.terms = NULL,
  synonyms = NULL
)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original WoS or SCOPUS file.
Tag	is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify.
sep	is the field separator character. This character separates strings in each column of the data frame. The default is sep = ";".
ngrams	is an integer between 1 and 3. It indicates the type of n-gram to extract from titles or abstracts.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.

Details

tableTag is an internal routine of main function biblioAnalysis.

Value

an object of class table

Examples

data(scientometrics, package = "bibliometrixData")
Tab <- tableTag(scientometrics, Tag = "CR", sep = ";")
Tab[1:10]

---

Term extraction tool from textual fields of a manuscript

Description

It extracts terms from a text field (abstract, title, author's keywords, etc.) of a bibliographic data frame.

Usage

termExtraction(
  M,
  Field = "TI",
  ngrams = 1,
  stemming = FALSE,
  language = "english",
  remove.numbers = TRUE,
  remove.terms = NULL,
  keep.terms = NULL,
  synonyms = NULL,
  verbose = TRUE
)

Arguments

M	is a data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to articles and variables to Field Tag in the original WoS or SCOPUS file.
Field	is a character object. It indicates the field tag of textual data : "TI" Manuscript title "AB" Manuscript abstract "ID" Manuscript keywords plus "DE" Manuscript author's keywords The default is Field = "TI".
ngrams	is an integer between 1 and 3. 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.
stemming	is logical. If TRUE the Porter Stemming algorithm is applied to all extracted terms. The default is stemming = FALSE.
language	is a character. It is the language of textual contents ("english", "german","italian","french","spanish"). The default is language="english".
remove.numbers	is logical. If TRUE all numbers are deleted from the documents before term extraction. The default is remove.numbers = TRUE.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
keep.terms	is a character vector. It contains a list of compound words "formed by two or more terms" to keep in their original form in the term extraction process. The default is keep.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
verbose	is logical. If TRUE the function prints the most frequent terms extracted from documents. The default is verbose=TRUE.

Value

the bibliometric data frame with a new column containing terms about the field tag indicated in the argument Field.

See Also

convert2df to import and convert an WoS or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis

Examples

# Example 1: Term extraction from titles

data(scientometrics, package = "bibliometrixData")

# vector of compound words
keep.terms <- c("co-citation analysis","bibliographic coupling")

# term extraction
scientometrics <- termExtraction(scientometrics, Field = "TI", ngrams = 1,
remove.numbers=TRUE, remove.terms=NULL, keep.terms=keep.terms, verbose=TRUE)

# terms extracted from the first 10 titles
scientometrics$TI_TM[1:10]


#Example 2: Term extraction from abstracts

data(scientometrics)

# term extraction
scientometrics <- termExtraction(scientometrics, Field = "AB", ngrams = 2, 
 stemming=TRUE,language="english",
 remove.numbers=TRUE, remove.terms=NULL, keep.terms=NULL, verbose=TRUE)

# terms extracted from the first abstract
scientometrics$AB_TM[1]

# Example 3: Term extraction from keywords with synonyms

data(scientometrics)

# vector of synonyms 
synonyms <- c("citation; citation analysis", "h-index; index; impact factor")

# term extraction
scientometrics <- termExtraction(scientometrics, Field = "ID", ngrams = 1,
synonyms=synonyms, verbose=TRUE)

---

Perform a Thematic Evolution Analysis

Description

It performs a Thematic Evolution Analysis based on co-word network analysis and clustering. The methodology is inspired by the proposal of Cobo et al. (2011).

Usage

thematicEvolution(
  M,
  field = "ID",
  years,
  n = 250,
  minFreq = 2,
  size = 0.5,
  ngrams = 1,
  stemming = FALSE,
  n.labels = 1,
  repel = TRUE,
  remove.terms = NULL,
  synonyms = NULL,
  cluster = "walktrap"
)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df.
field	is a character object. It indicates the content field to use. Field can be one of c=("ID","DE","TI","AB"). Default value is field="ID".
years	is a numeric vector of one or more unique cut points.
n	is numerical. It indicates the number of words to use in the network analysis
minFreq	is numerical. It indicates the min frequency of words included in to a cluster.
size	is numerical. It indicates del size of the cluster circles and is a number in the range (0.01,1).
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.
stemming	is logical. If it is TRUE the word (from titles or abstracts) will be stemmed (using the Porter's algorithm).
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.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
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

thematicEvolution starts from two or more thematic maps created by thematicMap function.

Reference:
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.

Value

a list containing:

nets		The thematic nexus graph for each comparison
incMatrix		Some useful statistics about the thematic nexus

See Also

thematicMap function to create a thematic map based on co-word network analysis and clustering.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

Examples

## Not run: 
data(managemeent, package = "bibliometrixData")
years=c(2004,2015)

nexus <- thematicEvolution(management,field="ID",years=years,n=100,minFreq=2)

## End(Not run)

---

Create a thematic map

Description

It creates a thematic map based on co-word network analysis and clustering. The methodology is inspired by the proposal of Cobo et al. (2011).

Usage

thematicMap(
  M,
  field = "ID",
  n = 250,
  minfreq = 5,
  ngrams = 1,
  stemming = FALSE,
  size = 0.5,
  n.labels = 1,
  community.repulsion = 0.1,
  repel = TRUE,
  remove.terms = NULL,
  synonyms = NULL,
  cluster = "walktrap",
  subgraphs = FALSE
)

Arguments

M	is a bibliographic dataframe.
field	is the textual attribute used to build up the thematic map. It can be field = c("ID","DE", "TI", "AB"). biblioNetwork or cocMatrix.
n	is an integer. It indicates the number of terms to include in the analysis.
minfreq	is a integer. It indicates the minimum frequency (per thousand) of a cluster. It is a number in the range (0,1000).
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.
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 del 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.
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.
repel	is logical. If it is TRUE ggplot uses geom_label_repel instead of geom_label.
remove.terms	is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
synonyms	is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
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").
subgraphs	is a logical. If TRUE cluster subgraphs are returned.

Details

thematicMap starts from a co-occurrence keyword network to plot in a two-dimensional map the typological themes of a domain.

Reference:
Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.

Value

a list containing:

map		The thematic map as ggplot2 object
clusters		Centrality and Density values for each cluster.
words		A list of words following in each cluster
nclust		The number of clusters
net		A list containing the network output (as provided from the networkPlot function)

See Also

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

Examples

## Not run: 
data(scientometrics, package = "bibliometrixData")
res <- thematicMap(scientometrics, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE)
plot(res$map)

## End(Not run)

---

Three Fields Plot

Description

Visualize the main items of three fields (e.g. authors, keywords, journals), and how they are related through a Sankey diagram.

Usage

threeFieldsPlot(M, fields = c("AU", "DE", "SO"), n = c(20, 20, 20))

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
fields	is a character vector. It indicates the fields to analyze using the standard WoS field tags. Default is fields = c("AU","DE", "SO").
n	is a integer vector. It indicates how many items to plot, for each of the three fields. Default is n = c(20, 20, 20)

Value

a sankeyPlot

Examples

#data(scientometrics, package = "bibliometrixData")

#threeFieldsPlot(scientometrics, fields=c("DE","AU","CR"),n=c(20,20,20))

---

Bibliographic data frame time slice

Description

Divide a bibliographic data frame into time slice

Usage

timeslice(M, breaks = NA, k = 5)

Arguments

M	is a bibliographic data frame obtained by the converting function convert2df. It is a data matrix with cases corresponding to manuscripts and variables to Field Tag in the original SCOPUS and Clarivate Analytics WoS file.
breaks	is a numeric vector of two or more unique cut points.
k	is an integer value giving the number of intervals into which the data frame is to be cut. k is used only in case breaks argument is not provided. The default is k = 5.

Value

the value returned from split is a list containing the data frames for each sub-period.

See Also

convert2df to import and convert an ISI or SCOPUS Export file in a bibliographic data frame.

biblioAnalysis function for bibliometric analysis.

summary to obtain a summary of the results.

plot to draw some useful plots of the results.

Examples

data(scientometrics, package = "bibliometrixData")

list_df <- timeslice(scientometrics, breaks = c(1995, 2005))

names(list_df)

---

Deleting leading and ending white spaces

Description

Deleting leading and ending white spaces from a character object.

Usage

trim(x)

Arguments

x	is a character object.

Details

tableTag is an internal routine of bibliometrics package.

Value

an object of class character

Examples

char <- c("  Alfred", "Mary", " John")
char
trim(char)

---

Deleting leading white spaces

Description

Deleting leading white spaces from a character object.

Usage

trim.leading(x)

Arguments

x	is a character object.

Details

tableTag is an internal routine of bibliometrics package.

Value

an object of class character

Examples

char <- c("  Alfred", "Mary", " John")
char
trim.leading(char)

---

Deleting extra white spaces

Description

Deleting extra white spaces from a character object.

Usage

trimES(x)

Arguments

x	is a character object.

Details

tableTag is an internal routine of bibliometrics package.

Value

an object of class character

Examples

char <- c("Alfred  BJ", "Mary    Beth", "John      John")
char
trimES(char)

---