Type:	Package
Title:	Comprehensive Science Mapping Analysis
Version:	5.1.0
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.
License:	GPL-3
URL:	https://www.bibliometrix.org, https://github.com/massimoaria/bibliometrix, https://www.k-synth.com
BugReports:	https://github.com/massimoaria/bibliometrix/issues
LazyData:	true
Encoding:	UTF-8
Depends:	R (≥ 3.3.0)
Imports:	stats, grDevices, bibliometrixData, dimensionsR, dplyr, DT, ca, forcats, ggplot2, ggrepel, igraph, Matrix, plotly, openalexR, openxlsx, pubmedR, purrr, readr, readxl, rscopus, shiny, shinycssloaders (≥ 1.1.0), SnowballC, stringdist, stringi, stringr, tibble, tidyr, tidytext, visNetwork
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), wordcloud2
RoxygenNote:	7.3.2
NeedsCompilation:	no
Config/testthat/edition:	3
Packaged:	2025-07-15 13:44:05 UTC; massimoaria
Author:	Massimo Aria [cre, aut, cph], Corrado Cuccurullo [aut]
Maintainer:	Massimo Aria <aria@unina.it>
Repository:	CRAN
Date/Publication:	2025-07-15 16:00:07 UTC

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

Maintainer: Massimo Aria <aria@unina.it>

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).

h-index calculation

Description

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

Usage

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

Arguments

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]])

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

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)

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

Value

The function authorProdOverTime returns a list containing two objects:

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

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:

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

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

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

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:

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

Value

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

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

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

Value

It is a list containing the following elements:

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

Value

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

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

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:

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 5 variables:

countries

country names

continent

continent names

iso2

country ISO 3166-1 alpha-2 code

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

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:

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

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:

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

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

Value

The function fieldByYear returns a list containing threeobjects:

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
)

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

Value

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

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

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

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

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]

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

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(M)

Arguments

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:

See Also

biblioAnalysis function for bibliometric analysis

summary method for class 'bibliometrix'

Examples

data(management, package = "bibliometrixData")
L <- lotka(management)
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

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)

Merge DE and ID Fields into a Unified Keywords Column

Description

This function creates a new column 'KW_Merged' by combining the contents of the 'DE' (author keywords) and 'ID' (keywords plus) fields in a bibliographic dataframe. Duplicate keywords within each record are removed, and leading/trailing spaces are trimmed. The merged keywords are separated by a semicolon (';').

Usage

mergeKeywords(M, force = FALSE)

Arguments

Details

If the 'KW_Merged' column already exists, it will not be overwritten unless 'force = TRUE' is specified.

Value

A dataframe with an added (or updated) 'KW_Merged' column containing deduplicated and cleaned keyword strings.

Examples

## Not run: 
data(management, package = "bibliometrix")
M <- mergeKeywords(management)
head(M$KW_Merged)

## End(Not run)

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

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

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:

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

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

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

Details

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

Value

It is a list containing the following elements:

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

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:

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

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

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

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

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 Thematic Evolution Network

Description

Visualizes the thematic evolution of clusters over time using a temporal network layout. Nodes are positioned along the x-axis according to time slices and vertically to minimize overlap. Edges represent links between themes across time periods, with customizable weights and styles. Nodes and Edges objects are the results of a Thematic Evolution Analysis performed using the thematicEvolution function.

Usage

plotThematicEvolution(
  Nodes,
  Edges,
  min.flow = 0,
  measure = "weighted",
  label_size = 5,
  edge_scale = 10,
  node_scale = 30
)

Arguments

Value

A 'visNetwork' object displaying a time-structured thematic evolution network.

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

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

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:

(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, median.window = "centered", graph = T)

Arguments

Details

References:

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.

Thor A., Bornmann L., Mark W. & Mutz R.(2018). Identifying single influential publications in a research field: new analysis opportunities of the CRExplorer. Scientometrics, 116:591–608 https://doi.org/10.1007/s11192-018-2733-7

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

## Not run: 
data(management, package = "bibliometrixData")
res <- rpys(management, sep = ";", graph = TRUE)

## End(Not run)

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

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

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

Value

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

the list contains the following objects:

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

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

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

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

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:

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(management, 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

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:

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("DE", "AU", "SO"), n = c(20, 20, 20))

Arguments

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

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

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

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

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)