bibliometrix package provides a set of tools for quantitative research in bibliometrics and scientometrics.
Bibliometrics turns the main tool of science, quantitative analysis, on itself. Essentially, bibliometrics is the application of quantitative analysis and statistics to publications such as journal articles and their accompanying citation counts. Quantitative evaluation of publication and citation data is now used in almost all scientific fields to evaluate growth, maturity, leading authors, conceptual and intellectual maps, trends of a scientific community.
Bibliometrics is also used in research performance evaluation, especially in university and government labs, and also by policymakers, research directors and administrators, information specialists and librarians, and scholars themselves.
bibliometrix supports scholars in three key phases of analysis:
Data importing and conversion to R format;
Bibliometric analysis of a publication dataset;
Building and plotting matrices for co-citation, coupling, collaboration, and co-word analysis. Matrices are the input data for performing network analysis, multiple correspondence analysis, and any other data reduction techniques.
bibliometrix includes biblioshiny: bibliometrix for no-coders
biblioshiny is a shiny app providing a web-interface for bibliometrix.
It supports scholars in easy use of the main features of bibliometrix:
Data importing and conversion to data frame collection
Analytics and Plots for three different level metrics:
Analysis of three structures of Knowledge (K-structures):
Please follow the biblioshiny tutorial at www.bibliometrix.org/biblioshiny
If you use this package for your research, we would appreciate a citation.
To cite bibliometrix in publications, please use:
Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
Official website: https://www.bibliometrix.org
GitHub repository: https://github.com/massimoaria/bibliometrix
Stable version from CRAN
Developers version from GitHub
The export file can be read and converted using by R using the function convert2df:
convert2df(file, dbsource, format)
The argument file is a character vector containing the name of export files downloaded from SCOPUS, Clarivate Analytics WOS, Digital Science Dimenions, PubMed or Cochrane CDSR website. file can also contains the name of a json/xlm object download using Digital Science Dimenions or PubMed APIs (through the packages dimensionsR and pubmedR.
es. file <- c("file1.txt","file2.txt", ...)
## An example from bibliometrix vignettes file <- "https://www.bibliometrix.org/datasets/savedrecs.bib" M <- convert2df(file = file, dbsource = "isi", 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 (https://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf).
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)
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.
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 = ". ")
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.
# 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), Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.8)
# 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)
# 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)
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="CA") CS <- conceptualStructure(M,field="ID", method="CA", minDegree=4, k.max=8, stemming=FALSE, labelsize=10, documents=10)
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, n=20, size = FALSE,label=TRUE, arrowsize = 0.5)
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).