Part IV

Topic 17 Bibliometrix Analysis using R

17.1 Introduction to Bibliometric Analysis

  • Bibliometric analysis is a widely used method for explorative and analytical studies of large volumes of research data.
  • The analysis is helpful in discovering various evolutionary variations in a specific field of study as well as highligting emerging topics in the field.
  • Bibliometrics is the application of quantitative analysis and statistics to publications such as journal articles and their accompanying citation counts. (https://en.wikipedia.org/wiki/Bibliometrix)
  • Various methods are used to analyse the publication data to evaluate growth, maturity, leading authors, conceptual stuctures, trends, topical evolution etc.

17.2 R and Bibliometric analysis

R’s package ecosystem is one of its major advantages, there are packages available for most widely used statistical and data analysis & visualisation techniques used several packages added almost daily on new and upcoming methods published by academic researchers or industry practitioners.

  • R provide packages for various areas of interest (see https://cran.r-project.org/web/views/ for a list of task views grouping packages according to their functionality ) including systematic literature review or the related field of meta analysis.

  • Bibliometrix (Aria & Cuccurullo (2017)), Revtools (Westgate (2018)) and Litsearchr (E. Grames, Stillman, Tingley, & Elphick (2019),E. M. Grames, Stillman, Tingley, & Elphick (2019)) of the Metaverse (https://rmetaverse.github.io/) project, Adjutant (Crisan, Munzner, & Gardy (2018)), Metagear (Lajeunesse (2016))) are a few providing various functionality.

  • Bibliometrix is by far the most popular with several publications using the package

  • The package webpage (http://www.bibliometrix.org/Papers.html) provides a list of publications utilising the package. (for example see, Lajeunesse (2016); Addor & Melsen (2019)) and hence we will use the package to demonstrate some of its functionality.

  • Linnenluecke, Marrone, & Singh (2020), Ahadi, Singh, Bower, & Garrett (2022) provide two examples of using Bibliometric analysis in a Systematic Literature Review

17.3 Bibliometrix Example

  • Bibliometrix (https://www.bibliometrix.org/) allows R users to import a bibliography database generated using SCOPUS and Web of Science stored either as a Bibtex (.bib) or Plain Text (.txt) file.

  • The package has simple functions which allows for descriptive analyses as shown in table-1 to table-3.

  • The analysis can also be easily visualised as shown in figure-17.1 to 17.5.

library(bibliometrix)  #load the package
library(pander)  #other required packages
library(knitr)
library(kableExtra)
library(ggplot2)
library(bibliometrixData)
# use scopuscollection data from the package

data("scientometrics")
# M=convert2df(file='scopus.bib',format='bibtex',dbsource = 'scopus')#convert
# external data to data frame

17.4 Descriptive Analysis

# Descriptive analysis
M = scientometrics  #just to reuse the other code
res1 = biblioAnalysis(M, sep = ";")
s1 = summary(res1, k = 10, pause = FALSE, verbose = FALSE)

d1 = s1$MainInformationDF  #main information 
d2 = s1$MostProdAuthors  #Most productive Authors 
d3 = s1$MostCitedPapers  #most cited papers 
pander(d1, caption = "Summary Information")
Summary Information
Description Results
MAIN INFORMATION ABOUT DATA
Timespan 1985:2015
Sources (Journals, Books, etc) 1
Documents 147
Average years from publication 14.1
Average citations per documents 14.81
Average citations per year per doc 0.8168
References 4444
DOCUMENT TYPES
article 125
article; proceedings paper 19
review 3
DOCUMENT CONTENTS
Keywords Plus (ID) 392
Author’s Keywords (DE) 342
AUTHORS
Authors 269
Author Appearances 337
Authors of single-authored documents 32
Authors of multi-authored documents 237
AUTHORS COLLABORATION
Single-authored documents 38
Documents per Author 0.546
Authors per Document 1.83
Co-Authors per Documents 2.29
Collaboration Index 2.17

17.4.1 Productive Authors

s1$MostProdAuthors
   Authors        Articles Authors        Articles Fractionalized
1  SMALL H               8 SMALL H                           6.33
2  ZITT M                6 ZITT M                            3.00
3  BASSECOULARD E        5 JARNEVING B                       2.50
4  GLANZEL W             5 GLANZEL W                         2.17
5  HUANG MH              5 BASSECOULARD E                    2.00
6  THIJS B               4 LO SC                             2.00
7  AHLGREN P             3 HUANG MH                          1.79
8  CHEN DZ               3 THIJS B                           1.67
9  JARNEVING B           3 LEYDESDORFF L                     1.50
10 QIU JP                3 MILMAN BL                         1.50
pander(d2, caption = "Most Productive Authors", table.split = Inf)
Most Productive Authors
Authors Articles Authors Articles Fractionalized
SMALL H 8 SMALL H 6.33
ZITT M 6 ZITT M 3.00
BASSECOULARD E 5 JARNEVING B 2.50
GLANZEL W 5 GLANZEL W 2.17
HUANG MH 5 BASSECOULARD E 2.00
THIJS B 4 LO SC 2.00
AHLGREN P 3 HUANG MH 1.79
CHEN DZ 3 THIJS B 1.67
JARNEVING B 3 LEYDESDORFF L 1.50
QIU JP 3 MILMAN BL 1.50

17.4.2 Most cited papers

pander(d3, caption = "Most Cited Papers")
Most Cited Papers
Paper DOI TC TCperYear NTC
BOYACK KW, 2005, SCIENTOMETRICS 283 15.72 3.997
SMALL H, 1985, SCIENTOMETRICS-a 148 3.89 1.065
VAN ECK NJ, 2010, SCIENTOMETRICS 142 10.92 5.004
SMALL H, 1985, SCIENTOMETRICS 130 3.42 0.935
SMALL H, 2006, SCIENTOMETRICS 83 4.88 3.487
GMUR M, 2003, SCIENTOMETRICS 78 3.90 2.806
ZITT M, 1994, SCIENTOMETRICS 60 2.07 2.353
GLANZEL W, 1996, SCIENTOMETRICS 58 2.15 1.798
DING Y, 2000, SCIENTOMETRICS 46 2.00 2.667
PONZI LJ, 2002, SCIENTOMETRICS 44 2.10 1.234

17.5 Information Plots

p1 = plot(res1, pause = FALSE)

17.5.1 Summary Plot-1 (Most Porductive Authors)

library(ggplot2)
theme_set(theme_bw())


p1[[1]] + theme_bw() + scale_x_discrete(limits = rev(levels(as.factor(p1[[1]]$data$AU))))
Most Productive Authors

Figure 17.1: Most Productive Authors

17.5.2 Summary Plot-2 (Most Productive Countries)

p1[[2]]
Most Productive Countries

Figure 17.2: Most Productive Countries

17.5.3 Summary Plot-3 (Annual Scientific Production)

p1[[3]]
Annual Scientific Production

Figure 17.3: Annual Scientific Production

17.5.4 Summary Plot-4 (Average Article Citation)

p1[[4]]
Average Article Citation

Figure 17.4: Average Article Citation

17.5.5 Summary Plot-5 (Author Production Over Time)

  • A graph for author statistics over time can also be produced.

  • Figure-17.5 shows a graph of top 10 authors over time. The information from these plots can be easily extracted to summarise them in a table.

topAU = authorProdOverTime(M, k = 10, graph = TRUE)
Author Production Over Time

Figure 17.5: Author Production Over Time

17.5.6 Sankey plot

  • Bibliometrix provides another useful function to plot a Sankey diagram to visualise multiple attributes at the same time. For example, figure-9 provides a three fields plot for Author, Author Keywords and Cited References.
threeFieldsPlot(M, fields = c("DE", "AU", "AU_CO"))

Figure 17.6: Sankey Diagram

17.6 Co-word Analysis

  • Analysis of the conceptual structure among the articles analysed.
  • Bibliomentrix can conduct a co-word analysis to map the conceptual structure of a framework using the word co-occurrences in a bibliographic database.
  • The analysis in Figure-2 is conducted using the Correspondence Analysis and K-Means clustering using Author’s keywords. This analysis includes Natural Language Processing and is conducted without stemming.
library(gridExtra)
CS = conceptualStructure(M, field = "DE", method = "CA", minDegree = 4, clust = "auto",
    stemming = FALSE, labelsize = 8, documents = 10, graph = FALSE)

grid.arrange(CS[[4]], CS[[5]], ncol = 2, nrow = 1)
Conceptual Structures-1

Figure 17.7: Conceptual Structures-1

17.7 Author collaboration network

NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net = networkPlot(NetMatrix, n = 50, Title = "Author collaboration", type = "auto",
    size = 10, size.cex = T, edgesize = 3, labelsize = 0.6)
Author Collaboration Network

Figure 17.8: Author Collaboration Network

17.8 Keyword co-occurance

Netmatrix2 = biblioNetwork(M, analysis = "co-occurrences", network = "keywords",
    sep = ";")

# Plot the network
net = networkPlot(Netmatrix2, normalize = "association", weighted = T, n = 50, Title = "Keyword Co-occurrences",
    type = "fruchterman", size = T, edgesize = 5, labelsize = 0.7)
Keyword co-occurance

Figure 17.9: Keyword co-occurance

17.9 Thematic Map

Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.

Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.

Map = thematicMap(M, field = "ID", n = 1000, minfreq = 5, stemming = FALSE, size = 0.5,
    n.labels = 4, repel = TRUE)
plot(Map$map)
Thematic Map

Figure 17.10: Thematic Map

Finally there is a shiny based GUI also available biblioshiny()

References

Addor, N., & Melsen, L. (2019). Legacy, rather than adequacy, drives the selection of hydrological models. Water Resources Research, 55(1), 378–390.
Ahadi, A., Singh, A., Bower, M., & Garrett, M. (2022). Text mining in education&mdash;a bibliometrics-based systematic review. Education Sciences, 12(3). https://doi.org/10.3390/educsci12030210
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. Retrieved from https://doi.org/10.1016/j.joi.2017.08.007
Crisan, A., Munzner, T., & Gardy, J. L. (2018). Adjutant: An r-based tool to support topic discovery for systematic and literature reviews. Bioinformatics, 35(6), 1070–1072.
Grames, E. M., Stillman, A. N., Tingley, M. W., & Elphick, C. S. (2019). An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks. Methods in Ecology and Evolution, 0(ja). https://doi.org/10.1111/2041-210X.13268
Grames, E., Stillman, A., Tingley, M., & Elphick, C. (2019). Litsearchr: Automated search term selection and search strategy for systematic reviews.
Lajeunesse, M. J. (2016). Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for r. Methods in Ecology and Evolution, 7(3), 323–330.
Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678
Westgate, M. J. (2018). Revtools: Bibliographic data visualization for evidence synthesis in r. bioArXiv. https://doi.org/10.1101/262881