Bibliometrix package

A package for quantitative research in scientometrics and bibliometrics that includes all the main bibliometric methods of analysis.

1. Get the dataset

I obtained the data from SCOPUS and Web of Science repositories (WOS). I searched for articles using a generic query for test purposes. It is important to emphasize that building a correct query requires some steps

Generic query just to test package: Reforestation * Temperate forest

Years: from 2000 to 2021

Finally, select all items of search to export. In this tutorial I exported as “.bib” file

SCOPUS

WOS

2. The package

2.1 To install

#install.packages("bibliometrix")

2.2 Shiny

The package presents a Shiny version, which makes it possible to run analyses in a point and click interface, without write code. This is a really cool possibility for people who want to use the tool but don’t want to write code in R. To use biblioshiny, you need to run the below code without # :

# bibliometrix::biblioshiny()

2.3 Despite Shiny it’s really cool, writing the code it’s a better way for reproducibility in future analyses. Also, It’s easy to use codes for this package.

Loading some libraries (make sure that all libraries are installed)

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(bibliometrix)

## 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.
##                         
## 
## https://www.bibliometrix.org
## 
##                         
## For information and bug reports:
##                         - Send an email to info@bibliometrix.org   
##                         - Write a post on https://github.com/massimoaria/bibliometrix/issues
##                         
## Help us to keep Bibliometrix free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
## 
##                         
## To start with the shiny web-interface, please digit:
## biblioshiny()

2.4 The next step is to import the data. The import function is bibliometrix::convert2df(). It is important to inform as an argument the path of .bib file, the data source (dbsource) and the file format.

SCOPUS

library(bibliometrix)
file_scopus <- "https://github.com/Tai-Rocha/bibliometrix_test/raw/main/data/scopus.bib"
#file <- "https://www.bibliometrix.org/datasets/savedrecs.bib"
scopus_data <- convert2df(file = file_scopus, dbsource = "scopus", format = "bibtex")

## 
## Converting your scopus collection into a bibliographic dataframe
## 
## 
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
## 
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)
## 
## 
## Missing fields:  CR 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

WOS

file_wos <- "https://github.com/Tai-Rocha/bibliometrix_test/raw/main/data/wos.bib"
wos_data <- convert2df(file = file_wos, dbsource = "wos", format = "bibtex")

## 
## Converting your wos collection into a bibliographic dataframe
## 
## 
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
## 
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)
## 
## 
## Missing fields:  CR 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

2.5 Merge the datasets into a single dataset, which will be used to carry out the analyses.

The bibliometrix::mergeDbSources() function performs this task and using remove.duplicated = TRUE argument (for obviously remove the duplicates).

merge_all <-
  bibliometrix::mergeDbSources(scopus_data, wos_data,
                               remove.duplicated = TRUE) 

## 
##  111 duplicated documents have been removed

2.6 The exported data returned different types of work, such as editorials, errata, etc.

dplyr::distinct(merge_all, DT) %>% 
  knitr::kable(row.names = FALSE)

2.7 Filter only articles

filter_data <- merge_all %>%
  filter(DT == "ARTICLE")

2.8 Now the dataset it’s ok for bibliometrics analyses. The meaning of the column names can be found in the package documentation.

dplyr::glimpse(filter_data)

## Rows: 460
## Columns: 30
## $ AU       <chr> "ZHANG T;YAN Q;WANG G;ZHU J", "FISCHER S;GREET J;WALSH C;CATF…
## $ DE       <chr> "NON-STRUCTURAL CARBOHYDRATES;  SPROUT GROWTH;  SPROUT REGENE…
## $ ID       <chr> "CARBOHYDRATES;  CONSERVATION;  CULTIVATION;  ECOSYSTEMS;  FI…
## $ C1       <chr> "CAS KEY LABORATORY OF FOREST ECOLOGY AND MANAGEMENT, INSTITU…
## $ JI       <chr> "ECOL. PROCESSES", "FOR. ECOL. MANAGE.", "FOR. ECOL. MANAGE."…
## $ AB       <chr> "BACKGROUND: TO RESTORE SECONDARY FORESTS (MAJOR FOREST RESOU…
## $ AR       <chr> "25", "119264", "119253", "119220", "119175", "119202", "1191…
## $ RP       <chr> "YAN, Q.; QINGYUAN FOREST CERN, CHINA; EMAIL: QLYAN@IAE.AC.CN…
## $ DT       <chr> "ARTICLE", "ARTICLE", "ARTICLE", "ARTICLE", "ARTICLE", "ARTIC…
## $ DI       <chr> "10.1186/s13717-021-00300-w", "10.1016/j.foreco.2021.119264",…
## $ FU       <chr> "NATIONAL NATURAL SCIENCE FOUNDATION OF CHINANATIONAL NATURAL…
## $ BN       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ SN       <chr> "21921709", "03781127", "03781127", "03781127", "03781127", "…
## $ SO       <chr> "ECOLOGICAL PROCESSES", "FOREST ECOLOGY AND MANAGEMENT", "FOR…
## $ LA       <chr> "ENGLISH", "ENGLISH", "ENGLISH", "ENGLISH", "ENGLISH", "ENGLI…
## $ TC       <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 1, 0, 0…
## $ PN       <chr> "1", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "10", "5", N…
## $ PP       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "…
## $ PU       <chr> "SPRINGER SCIENCE AND BUSINESS MEDIA DEUTSCHLAND GMBH", "ELSE…
## $ DB       <chr> "ISI", "ISI", "ISI", "ISI", "ISI", "ISI", "ISI", "ISI", "ISI"…
## $ TI       <chr> "THE EFFECTS OF STUMP SIZE AND WITHINGAP POSITION ON SPROUT N…
## $ VL       <chr> "10", "493", "493", "492", "491", "491", "491", "490", "489",…
## $ PY       <dbl> 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2…
## $ FX       <chr> "THIS RESEARCH WAS SUPPORTED BY GRANTS FROM THE STRATEGIC LEA…
## $ CR       <chr> "none", "none", "none", "none", "none", "none", "none", "none…
## $ AU_UN    <chr> "INSTITUTE OF APPLIED ECOLOGY;CHINESE ACADEMY OF SCIENCES;CLE…
## $ AU1_UN   <chr> "NOTREPORTED;QINGYUAN FOREST CERN;NOTREPORTED", "FISCHER;SCHO…
## $ AU_UN_NR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ SR_FULL  <chr> "ZHANG T, 2021, ECOL PROCESSES", "FISCHER S, 2021, FOR ECOL M…
## $ SR       <chr> "ZHANG T, 2021, ECOL PROCESSES", "FISCHER S, 2021, FOR ECOL M…

3. Bibliometrix functions for analyzing data

tutorial.

summary <- bibliometrix::biblioAnalysis(filter_data)

The plot() function builds graphics when we offer the result of the bibliometrix::biblioAnalysis() function. Several standardized graphics are automatically generated:

graphics_bibliometrix <- plot(summary)

4 References

Aria, Massimo, and Corrado Cuccurullo. 2017. “Bibliometrix: An r-Tool for Comprehensive Science Mapping Analysis.” Journal of Informetrics 11 (4): 959–75. https://doi.org/10.1016/j.joi.2017.08.007.

Bibliometrix: Comprehensive Science Mapping Analysis (2021). https://CRAN.R-project.org/package=bibliometrix .

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