library(dplyr)
library(knitr)
library(bibliometrix)Using R to analyze publications - part 2
Some examples using bibliometrix
R
Data Analysis
bibliometrics
Code and examples showing how to analyze meta-data for a set of publications using the bibilometrix R package.
Overview
I needed some information on all my publications for “bean counting” purposes related to preparing my promotion materials. In the past, I also needed similar information for NSF grant applications.
Instead of doing things by hand, there are nicer/faster ways using R. in part 1, I did a few things using the scholar package. While some parts worked nicely, I encountered 2 problems. First, since my Google Scholar record lists items other than peer-reviewed journal articles, they show up in the analysis and need to be cleaned out. Second, Google Scholar doesn’t like automated queries through the API and is quick to block, at which point things don’t work anymore.
To get around these issues, I decided to give a different R package a try, namely bibliometrix. The workflow is somewhat different.
Required packages
Loading data
Old: I keep all references to my published papers in a BibTeX file, managed through Zotero/Jabref. I know this file is clean and correct. I’m loading it here for processing. If you don’t have such a file, make one using your favorite reference manager. Or create it through a saved search on a bibliographic database, as explained on the bibliometrix website.
New: In the current version of bibliometrix, reading in my bibtex file failed. A fairly good alternative is to go to your NIH “My Bibliography” (which anyone with NIH funding needs to have anyway) and export it in MEDLINE format. Then read in the file with the code below. As of the time of writing this, it requires the Github version of bibliometrix.
#read bib file, turn file of references into data frame
pubs <- bibliometrix::convert2df("medline.txt", dbsource="pubmed",format="pubmed")
Converting your pubmed collection into a bibliographic dataframe
Done!
Generating affiliation field tag AU_UN from C1: Done!Each row of the data frame created by the convert2df function is a publication, the columns contain information for each publication. For a list of what each column variable codes for, see the bibliometrix documentation.
Analyzing 2 time periods
For my purpose, I want to analyze 2 different time periods and compare them. Therefore, I split the data frame containing publications, then run the analysis on each.
#get all pubs for an author (or multiple)
period_1_start = 2009
period_2_start = 2015
#here I want to separately look at publications in the 2 time periods I defined above
pubs_old <- data.frame(pubs) %>% dplyr::filter((PY>=period_1_start & PY<period_2_start ))
pubs_new <- data.frame(pubs) %>% dplyr::filter(PY>=period_2_start)
res_old <- bibliometrix::biblioAnalysis(pubs_old, sep = ";") #perform analysis
res_new <- bibliometrix::biblioAnalysis(pubs_new, sep = ";") #perform analysisGeneral information
The summary functions provide a lot of information in a fairly readable format. I apply them here to both time periods so I can compare.
Time period 1
summary(res_old, k = 10)
MAIN INFORMATION ABOUT DATA
Timespan 2009 : 2014
Sources (Journals, Books, etc) 12
Documents 19
Annual Growth Rate % 3.71
Document Average Age 12.3
Average citations per doc 0
Average citations per year per doc 0
References 1
DOCUMENT TYPES
clinical trial;journal article;research support, non-u.s. gov't 1
comparative study;journal article;research support, n.i.h., extramural;research support, non-u.s. gov't 1
journal article 2
journal article;research support, n.i.h., extramural 5
journal article;research support, n.i.h., extramural;research support, non-u.s. gov't 3
journal article;research support, n.i.h., extramural;research support, non-u.s. gov't;research support, u.s. gov't, non-p.h.s. 1
journal article;research support, n.i.h., extramural;research support, non-u.s. gov't;research support, u.s. gov't, non-p.h.s.;review;systematic review 1
journal article;research support, non-u.s. gov't 3
journal article;research support, non-u.s. gov't;research support, u.s. gov't, non-p.h.s.;research support, u.s. gov't, p.h.s. 1
journal article;review 1
DOCUMENT CONTENTS
Keywords Plus (ID) 148
Author's Keywords (DE) 148
AUTHORS
Authors 45
Author Appearances 80
Authors of single-authored docs 0
AUTHORS COLLABORATION
Single-authored docs 0
Documents per Author 0.422
Co-Authors per Doc 4.21
International co-authorships % 0
Annual Scientific Production
Year Articles
2009 5
2010 2
2011 1
2012 3
2013 2
2014 6
Annual Percentage Growth Rate 3.71
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 HANDEL A 19 HANDEL A 5.55
2 ANTIA R 6 ANTIA R 1.78
3 DOHERTY PC 3 LONGINI IM JR 1.00
4 LA GRUTA NL 3 DOHERTY PC 0.56
5 LONGINI IM JR 3 LA GRUTA NL 0.56
6 THOMAS PG 3 THOMAS PG 0.56
7 PILYUGIN SS 2 BEAUCHEMIN CA 0.50
8 ROHANI P 2 LI Y 0.50
9 STALLKNECHT D 2 ROHANI P 0.50
10 TURNER SJ 2 ROZEN DE 0.50
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 ZHENG N, 2014, PLOS ONE 10.1371/JOURNAL.PONE.0105721 0 0 NaN
2 HANDEL A, 2014, PROC BIOL SCI 10.1098/RSPB.2013.3051 0 0 NaN
3 NGUYEN TH, 2014, J IMMUNOL 10.4049/JIMMUNOL.1303147 0 0 NaN
4 LI Y, 2014, J THEOR BIOL 10.1016/J.JTBI.2014.01.008 0 0 NaN
5 HANDEL A, 2014, J R SOC INTERFACE 10.1098/RSIF.2013.1083 0 0 NaN
6 CUKALAC T, 2014, PROC NATL ACAD SCI U S A 10.1073/PNAS.1323736111 0 0 NaN
7 HANDEL A, 2013, PLOS COMPUT BIOL 10.1371/JOURNAL.PCBI.1002989 0 0 NaN
8 THOMAS PG, 2013, PROC NATL ACAD SCI U S A 10.1073/PNAS.1222149110 0 0 NaN
9 JACKWOOD MW, 2012, INFECT GENET EVOL 10.1016/J.MEEGID.2012.05.003 0 0 NaN
10 DESAI R, 2012, CLIN INFECT DIS 10.1093/CID/CIS372 0 0 NaN
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 USA 14 0.7778 11 3 0.214
2 AUSTRALIA 3 0.1667 2 1 0.333
3 CANADA 1 0.0556 1 0 0.000
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 AUSTRALIA 0 0
2 CANADA 0 0
3 USA 0 0
Most Relevant Sources
Sources
1 JOURNAL OF THE ROYAL SOCIETY INTERFACE
2 JOURNAL OF THEORETICAL BIOLOGY
3 JOURNAL OF IMMUNOLOGY (BALTIMORE MD. : 1950)
4 PLOS ONE
5 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
6 BMC EVOLUTIONARY BIOLOGY
7 BMC PUBLIC HEALTH
8 CLINICAL INFECTIOUS DISEASES : AN OFFICIAL PUBLICATION OF THE INFECTIOUS DISEASES SOCIETY OF AMERICA
9 EPIDEMICS
10 INFECTION GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES
Articles
1 3
2 3
3 2
4 2
5 2
6 1
7 1
8 1
9 1
10 1
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 HUMANS 13 HUMANS 13
2 MODELS BIOLOGICAL 8 MODELS BIOLOGICAL 8
3 ANIMALS 7 ANIMALS 7
4 COMPUTER SIMULATION 5 COMPUTER SIMULATION 5
5 BIOLOGICAL EVOLUTION 4 BIOLOGICAL EVOLUTION 4
6 MODELS IMMUNOLOGICAL 4 MODELS IMMUNOLOGICAL 4
7 FEMALE 3 FEMALE 3
8 MICE 3 MICE 3
9 MUTATION 3 MUTATION 3
10 AMINO ACID SEQUENCE 2 AMINO ACID SEQUENCE 2Time period 2
summary(res_new, k = 10)
MAIN INFORMATION ABOUT DATA
Timespan 2015 : 2020
Sources (Journals, Books, etc) 22
Documents 29
Annual Growth Rate % -9.71
Document Average Age 6.72
Average citations per doc 0
Average citations per year per doc 0
References 1
DOCUMENT TYPES
comparative study;journal article;research support, n.i.h., extramural;research support, non-u.s. gov't 1
journal article 7
journal article;multicenter study;research support, n.i.h., extramural 1
journal article;research support, n.i.h., extramural 5
journal article;research support, n.i.h., extramural;research support, non-u.s. gov't 5
journal article;research support, n.i.h., extramural;research support, non-u.s. gov't;review 1
journal article;research support, n.i.h., extramural;research support, u.s. gov't, non-p.h.s. 1
journal article;research support, n.i.h., extramural;research support, u.s. gov't, non-p.h.s.;review 1
journal article;research support, non-u.s. gov't 4
journal article;research support, non-u.s. gov't;research support, n.i.h., extramural 1
journal article;research support, non-u.s. gov't;research support, u.s. gov't, non-p.h.s.;research support, n.i.h., extramural 1
letter 1
DOCUMENT CONTENTS
Keywords Plus (ID) 198
Author's Keywords (DE) 198
AUTHORS
Authors 209
Author Appearances 332
Authors of single-authored docs 1
AUTHORS COLLABORATION
Single-authored docs 1
Documents per Author 0.139
Co-Authors per Doc 11.4
International co-authorships % 41.38
Annual Scientific Production
Year Articles
2015 5
2016 7
2017 3
2018 6
2019 5
2020 3
Annual Percentage Growth Rate -9.71
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 HANDEL A 29 HANDEL A 5.494
2 WHALEN CC 7 ANTIA R 0.810
3 ANTIA R 5 SHEN Y 0.723
4 MARTINEZ L 5 WHALEN CC 0.651
5 SHEN Y 5 MCKAY B 0.629
6 LA GRUTA NL 4 EBELL MH 0.571
7 MCKAY B 4 THOMAS PG 0.571
8 THOMAS PG 4 LA GRUTA NL 0.540
9 ZALWANGO S 4 ROHANI P 0.500
10 DENHOLM JT 3 MARTINEZ L 0.485
Top manuscripts per citations
Paper DOI TC TCperYear NTC
1 MCKAY B, 2020, PROC BIOL SCI 10.1098/RSPB.2020.0496 0 0 NaN
2 MOORE JR, 2020, BULL MATH BIOL 10.1007/S11538-020-00711-4 0 0 NaN
3 HANDEL A, 2020, NAT REV IMMUNOL 10.1038/S41577-019-0235-3 0 0 NaN
4 MARTINEZ L, 2019, J INFECT DIS 10.1093/INFDIS/JIZ328 0 0 NaN
5 WU T, 2019, NAT COMMUN 10.1038/S41467-019-10661-8 0 0 NaN
6 MCKAY B, 2019, PLOS ONE 10.1371/JOURNAL.PONE.0217219 0 0 NaN
7 DALE AP, 2019, J AM BOARD FAM MED SOCIOLOGICAL METHODS & RESEARCH 10.3122/JABFM.2019.02.180183 0 0 NaN
8 WOLDU H, 2019, J APPL STAT 10.1080/02664763.2018.1470231 0 0 NaN
9 HANDEL A, 2018, PLOS COMPUT BIOL 10.1371/JOURNAL.PCBI.1006505 0 0 NaN
10 CASTELLANOS ME, 2018, INT J TUBERC LUNG DIS 10.5588/IJTLD.18.0073 0 0 NaN
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 USA 16 0.696 7 9 0.562
2 AUSTRALIA 5 0.217 1 4 0.800
3 GEORGIA 2 0.087 0 2 1.000
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 AUSTRALIA 0 0
2 GEORGIA 0 0
3 USA 0 0
Most Relevant Sources
Sources
1 PLOS ONE
2 PLOS COMPUTATIONAL BIOLOGY
3 THE INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE : THE OFFICIAL JOURNAL OF THE INTERNATIONAL UNION AGAINST TUBERCULOSIS AND LUNG DISEASE
4 THE LANCET. GLOBAL HEALTH
5 THE LANCET. RESPIRATORY MEDICINE
6 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
7 BMC INFECTIOUS DISEASES
8 BULLETIN OF MATHEMATICAL BIOLOGY
9 ELIFE
10 EPIDEMICS
Articles
1 4
2 2
3 2
4 2
5 2
6 1
7 1
8 1
9 1
10 1
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 HUMANS 23 HUMANS 23
2 ANIMALS 8 ANIMALS 8
3 FEMALE 7 FEMALE 7
4 MALE 7 MALE 7
5 MICE 6 MICE 6
6 ADULT 5 ADULT 5
7 CHILD 5 CHILD 5
8 ADOLESCENT 4 ADOLESCENT 4
9 ANTIVIRAL AGENTS/THERAPEUTIC USE 4 ANTIVIRAL AGENTS/THERAPEUTIC USE 4
10 CHILD PRESCHOOL 4 CHILD PRESCHOOL 4Note that some values are reported as NA, e.g. the citations. Depending on which source you got the original data from, that information might be included or not. In my case, it is not.
Making a table of journals
It can be useful to get a list of all journals in which you published. I’m doing this here for the second time period. With just the bibliometrix package, I can get a list of publications and how often I have published in each.
journaltable = data.frame(res_new$Sources)
#knitr::kable(journaltable) #uncomment this to print the tableAs mentioned in part 1 of this series of posts, the Impact Factor feature from the scholar package doesn’t work anymore. I’m leaving the old code in there in case it ever comes back. For now, there is no Impact Factor information. (I haven’t tried to figure out if there is another way to get it.)
It might also be nice to get some journal metrics, such as impact factors. While this is possible with the scholar package, the bibliometrix package doesn’t have it.
However, the scholar package doesn’t really get that data from Google Scholar, instead it has an internal spreadsheet/table with impact factors (according to the documentation, taken - probably not fully legally - from some spreadsheet posted on ResearchGate). We can thus access those impact factors stored in the scholar package without having to connect to Google Scholar. As long as the journal names stored in the scholar package are close to the ones we have here, we might get matches.
#library(scholar)
#ifvalues = scholar::get_impactfactor(journaltable[,1], max.distance = 0.1)
#journaltable = cbind(journaltable, ifvalues$ImpactFactor)
#colnames(journaltable) = c('Journal','Number of Pubs','Impact Factor')
colnames(journaltable) = c('Journal','Number of Pubs')
knitr::kable(journaltable)| Journal | Number of Pubs |
|---|---|
| PLOS ONE | 4 |
| PLOS COMPUTATIONAL BIOLOGY | 2 |
| THE INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE : THE OFFICIAL JOURNAL OF THE INTERNATIONAL UNION AGAINST TUBERCULOSIS AND LUNG DISEASE | 2 |
| THE LANCET. GLOBAL HEALTH | 2 |
| THE LANCET. RESPIRATORY MEDICINE | 2 |
| AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE | 1 |
| BMC INFECTIOUS DISEASES | 1 |
| BULLETIN OF MATHEMATICAL BIOLOGY | 1 |
| ELIFE | 1 |
| EPIDEMICS | 1 |
| EPIDEMIOLOGY AND INFECTION | 1 |
| FRONTIERS IN IMMUNOLOGY | 1 |
| JOURNAL OF APPLIED STATISTICS | 1 |
| JOURNAL OF THE AMERICAN BOARD OF FAMILY MEDICINE : JABFM | 1 |
| NATURE | 1 |
| NATURE COMMUNICATIONS | 1 |
| NATURE REVIEWS. IMMUNOLOGY | 1 |
| PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON. SERIES B BIOLOGICAL SCIENCES | 1 |
| PLOS BIOLOGY | 1 |
| PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 1 |
| PROCEEDINGS. BIOLOGICAL SCIENCES | 1 |
| THE JOURNAL OF INFECTIOUS DISEASES | 1 |
Ok that worked somewhat. It couldn’t find several journals. The reported IF seem reasonable. But since I don’t know what year those IF are from, and if the rest is fully reliable, I would take this with a grain of salt.
Discussion
The bibliometrix package doesn’t suffer from the problems that I encountered in part 1 of this post when I tried the scholar package (and Google Scholar). The downside is that I can’t get some of the information, e.g. my annual citations. So it seems there is not (yet) a comprehensive solution, and using both packages seems best.
A larger overall problem is that a lot of this information is controlled by corporations (Google, Elsevier, Clarivate Analytics, etc.), which might or might not allow R packages and individual users (who don’t subscribe to their offerings) to access certain information. As such, R packages accessing this information will need to adjust to whatever the companies allow.