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A Brief Introduction to openalexR

Source: vignettes/articles/A_Brief_Introduction_to_openalexR.Rmd
A_Brief_Introduction_to_openalexR.Rmd

 

https://github.com/ropensci/openalexR

Latest version: 1.0.2.9000, 2023-03-11

 

by Massimo Aria

Full Professor in Social Statistics

PhD in Computational Statistics

Laboratory and Research Group STAD Statistics, Technology, Data Analysis

Department of Economics and Statistics

University of Naples Federico II

email

https://massimoaria.com

 

An R-package to gather bibliographic data from OpenAlex

openalexR helps you interface with the OpenAlex API to retrieve bibliographic infomation about publications, authors, venues, institutions and concepts with 4 main functions:

  • oa_query(): generates a valid query, written following the OpenAlex API syntax, from a set of arguments provided by the user.

  • oa_request(): downloads a collection of entities matching the query created by oa_query() or manually written by the user, and returns a JSON object in a list format.

  • oa2df(): converts the JSON object in classical bibliographic tibble/data frame.

  • oa_fetch(): composes three functions above so the user can execute everything in one step, i.e., oa_query |> oa_request |> oa2df

  • oa_random(): to get random entity, e.g., oa_random("works") gives a different work each time you run it

Works (think papers, publications)

This paper:

Aria, M., & Cuccurullo, C. (2017). bibliometrix: 
An R-tool for comprehensive science mapping analysis. 
Journal of informetrics, 11(4), 959-975.

is associated to the OpenAlex-id W2755950973. If you know your paper’s OpenAlex ID, all you need to do is passing identifier = <openalex id> as an argument in oa_fetch():

paper_id <- oa_fetch(
  identifier = "W2755950973",
  entity = "works",
  verbose = TRUE
)
## Requesting url: https://api.openalex.org/works/W2755950973
dplyr::glimpse(paper_id)
## Rows: 1
## Columns: 28
## $ id               <chr> "https://openalex.org/W2755950973"
## $ display_name     <chr> "bibliometrix : An R-tool for comprehensive science m…
## $ author           <list> [<data.frame[2 x 10]>]
## $ ab               <lgl> NA
## $ publication_date <chr> "2017-11-01"
## $ relevance_score  <lgl> NA
## $ so               <chr> "Journal of Informetrics"
## $ so_id            <chr> "https://openalex.org/S205292342"
## $ publisher        <chr> "Elsevier BV"
## $ issn             <list> <"1875-5879", "1751-1577">
## $ url              <lgl> NA
## $ first_page       <chr> "959"
## $ last_page        <chr> "975"
## $ volume           <chr> "11"
## $ issue            <chr> "4"
## $ is_oa            <lgl> FALSE
## $ cited_by_count   <int> 2431
## $ counts_by_year   <list> [<data.frame[8 x 2]>]
## $ publication_year <int> 2017
## $ cited_by_api_url <chr> "https://api.openalex.org/works?filter=cites:W2755950…
## $ ids              <list> [<tbl_df[3 x 2]>]
## $ doi              <chr> "https://doi.org/10.1016/j.joi.2017.08.007"
## $ type             <chr> "journal-article"
## $ referenced_works <list> <"https://openalex.org/W767067438", "https://openale…
## $ related_works    <list> <"https://openalex.org/W1996408511", "https://openale…
## $ is_paratext      <lgl> FALSE
## $ is_retracted     <lgl> FALSE
## $ concepts         <list> [<data.frame[2 x 5]>]

oa_fetch() is a composition of functions: oa_query |> oa_request |> oa2df. As results, oa_query() returns the query string including the OpenAlex endpoint API server address (default). oa_request() downloads the bibliographic records matching the query. Finally, oa2df() converts the final result list to a tibble. The final result is a complicated tibble, but we can use show_works() to display a simplified version:

paper_id %>% 
  show_works() %>%
  knitr::kable()
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science

External id formats

OpenAlex endpoint accepts OpenAlex IDs and other external IDs (e.g., DOI, ISSN) in several formats, including Digital Object Identifier (DOI) and Persistent Identifiers (PIDs).

oa_fetch(
  # identifier = "https://doi.org/10.1016/j.joi.2017.08.007", # would also work (PIDs)
  identifier = "doi:10.1016/j.joi.2017.08.007",
  entity = "works"
) %>% 
  show_works() %>%
  knitr::kable()
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science

More than one publications/authors

https://api.openalex.org/authors/https://orcid.org/

If you know the OpenAlex IDs of these entities, you can also feed them into the identifier argument.

oa_fetch(
  identifier = c("W2741809807", "W2755950973"),
  # identifier = c("https://doi.org/10.1016/j.joi.2017.08.007", "https://doi.org/10.1016/j.joi.2017.08.007"), # TODO
  entity = "works",
  verbose = TRUE
) %>% 
  show_works() %>%
  knitr::kable()
## Requesting url: https://api.openalex.org/works?filter=openalex_id%3AW2741809807%7CW2755950973
## Getting 1 page of results with a total of 2 records...
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science
W2741809807 The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles Heather A. Piwowar Stefanie Haustein PeerJ https://doi.org/10.7717/peerj.4375 TRUE Citation, License, Open science

However, if you only know their external identifies, say, DOIs, you would need to use doi as a filter (either the canonical form with https://doi.org/ or without should work):

oa_fetch(
  # identifier = c("W2741809807", "W2755950973"),
  doi = c("10.1016/j.joi.2017.08.007", "https://doi.org/10.1093/bioinformatics/btab727"),
  entity = "works",
  verbose = TRUE
) %>% 
  show_works() %>%
  knitr::kable()
## Requesting url: https://api.openalex.org/works?filter=doi%3A10.1016%2Fj.joi.2017.08.007%7Chttps%3A%2F%2Fdoi.org%2F10.1093%2Fbioinformatics%2Fbtab727
## Getting 1 page of results with a total of 2 records...
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science
W3206431085 PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods Joseph P. Romano Jason H. Moore Bioinformatics https://academic.oup.com/bioinformatics/article-pdf/38/3/878/42167734/btab727.pdf TRUE Python (programming language), Benchmarking, Benchmark (surveying)

Filters

In most cases, we are interested in downloading a collection of items that meet one or more inclusion/exclusion criteria (filters). Supported filters for each entity are listed here.

Example: We want to download all works published by a set of authors. We can do this by filtering on the authorships.author.id/author.id or authorships.author.orcid/author.orcid attribute (see more on works attributes):

oa_fetch(
  entity = "works",
  author.id = c("A923435168", "A2208157607"),
  verbose = TRUE
) %>% 
  show_works() %>% 
  knitr::kable()
## Requesting url: https://api.openalex.org/works?filter=author.id%3AA923435168%7CA2208157607
## Getting 2 pages of results with a total of 212 records...
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science
W2741809807 The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles Heather A. Piwowar Stefanie Haustein PeerJ https://doi.org/10.7717/peerj.4375 TRUE Citation, License, Open science
W2122130843 Scientometrics 2.0: New metrics of scholarly impact on the social Web Jason Priem Bradely H. Hemminger First Monday NA FALSE Bookmarking, Altmetrics, Social media
W2041540760 How and why scholars cite on Twitter Jason Priem Kaitlin L. Costello Proceedings Of The Association For Information Science And Technology https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/meet.14504701201 TRUE Citation, Conversation, Social media
W2038196424 Coverage and adoption of altmetrics sources in the bibliometric community Stefanie Haustein Jens Terliesner Scientometrics NA FALSE Altmetrics, Bookmarking, Social media
W2396414759 The Altmetrics Collection Jason Priem Dario Taraborelli PLOS ONE https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0048753&type=printable TRUE Altmetrics 
orcids <- c("0000-0003-3737-6565", "0000-0002-8517-9411")
canonical_orcids <- paste0("https://orcid.org/", orcids)
oa_fetch(
  entity = "works",
  author.orcid = canonical_orcids,
  verbose = TRUE
) %>% 
  show_works() %>% 
  knitr::kable()
## Requesting url: https://api.openalex.org/works?filter=author.orcid%3Ahttps%3A%2F%2Forcid.org%2F0000-0003-3737-6565%7Chttps%3A%2F%2Forcid.org%2F0000-0002-8517-9411
## Getting 3 pages of results with a total of 463 records...
id display_name first_author last_author so url is_oa top_concepts
W2755950973 bibliometrix : An R-tool for comprehensive science mapping analysis Massimo Aria Corrado Cuccurullo Journal of Informetrics NA FALSE Data science
W2955219525 Scaling tree-based automated machine learning to biomedical big data with a feature set selector Trang T. Le Jason H. Moore Bioinformatics https://academic.oup.com/bioinformatics/article-pdf/36/1/250/31813758/btz470.pdf TRUE Pipeline (software), Scalability, Feature (linguistics)
W227035261 Follicular lymphomas’ BCL-2/IgH junctions contain templated nucleotide insertions: novel insights into the mechanism of t(14;18) translocation Ulrich Jaeger Bertrand Nadel Blood NA FALSE Gene
W2408216567 Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains Corrado Cuccurullo Fabrizia Sarto Scientometrics NA FALSE Administration (probate law), Bibliometrics, Public management
W2952824318 A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE Trang T. Le Tulsa Investigators Frontiers in Aging Neuroscience https://www.frontiersin.org/articles/10.3389/fnagi.2018.00317/pdf TRUE Correlation, Mood, Contrast (vision)
W2118077304 V(D)J-mediated Translocations in Lymphoid Neoplasms: A Functional Assessment of Genomic Instability by Cryptic Sites⋆ Rodrig Marculescu Bertrand Nadel Journal of Experimental Medicine http://jem.rupress.org/content/195/1/85.full.pdf TRUE Gene, DNA, Immune system

Example: We want to download all works that have been cited more than 50 times, published between 2020 and 2021, and include the strings “bibliometric analysis” or “science mapping” in the title. Maybe we also want the results to be sorted by total citations in a descending order.

Setting the argument count_only = TRUE, the function oa_request() returns the number of items matching the query without downloading the collection.

oa_fetch(
  entity = "works",
  title.search = c("bibliometric analysis", "science mapping"),
  cited_by_count = ">50", 
  from_publication_date = "2020-01-01",
  to_publication_date = "2021-12-31",
  sort = "cited_by_count:desc",
  count_only = TRUE,
  verbose = TRUE
)
## Requesting url: https://api.openalex.org/works?filter=title.search%3Abibliometric%20analysis%7Cscience%20mapping%2Ccited_by_count%3A%3E50%2Cfrom_publication_date%3A2020-01-01%2Cto_publication_date%3A2021-12-31&sort=cited_by_count%3Adesc
##      count db_response_time_ms page per_page
## [1,]    77                  18    1        1

We can now download the records and transform it into a tibble/data frame by setting count_only = FALSE (also the default value):

oa_fetch(
  entity = "works",
  title.search = c("bibliometric analysis", "science mapping"),
  cited_by_count = ">50", 
  from_publication_date = "2020-01-01",
  to_publication_date = "2021-12-31",
  sort = "cited_by_count:desc",
  count_only = FALSE
) %>%
  show_works() %>%
  knitr::kable()
id display_name first_author last_author so url is_oa top_concepts
W3160856016 How to conduct a bibliometric analysis: An overview and guidelines Naveen Donthu Weng Marc Lim Journal of Business Research https://doi.org/10.1016/j.jbusres.2021.04.070 TRUE Bibliometrics, Field (mathematics), Resource (disambiguation)
W3038273726 Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach Surabhi Verma Anders Gustafsson Journal of Business Research NA FALSE Bibliometrics, Field (mathematics), MEDLINE
W2990450011 Forty-five years of Journal of Business Research: A bibliometric analysis Naveen Donthu Debidutta Pattnaik Journal of Business Research NA FALSE Bibliometrics
W3001491100 Software tools for conducting bibliometric analysis in science: An up-to-date review Jose A. Moral-Munoz Manuel Cobo Profesional De La Informacion https://revista.profesionaldelainformacion.com/index.php/EPI/article/download/epi.2020.ene.03/47883 TRUE Bibliometrics, Software
W3044902155 Financial literacy: A systematic review and bibliometric analysis Kirti Savyasacchi Goyal Satish Kumar International Journal of Consumer Studies NA FALSE Financial literacy, Citation, Content analysis
W3011866596 A Bibliometric Analysis of COVID-19 Research Activity: A Call for Increased Output Mohamad A. Chahrour Hussein H. Khachfe Cureus https://assets.cureus.com/uploads/original_article/pdf/29507/1612429991-1612429986-20210204-30437-1t0hywm.pdf TRUE Observational study, Gross domestic product, Population

Read on to see how we can shorten these two function calls.

Authors

Similarly to work, we can use identifier to pass in authors’ OpenAlex ID.

Example: We want more information on authors with IDs A923435168 and A2208157607.

oa_fetch(
  identifier = c("A923435168", "A2208157607"),
  verbose = TRUE
) %>%
  show_authors() %>%
  knitr::kable()
## Requesting url: https://api.openalex.org/authors?filter=openalex_id%3AA923435168%7CA2208157607
## Getting 1 page of results with a total of 2 records...
id display_name orcid works_count cited_by_count affiliation_display_name top_concepts
A923435168 Massimo Aria 0000-0002-8517-9411 161 3839 University of Naples Federico II Statistics, Internal medicine, Pathology
A2208157607 Jason Priem 0000-0001-6187-6610 51 1678 HortResearch World Wide Web, Library science, Law

Example: We want download all authors’ records of scholars who work at the University of Naples Federico II (OpenAlex ID: I71267560) and who have published more than 499 works.

Let’s first check how many records match the query, then set count_only = FALSE to download the entire collection. We can do this by first defining a list of arguments, then adding count_only (default FALSE) to this list:

my_arguments <- list(
  entity = "authors",
  last_known_institution.id = "I71267560",
  works_count = ">499"
  )

do.call(oa_fetch, c(my_arguments, list(count_only = TRUE)))
##      count db_response_time_ms page per_page
## [1,]    57                  75    1        1
do.call(oa_fetch, my_arguments) %>% 
  show_authors() %>%
  knitr::kable()
id display_name orcid works_count cited_by_count affiliation_display_name top_concepts
A2061787601 Luca Lista 0000-0001-6471-5492 2713 35395 University of Naples Federico II Nuclear physics, Particle physics, Quantum mechanics
A2609805198 Giovanni Esposito 0000-0001-7960-5253 2072 33356 University of Naples Federico II Internal medicine, Genetics, Biochemistry
A3088244307 A. K. Sanchez NA 2047 38686 University of Naples Federico II Quantum mechanics, Nuclear physics, Particle physics
A2011452631 Leonardo Merola NA 1575 27201 University of Naples Federico II Quantum mechanics, Particle physics, Nuclear physics
A2725087388 Mariagrazia Alviggi NA 1561 26628 University of Naples Federico II Quantum mechanics, Particle physics, Nuclear physics
A2103058924 Mario Mancini NA 1558 16568 University of Naples Federico II Internal medicine, Endocrinology, Biochemistry

You can also use other filters such as display_name, has_orcid, and orcid:

oa_fetch(
  entity = "authors",
  display_name = "Massimo Aria",
  has_orcid = "true"
) %>%
  show_authors() %>%
  knitr::kable()
id display_name orcid works_count cited_by_count affiliation_display_name top_concepts
A923435168 Massimo Aria 0000-0002-8517-9411 161 3839 University of Naples Federico II Statistics, Internal medicine, Pathology 
oa_fetch(
  entity = "authors",
  orcid = "0000-0002-8517-9411"
) %>%
  show_authors() %>%
  knitr::kable()
id display_name orcid works_count cited_by_count affiliation_display_name top_concepts
A923435168 Massimo Aria 0000-0002-8517-9411 161 3839 University of Naples Federico II Statistics, Internal medicine, Pathology

Institutions

Example: We want download all records regarding Italian institutions (country_code:it) that are classified as educational (type:education). Again, we check how many records match the query then download the collection:

italian_insts <- list(
  entity = "institutions",
  country_code = "it",
  type = "education",
  verbose = TRUE
)

do.call(oa_fetch, c(italian_insts, list(count_only = TRUE)))
## Requesting url: https://api.openalex.org/institutions?filter=country_code%3Ait%2Ctype%3Aeducation
##      count db_response_time_ms page per_page
## [1,]   231                  33    1        1
dplyr::glimpse(do.call(oa_fetch, italian_insts))
## Requesting url: https://api.openalex.org/institutions?filter=country_code%3Ait%2Ctype%3Aeducation
## Getting 2 pages of results with a total of 231 records...
## Rows: 231
## Columns: 22
## $ id                        <chr> "https://openalex.org/I861853513", "https://…
## $ display_name              <chr> "Sapienza University of Rome", "University o…
## $ display_name_alternatives <list> "Università degli Studi di Roma \"La Sapien…
## $ display_name_acronyms     <list> NA, "UNIBO", "UNIPD", "UNIMI", NA, NA, "UNI…
## $ international             <list> [<data.frame[1 x 87]>], [<data.frame[1 x 10…
## $ ror                       <chr> "https://ror.org/02be6w209", "https://ror.or…
## $ ids                       <list> [<tbl_df[6 x 2]>], [<tbl_df[6 x 2]>], [<tbl…
## $ country_code              <chr> "IT", "IT", "IT", "IT", "IT", "IT", "IT", "I…
## $ geo                       <list> [<data.frame[1 x 7]>], [<data.frame[1 x 7]>…
## $ type                      <chr> "education", "education", "education", "educ…
## $ homepage_url              <chr> "http://www.uniroma1.it/", "http://www.unibo…
## $ image_url                 <chr> "https://upload.wikimedia.org/wikipedia/en/4…
## $ image_thumbnail_url       <chr> "https://upload.wikimedia.org/wikipedia/en/t…
## $ associated_institutions   <list> [<data.frame[4 x 6]>], [<data.frame[2 x 6]>…
## $ relevance_score           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ works_count               <int> 168778, 134707, 133162, 131067, 94633, 89536…
## $ cited_by_count            <int> 11676356, 10516776, 10570795, 9905262, 65705…
## $ counts_by_year            <list> [<data.frame[12 x 3]>], [<data.frame[12 x 3…
## $ works_api_url             <chr> "https://api.openalex.org/works?filter=insti…
## $ x_concepts                <list> [<data.frame[13 x 5]>], [<data.frame[15 x 5…
## $ updated_date              <chr> "2023-03-11T07:22:28.530204", "2023-03-11T15…
## $ created_date              <chr> "2016-06-24", "2016-06-24", "2016-06-24", "2…

Venues (think journals)

Example: We want download all records regarding journals that have published more than 100,000 works:

big_journals <- list(
  entity = "venues",
  works_count = ">100000",
  verbose = TRUE
)

do.call(oa_fetch, c(big_journals, list(count_only = TRUE)))
## Requesting url: https://api.openalex.org/venues?filter=works_count%3A%3E100000
##      count db_response_time_ms page per_page
## [1,]   114                  16    1        1
dplyr::glimpse(do.call(oa_fetch, big_journals))
## Requesting url: https://api.openalex.org/venues?filter=works_count%3A%3E100000
## Getting 1 page of results with a total of 114 records...
## Rows: 114
## Columns: 15
## $ id              <chr> "https://openalex.org/S2764455111", "https://openalex.…
## $ display_name    <chr> "PubMed Central", "Europe PMC (PubMed Central)", "Spri…
## $ publisher       <chr> NA, "PubMed Central", "Springer Nature", "Le Centre po…
## $ issn            <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ issn_l          <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ is_oa           <lgl> TRUE, TRUE, NA, TRUE, TRUE, TRUE, TRUE, NA, NA, NA, NA…
## $ is_in_doaj      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ids             <list> [<tbl_df[2 x 2]>], [<tbl_df[1 x 2]>], [<tbl_df[1 x 2]…
## $ homepage_url    <chr> NA, NA, NA, NA, NA, NA, "http://www.repec.org", NA, NA…
## $ relevance_score <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ works_count     <int> 6819376, 5104577, 2789009, 2034266, 1950641, 1281583, …
## $ cited_by_count  <int> 224063913, 215293374, 13591001, 6346713, 30810362, 522…
## $ counts_by_year  <list> [<data.frame[12 x 3]>], [<data.frame[12 x 3]>], [<dat…
## $ x_concepts      <list> NA, NA, [<data.frame[4 x 5]>], [<data.frame[4 x 5]>],…
## $ works_api_url   <chr> "https://api.openalex.org/works?filter=host_venue.id:S…

Concepts (think theme, keywords)

Example: We want to download the records of all the concepts that concern at least one million works:

popular_concepts <- list(
  entity = "concepts",
  works_count = ">1000000",
  verbose = TRUE
)

do.call(oa_fetch, c(popular_concepts, list(count_only = TRUE)))
## Requesting url: https://api.openalex.org/concepts?filter=works_count%3A%3E1000000
##      count db_response_time_ms page per_page
## [1,]   238                  12    1        1
dplyr::glimpse(do.call(oa_fetch, popular_concepts))
## Requesting url: https://api.openalex.org/concepts?filter=works_count%3A%3E1000000
## Getting 2 pages of results with a total of 238 records...
## Rows: 238
## Columns: 17
## $ id                         <chr> "https://openalex.org/C41008148", "https://…
## $ display_name               <chr> "Computer science", "Medicine", "Biology", …
## $ display_name_international <list> [<data.frame[1 x 186]>], [<data.frame[1 x …
## $ description                <chr> "study of computation", "field of study for…
## $ description_international  <list> [<data.frame[1 x 41]>], [<data.frame[1 x 4…
## $ wikidata                   <chr> "https://www.wikidata.org/wiki/Q21198", "ht…
## $ level                      <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ ids                        <list> [<tbl_df[5 x 2]>], [<tbl_df[5 x 2]>], [<tb…
## $ image_url                  <chr> "https://upload.wikimedia.org/wikipedia/com…
## $ image_thumbnail_url        <chr> "https://upload.wikimedia.org/wikipedia/com…
## $ ancestors                  <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, [<…
## $ related_concepts           <list> [<data.frame[93 x 5]>], [<data.frame[51 x …
## $ relevance_score            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ works_count                <int> 77706395, 58288400, 42025367, 38411305, 340…
## $ cited_by_count             <int> 399515930, 592174423, 645063596, 397090377,…
## $ counts_by_year             <list> [<data.frame[12 x 3]>], [<data.frame[12 x …
## $ works_api_url              <chr> "https://api.openalex.org/works?filter=conc…

Other examples

Get all works citing a particular work

We can download all publications citing another publication by using the filter attribute cites.

For example, if we want to download all publications citing the article Aria and Cuccurullo (2017), we have just to set the argument filter as cites = "W2755950973" where “W2755950973” is the OA id for the article by Aria and Cuccurullo.

aria_count <- oa_fetch(
  entity = "works",
  cites = "W2755950973",
  count_only = TRUE,
  verbose = TRUE
) 
## Requesting url: https://api.openalex.org/works?filter=cites%3AW2755950973
aria_count
##      count db_response_time_ms page per_page
## [1,]  2461                 207    1        1

This query will return a collection of NA publications. Among these articles, let’s download the ones published in the following year:

oa_fetch(
  entity = "works",
  cites = "W2755950973",
  publication_year = 2018,
  count_only = FALSE,
  verbose = TRUE
) %>% 
  dplyr::glimpse()
## Requesting url: https://api.openalex.org/works?filter=cites%3AW2755950973%2Cpublication_year%3A2018
## Getting 1 page of results with a total of 17 records...
## Rows: 17
## Columns: 28
## $ id               <chr> "https://openalex.org/W2902888572", "https://openalex…
## $ display_name     <chr> "A global bibliometric analysis of Plesiomonas-relate…
## $ author           <list> [<data.frame[2 x 10]>], [<data.frame[7 x 10]>], [<da…
## $ ab               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ publication_date <chr> "2018-11-29", "2018-04-01", "2018-06-25", "2018-09-27…
## $ relevance_score  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ so               <chr> "PLOS ONE", "Journal of Experimental Zoology Part A: …
## $ so_id            <chr> "https://openalex.org/S202381698", "https://openalex.…
## $ publisher        <chr> "Public Library of Science", "Wiley", "Public Library…
## $ issn             <list> "1932-6203", <"2471-5638", "2471-5646">, "1932-6203"…
## $ url              <chr> "https://journals.plos.org/plosone/article/file?id=10…
## $ first_page       <chr> "e0207655", "162", "e0199706", "10589", "3", "38", NA…
## $ last_page        <chr> "e0207655", "176", "e0199706", "10604", "15", "38", N…
## $ volume           <chr> "13", "329", "13", "101", NA, "4", NA, "4", "9", NA, …
## $ issue            <chr> "11", "4-5", "6", "12", NA, "3", NA, "11", NA, NA, "3…
## $ is_oa            <lgl> TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TR…
## $ cited_by_count   <int> 73, 56, 56, 48, 35, 32, 20, 17, 15, 14, 10, 10, 7, 4,…
## $ counts_by_year   <list> [<data.frame[5 x 2]>], [<data.frame[6 x 2]>], [<data…
## $ publication_year <int> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,…
## $ cited_by_api_url <chr> "https://api.openalex.org/works?filter=cites:W2902888…
## $ ids              <list> [<tbl_df[5 x 2]>], [<tbl_df[4 x 2]>], [<tbl_df[5 x 2…
## $ doi              <chr> "https://doi.org/10.1371/journal.pone.0207655", "http…
## $ type             <chr> "journal-article", "journal-article", "journal-articl…
## $ referenced_works <list> <"https://openalex.org/W177697404", "https://openale…
## $ related_works    <list> <"https://openalex.org/W2150273412", "https://openal…
## $ is_paratext      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ is_retracted     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ concepts         <list> [<data.frame[11 x 5]>], [<data.frame[17 x 5]>], [<da…

Convert an OpenAlex data frame to a bibliometrix object

The bibliometrix R-package (https://www.bibliometrix.org) provides a set of tools for quantitative research in bibliometrics and scientometrics. Today it represents one of the most used science mapping software in the world. In a recent survey on bibliometric analysis tools, Moral-Muñoz et al. (2020) wrote: “At this moment, maybe Bibliometrix and its Shiny platform contain the more extensive set of techniques implemented, and together with the easiness of its interface, could be a great software for practitioners”.

The function oa2bibliometrix converts a bibliographic data frame of works into a bibliometrix object. This object can be used as input collection of a science mapping workflow.

bib_ls <- list(
  identifier = NULL,
  entity = "works",
  cites = "W2755950973",
  from_publication_date = "2022-01-01",
  to_publication_date = "2022-03-31"
)

do.call(oa_fetch, c(bib_ls, list(count_only = TRUE)))
##      count db_response_time_ms page per_page
## [1,]   282                  59    1        1
do.call(oa_fetch, bib_ls) %>% 
  oa2bibliometrix() %>% 
  dplyr::glimpse()
## Rows: 282
## Columns: 41
## $ AU               <chr> "YIXIA CHEN;MINGWEI LIN;DAN ZHUANG", "YONG QIN;ZESHUI…
## $ RP               <chr> "COLLEGE OF COMPUTER AND CYBER SECURITY, FUJIAN NORMA…
## $ C1               <chr> "COLLEGE OF COMPUTER AND CYBER SECURITY, FUJIAN NORMA…
## $ AU_UN            <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "…
## $ AU_CO            <chr> "CHINA;CHINA;CHINA", "CHINA;CHINA;CHINA;CROATIA", "BR…
## $ ID               <chr> "WASTEWATER;ENVIRONMENTAL SCIENCE;CONTAMINATION;WEB O…
## $ id_url           <chr> "https://openalex.org/W4210864411", "https://openalex…
## $ author           <list> [<data.frame[3 x 10]>], [<data.frame[4 x 10]>], [<da…
## $ publication_date <chr> "2022-02-01", "2022-01-01", "2022-01-01", "2022-03-08…
## $ relevance_score  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ so_id            <chr> "https://openalex.org/S203465130", "https://openalex.…
## $ publisher        <chr> "Elsevier BV", "Elsevier BV", "Elsevier BV", "Wiley-B…
## $ issn             <list> <"0045-6535", "1879-1298">, <"1879-0690", "1364-0321…
## $ url              <chr> NA, NA, "https://doi.org/10.1016/j.procs.2022.01.054"…
## $ first_page       <chr> "133932", "111780", "448", "1129", NA, "132941", "497…
## $ last_page        <chr> "133932", "111780", "455", "1155", NA, "132941", "506…
## $ volume           <chr> "297", "153", "199", "39", "42", "291", "55", "19", "…
## $ issue            <chr> NA, NA, NA, "6", NA, NA, "6", "5", NA, "2", "1", NA, …
## $ is_oa            <lgl> FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, F…
## $ counts_by_year   <list> [<data.frame[2 x 2]>], [<data.frame[2 x 2]>], [<data…
## $ cited_by_api_url <chr> "https://api.openalex.org/works?filter=cites:W4210864…
## $ ids              <list> [<tbl_df[3 x 2]>], [<tbl_df[3 x 2]>], [<tbl_df[2 x 2…
## $ doi              <chr> "https://doi.org/10.1016/j.chemosphere.2022.133932", …
## $ referenced_works <list> <"https://openalex.org/W1854025783", "https://openal…
## $ related_works    <list> <"https://openalex.org/W1547839164", "https://openal…
## $ is_paratext      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ is_retracted     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
## $ concepts         <list> [<data.frame[16 x 5]>], [<data.frame[19 x 5]>], [<da…
## $ id_oa            <chr> "W4210864411", "W3208801174", "W4210817604", "W422099…
## $ CR               <chr> "https://openalex.org/W1854025783;https://openalex.or…
## $ TI               <chr> "WASTEWATER TREATMENT AND EMERGING CONTAMINANTS: BIBL…
## $ AB               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ SO               <chr> "CHEMOSPHERE", "RENEWABLE & SUSTAINABLE ENERGY REVIEW…
## $ DT               <chr> "JOURNAL-ARTICLE", "JOURNAL-ARTICLE", "JOURNAL-ARTICL…
## $ DB               <chr> "openalex", "openalex", "openalex", "openalex", "open…
## $ JI               <chr> "S203465130", "S68497187", "S120348307", "S102896891"…
## $ J9               <chr> "S203465130", "S68497187", "S120348307", "S102896891"…
## $ PY               <int> 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,…
## $ TC               <int> 37, 25, 19, 19, 19, 16, 16, 15, 15, 13, 12, 12, 12, 1…
## $ SR_FULL          <chr> "YIXIA CHEN, 2022, CHEMOSPHERE", "YONG QIN, 2022, REN…
## $ SR               <chr> "YIXIA CHEN, 2022, CHEMOSPHERE", "YONG QIN, 2022, REN…

About OpenAlex

oar-img

Schema credits: @dhimmel

OpenAlex is a fully open catalog of the global research system. It’s named after the ancient Library of Alexandria. The OpenAlex dataset describes scholarly entities and how those entities are connected to each other. There are five types of entities:

  • Works are papers, books, datasets, etc; they cite other works

  • Authors are people who create works

  • Venues are journals and repositories that host works

  • Institutions are universities and other orgs that are affiliated with works (via authors)

  • Concepts tag Works with a topic

Acknowledgements

Package hex was made with Midjourney and thus inherits a CC BY-NC 4.0 license.