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openalexR helps you interface with the OpenAlex API to retrieve bibliographic infomation about publications, authors, venues, institutions and concepts with 5 main functions:

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

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

Setup

You can install the developer version of openalexR from GitHub with:

install.packages("remotes")
remotes::install_github("massimoaria/openalexR")

You can install the released version of openalexR from CRAN with:

install.packages("openalexR")

Before we go any further, we highly recommend you set openalexR.mailto option so that your requests go to the polite pool for faster response times:

options(openalexR.mailto = "example@email.com")

Bonus point if you put this in your .Rprofile with file.edit("~/.Rprofile").

Examples

There are different filters/arguments you can use in oa_fetch, depending on which entity you’re interested in: works, authors, venues, institutions, or concepts. We show a few examples below.

Works

Goal: Download all information about a givens set of publications (known DOIs).

Use doi as a works filter (either the canonical form with https://doi.org/ or without):

oa_fetch(
  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 https://doi.org/10.1016/j.joi.2017.08.007 FALSE Data science
W3206431085 PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods Joseph D. Romano Jason H. Moore Bioinformatics https://doi.org/10.1093/bioinformatics/btab727 TRUE Benchmarking, Python (programming language), Benchmark (surveying)

Goal: Download all works published by a set of authors (known ORCIDs).

Use author.orcid as a filter (either canonical form with https://orcid.org/ or without will work):

oa_fetch(
  entity = "works",
  author.orcid = c("0000-0003-3737-6565", "0000-0002-8517-9411"),
  verbose = TRUE
) %>%
  show_works() %>%
  knitr::kable()
#> Requesting url: https://api.openalex.org/works?filter=author.orcid%3A0000-0003-3737-6565%7C0000-0002-8517-9411
#> Getting 2 pages of results with a total of 211 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 https://doi.org/10.1016/j.joi.2017.08.007 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://doi.org/10.1093/bioinformatics/btz470 TRUE Pipeline (software), Scalability, Feature (linguistics)
W1979874437 Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery Alfonso Montella Filomena Mauriello Accident Analysis & Prevention https://doi.org/10.1016/j.aap.2011.04.025 FALSE Crash, Identification (biology), Decision tree
W2952824318 A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE Trang T. Le Tulsa Investigators Frontiers in Aging Neuroscience https://doi.org/10.3389/fnagi.2018.00317 TRUE Correlation, Mood, Contrast (vision)
W2408216567 Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains Corrado Cuccurullo Fabrizia Sarto Scientometrics https://doi.org/10.1007/s11192-016-1948-8 FALSE Administration (probate law), Bibliometrics, Public management
W2281330131 Coopetition and sustainable competitive advantage. The case of tourist destinations Valentina Della Corte Massimo Aria Tourism Management https://doi.org/10.1016/j.tourman.2015.12.009 FALSE Competitive advantage, Tourism, Game theory

Goal: 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.

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",
  verbose = TRUE
) %>%
  show_works() %>%
  knitr::kable()
#> 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
#> Getting 1 page of results with a total of 45 records...
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 https://doi.org/10.1016/j.jbusres.2020.06.057 TRUE Bibliometrics, Field (mathematics), MEDLINE
W2990450011 Forty-five years of Journal of Business Research: A bibliometric analysis Naveen Donthu Debidutta Pattnaik Journal of Business Research https://doi.org/10.1016/j.jbusres.2019.10.039 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://doi.org/10.3145/epi.2020.ene.03 TRUE Bibliometrics, Software
W3044902155 Financial literacy: A systematic review and bibliometric analysis Kirti Goyal Satish Kumar International Journal of Consumer Studies https://doi.org/10.1111/ijcs.12605 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://doi.org/10.7759/cureus.7357 TRUE Observational study, Gross domestic product, Population

Authors

Goal: Download author information when we know their ORCID.

Here, instead of author.orcid like earlier, we have to use orcid as an argument. This may be a little confusing, but again, a different entity (authors instead of works) requires a different set of filters.

oa_fetch(
  entity = "authors",
  orcid = c("0000-0003-3737-6565", "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 131 3116 University of Naples Federico II Statistics, Internal medicine, Pathology
A2610192943 Trang T. Le 0000-0003-3737-6565 80 630 University of Pennsylvania Genetics, Internal medicine, Statistics

Goal: Acquire information on the authors of this package.

We can use other filters such as display_name and has_orcid:

oa_fetch(
  entity = "authors",
  display_name = c("Massimo Aria", "Trang T. Le"),
  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 131 3116 University of Naples Federico II Statistics, Internal medicine, Pathology
A2610192943 Trang T. Le 0000-0003-3737-6565 80 630 University of Pennsylvania Genetics, Internal medicine, Statistics

Goal: Download all authors’ records of scholars who work at the University of Naples Federico II (OpenAlex ID: I71267560) and have published at least 500 publications.

Let’s first check how many records match the query, then 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,]    27                  59    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 2475 34442 University of Naples Federico II Nuclear physics, Particle physics, Quantum mechanics
A2600338221 Alberto Orso Maria Iorio 0000-0002-3798-1135 1183 21245 University of Naples Federico II Nuclear physics, Particle physics, Quantum mechanics
A2011452631 Leonardo Merola NA 1115 18805 University of Naples Federico II Quantum mechanics, Particle physics, Nuclear physics
A3113327292 Vincenzo Canale NA 989 15199 University of Naples Federico II Quantum mechanics, Particle physics, Nuclear physics
A223517670 Ettore Novellino 0000-0002-2181-2142 962 17905 University of Naples Federico II Biochemistry, Genetics, Organic chemistry
A2062713547 G. De Nardo NA 959 12481 University of Naples Federico II Particle physics, Nuclear physics, Quantum mechanics

Example analyses

Goal: track the popularity of Biology concepts over time.

We first download the records of all level-1 concepts/keywords that concern over one million works:

library(gghighlight)
concept_df <- oa_fetch(
  entity = "concepts",
  level = 1,
  ancestors.id = "https://openalex.org/C86803240", # Biology
  works_count = ">1000000"
)

concept_df %>%
  select(display_name, counts_by_year) %>%
  tidyr::unnest(counts_by_year) %>%
  filter(year < 2022) %>%
  ggplot() +
  aes(x = year, y = works_count, color = display_name) +
  facet_wrap(~display_name) +
  geom_line(linewidth = 0.7) +
  scale_color_brewer(palette = "Dark2") +
  labs(
    x = NULL, y = "Works count",
    title = "Virology spiked in 2020."
  ) +
  guides(color = "none") +
  gghighlight(
    max(works_count) > 244000, 
    label_params = list(nudge_y = 10^5, segment.color = NA)
  )
#> label_key: display_name

Goal: Rank institutions in Italy by total number of citations.

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:

italy_insts <- oa_fetch(
  entity = "institutions",
  country_code = "it",
  type = "education",
  verbose = TRUE
)
#> Requesting url: https://api.openalex.org/institutions?filter=country_code%3Ait%2Ctype%3Aeducation
#> Getting 2 pages of results with a total of 231 records...

italy_insts %>%
  slice_max(cited_by_count, n = 8) %>%
  mutate(display_name = forcats::fct_reorder(display_name, cited_by_count)) %>%
  ggplot() +
  aes(x = cited_by_count, y = display_name, fill = display_name) +
  geom_col() +
  scale_fill_viridis_d(option = "E") +
  guides(fill = "none") +
  labs(
    x = "Total citations", y = NULL,
    title = "Italian references"
  ) +
  coord_cartesian(expand = FALSE)

And what do they publish on?

concept_cloud <- italy_insts %>%
  select(inst_id = id, x_concepts) %>%
  tidyr::unnest(x_concepts) %>%
  filter(level == 1) %>%
  select(display_name, score) %>%
  group_by(display_name) %>%
  summarise(score = sum(score))

pal <- c("black", scales::brewer_pal(palette = "Set1")(5))
set.seed(1)
wordcloud::wordcloud(
  concept_cloud$display_name,
  concept_cloud$score,
  scale = c(2, .4),
  colors = pal
)

Goal: Visualize big journals’ topics.

We first download all records regarding journals that have published more than 300,000 works, then visualize their scored concepts:

jours <- oa_fetch(
  entity = "venues",
  works_count = ">500000",
  verbose = TRUE
) %>%
  filter(publisher != "Elsevier"|is.na(publisher)) %>%
  distinct(display_name, .keep_all = TRUE) %>%
  select(jour = display_name, x_concepts) %>%
  tidyr::unnest(x_concepts) %>%
  filter(level == 0) %>%
  left_join(concept_abbrev) %>%
  mutate(abbreviation = gsub(" ", "<br>", abbreviation)) %>%
  tidyr::complete(jour, abbreviation, fill = list(score = 0)) %>%
  group_by(jour) %>%
  mutate(
    color = if_else(score > 10, "#1A1A1A", "#D9D9D9"), # CCCCCC
    label = paste0("<span style='color:", color, "'>", abbreviation, "</span>")
  )

jours %>%
  ggplot() +
  aes(fill = jour, y = score, x = abbreviation, group = jour) +
  facet_wrap(~jour) +
  geom_hline(yintercept = c(45, 90), colour = "grey90", linewidth = 0.2) +
  geom_segment(
    aes(x = abbreviation, xend = abbreviation, y = 0, yend = 100),
    color = "grey95"
  ) +
  geom_col(color = "grey20") +
  coord_polar(clip = "off") +
  theme_bw() +
  theme(
    plot.background = element_rect(fill = "transparent", colour = NA),
    panel.background = element_rect(fill = "transparent", colour = NA),
    panel.grid = element_blank(),
    panel.border = element_blank(),
    axis.text = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  ggtext::geom_richtext(
    aes(y = 120, label = label),
    fill = NA, label.color = NA, size = 3
  ) +
  scale_fill_brewer(palette = "Set1") +
  guides(fill = "none") +
  labs(y = NULL, x = NULL, title = "Journal clocks")

The user can also perform snowballing with oa_snowball. Snowballing is a literature search technique where the researcher starts with a set of articles and find articles that cite or were cited by the original set. oa_snowball returns a list of 2 elements: nodes and edges. Similar to oa_fetch, oa_snowball finds and returns information on a core set of articles satisfying certain criteria, but, unlike oa_fetch, it also returns information the articles that cite and are cited by this core set.

library(ggraph)
library(tidygraph)
#> 
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#> 
#>     filter

snowball_docs <- oa_snowball(
  identifier = c("W1964141474", "W1963991285"),
  verbose = TRUE
)
#> Requesting url: https://api.openalex.org/works?filter=openalex_id%3AW1964141474%7CW1963991285
#> Getting 1 page of results with a total of 2 records...
#> Collecting all documents citing the target papers...
#> Requesting url: https://api.openalex.org/works?filter=cites%3AW1963991285%7CW1964141474
#> Getting 3 pages of results with a total of 451 records...
#> Collecting all documents cited by the target papers...
#> Requesting url: https://api.openalex.org/works?filter=cited_by%3AW1963991285%7CW1964141474
#> Getting 1 page of results with a total of 87 records...

ggraph(graph = as_tbl_graph(snowball_docs), layout = "stress") +
  geom_edge_link(aes(alpha = after_stat(index)), show.legend = FALSE) +
  geom_node_point(aes(fill = oa_input, size = cited_by_count), shape = 21) +
  geom_node_label(aes(filter = oa_input, label = id), nudge_y = 0.2, size = 3) +
  scale_edge_width(range = c(0.1, 1.5), guide = "none") +
  scale_size(range = c(3, 10), guide = "none") +
  scale_fill_manual(values = c("#1A5878", "#C44237"), na.value = "grey", name = "") +
  theme_graph() +
  theme(legend.position = "bottom") +
  guides(fill = "none")

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

Code of Conduct

Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Acknowledgements

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