openalexR

openalexR helps you interface with the OpenAlex API to retrieve bibliographic information about publications, authors, institutions, sources, funders, publishers, topics 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

📜 Citation

If you use openalexR in research, please cite:

Aria, M., Le T., Cuccurullo, C., Belfiore, A. & Choe, J. (2024), openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlex, The R Journal, 15(4), 167-180, DOI: https://doi.org/10.32614/RJ-2023-089.

🙌 Support OpenAlex

If OpenAlex has helped you, consider writing a Testimonial which will help support the OpenAlex team and show that their work is making a real and necessary impact.

⚙️ Setup

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

install.packages("remotes")
remotes::install_github("ropensci/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. If you have OpenAlex Premium, you can add your API key to the openalexR.apikey option as well. These lines best go into .Rprofile with file.edit("~/.Rprofile").

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

Alternatively, you can open .Renviron with file.edit("~/.Renviron") and add:

openalexR.mailto = example@email.com
openalexR.apikey = EXAMPLE_APIKEY

library(openalexR)
library(dplyr)
library(ggplot2)

🌿 Examples

There are different filters/arguments you can use in oa_fetch, depending on which entity you’re interested in: works, authors, sources, funders, 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:

works_from_dois <- oa_fetch(
  entity = "works",
  doi = c("10.1016/j.joi.2017.08.007", "https://doi.org/10.1007/s11192-013-1221-3"),
  verbose = TRUE
)
#> Requesting url: https://api.openalex.org/works?filter=doi%3A10.1016%2Fj.joi.2017.08.007%7Chttps%3A%2F%2Fdoi.org%2F10.1007%2Fs11192-013-1221-3
#> Getting 1 page of results with a total of 2 records...

We can view the output tibble/dataframe, works_from_dois, interactively in RStudio or inspect it with base functions like str or head. We also provide the experimental show_works function to simplify the result (e.g., remove some columns, keep first/last author) for easy viewing.

Note: the following table is wrapped in knitr::kable() to be displayed nicely in this README, but you will most likely not need this function.

# str(works_from_dois, max.level = 2)
# head(works_from_dois)
# show_works(works_from_dois)

works_from_dois |>
  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	https://doi.org/10.1016/j.joi.2017.08.007	FALSE	Workflow, Bibliometrics, Software
W2038196424	Coverage and adoption of altmetrics sources in the bibliometric community	Stefanie Haustein	Jens Terliesner	Scientometrics	https://doi.org/10.1007/s11192-013-1221-3	FALSE	Altmetrics, Bookmarking, Social media

Goal: Download all works given their PMIDs.

Use pmid as a filter:

works_from_pmids <- oa_fetch(
  entity = "works",
  pmid = c("14907713", 32572199),
  verbose = TRUE
)
#> Requesting url: https://api.openalex.org/works?filter=pmid%3A14907713%7C32572199
#> Getting 1 page of results with a total of 2 records...

works_from_pmids |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	so	url	is_oa	top_concepts
W1775749144	PROTEIN MEASUREMENT WITH THE FOLIN PHENOL REAGENT	Oliver H. Lowry	ROSE J. RANDALL	Journal of Biological Chemistry	https://doi.org/10.1016/s0021-9258(19)52451-6	TRUE	Reagent, Phenol
W3036882247	Integrating spatial gene expression and breast tumour morphology via deep learning	Bryan He	James Zou	Nature Biomedical Engineering	https://doi.org/10.1038/s41551-020-0578-x	FALSE	Histopathology, Gene, Cancer

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):

works_from_orcids <- oa_fetch(
  entity = "works",
  author.orcid = c("0000-0001-6187-6610", "0000-0002-8517-9411"),
  verbose = TRUE
)
#> Requesting url: https://api.openalex.org/works?filter=author.orcid%3A0000-0001-6187-6610%7C0000-0002-8517-9411
#> Getting 2 pages of results with a total of 259 records...
#> Warning in oa_request(oa_query(filter = filter_i, multiple_id = multiple_id, : 
#> The following work(s) have truncated lists of authors: W4230863633.
#> Query each work separately by its identifier to get full list of authors.
#> For example:
#>   lapply(c("W4230863633"), \(x) oa_fetch(identifier = x))
#> Details at https://docs.openalex.org/api-entities/authors/limitations.

works_from_orcids |>
  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	https://doi.org/10.1016/j.joi.2017.08.007	FALSE	Workflow, Bibliometrics, Software
W2741809807	The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles	Heather Piwowar	Stefanie Haustein	PeerJ	https://doi.org/10.7717/peerj.4375	TRUE	Citation, License, Bibliometrics
W2122130843	Scientometrics 2.0: New metrics of scholarly impact on the social Web	Jason Priem	Bradely H. Hemminger	First Monday	https://doi.org/10.5210/fm.v15i7.2874	FALSE	Bookmarking, Altmetrics, Social media
W1553564559	Altmetrics in the wild: Using social media to explore scholarly impact	Jason Priem	Bradley M. Hemminger	arXiv (Cornell University)	https://arxiv.org/abs/1203.4745	TRUE	Altmetrics, Social media, Citation
W3005144120	Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research	Massimo Aria	Maria Spano	Social Indicators Research	https://doi.org/10.1007/s11205-020-02281-3	FALSE	Human geography, Data collection, Position (finance)
W3130540911	altmetrics: a manifesto	Jason Priem	Cameron Neylon	NA	https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1187&context=scholcom	FALSE	Altmetrics, Manifesto

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.

works_search <- 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",
  options = list(sort = "cited_by_count:desc"),
  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
#> Getting 2 pages of results with a total of 372 records...

works_search |>
  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)
W3001491100	Software tools for conducting bibliometric analysis in science: An up-to-date review	José A. Moral-Muñoz	Manuel J. Cobo	El Profesional de la Informacion	https://doi.org/10.3145/epi.2020.ene.03	TRUE	Bibliometrics, Visualization, Set (abstract data type)
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), Empirical research
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, Content analysis, Citation
W3042215340	A bibliometric analysis using VOSviewer of publications on COVID-19	Yuetian Yu	Erzhen Chen	Annals of Translational Medicine	https://doi.org/10.21037/atm-20-4235	TRUE	Citation, Bibliometrics, China
W3198357836	Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis	John W. Goodell	Debidutta Pattnaik	Journal of Behavioral and Experimental Finance	https://doi.org/10.1016/j.jbef.2021.100577	FALSE	Scholarship, Valuation (finance), Corporate finance

🧑 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.

authors_from_orcids <- oa_fetch(
  entity = "authors",
  orcid = c("0000-0001-6187-6610", "0000-0002-8517-9411")
)
authors_from_orcids |>
  show_authors() |>
  knitr::kable()

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	197	10881	University of Naples Federico II	Physiology, Pathology and Forensic Medicine, Periodontics
A5023888391	Jason Priem	0000-0001-6187-6610	62	3673	OurResearch	Statistics, Probability and Uncertainty, Information Systems, Communication

Goal: Acquire information on the authors of this package.

We can use other filters such as display_name and has_orcid:

authors_from_names <- oa_fetch(
  entity = "authors",
  display_name = c("Massimo Aria", "Jason Priem"),
  has_orcid = TRUE
)
authors_from_names |>
  show_authors() |>
  knitr::kable()

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	197	10881	University of Naples Federico II	Physiology, Pathology and Forensic Medicine, Periodontics
A5023888391	Jason Priem	0000-0001-6187-6610	62	3673	OurResearch	Statistics, Probability and Uncertainty, Information Systems, Communication

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_institutions.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,]    44                  81    1        1

if (do.call(oa_fetch, c(my_arguments, list(count_only = TRUE)))[1]>0){
do.call(oa_fetch, my_arguments) |>
  show_authors() |>
  knitr::kable()
}
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.
#> Warning: Unknown or uninitialised column: `name`.
#> Warning: Unknown or uninitialised column: `display_name`.

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5106552509	C. Sciacca	0000-0002-8412-4072	3803	140341	INFN Sezione di Napoli
A5091797706	L. Lista	0000-0001-6471-5492	3730	162016	INFN Sezione di Napoli	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics
A5003544129	Annamaria Colao	0000-0001-6986-266X	1305	44071	University of Naples Federico II	Endocrinology, Diabetes and Metabolism, Endocrinology, Diabetes and Metabolism, Surgery
A5106315809	M. Merola	0000-0002-7082-8108	1194	66583	INFN Sezione di Napoli
A5026402548	Gabriella Fabbrocini	0000-0002-0064-1874	992	16411	University of Naples Federico II	Dermatology, Immunology, Dermatology
A5064690950	R. De Rosa	0000-0002-4004-947X	915	89676	University of Naples Federico II	Astronomy and Astrophysics, Ocean Engineering, Astronomy and Astrophysics

🍒 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) > 200000,
    min(works_count) < 400000,
    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 232 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?

# The package wordcloud needs to be installed to run this chunk
# library(wordcloud)
concept_cloud <- italy_insts |>
  select(inst_id = id, topics) |>
  tidyr::unnest(topics) |>
  filter(name == "field") |>
  select(display_name, count) |>
  group_by(display_name) |>
  summarise(score = sqrt(sum(count)))
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:

# The package ggtext needs to be installed to run this chunk
# library(ggtext)
jours_all <- oa_fetch(
  entity = "sources",
  works_count = ">200000",
  verbose = TRUE
)

clean_journal_name <- function(x) {
  x |>
    gsub("\\(.*?\\)", "", x = _) |>
    gsub("Journal of the|Journal of", "J.", x = _) |>
    gsub("/.*", "", x = _)
}

jours <- jours_all |>
  filter(type == "journal") |>
  slice_max(cited_by_count, n = 9) |>
  distinct(display_name, .keep_all = TRUE) |>
  select(jour = display_name, topics) |>
  tidyr::unnest(topics) |>
  filter(name == "field") |>
  group_by(id, jour, display_name) |> 
  summarise(score = (sum(count))^(1/3), .groups = "drop") |> 
  left_join(concept_abbrev, by = join_by(id, display_name)) |>
  mutate(
    abbreviation = gsub(" ", "<br>", abbreviation),
    jour = clean_journal_name(jour),
  ) |>
  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>")
  ) |>
  ungroup()

jours |>
  ggplot() +
  aes(fill = jour, y = score, x = abbreviation, group = jour) +
  facet_wrap(~jour) +
  geom_hline(yintercept = c(25, 50), colour = "grey90", linewidth = 0.2) +
  geom_segment(
    aes(x = abbreviation, xend = abbreviation, y = 0, yend = 55),
    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 = 75, label = label),
    fill = NA, label.color = NA, size = 3
  ) +
  scale_fill_brewer(palette = "Set1", guide = "none") +
  labs(y = NULL, x = NULL, title = "Journal clocks")

❄️ Snowball search

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.

# The packages ggraph and tidygraph need to be installed to run this chunk
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%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 593 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 94 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, color = "white") +
  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("#a3ad62", "#d46780"), na.value = "grey", name = "") +
  theme_graph() +
  theme(
    plot.background = element_rect(fill = "transparent", colour = NA),
    panel.background = element_rect(fill = "transparent", colour = NA),
    legend.position = "bottom"
  ) +
  guides(fill = "none")

🌾 N-grams

Update 2024-09-15: The n-gram API endpoint is not currently in service. The following code chunk is not evaluated.

OpenAlex offers (limited) support for fulltext N-grams of Work entities (these have IDs starting with "W"). Given a vector of work IDs, oa_ngrams returns a dataframe of N-gram data (in the ngrams list-column) for each work.

ngrams_data <- oa_ngrams(
  works_identifier = c("W1964141474", "W1963991285"),
  verbose = TRUE
)

ngrams_data

lapply(ngrams_data$ngrams, head, 3)

ngrams_data |>
  tidyr::unnest(ngrams) |>
  filter(ngram_tokens == 2) |>
  select(id, ngram, ngram_count) |>
  group_by(id) |>
  slice_max(ngram_count, n = 10, with_ties = FALSE) |>
  ggplot(aes(ngram_count, forcats::fct_reorder(ngram, ngram_count))) +
  geom_col(aes(fill = id), show.legend = FALSE) +
  facet_wrap(~id, scales = "free_y") +
  labs(
    title = "Top 10 fulltext bigrams",
    x = "Count",
    y = NULL
  )

oa_ngrams can sometimes be slow because the N-grams data can get pretty big, but given that the N-grams are "cached via CDN"](https://docs.openalex.org/api-entities/works/get-n-grams#api-endpoint), you may also consider parallelizing for this special case (oa_ngrams does this automatically if you have {curl} >= v5.0.0).

💫 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
Sources 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.

Name		Name	Last commit message	Last commit date
Latest commit History 503 Commits
.github		.github
R		R
data-raw		data-raw
data		data
inst		inst
man		man
pkgdown/favicon		pkgdown/favicon
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
codecov.yml		codecov.yml
codemeta.json		codemeta.json
cran-comments.md		cran-comments.md
openalexR.Rproj		openalexR.Rproj

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⚙️ Setup

🌿 Examples

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❄️ Snowball search

🌾 N-grams

💫 About OpenAlex

🤝 Code of Conduct

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