openalexR

openalexR helps you interface with the OpenAlex API to retrieve bibliographic information about publications, authors, institutions, sources, funders, publishers, topics and keywords 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: . 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>
#> ℹ 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	is_oa	top_concepts
W2755950973	bibliometrix : An R-tool for comprehensive science mapping analysis	Massimo Aria	Corrado Cuccurullo	FALSE	Workflow, Bibliometrics, Software
W2038196424	Coverage and adoption of altmetrics sources in the bibliometric community	Stefanie Haustein	Jens Terliesner	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>
#> ℹ 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	is_oa	top_concepts
W1775749144	PROTEIN MEASUREMENT WITH THE FOLIN PHENOL REAGENT	OliverH. Lowry	RoseJ. Randall	TRUE	Reagent, Phenol
W3036882247	Integrating spatial gene expression and breast tumour morphology via deep learning	Bryan He	James Zou	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>
#> ℹ Getting 2 pages of results with a total of 293 records...
works_from_orcids |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	is_oa	top_concepts
W2755950973	bibliometrix : An R-tool for comprehensive science mapping analysis	Massimo Aria	Corrado Cuccurullo	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	TRUE	Citation, License, Bibliometrics
W3005144120	Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research	Massimo Aria	Maria Spano	FALSE	Human geography, Data collection, Position (finance)
W2122130843	Scientometrics 2.0: New metrics of scholarly impact on the social Web	Jason Priem	Bradely H. Hemminger	TRUE	Bookmarking, Altmetrics, Social media
W2408216567	Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains	Corrado Cuccurullo	Fabrizia Sarto	FALSE	Domain (mathematical analysis), Content analysis, Public domain
W2396414759	The Altmetrics Collection	Jason Priem	Dario Taraborelli	TRUE	Social media, Citation, Altmetrics

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>
#> ℹ Getting 3 pages of results with a total of 550 records...
#> ⠙ OpenAlex downloading [2/3] ■■■■■■■■■■■■■■■■■■■■■             67% ETA:  1s
#> 
#> ⠙ OpenAlex downloading [3/3] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% ETA:  0s
works_search |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	is_oa	top_concepts
W3160856016	How to conduct a bibliometric analysis: An overview and guidelines	Naveen Donthu	Weng Marc Lim	FALSE	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	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	FALSE	Bibliometrics, Field (mathematics), Disease
W3044902155	Financial literacy: A systematic review and bibliometric analysis	Kirti Goyal	Satish Kumar	FALSE	Financial literacy, Citation, Content analysis
W3198357836	Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis	John W. Goodell	Debidutta Pattnaik	FALSE	Scholarship, Valuation (finance), Corporate finance
W2998021954	Bibliometric Analysis using Bibliometrix an R Package	Hamid Derviş	NA	TRUE	Bibliometrics, Citation, Field (mathematics)

🧑 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	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	227	16557	Physiology, Pathology and Forensic Medicine, Psychiatry and Mental health
A5023888391	Jason Priem	0000-0001-6187-6610	61	4196	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	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	227	16557	Physiology, Pathology and Forensic Medicine, Psychiatry and Mental health
A5023888391	Jason Priem	0000-0001-6187-6610	61	4196	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
#> [1] 88
#> 
#> $db_response_time_ms
#> [1] 18
#> 
#> $page
#> [1] 1
#> 
#> $per_page
#> [1] 1
#> 
#> $groups_count
#> NULL

if (do.call(oa_fetch, c(my_arguments, list(count_only = TRUE)))[1]>0){
do.call(oa_fetch, my_arguments) |>
  show_authors() |>
  knitr::kable()
}

id	display_name	orcid	works_count	cited_by_count	top_concepts
A5034359289	E. Rossi	0000-0002-2146-677X	4112	225222	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics
A5114377868	L. Lista	0000-0001-6471-5492	2701	136057	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics
A5106552509	C. Sciacca	0000-0002-8412-4072	2503	101360	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics
A5106315436	S. Ragazzi	0000-0001-8219-2074	2116	113665	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics
A5058925624	Arnon Nagler	0000-0002-0763-1265	2103	70732	Hematology, Hematology, Public Health, Environmental and Occupational Health
A5066300938	S. D’Auria	0000-0003-3393-6318	2084	122832	Nuclear and High Energy Physics, Nuclear and High Energy Physics, Nuclear and High Energy Physics

🍒 Example analyses

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>
#> ℹ Getting 2 pages of results with a total of 236 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(type == "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 topics:

# 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(type == "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>
#> ℹ 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>
#> ℹ Getting 4 pages of results with a total of 658 records...
#> ⠙ OpenAlex downloading [2/4] ■■■■■■■■■■■■■■■■                  50% ETA: 18s
#> 
#> ⠹ OpenAlex downloading [3/4] ■■■■■■■■■■■■■■■■■■■■■■■           75% ETA:  7s
#> 
#> ⠹ OpenAlex downloading [4/4] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% ETA:  0s
#> 
#> 
#> 
#> Collecting all documents cited by the target papers...
#> Requesting url:
#> <https://api.openalex.org/works?filter=cited_by%3AW1963991285%7CW1964141474>
#> ℹ Getting 1 page of results with a total of 93 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

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)
Publishers are companies that publish works
Funders are organizations that fund works
Topics are tags automatically given to works based on information about the work; grouped into subfields, which are grouped into fields, which are grouped into top-level domains
Keywords are terms associated with works derived from Topics (at most 5 per work)

🤝 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.