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 byoa_queryor 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
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:
options(openalexR.mailto = "example@email.com")Bonus point if you put this in your .Rprofile with
file.edit("~/.Rprofile").
library(openalexR)
library(dplyr)
library(ggplot2)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.
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):
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 | Data science |
| 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 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 1 page of results with a total of 173 records...
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 | 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 | https://doi.org/10.5210/fm.v15i7.2874 | FALSE | Bookmarking, Altmetrics, Social media |
| 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 |
| 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 |
| W2059275568 | Beyond the paper | Jason Priem | NA | Nature | https://doi.org/10.1038/495437a | TRUE | MEDLINE |
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",
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 1 page of results with a total of 79 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) |
| 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 | 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 | 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 Savyasacchi 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 |
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 |
|---|---|---|---|---|---|---|
| A923435168 | Massimo Aria | 0000-0002-8517-9411 | 161 | 3910 | University of Naples Federico II | Statistics, Internal medicine, Pathology |
| A2208157607 | Jason Priem | 0000-0001-6187-6610 | 51 | 1680 | HortResearch | World Wide Web, Library science, Law |
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 |
|---|---|---|---|---|---|---|
| A923435168 | Massimo Aria | 0000-0002-8517-9411 | 161 | 3910 | University of Naples Federico II | Statistics, Internal medicine, Pathology |
| A2208157607 | Jason Priem | 0000-0001-6187-6610 | 51 | 1680 | HortResearch | World Wide Web, Library science, Law |
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,] 54 54 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 | 35462 | University of Naples Federico II | Nuclear physics, Particle physics, Quantum mechanics |
| A2609805198 | Giovanni Esposito | 0000-0001-7960-5253 | 2070 | 33506 | University of Naples Federico II | Internal medicine, Genetics, Biochemistry |
| A3088244307 | A. K. Sanchez | NA | 2047 | 38758 | University of Naples Federico II | Quantum mechanics, Nuclear physics, Particle physics |
| A2011452631 | Leonardo Merola | NA | 1575 | 27247 | University of Naples Federico II | Quantum mechanics, Particle physics, Nuclear physics |
| A2725087388 | Mariagrazia Alviggi | NA | 1561 | 26667 | University of Naples Federico II | Quantum mechanics, Particle physics, Nuclear physics |
| A2103058924 | Mario Mancini | NA | 1558 | 16591 | University of Naples Federico II | Internal medicine, Endocrinology, Biochemistry |
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 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, 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:
# The package ggtext needs to be installed to run this chunk
# library(ggtext)
jours_all <- oa_fetch(
entity = "venues",
works_count = ">200000",
verbose = TRUE
)
jours <- jours_all |>
filter(!is.na(x_concepts), type != "ebook platform") |>
slice_max(cited_by_count, n = 9) |>
distinct(display_name, .keep_all = TRUE) |>
select(jour = display_name, x_concepts) |>
tidyr::unnest(x_concepts) |>
filter(level == 0) |>
left_join(concept_abbrev, by = join_by(id, display_name)) |>
mutate(
abbreviation = gsub(" ", "<br>", abbreviation),
jour = gsub("Journal of|Journal of the", "J.", gsub("\\(.*?\\)", "", 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(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", guide = "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.
# 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_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 475 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, 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")
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
#> # A tibble: 2 × 4
#> id doi count ngrams
#> <chr> <chr> <int> <list>
#> 1 https://openalex.org/W1964141474 https://doi.org/10.1016/j.conb.… 2733 <df>
#> 2 https://openalex.org/W1963991285 https://doi.org/10.1126/science… 2338 <df>
lapply(ngrams_data$ngrams, head, 3)
#> [[1]]
#> ngram ngram_tokens ngram_count
#> 1 brain basis and core cause 5 2
#> 2 cause be not yet fully 5 2
#> 3 include structural and functional magnetic 5 2
#> term_frequency
#> 1 0.0006637902
#> 2 0.0006637902
#> 3 0.0006637902
#>
#> [[2]]
#> ngram ngram_tokens ngram_count
#> 1 intact but less accessible phonetic 5 1
#> 2 accessible phonetic representation in Adults 5 1
#> 3 representation in Adults with Dyslexia 5 1
#> term_frequency
#> 1 0.0003756574
#> 2 0.0003756574
#> 3 0.0003756574
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).
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
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.
Package hex was made with Midjourney and thus inherits a CC BY-NC 4.0 license.