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 = "[email protected]")
options(openalexR.apikey = "EXAMPLE_APIKEY")

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

openalexR.mailto = [email protected]
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()

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 252 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()

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 239 records...

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

🧑 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()

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()

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,]    31                 116    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()
}

🍒 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, 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 = "sources",
  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")

❄️ 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 533 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 91 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

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_count ngram_tokens
#> 1                 brain basis and core cause           2            5
#> 2                     cause be not yet fully           2            5
#> 3 include structural and functional magnetic           2            5
#>   term_frequency
#> 1   0.0006637902
#> 2   0.0006637902
#> 3   0.0006637902
#> 
#> [[2]]
#>                                          ngram ngram_count ngram_tokens
#> 1          intact but less accessible phonetic           1            5
#> 2 accessible phonetic representation in Adults           1            5
#> 3       representation in Adults with Dyslexia           1            5
#>   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).

💫 About OpenAlex

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.