scholar: Analyse Citation Data from Google Scholar

The scholar package extracts citation and publication data from Google Scholar profiles. It can be used to:

Google Scholar does not provide an official public API. Results can change when Google changes page markup, and repeated requests may be rate limited. The examples below use small requests and are skipped automatically when Google Scholar is not reachable.

Scholar IDs

Most functions need a Google Scholar profile ID. In a profile URL such as https://scholar.google.com/citations?user=B7vSqZsAAAAJ, the ID is B7vSqZsAAAAJ.

id <- "B7vSqZsAAAAJ"

If you have a URL or an ID with trailing query parameters, tidy_id() keeps only the Scholar ID.

tidy_id("https://scholar.google.com/citations?user=B7vSqZsAAAAJ&hl=en")

[1] "B7vSqZsAAAAJ"

tidy_id("B7vSqZsAAAAJ&hl=en")

[1] "B7vSqZsAAAAJ"

Author search is the endpoint Google has put behind a login, so get_scholar_id() and search_scholar_ids() can come back empty on a machine where a browser shows the profile plainly; the package warns when the result is a refusal rather than an absence of matches. If you have the profile URL, take the ID from it. If you are matching authors programmatically, OpenAlex is the sanctioned source.

get_scholar_id(first_name = "Richard", last_name = "Feynman")

For ambiguous names, search_scholar_ids() returns all author-search matches it can find, including IDs, affiliations, verified email text, research interests, and profile URLs. max_pages limits pagination so that examples and scripts do not make excessive requests.

search_scholar_ids("hao xu", max_pages = 1)

Profile Data

get_profile() returns a list with name, affiliation, citation metrics, fields, homepage, coauthors, and availability counts.

profile <- get_profile(id)
profile$name
profile[c("total_cites", "h_index", "i10_index")]
profile$fields

Publications

get_publications() returns a data frame of publications from a Scholar profile. It includes the publication title, displayed author list, venue text, volume/page details, citation count, year, the Google Scholar citation ID (cid), and the publication ID (pubid).

pubs <- get_publications(id, pagesize = 20)
head(pubs, 3)

head(get_publications(id, pagesize = 10, sortby = "year"), 3)

Google Scholar sometimes truncates long author lists with .... Use get_publications_all_authors() when you need the complete author list for those rows. It only calls get_complete_authors() for rows that appear to be truncated.

pubs_full <- get_publications_all_authors(id, pagesize = 20, delay = 1)
head(pubs_full, 3)

get_complete_authors(id, pubs$pubid[1], delay = 0)

author_position() helps analyse where a named author appears in publication author lists. Position_Normalized is 0 for first author, 1 for last author, and values between 0 and 1 for middle positions.

author_position(pubs$author[1:5], profile$name)

Citation Histories

ct <- get_citation_history(id)
ct

ggplot(ct, aes(year, cites)) +
  geom_line() +
  geom_point() +
  labs(x = "Year", y = "Citations")

get_article_cite_history() retrieves the yearly citation history for one publication. Missing years are filled with zero citations.

article_history <- get_article_cite_history(id, pubs$pubid[1])
article_history

ggplot(article_history, aes(year, cites)) +
  geom_segment(aes(xend = year, yend = 0), linewidth = 1, colour = "grey70") +
  geom_point(size = 3, colour = "firebrick") +
  labs(x = "Year", y = "Citations")

get_citing_articles() goes one step further than a citation count and lists the works citing a publication, with their venue and year, which is what you need to split a citation curve by community or to measure an autocitation rate. Google caps the list the same way the “cited by” link does, and guards it more closely than a profile page, so expect it to be refused from a server.

pubs <- get_publications(id)
top <- pubs[which.max(pubs$cites), ]
get_citing_articles(id, top$pubid, publications = pubs, max_pages = 2)

How the h-index Grew

get_profile() reports one h-index at one moment. get_h_index_history() rebuilds it year by year from the citation history of each publication, which is the difference between knowing someone is at h-index 39 and knowing whether they got there in five years or thirty.

history <- get_h_index_history(id, delay = 1)
head(history)

ggplot(history, aes(year, h_index)) +
  geom_step(direction = "hv") +
  labs(x = NULL, y = "h-index",
       title = "How the h-index accumulated",
       subtitle = "each step is a year in which another paper reached the threshold")

The cost is one request per cited publication, so the histories are cached with R.cache and a second call for the same profile makes no requests. Pass flush = TRUE to refresh. Publications with no citations are skipped, since they cannot enter an h-index in any year, and the curve is only as long as the years Google reports per publication.

Citation Metrics

get_publication_metrics() calculates metrics from a publication data frame. get_scholar_metrics() fetches publications and calculates the same summary in one call.

get_publication_metrics(pubs)
get_scholar_metrics(id, pagesize = 20)

get_num_articles(id)
get_oldest_article(id)
get_num_distinct_journals(id)
get_num_top_journals(id)

Publication Details

Google Scholar publication detail pages can expose additional fields. These helpers accept a Scholar ID and a publication ID.

pubid <- pubs$pubid[1]

get_article_scholar_url(id, pubid)
get_publication_url(id, pubid)
get_publication_date(id, pubid)
get_publication_abstract(id, pubid)
get_publication_data_extended(id, pubid)

Some publications have no linked PDF, abstract, or publication date on Google Scholar. In those cases the corresponding helper may return an empty value or NA.

Comparing Scholars

compare_scholars() compares citation totals from publication records by publication year.

ids <- c("B7vSqZsAAAAJ", "DO5oG40AAAAJ")
cs <- compare_scholars(ids)
head(cs, 3)

cs <- dplyr::filter(cs, !is.na(year) & year > 1900)

ggplot(cs, aes(year, cites, colour = name, group = name)) +
  geom_line() +
  labs(x = "Publication year", y = "Citations", colour = NULL) +
  theme(legend.position = "bottom")

compare_scholar_careers() compares citation histories after aligning each scholar by career year.

csc <- compare_scholar_careers(ids)
head(csc, 3)

ggplot(csc, aes(career_year, cites, colour = name, group = name)) +
  geom_line() +
  geom_point() +
  labs(x = "Career year", y = "Citations", colour = NULL) +
  theme(legend.position = "bottom")

Coauthor Networks

get_coauthors() retrieves the coauthors listed by Google Scholar for a profile and can optionally expand the network by following coauthors’ profiles. Keep n_coauthors and n_deep small for exploratory work; network plots get messy quickly and repeated requests can be rate limited.

coauthor_network <- get_coauthors("DO5oG40AAAAJ", n_coauthors = 4, n_deep = 0)
coauthor_network

if (nrow(coauthor_network) > 1) {
  plot_coauthors(coauthor_network)
}

Journal Rankings

get_journalrank() is deprecated and fetches nothing. SCImago has served its ranking page behind a JavaScript challenge since late 2025, so a script is answered with HTTP 403 and cannot read the table, and the table carries no published licence. The function remains so that old code fails with an explanation rather than a stack trace:

get_journalrank(c("Nature", "Science"))

Error:
! get_journalrank() is deprecated and downloads nothing.
  SCImago answers a script with a JavaScript challenge (HTTP 403), and the table has no published licence.
  For journal metrics use OpenAlex, which is openly licensed and needs no scraping:
    https://api.openalex.org/sources?search=Nature
  For SJR quartiles specifically, save the table by hand from https://www.scimagojr.com/journalrank.php in a browser and read it with utils::read.csv2().

For journal metrics, use OpenAlex, which is openly licensed, built for programmatic use, and needs no session or scraping. It publishes the h-index, i10-index, work and citation counts, and the ISSNs of a journal:

openalex <- jsonlite::fromJSON(
  "https://api.openalex.org/sources?search=Nature&per-page=1")
openalex$results[, c("display_name", "works_count", "cited_by_count")]
openalex$results$summary_stats

If you need SCImago’s SJR quartiles specifically, save the table by hand in a browser from https://www.scimagojr.com/journalrank.php and read it with utils::read.csv2() — the separators are semicolons.

Predicting Future h-index

predict_h_index() implements the model from Acuna et al. for predicting future h-index values. The original model was calibrated on neuroscientists, so results for other fields should be treated as illustrative rather than authoritative.

predict_h_index("GAi23ssAAAAJ")

You can provide a custom list of top journals for the model’s top-journal predictor.

predict_h_index(
  id,
  journals = c("Nature", "Science", "Proceedings of the National Academy of Sciences")
)

Formatting Publications for CVs

format_publications() returns publication strings that can be printed in R Markdown, Quarto, or a CV workflow. The author.name argument highlights a chosen author in bold after converting the supplied name to the abbreviated format used by the output.

format_publications("DO5oG40AAAAJ", "Guangchuang Yu") |>
  head() |>
  cat(sep = "\n\n")

format_publications("DO5oG40AAAAJ", "Guangchuang Yu") |>
  head() |>
  print(quote = FALSE)

Mirrors, Retries, and Caching

Use set_scholar_mirror() if you need to point the package at a compatible Scholar mirror.

set_scholar_mirror("https://scholar.google.com")

All network requests go through get_scholar_resp(), which stores Google Scholar session cookies and retries transient failures. get_publications() also caches profile publication data with R.cache in a temporary cache directory. Use flush = TRUE in get_publications() or functions that forward to it when you need fresh data.

get_publications(id, flush = TRUE)
get_scholar_metrics(id, flush = TRUE)

When Scholar refuses: servers, CI, and hosted apps

Google guards Scholar against automated clients, and how it responds depends on where the request comes from. On your machine a profile page opens; on shinyapps.io, a GitLab runner or a GitHub Actions job the same request can come back as a sign-in redirect, a captcha, or HTTP 403. The package no longer describes that as a bad ID: get_scholar_resp() inspects the address the request finally landed on and the body it carries, then warns with the reason.

get_profile("B7vSqZsAAAAJ")
#> Warning: Google Scholar returned a captcha page that needs JavaScript, so the
#>   response contains no data. ... Carry the session of a browser that opens the
#>   page with set_scholar_cookie(), ... or run it from a workstation.

The settings the package reads, each with an environment variable of the same name in upper case: scholar_site, scholar_user_agent, scholar_accept_language, scholar_request_delay, scholar_retry_delay, scholar_retry_max_delay, scholar_timeout, scholar_cookie, and scholar_cookie_file.

For large workflows, prefer fetching each scholar’s publications once and then reusing the returned data frame for multiple summaries.

pubs <- get_publications(id)

recent <- subset(pubs, !is.na(year) & year >= 2020)
nrow(recent)
sum(recent$cites, na.rm = TRUE)
get_publication_metrics(recent)

Overview