Provides functions to extract citation data from Google Scholar. Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.
The scholar R package provides functions to extract citation data from Google Scholar. In addition to retrieving basic information about a single scholar, the package also allows you to compare multiple scholars and predict future h-index values.
Development of the scholar package has resumed and a new maintainer should be confirmed shortly. Please continue to file issues and make pull requests against https://github.com/jkeirstead/scholar going forwards.
Individual scholars are referenced by a unique character string, which can be found by searching for an author and inspecting the resulting scholar homepage. For example, the profile of physicist Richard Feynman is located at http://scholar.google.com/citations?user=B7vSqZsAAAAJ and so his unique id is
Basic information on a scholar can be retrieved as follows:
# Define the id for Richard Feynman id <- 'B7vSqZsAAAAJ' # Get his profile and print his name l <- get_profile(id) l$name # Get his citation history, i.e. citations to his work in a given year get_citation_history(id) # Get his publications (a large data frame) get_publications(id)
Additional functions allow the user to query the publications list, e.g.
get_num_top_journals. Note that Google doesn't explicit categorize publications as journal articles, book chapters, etc, and so journal or article in these function names is just a generic term for a publication.
You can also compare multiple scholars, as shown below. Note that these two particular scholars are rather profilic and these queries will take a very long time to run.
# Compare Feynman and Stephen Hawking ids <- c('B7vSqZsAAAAJ', 'qj74uXkAAAAJ') # Get a data frame comparing the number of citations to their work in # a given year compare_scholars(ids) # Compare their career trajectories, based on year of first citation compare_scholar_careers(ids)
Finally users can predict the future h-index of a scholar, based on the method of Acuna et al.. Since the method was originally calibrated on data from neuroscientists, it goes without saying that, if the scholar is from another discipline, then the results should be taken with a large pinch of salt. A more general critique of the original paper is available here. Still, it's a bit of fun.
## Predict h-index of original method author, Daniel Acuna id <- 'GAi23ssAAAAJ' predict_h_index(id)
update impact factor data to 2017 (released on 2018-06-26)
plot_coauthors function to plot co-author network (thanks @cimentadaj)
get_impactfactor function to query journal's impact factor (thanks @DominiqueMakowski)
getCompleteAuthors to get the complete list of authors for a publication (thanks @abfleishman)
get_article_cite_historyaccording to the change of Google Scholar (thanks @guangchuangyu)
Fixed bug with missing cookies that was preventing data from being downloaded.
Converted code from XML to rvest/dplyr for legibility
get_publications now uses
cid as the name of the
column used to link to a publication's full citation history. This
avoids any confusion when you add the scholar's id, which is
elsewhere in the package.
get_article_cite_history function to get the citation
history of a single article (#6, thanks @mkiang)
pagesize argument to
get_publications. By default 100
publications will be fetched.
Added an option to flush cache in
Improved performance of large numbers of publications (#15, thanks @jefferis)
Updated functions to work with new Google Scholar layout
Added a CITATION file
Added the publication id to
get_publications (thanks @dfalster)
Fixed bug with incorrect parsing of profile summary table (#2)
predict_h_index now predicts a scholar's h-index for every year in
the next ten years, not just at 1, 5, and 10 year intervals. Thanks
to Daniel Acuna for providing the necessary regression coefficients.
get profile and publications data for researchers on Google Scholar
compare multiple scholars and predict future h-index values