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.
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, for example:
# 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)