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https://github.com/jkeirstead/scholar
Analyse citation data from Google Scholar
https://github.com/jkeirstead/scholar
Last synced: 3 days ago
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Analyse citation data from Google Scholar
- Host: GitHub
- URL: https://github.com/jkeirstead/scholar
- Owner: jkeirstead
- License: other
- Created: 2013-10-21T20:10:00.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2023-12-02T22:01:06.000Z (12 months ago)
- Last Synced: 2024-08-02T06:03:40.365Z (3 months ago)
- Language: R
- Size: 3.66 MB
- Stars: 312
- Watchers: 25
- Forks: 83
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
- License: LICENSE
Awesome Lists containing this project
README
# scholar
[](https://CRAN.R-project.org/package=scholar)
[](https://github.com/jkeirstead/scholar/actions)
The scholar R package provides functions to extract citation data from [Google Scholar](http://scholar.google.com). 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 is ongoing with [GuangchuangYu](https://github.com/GuangchuangYu) acting as maintainer.
Please continue to file issues and make pull requests against https://github.com/jkeirstead/scholar going forwards.*
## Installation
```r
# from CRAN
install.packages("scholar")
# from GitHub
if(!requireNamespace('remotes')) install.packages("remotes")
remotes::install_github('jkeirstead/scholar')
```
## Basic features
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 `B7vSqZsAAAAJ`.
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_articles`, `get_num_distinct_journals`, `get_oldest_article`, `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.
## Comparing scholars
You can also compare multiple scholars, as shown below. Note that these two particular scholars are rather prolific 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)
```
## Predicting future h-index values
Finally users can predict the future [h-index](http://en.wikipedia.org/wiki/H-index) of a scholar, based on the method of [Acuna et al.](https://www.nature.com/nature/articles/489201a). 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](http://simplystatistics.org/2012/10/10/whats-wrong-with-the-predicting-h-index-paper/). Still, it's a bit of fun.
```
## Predict h-index of original method author, Daniel Acuna
id <- 'GAi23ssAAAAJ'
predict_h_index(id)
```