scholar

Analyse citation data from Google Scholar

https://github.com/yulab-smu/scholar

Science Score: 20.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: scholar.google, nature.com
  • Committers with academic emails
    2 of 18 committers (11.1%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (15.0%) to scientific vocabulary

Keywords from Contributors

visualisation standardization report
Last synced: 10 months ago · JSON representation

Repository

Analyse citation data from Google Scholar

Basic Info
  • Host: GitHub
  • Owner: YuLab-SMU
  • License: other
  • Language: R
  • Default Branch: master
  • Size: 4.83 MB
Statistics
  • Stars: 46
  • Watchers: 2
  • Forks: 6
  • Open Issues: 0
  • Releases: 0
Fork of jkeirstead/scholar
Created over 5 years ago · Last pushed about 1 year ago

https://github.com/YuLab-SMU/scholar/blob/master/

# scholar

[![CRAN status](https://www.r-pkg.org/badges/version/scholar)](https://CRAN.R-project.org/package=scholar)
[![R-CMD-check](https://github.com/YuLab-SMU/scholar/workflows/R-CMD-check/badge.svg)](https://github.com/YuLab-SMU/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.


## Installation

```r
# from CRAN
install.packages("scholar")

# from GitHub
if(!requireNamespace('remotes')) install.packages("remotes")
remotes::install_github('YuLab-SMU/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

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)
```

## Formatting publications for CV

Finally, the `format_publications` function can be used (e.g., in conjunction with the [`vitae`](https://pkg.mitchelloharawild.com/vitae/) package) to format publications in APA Style. The short name of the author of interest (e.g., of the person whose CV is being made) can be highlighted in bold with the `author.name` argument. The function after the pipe allows rmarkdown to format them properly, and the code chunk should be set to `results = "asis"`.

```
# APA style:
format_publications("NrfwEncAAAAJ", "R Thriault") |> cat(sep='\n\n')

# Numbering format:
format_publications("NrfwEncAAAAJ", "R Thriault") |> print(quote=FALSE)
```

Owner

  • Name: Bioinformatics Group @ SMU
  • Login: YuLab-SMU
  • Kind: organization
  • Email: gcyu1@smu.edu.cn
  • Location: Guangzhou

Research group led by Prof. Guangchuang Yu in School of Basic Medical Sciences, Southern Medical University

GitHub Events

Total
  • Issues event: 2
  • Watch event: 6
  • Issue comment event: 1
  • Push event: 3
Last Year
  • Issues event: 2
  • Watch event: 6
  • Issue comment event: 1
  • Push event: 3

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 199
  • Total Committers: 18
  • Avg Commits per committer: 11.056
  • Development Distribution Score (DDS): 0.719
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
James Keirstead j****d@g****m 56
Gregory Jefferis j****s@g****m 52
Guangchuang Yu g****u@g****m 23
Thomas Shafee T****4 13
guangchuang yu g****u@g****m 13
cimentadaj c****j@g****m 11
DominiqueMakowski d****9@g****m 9
JConigrave j****e@g****m 6
André Calero Valdez a****z@g****m 4
abfleishman a****n@g****m 2
DarioBoh b****o@g****m 2
Raoul Wadhwa r****a@g****m 2
gjgetzinger g****r@d****u 1
Jaume Bonet j****t@g****m 1
Timon Elmer t****r@g****m 1
Anthony a****t@g****m 1
Daniel Falster d****r@m****u 1
RemPsyc R****c 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 1
  • Total pull requests: 2
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 24 days
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.5
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: about 2 months
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • raffaem (1)
Pull Request Authors
  • rempsyc (1)
  • Sumidu (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 1,812 last-month
  • Total docker downloads: 21,934
  • Total dependent packages: 2
  • Total dependent repositories: 13
  • Total versions: 13
  • Total maintainers: 1
cran.r-project.org: scholar

Analyse Citation Data from Google Scholar

  • Versions: 13
  • Dependent Packages: 2
  • Dependent Repositories: 13
  • Downloads: 1,812 Last month
  • Docker Downloads: 21,934
Rankings
Dependent repos count: 8.0%
Stargazers count: 9.2%
Forks count: 10.8%
Average: 11.4%
Docker downloads count: 12.6%
Dependent packages count: 13.7%
Downloads: 14.3%
Maintainers (1)
Last synced: 11 months ago