tinyscholar

The goal of tinyscholar is to provide a simple way to get and show Google scholar profile.

Installation

You can install the released version of tinyscholar from CRAN with:

install.packages("tinyscholar")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ShixiangWang/tinyscholar")
# devtools::install_git("https://gitee.com/ShixiangWang/tinyscholar")

Usage

Here I will use my profile as an example.

library(tinyscholar)

Get You Google Scholar ID

Firstly, you need to get your Google scholar ID from URL of your Google scholar profile or by running the following function with a keyword:

scholar_search("Shixiang Wang")
#> Searching author Shixiang Wang
#> Using API server: https://api.scaleserp.com
#> Using Shixiang's personal API key, only 125 free searches per month for all packages users
#> Search times used: 38
#> Search times left: 87
#>             id
#> 1 FvNp0NkAAAAJ
#>                                                                                               desc
#> 1 Wang, Shixiang (王诗翔)ShanghaiTech. UniversityVerified email at shanghaitech.edu.cnCited by 127

Copy your ID and go to the next step.

Get Personal Profile

Then you can use function tinyscholar() to get the tidy data, which is a list of two data.frame with added profile class.

profile <- tinyscholar("FvNp0NkAAAAJ")
#> Using cache directory: /tmp/RtmpMKzAYT/tinyscholar
#> Cannot find cache file /tmp/RtmpMKzAYT/tinyscholar/unsorted_2024-11-02_FvNp0NkAAAAJ.rds
#> Try quering data from server: hiplot
#> Save data to cache file /tmp/RtmpMKzAYT/tinyscholar/unsorted_2024-11-02_FvNp0NkAAAAJ.rds
#> Done
str(profile, max.level = 1)
#> List of 2
#>  $ publications:'data.frame':    20 obs. of  5 variables:
#>  $ citations   :'data.frame':    7 obs. of  2 variables:
#>  - attr(*, "class")= chr [1:2] "ScholarProfile" "list"

You can use this data in your way. The following parts provide two simple ways to show the profile.

Show Table

Table is the best way to show the scholar profile. Tinyscholar uses gt package to generate tables which can be easily modified.

tb <- scholar_table(profile)
tb$citations
Citations
when count
total 1268
2019 39
2020 121
2021 190
2022 291
2023 319
2024 303
Update: 2024-11-02 
tb$publications
Publications
title authors venue citations year
Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction S Wang, Z He, X Wang, H Li, XS Liu eLife, 2019 283 2019
APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer S Wang, M Jia, Z He, XS Liu Oncogene 37 (29), 3924-3936, 2018 238 2018
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization J Li, B Miao, S Wang, W Dong, H Xu, C Si, W Wang, S Duan, J Lou, Z Bao, ... Briefings in bioinformatics 23 (4), bbac261, 2022 158 2022
Sex Differences in Cancer Immunotherapy Efficacy, Biomarkers, and Therapeutic Strategy S Wang, LA Cowley, XS Liu Molecules 24 (18), 3214, 2019 149 2019
UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data S Wang, Y Xiong, L Zhao, K Gu, Y Li, F Zhao, J Li, M Wang, H Wang, ... Bioinformatics 38 (2), 527-529, 2022 97 2022
Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes S Wang, H Li, M Song, Z Tao, T Wu, Z He, X Zhao, K Wu, XS Liu PLoS genetics 17 (5), e1009557, 2021 88 2021
The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex S Wang, J Zhang, Z He, K Wu, XS Liu International journal of cancer 145 (10), 2840-2849, 2019 68 2019
The UCSCXenaTools R package: a toolkit for accessing genomics data from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq S Wang, XS Liu Journal of Open Source Software 4 (40), 1627, 2019 61 2019
Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis S Wang, Z Tao, T Wu, XS Liu Bioinformatics 37 (11), 1590-1592, 2020 41 2020
Ras downstream effector GGCT alleviates oncogenic stress Z He, S Wang, Y Shao, J Zhang, X Wu, Y Chen, J Hu, F Zhang, XS Liu Iscience 19, 256-266, 2019 16 2019
The repertoire of copy number alteration signatures in human cancer Z Tao, S Wang, C Wu, T Wu, X Zhao, W Ning, G Wang, J Wang, J Chen, ... Briefings in Bioinformatics 24 (2), bbad053, 2023 11 2023
Quantification of neoantigen-mediated immunoediting in cancer evolution T Wu, G Wang, X Wang, S Wang, X Zhao, C Wu, W Ning, Z Tao, F Chen, ... Cancer Research 82 (12), 2226-2238, 2022 9 2022
Copy number signature analyses in prostate cancer reveal distinct etiologies and clinical outcomes S Wang, H Li, M Song, Z He, T Wu, X Wang, Z Tao, K Wu, XS Liu MedRxiv, 2020.04. 27.20082404, 2020 9 2020
Pan-cancer noncoding genomic analysis identifies functional CDC20 promoter mutation hotspots Z He, T Wu, S Wang, J Zhang, X Sun, Z Tao, X Zhao, H Li, K Wu, XS Liu Iscience 24 (4), 2021 8 2021
Association of CSMD1 with tumor mutation burden and other clinical outcomes in Gastric Cancer X Wang, S Wang, Y Han, M Xu, P Li, M Ke, Z Teng, P Huang, Z Diao, ... International Journal of General Medicine, 8293-8299, 2021 5 2021
Ggct (&#947;&#8208;glutamyl cyclotransferase) plays an important role in erythrocyte antioxidant defense and red blood cell survival Z He, X Sun, S Wang, D Bai, X Zhao, Y Han, P Hao, XS Liu British Journal of Haematology 195 (2), 267-275, 2021 4 2021
Can tumor mutational burden determine the most effective treatment for lung cancer patients? S Wang, Z He, X Wang, H Li, T Wu, X Sun, K Wu, XS Liu Lung Cancer Management 8 (4), LMT21, 2019 4 2019
Revisiting neoantigen depletion signal in the untreated cancer genome S Wang, X Wang, T Wu, Z He, H Li, X Sun, XS Liu bioRxiv, 2020.05. 11.089540, 2020 3 2020
STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability J Chen, L Zhu, W Mou, A Lin, D Zeng, C Qi, Z Liu, A Jiang, B Tang, W Shi, ... iMetaOmics 1 (1), e7, 2024 2 2024
Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer S Wang, CY Wu, MM He, JX Yong, YX Chen, LM Qian, JL Zhang, ZL Zeng, ... Nature Communications 15 (1), 1515, 2024 2 2024
Update: 2024-11-02

Show Plot

Similarly, you can show numeric data with ggplot2 package.

pl <- scholar_plot(profile)
pl$citations

pl$publications

Similar R package

R package scholar is a more comprehensive package to get and visualize the Google scholar profile. However, tinyscholar is lightweight and not limited in China.