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:
And the development version from GitHub with:
Usage
Here I will use my profile as an example.
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 127Copy 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/RtmpYpUmbG/tinyscholar
#> Cannot find cache file /tmp/RtmpYpUmbG/tinyscholar/unsorted_2026-06-01_FvNp0NkAAAAJ.rds
#> Try quering data from server: hiplot
#> Save data to cache file /tmp/RtmpYpUmbG/tinyscholar/unsorted_2026-06-01_FvNp0NkAAAAJ.rds
#> Done
str(profile, max.level = 1)
#> List of 2
#> $ publications:'data.frame': 20 obs. of 5 variables:
#> $ citations :'data.frame': 9 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.
| Citations | |
| when | count |
|---|---|
| total | 2041 |
| 2019 | 39 |
| 2020 | 122 |
| 2021 | 190 |
| 2022 | 293 |
| 2023 | 329 |
| 2024 | 356 |
| 2025 | 458 |
| 2026 | 245 |
| Update: 2026-06-01 | |
| 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 | 335 | 2019 |
| 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 | 293 | 2022 |
| 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 | 277 | 2018 |
| Sex Differences in Cancer Immunotherapy Efficacy, Biomarkers, and Therapeutic Strategy | S Wang, LA Cowley, XS Liu | Molecules 24 (18), 3214, 2019 | 198 | 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 | 156 | 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 | 132 | 2021 |
| 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 | 94 | 2019 |
| Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0 | D Zeng, Y Fang, W Qiu, P Luo, S Wang, R Shen, W Gu, X Huang, Q Mao, ... | Cell reports methods 4 (12), 2024 | 90 | 2024 |
| 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 | 78 | 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 | 57 | 2020 |
| 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 | 41 | 2024 |
| 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 | 37 | 2023 |
| 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 | 31 | 2024 |
| CircRNAs: functions and emerging roles in cancer and immunotherapy | Y Wang, Y Cui, X Li, SH Jin, H Wang, US Gaipl, H Ma, S Wang, JG Zhou | BMC medicine 23 (1), 477, 2025 | 23 | 2025 |
| 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 | 20 | 2019 |
| 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 | 18 | 2022 |
| Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny | S Li, Y Peng, M Chen, Y Zhao, Y Xiong, J Li, P Luo, H Wang, F Zhao, ... | Communications Biology 7 (1), 1200, 2024 | 17 | 2024 |
| Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites | W Ning, T Wu, C Wu, S Wang, Z Tao, G Wang, X Zhao, K Diao, J Wang, ... | Journal of Molecular Cell Biology 15 (4), mjad023, 2023 | 17 | 2023 |
| Ggct (γ‐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 | 16 | 2021 |
| TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy | S Wang, Y Xiong, Y Zhang, H Wang, M Chen, J Li, P Luo, YH Luo, ... | Journal for ImmunoTherapy of Cancer 12, e008040, 2024 | 15 | 2024 |
| Update: 2026-06-01 | ||||
Show Plot
Similarly, you can show numeric data with ggplot2 package.
pl <- scholar_plot(profile)
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the tinyscholar package.
#> Please report the issue at
#> <https://github.com/ShixiangWang/tinyscholar/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
pl$citations
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.