# 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: /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpXzBH2h/tinyscholar
    #> Cannot find cache file /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpXzBH2h/tinyscholar/unsorted_2022-08-09_FvNp0NkAAAAJ.rds
    #> Try quering data from server: hiplot
    #> Timeout/error when use hiplot server. Switch to use the server: cse.
    #> Save data to cache file /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpXzBH2h/tinyscholar/unsorted_2022-08-09_FvNp0NkAAAAJ.rds
    #> Done
    str(profile, max.level = 1)
    #> List of 2
    #>  $ publications:'data.frame':    20 obs. of  5 variables:
    #>  $ citations   :'data.frame':    6 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 | 509
2018 | 2
2019 | 36
2020 | 117
2021 | 195
2022 | 154
Update: 2022-08-09

tb$publications

Publications
title | authors | venue | citations | year
--- | --- | --- | --- | ---
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 | 144 | 2018
Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction | S Wang, Z He, X Wang, H Li, XS Liu | eLife, 2019 | 133 | 2019
Sex Differences in Cancer Immunotherapy Efficacy, Biomarkers, and Therapeutic Strategy | S Wang, LA Cowley, XS Liu | Molecules 24 (18), 3214, 2019 | 84 | 2019
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 | 47 | 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 | 29 | 2019
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 | 21 | 2021
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 | 18 | 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 | 8 | 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 | 8 | 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 | 6 | 2020
Hiplot: A comprehensive and easy-to-use web service 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, ... | bioRxiv, 2022 | 4 | 2022
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), 102285, 2021 | 2 | 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 | 2 | 2019
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 | 1 | 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 14, 8293, 2021 | 1 | 2021
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 | 1 | 2020
Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol | Q Chen, QN Wu, YM Rong, S Wang, Z Zuo, L Bai, B Zhang, S Yuan, ... | Briefings in Bioinformatics, 2022 | 0 | 2022
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, 2022 | 0 | 2022
Onlinemeta: A Web Serve For Meta-Analysis Based On R-shiny | Y Yi, A Lin, C Zhou, J Zhang, S Wang, P Luo | bioRxiv, 2022 | 0 | 2022
Pan-cancer 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, ... | bioRxiv, 2022 | 0 | 2022
Update: 2022-08-09

### 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.