The goal of tinyscholar is to provide a simple way to get and show Google scholar profile.
You can install the released version of tinyscholar from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") ::install_github("ShixiangWang/tinyscholar") devtools# devtools::install_git("https://gitee.com/ShixiangWang/tinyscholar")
Here I will use my profile as an example.
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.
Then you can use function
tinyscholar() to get the tidy data, which is a list of two
data.frame with added
<- tinyscholar("FvNp0NkAAAAJ") profile #> Using cache directory: /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpzNbccn/tinyscholar #> Cannot find cache file /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpzNbccn/tinyscholar/unsorted_2021-05-16_FvNp0NkAAAAJ.rds #> Try quering data from server: hiplot #> Save data to cache file /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpzNbccn/tinyscholar/unsorted_2021-05-16_FvNp0NkAAAAJ.rds #> Done str(profile, max.level = 1) #> List of 2 #> $ publications:'data.frame': 13 obs. of 5 variables: #> $ citations :'data.frame': 5 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.
Table is the best way to show the scholar profile. Tinyscholar uses gt package to generate tables which can be easily modified.
<- scholar_table(profile) tb $citationstb
|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||85||2018|
|Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction||S Wang, Z He, X Wang, H Li, XS Liu||eLife, 2019||55||2019|
|Sex Differences in Cancer Immunotherapy Efficacy, Biomarkers, and Therapeutic Strategy||S Wang, LA Cowley, XS Liu||Molecules 24 (18), 3214, 2019||37||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||30||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||11||2019|
|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||3||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||3||2019|
|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, 2020||2||2020|
|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||0||2021|
|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||0||2021|
|UCSCXenaShiny: an R package for exploring and analyzing UCSC Xena public datasets in web browser||S Wang, Y Xiong, K Gu, L Zhao, Y Li, F Zhao, X Li, XS Liu||Preprints, 2020||0||2020|
|Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis||S Wang, Z Tao, T Wu, XS Liu||bioRxiv, 2020||0||2020|
|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||0||2020|
Similarly, you can show numeric data with ggplot2 package.
<- scholar_plot(profile) pl $citationspl
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.