scimeetr-package: Scientometrics Meet R.
In MaximeRivest/scimeetr: Scientometrics Meet R

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scimeetr provides several tools to analyse a corpus of scientific articles. When used in a systematic way, I argue that scimeetr can provide and systematic and effecient introduction to a new research field. With scimeetr a researcher can do several coupling and clustering of scientific papers. Scimeetr can also be used to calculate several metrics about that scientific corpus (e.g. most prolific journals, authors, country, universities, most cited paper, paper citing most other papers, papers with citation number farthest from expectations). Although these metrics can be used and calculated independently scimeetr's functions were design to facilitate the completion of a complete and cohesive analysis of a corpus of scholarly articles which should be usefull for a novice to a research field.

A classic workflow to use scimeetr will be as follow. (1) Import bibliometric data that were acquired from https://www.scopus.com/ or http://apps.webofknowledge.com/ using the function import_scopus_files or the function import_wos_files respectively. (2) find research community with scimap. (3) Characterize each research communities with summary.scimeetr, characterize_kw, characterize_ti, characterize_ab, characterize_jo, characterize_un, and characterize_au. And, (4) generate reading list with scilist or scilist_all