characterize_jo: Characterize bibliometric corpus with journals.
In MaximeRivest/scimeetr: Scientometrics Meet R

Description Usage Arguments Value See Also Examples

characterize_jo calculates several journal bibliometrics from a scimeetr object. The results are returned in a list of data frame. The metrics in the table are: number of citations, H-index, impact factor, number of different papers that were cited by papers in the scimeetr dataframe, number of papers that are within the community. _rel, _rank and _relevance at the end of a column name refers to the fact that the relativem the rank change or the relevance of the journal were calculated based on the metrics that matches the start of the column name.

1	characterize_jo(scimeetr_data, lambda = 0.6)

`scimeetr_data`	An object of class scimeetr.
`lambda`	A number from 0 to 1. If 0 the relevance score would be equal to the relative frequency. If 1 for the relevance score would be equal to the frequency.

A list of dataframe. The list length matchs the number of communities that the scimeetr object contains.

characterize_kw for keyword characterization, characterize_ti for title-word characterization, characterize_ab for abstract-word characterization, characterize_au for author characterization, characterize_un for university characterization, characterize_co for country characterization

# Example with an object of class scimeetr (see import_wos_files() or 
# import_scopus_files()) already in the workspace
journals <- characterize_jo(scimeetr_list)
# Since this example shows how to load WOS from your system we need to run 
# the following line to find the path to the exemple file
fpath <- system.file("extdata", package="scimeetr") 
fpath <- paste(fpath, "/wos_folder/", sep = "") 
# Then we can run the actual example
example_scimeetr_object <- import_wos_files(files_directory = fpath)
characterize_jo(example_scimeetr_object)