Diderot (version 0.13)

compute_Ji: Function to compute the evolution of the Ji metric for a term (e.g. publication title) present in the reference list of a bibliographic dataset.

Description

This function computes the Ji metric for a given term from a bibliographic dataset and returns its annual evolution within the timeframe specified. This metric indicates how much the term (e.g. publication title, software name) is cited simulteaneously in the references of both corpora and is thus important for cross-fertilization between the two communities. This function is run on the bibliographic dataset (created with create_bibliography) and is thus useful before graph creation or when the term to be searched is not the title of a node in the resulting graph. For instance, if the user knows that a publication (or, e.g. software or scientific database referenced only through a URL or grey literature) is cited and may have an impact on cross-fertilization between the two communities (the literature of which is represented by the two corpora) but does not have its own entry in the bibliographic database and would therefore not be featured as a node in the graph created by build_graph, the compute_Ji function can be used to assess its importance.

Usage

compute_Ji(db, pubtitle, labels, from = -1, to = -1)

Arguments

db

Bibliographic database created with created_bibliography.

pubtitle

Publication title, or more generally term to be searched (e.g. software name).

labels

Labels (i.e. names) of the two corpora featured in the graph.

from

Start year of the time window considered (included)

to

End year of the time window considered (*excluded*)

Value

Dataframe containing year and Ji metric value.

See Also

create_bibliography

Examples

# NOT RUN {
labels<-c("Corpus1","Corpus2")

# Build a bibliographical dataset from Scopus exports
db<-create_bibliography(corpora_files=c(tempfi1,tempfi2), 
                        labels=labels, keywords=NA)
# }
# NOT RUN {
# Compute Ji
compute_Ji(db, "Title1", labels, from=1990, to=2018)

# }