This release adds a code-free app and reference-manager export.
run_app()] launches a local, code-free Shiny app for building a search,
retrieving records with a live progress terminal, and exporting them. A panel
mirrors every choice as a runnable R script, so the app is an on-ramp to the
package. It runs on your own machine, so the API key never leaves it. The app
also has a Compare topics tab (with highlight, stability-band and
counts-in-label controls, a per-term progress indicator, a quota estimate and a
CSV export) and a Demo mode, on by default, that synthesises records and a
comparison so the whole workflow can be explored with no key and no network. A
new vignette, Using the code-free app, walks through every panel.as_bibtex()] and [as_ris()] export a record set to the BibTeX and RIS
interchange formats, so a search can be carried into Zotero, EndNote, Mendeley
or a LaTeX bibliography.plot_scopus_comparison()] now spreads the direct line labels vertically and
draws a leader from each label to its line, so topics that converge near the
final year no longer overlap, and falls back to a legend when there are too
many topics to label legibly.This release reaches further into the API and adds an analysis layer on top of a retrieval.
scopus_fetch()] gains cursor = TRUE, cursor-based pagination that retrieves
a whole large query without the 5000-record ceiling of offset paging.scopus_abstract()] retrieves the abstract and fuller metadata for one or many
records from the 'Scopus' Abstract Retrieval API, resilient to an identifier
that cannot be found.scopus_trend()] reports annual record counts for a query (the size of a
literature over time), with [plot_scopus_trend()].scopus_top()] tallies the most frequent sources or authors in a record set,
with [plot_scopus_top()]. An autoplot() method draws a record set's
publications per year.First release.
scopus_plan()], and cheap sizing with
[scopus_count()].scopus_fetch()], with the largest
page each view allows requested by default to keep request counts low, and
resumable, cached, partitioned retrieval through [scopus_fetch_plan()].scopus_records()], with a
summary() method that gives a quick overview.scopus_extract_dois()] and
[scopus_diff_dois()].scopus_compare_topics()], and a plot from
[plot_scopus_comparison()] or autoplot().as_bibliometrix()],
[write_scopus_records()] and [read_scopus_records()].scopus_field_tags()], a
safe query composer in [scopus_query()], and a bundled [example_records]
dataset for offline exploration.scopus_combine()] (and a c() method), plus
as_tibble() and as.data.frame() coercion.scopus_error and its subclasses) and quota-header
parsing with [scopus_quota()].highlight argument and a shaded Wilson
stability band (an illustrative range, switchable with interval).example_records spans several disciplines, and the examples and
workflow vignettes draw on a wide range of fields.authors column rather than truncated to
the first. Very large result totals are handled without overflow. DOI cleaning
copes with www.doi.org hosts and DOI: labels.