open_alex_gold_open_access_learning_analytics is renamed to
learning_analytics. Update data(...) calls
accordingly.id argument on every network builder.
author_network(), keyword_network(),
reference_network(), document_network(),
source_network(), country_network(),
institution_network(), conetwork(),
local_citations(), and historiograph() gain an
id argument that names the work-identifier column (the rows
of the works x entities matrix). This completes the
custom-column workflow introduced in 0.5.0, which had given every
entity field a self-naming argument but still required the
works column to be named id.
id = NULL (default): use an existing id
column when present, otherwise fall back to row numbers (each row is one
document). A custom data frame with no identifier column now builds
without error.id = "paper_id": use any named column as the
identifier.id names a column other
than "id" while the data already has a distinct
"id" column, the call errors rather than silently
overwriting that column (which might itself be an entity field).split_field() (and therefore every builder) now coerces
a factor entity column to character before splitting,
instead of failing with “non-character argument”. Hand-built data frames
with stringsAsFactors = TRUE now work like character
columns.read_biblio(format = "generic", ...) now takes
entity-named arguments — authors, keywords,
references, countries,
affiliations, and journal — each naming the
source column to map onto that standard field. Multi-valued fields are
split on sep into the standard list-column;
journal is kept scalar. This mirrors the network-builder
vocabulary, so the same field names are used end to end:
read_biblio("my.csv", format = "generic", id = "paper_id",
authors = "Author Names", keywords = "Tags", sep = ",")list_cols is retained for splitting any further columns
in place (keeping their original names). Naming any of these columns (or
id) implies format = "generic", so passing
format is optional:
read_biblio("my.csv", id = "paper_id", authors = "Author Names", sep = ",")actors argument is deprecated —
the columns it named were never only “actors”. Use the entity arguments
above (or list_cols for arbitrary columns).
actors still works, mapped to list_cols, with
a deprecation warning.authors,
keywords, references, journal,
countries, affiliations), sep,
references_sep, and strip_quotes, with a
per-builder default-argument table. The deprecated
keyword_network(field = ) example was updated to the
keywords = form.Custom data sets, any column name, any
separator. Every network builder now takes a self-describing
column argument plus sep, so a non-standard CSV works in a
single call without renaming columns or pre-splitting strings:
author_network(d, authors = "Author Names", sep = ",")keyword_network(d, keywords = "Tags", sep = ",")reference_network(d, references = "Cited Refs", sep = ",")document_network(d, references = "Cited Refs", sep = ",")source_network(d, journal = "Source title")country_network(d, countries = "Nations", sep = ",")institution_network(d, affiliations = "Orgs", sep = ",")local_citations() and historiograph() gain
references + sep.sep accepts any delimiter (",",
"|", " and ", …) and applies to the named
entity column. The new arguments sit at the end of each signature, so
existing positional calls are unaffected.
references_sep —
author_network(), country_network(),
institution_network(), and source_network()
gain a references_sep argument (default ";")
so the references column used for coupling can have its own separator,
independent of the entity sep. (Reference strings often
contain internal commas, which is why it is separate.)
strip_quotes — every builder gains
a strip_quotes argument (default TRUE) that
removes surrounding quote characters (straight ", doubled
"", and curly quotes) from each entity, so a quoted CSV
value like "Alice" or ""Alice"" is treated as
Alice. Set strip_quotes = FALSE to keep quotes
as part of the label. Applies to both freshly-split strings and entities
supplied in a list-column.
parse_names(): optional, standalone utility that
reorders author names to "First Last" and parses each into
first/last/particle/suffix
components (attached as the "parts" attribute). Handles
three conventions: "Last, First" (comma), the Scopus /
bibnets "SURNAME Initials" label form
("WANG Y", "AYALA-ROMERO JA"), and
"First Last". The surname_first argument
("auto"/"yes"/"no", default
"auto") controls comma-less interpretation, with
auto-detection biased toward the bibnets/Scopus convention so native
bibnets labels parse without extra arguments. Case-insensitive
(recognises particles in bibnets’ uppercased labels).
format argument selects output style:
"first_last" (default), "last_initials"
("Saqr M."), or "last" ("Saqr").
Detects group/corporate authors and leaves them, NA, and
empty strings unchanged. Not called by any reader or network builder —
entity labels are still matched verbatim unless you apply this yourself.
Base R only; no new dependencies. Documented in detail in
?parse_names and the new vignette
vignette("parsing-author-names").
"harmonic", "first",
etc.) on a plain character author column now splits correctly.
Previously a delimited string was treated as a single author, silently
producing a wrong network.author_network(type = "co_citation", self_loops = TRUE)
now honors self_loops (previously ignored).author_network(type = "equivalence") now forwards
deduplicate (previously ignored).";", "|", or
tab) now emits a warning instead of silently building a degenerate
network. The heuristic deliberately ignores commas and
" and ", which occur inside valid single labels
("Last, First" names, reference strings,
"Smith and Sons"), so correct data is never warned
about.read_biblio(format = "generic") now warns, listing the
available columns, when an actors column is not found
(previously skipped silently).keyword_network(field = ) is deprecated in favor of
keyword_network(keywords = ). The old argument still works
(with a warning) and stays in its original second position, so
keyword_network(d, "author_keywords") is unchanged.test-equiv-*.R equivalence suites (vs
bibliometrix and biblionetwork) have been
moved out of the package into a local-only
local_testing_and_equivalence/ directory. These developer
checks pulled in data.table/biblionetwork,
whose OpenMP parallelism caused the “CPU time 4 times elapsed time” NOTE
on the Debian r-devel pre-test. They remain runnable locally but are no
longer part of R CMD check.bibliometrix, biblionetwork, and
data.table removed from Suggests — they were
used only by the relocated equivalence tests.tests/testthat.R keeps a 2-thread BLAS/OpenMP cap as
defence for crossprod() / tcrossprod() in
multiply_bipartite().read_scopus, read_wos,
read_dimensions, read_lens) now use small
bundled fixtures under inst/extdata/, reached via
system.file(). API-wrapper readers
(read_openalex, read_crossref) use an inline
data frame matching the upstream column shape so the conversion path
runs without a network call. read_biblio examples now
demonstrate multi-file, directory, and generic-CSV modes against the
bundled fixtures.inst/extdata/scopus_sample.csv,
wos_sample.txt, dimensions_sample.csv,
lens_sample.csv (2 records each).read_lens() no longer inflates output to
n^2 rows when neither Lens ID nor
ID columns are present.read_openalex() no longer inflates output to
n^2 rows when the id column is absent.read_scopus() now normalises empty-string DOIs to
NA, so is.na(doi) deduplication checks behave
as expected.read_wos() empty-file return now includes the
keywords_plus list-column to match the non-empty
schema.read_crossref() no longer crashes with “row names
contain missing values” when the issued column has
NA entries.to_igraph(),
to_tbl_graph(), to_cograph()) now use
@examplesIf requireNamespace(...) so they execute when the
suggested package is installed instead of being silently skipped.read_biblio(), read_bibtex(), and
read_ris() now ship runnable examples backed by either the
bundled extdata/openalex_works.csv fixture or a
tempfile()-based minimal record.read_scopus(),
read_wos(), read_ris(),
read_lens(), read_dimensions(),
read_crossref(), read_biblio(),
read_openalex(), plus dedicated coverage for
R/edgelist.R and build_bipartite_long().temporal_network() — builds time-windowed networks with
fixed, sliding, or cumulative strategies. Results include a
window column for easy stacking.historiograph() — Garfield-style chronological citation
network among the most locally cited documents.local_citations() — counts within-dataset citations
(Local Citation Score).backbone() — disparity filter for extracting
statistically significant edges from dense weighted networks.prune() — threshold and top-n edge pruning.read_biblio() — universal reader with auto-format
detection (Scopus, WoS, BibTeX, RIS, Dimensions, Lens.org).read_dimensions() — Dimensions CSV export reader.read_crossref() — converter for
rcrossref::cr_works() output.to_gephi() — exports node and edge tables in Gephi CSV
format; writes nodes.csv + edges.csv when a
directory path is supplied.to_graphml() — pure base-R GraphML writer; no XML
package required.to_cograph() — converts edge list to a
cograph_network object with optional node metadata for
direct use with cograph::splot().weight
descending and reset row names.local_citations() canonical column order:
id, lcs, gcs, year,
title, journal, doi.historiograph() empty-result schema matches non-empty
schema.id,
title, year, journal,
doi, cited_by_count, abstract,
type, authors, references,
keywords, then source-specific extras.backbone() and prune() use single-pass
O(m) node statistics via tapply() / split() —
faster on large networks.temporal_network() converted from for loop
to lapply.read_dimensions() / read_crossref() now
apply standardize_authors() and
standardize_refs() for consistency with other readers.count renamed to counting;
measure renamed to similarity across all
network functions.co_network() renamed to conetwork().read_openalex() — reads OpenAlex JSON export.filter_top() — keeps only the top-n most connected
nodes.normalize() — post-hoc normalisation of any edge
list.Initial release.
author_network(),
document_network(), reference_network(),
keyword_network(), institution_network(),
country_network(), source_network(),
conetwork().to_igraph(), to_tbl_graph(),
to_matrix().
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