Compares how often a set of comparison topics co-occur with a reference topic over time. For each year and each comparison term, the number of records matching the reference combined with that term is expressed as a percentage of the records matching the reference alone. This reveals which sub-topics are growing or shrinking within a literature.
Usage
scopus_compare_topics(
reference_query,
comparison_terms,
years,
field = NULL,
view = c("STANDARD", "COMPLETE"),
api_key = NULL,
inst_token = NULL,
verbose = FALSE
)Arguments
- reference_query
Character scalar. The reference topic that anchors the comparison (for example
"language learning").- comparison_terms
Character vector of topics to compare against the reference (for example
c("effect size", "Bayesian")). Each is combined with the reference using a logical AND.- years
Integer vector of publication years to span (for example
2015:2020).- field
Optional 'Scopus' field tag applied to every component of every query (see
scopus_plan()).- view
Either
"STANDARD"or"COMPLETE".- api_key, inst_token
Optional credentials (see
scopus_has_key()).- verbose
Logical. When
TRUE, progress is reported.
Value
A tibble of class scopus_comparison with the columns query (the
full query used), query_type ("reference" or "comparison"),
abridged_query (the topic label for plotting), year, n (records that
year), reference_n (reference records that year), comparison_percentage
(100 * n / reference_n, or NA when reference_n is 0) and
average_comparison_percentage (the same ratio computed on period totals).
Comparison rows are sorted by descending average percentage.
API access
This performs one count request per term per year, so it requires a valid API
key and internet access. The API access section of scopus_count() gives
the details. A modest number of terms and years keeps the call within quota.
See also
plot_scopus_comparison() to visualise the result.
Examples
if (FALSE) { # scopusflow::scopus_has_key()
cmp <- scopus_compare_topics(
reference_query = "deep learning",
comparison_terms = c("computer vision", "drug discovery", "medical imaging"),
years = 2015:2022,
field = "TITLE-ABS-KEY"
)
cmp
}