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, as a double so very large counts are exact), reference_n (reference
records that year, likewise a double), comparison_percentage
(100 * n / reference_n, or NA when reference_n is 0) and
average_comparison_percentage (the same ratio computed on period totals,
over the years where both counts are available). 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"),
years = 2018:2022,
field = "TITLE-ABS-KEY"
)
cmp
}
# The shape of the return value, built offline so it runs without a key.
years <- 2018:2022
ref_n <- c(4200, 5600, 7100, 8600, 10200)
counts <- list(`computer vision` = c(1500, 2000, 2500, 3000, 3600),
`drug discovery` = c(180, 260, 370, 500, 660))
cmp <- tibble::tibble(
query = "TITLE-ABS-KEY(deep learning)",
query_type = c(rep("reference", length(years)),
rep("comparison", length(counts) * length(years))),
abridged_query = c(rep("deep learning", length(years)),
rep(names(counts), each = length(years))),
year = rep(years, length(counts) + 1),
n = c(ref_n, unlist(counts, use.names = FALSE)),
reference_n = rep(ref_n, length(counts) + 1),
comparison_percentage = 100 * c(ref_n, unlist(counts, use.names = FALSE)) /
rep(ref_n, length(counts) + 1),
average_comparison_percentage = c(rep(100, length(years)),
rep(c(35.3, 5.4), each = length(years)))
)
class(cmp) <- c("scopus_comparison", class(cmp))
cmp
#> <scopus_comparison> (3 topics)
#> # A tibble: 15 × 8
#> query query_type abridged_query year n reference_n comparison_percentage
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 TITL… reference deep learning 2018 4200 4200 100
#> 2 TITL… reference deep learning 2019 5600 5600 100
#> 3 TITL… reference deep learning 2020 7100 7100 100
#> 4 TITL… reference deep learning 2021 8600 8600 100
#> 5 TITL… reference deep learning 2022 10200 10200 100
#> 6 TITL… comparison computer visi… 2018 1500 4200 35.7
#> 7 TITL… comparison computer visi… 2019 2000 5600 35.7
#> 8 TITL… comparison computer visi… 2020 2500 7100 35.2
#> 9 TITL… comparison computer visi… 2021 3000 8600 34.9
#> 10 TITL… comparison computer visi… 2022 3600 10200 35.3
#> 11 TITL… comparison drug discovery 2018 180 4200 4.29
#> 12 TITL… comparison drug discovery 2019 260 5600 4.64
#> 13 TITL… comparison drug discovery 2020 370 7100 5.21
#> 14 TITL… comparison drug discovery 2021 500 8600 5.81
#> 15 TITL… comparison drug discovery 2022 660 10200 6.47
#> # ℹ 1 more variable: average_comparison_percentage <dbl>