Most frequent values in a record set

Tallies the most common sources or authors across a scopus_records object. It works on records already in memory, so it makes no network request.

Usage

scopus_top(x, by = c("source", "author"), n = 10L)

Arguments

x

A scopus_records tibble.

by

What to tally: "source" (the publication titles) or "author". Author strings holding several names are split at the semicolon, with or without a space after it, so each contributor is counted once per record. Only a view = "COMPLETE" retrieval holds several names, since STANDARD records carry the first author alone (see scopus_records()). A tally by author of a set retrieved under STANDARD therefore counts first authorships, and says so with a warning of class scopus_warning_first_author_only, once per session.

n

The number of rows to return (the top n).

Value

A tibble of class scopus_top with columns value and n, sorted by descending count, with ties broken by value in byte order so the result is reproducible across platforms and locales. Exactly n rows are returned (fewer if there are fewer distinct values), so values tied at the cut-off rank may be dropped. The by choice is stored in the by attribute.

See also

plot_scopus_top(), summary.scopus_records()

Examples

# The bundled corpus of real articles stands in for a harvest of your own,
# since 'Scopus' records may not be redistributed.
scopus_top(example_records, by = "source")
#> # A tibble: 10 × 2
#>    value                                  n
#>  * <chr>                              <int>
#>  1 ACS Applied Materials & Interfaces     8
#>  2 Journal of Power Sources               5
#>  3 Synthetic Metals                       5
#>  4 Electrochimica Acta                    4
#>  5 Journal of Materials Chemistry A       4
#>  6 Scientific Reports                     4
#>  7 Journal of Alloys and Compounds        3
#>  8 Journal of Energy Storage              3
#>  9 Materials Chemistry and Physics        3
#> 10 Nanotechnology                         3

# That corpus names one author per article, so the author tally counts
# first authors. A harvest retrieved with view = "COMPLETE" lists every
# author, and the tally splits them.
scopus_top(example_records, by = "author", n = 5)
#> # A tibble: 5 × 2
#>   value                 n
#> * <chr>             <int>
#> 1 Hao Yang              3
#> 2 L. Ojeda              3
#> 3 R. Mendoza            3
#> 4 A.I. Mtz-Enríquez     2
#> 5 Bin Wang              2