Save a scopus_records tibble to disk and read it back, with a stable
round-trip. The file extension selects the format. An .rds file preserves
the types and class exactly, while a .csv file is portable plain text.
The optional authkeywords column a view = "COMPLETE" retrieval adds
(see scopus_records()) round-trips through both formats, and so do the
columns fields = "extended" adds. A .csv file reads them back as text,
apart from the integer open_access, so an ISSN keeps its leading zeros.
The attributes a live retrieval carries, including retrieved_at and
scopusflow_version (see scopus_fetch()), survive the .rds form only:
.csv is a table of columns and cannot hold them, so save as .rds when a
set is a baseline to be compared against later.
Arguments
- x
A scopus_records tibble to write.
- path
Explicit file path. The functions read from, or write to, exactly this path and leave the working directory alone. Parent directories are assumed to exist already.
Value
write_scopus_records() returns x invisibly. read_scopus_records()
returns a scopus_records tibble.
Details
A .csv file is written and read as UTF-8 in every locale. One written by
another tool reads back as well, and a blank field in it reads as missing,
since pandas, the Python twin and spreadsheet software write a missing value
that way, where R writes NA.
Examples
# A round trip on the bundled corpus of real articles, which stands in for
# a retrieval of your own because 'Scopus' records may not be redistributed.
# The .rds form restores the object exactly.
rds <- tempfile(fileext = ".rds")
write_scopus_records(example_records, rds)
identical(read_scopus_records(rds), example_records)
#> [1] TRUE
# The .csv form is portable plain text and reads back to the same schema.
csv <- tempfile(fileext = ".csv")
write_scopus_records(example_records, csv)
head(read_scopus_records(csv))
#> <scopus_records> 6 records
#> query: "graphene supercapacitor"
#> # A tibble: 6 × 9
#> entry_number scopus_id doi title authors year date publication citations
#> <int> <chr> <chr> <chr> <chr> <int> <chr> <chr> <int>
#> 1 1 NA 10.155… Enha… Jianhu… 2015 2015… Journal of… 1
#> 2 2 NA NA Fabr… Patric… 2015 2015… DigitalCom… 0
#> 3 3 NA 10.102… Flex… Zhiwei… 2015 2015… ACS Applie… 469
#> 4 4 NA 10.101… Heav… Vikran… 2015 2015… Electrochi… 195
#> 5 5 NA 10.100… Grap… Chih-T… 2015 2015… Small 108
#> 6 6 NA 10.101… Nano… Hao Ya… 2015 2015… Journal of… 47