This function is a simple wrapper to unify journal ids.
Arguments
- meta_data
Data which was processed via
jst_get_article().- remove_cols
Should the original columns be removed after unifying?
Details
Date on journal ids can be found in three columns:
journal_pub_id, journal_jcode and journal_doi. From my experience,
most of the time the relevant dat ais present in journal_pub_id or
journal_jcode, with journal_jcode being to most common identifier.
This function takes the value from journal_pub_id, and if it is missing,
that from journal_jcode. journal_doi is currently disregarded.
Examples
article <- jst_get_article(jst_example("article_with_references.xml"))
jst_unify_journal_id(article)
#> # A tibble: 1 × 17
#> file_name journal_title article_doi article_pub_id article_jcode article_type
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 article_w… Transactions… 10.2307/32… NA NA research-ar…
#> # ℹ 11 more variables: article_title <chr>, volume <chr>, issue <chr>,
#> # language <chr>, pub_day <chr>, pub_month <chr>, pub_year <int>,
#> # first_page <chr>, last_page <chr>, page_range <chr>, journal_id <chr>
# per default, original columns with data on the journal are removed
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
jst_unify_journal_id(article) %>%
select(contains("journal")) %>%
names()
#> [1] "journal_title" "journal_id"
# you can keep them by setting `remove_cols` to `FALSE`
jst_unify_journal_id(article, remove_cols = FALSE) %>%
select(contains("journal")) %>%
names()
#> [1] "journal_doi" "journal_jcode" "journal_pub_id" "journal_title"
#> [5] "journal_id"