Description Usage Arguments Value Examples
Bibliographic data can be stored in a number of different file types, meaning that detecting consistent attributes of those files is necessary if they are to be parsed accurately. These functions attempt to identify some of those key file attributes. Specifically, detect_parser
determines which parse_
function to use; detect_delimiter
and detect_lookup
identify different attributes of RIS files; and detect_year
attempts to fill gaps in publication years from other information stored in a data.frame
.
1 2 3 4 5 6 7 | detect_parser(x)
detect_delimiter(x)
detect_lookup(tags)
detect_year(df)
|
x |
A character vector containing bibliographic data |
tags |
A character vector containing RIS tags. |
df |
a data.frame containing bibliographic data |
detect_parser
and detect_delimiter
return a length-1 character; detect_year
returns a character vector listing estimated publication years; and detect_lookup
returns a data.frame
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | revtools <- c(
"",
"PMID- 31355546",
"VI - 10",
"IP - 4",
"DP - 2019 Dec",
"TI - revtools: An R package to support article
screening for evidence synthesis.",
"PG - 606-614",
"LID - 10.1002/jrsm.1374 [doi]",
"AU - Westgate MJ",
"LA - eng",
"PT - Journal Article",
"JT - Research Synthesis Methods",
""
)
# detect basic attributes of ris files
detect_parser(revtools)
detect_delimiter(revtools)
# determine which tag format to use
tags <- trimws(unlist(lapply(
strsplit(revtools, "- "),
function(a){a[1]}
)))
pubmed_tag_list <- detect_lookup(tags[!is.na(tags)])
# find year data in other columns
df <- as.data.frame(parse_pubmed(revtools))
df$year <- detect_year(df)
|
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