my_df <- data.frame(
title = c(
"EviAtlas: a tool for visualising evidence synthesis databases",
"revtools: An R package to support article screening for evidence synthesis",
"An automated approach to identifying search terms for systematic reviews
using keyword co-occurrence networks",
"Reproducible, flexible and high-throughput data extraction from primary literature:
The metaDigitise r package",
"eviatlas:tool for visualizing evidence synthesis databases.",
"REVTOOLS a package to support article-screening for evidence synthsis"
),
year = c("2019", "2019", "2019", "2019", NA, NA),
authors = c("Haddaway et al", "Westgate",
"Grames et al", "Pick et al", NA, NA),
stringsAsFactors = FALSE
)
# run deduplication
dups <- find_duplicates(
my_df$title,
method = "string_osa",
rm_punctuation = TRUE,
to_lower = TRUE
)
expect(
length(dups) == nrow(my_df),
"Not all rows in df have been classified as duplicates or unique entries"
)
expect(
all(dups[5:6] == dups[1:2]),
"Not detecting duplicated titles in example df"
)
deduped <- extract_unique_references(my_df, matches = dups)
expect(
length(unique(dups)) == nrow(deduped),
"Not all duplicate entries ignored when extracting unique references"
)
deduped2 <- deduplicate(my_df, "title",
rm_punctuation = TRUE,
to_lower = TRUE)
expect(
all.equal(deduped, deduped2),
"deduplicate not returning the same result as combining find_duplicates and extract_unique_references"
)
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