Package 'synthesisr' reference manual

Title:	Import, Assemble, and Deduplicate Bibliographic Datasets
Description:	A critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as 'Web of Science' or 'Scopus'. These databases often export in different formats or with different metadata tags. 'synthesisr' expands on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging and deduplication of the resulting dataset.
Authors:	Martin Westgate [aut, cre] , Eliza Grames [aut]
Maintainer:	Martin Westgate <[email protected]>
License:	GPL-3
Version:	0.3.0
Built:	2025-02-21 02:22:06 UTC
Source:	https://github.com/mjwestgate/synthesisr

synthesisr: Import, assemble, and deduplicate bibiliographic datasets

Description

Systematic review searches include multiple databases that export results in a variety of formats with overlap in coverage between databases. To streamline the process of importing, assembling, and deduplicating results, synthesisr recognizes bibliographic files exported from databases commonly used for systematic reviews and merges results into a standardized format.

Import & Export

The key task performed by synthesisr is flexible import and presentation of bibliographic data. This is typically achieved by read_refs(), which can import multiple files at once and link them together into a single data.frame. Conversely, export is via write_refs(). Users that require more detailed control can use the following functions:

read_refs Read bibliographic data
write_refs Write bibliographic data
detect_ Detect file attributes
parse_ Parse a vector containing bibliographic data
clean_ Cleaning functions for author and column names
code_lookup A dataset of potential ris tags

Formatting

bibliography Methods for class bibliography
format_citation Return a clean citation from a bibliography or data.frame
add_line_breaks Set a maximum character width for strings

Deduplication

When importing from multiple databases, it is likely that there will be duplicates in the resulting dataset. The easiest way to deal with this problem in synthesisr is using the deduplicate() function; but this can be risky, particularly if there are no DOIs in the dataset. To get finer control of the deduplication process, consider using the sub-functions:

deduplicate Semi-automated duplicate removal
find_duplicates Locate potentially duplicated references
extract_unique_references Return a data.frame with only 'unique' references
review_duplicates Manually review potential duplicates
override_duplicates Manually override identified duplicates
fuzz_ Fuzzy string matching c/o fuzzywuzzy
string_ Fuzzy string matching c/o stringdist

Deprecated

merge_columns Synonymous with dplyr::bind_rows

Author(s)

Maintainer: Martin Westgate [email protected] (ORCID)

Authors:

Eliza Grames [email protected] (ORCID)

Add line breaks to one or more strings

Description

This function takes a vector of strings and adds line breaks every n characters. Primarily built to be called internally by format_citation(), this function has been made available as it can be useful in other contexts.

Usage

add_line_breaks(x, n = 50, max_n = NULL, html = FALSE, max_time = NULL)
add_line_breaks(x, n = 50, max_n = NULL, html = FALSE, max_time = NULL)

Arguments

`x`	Either a string or a vector; if the vector is not of class character if will be coerced to one using `as.character()`.
`n`	Numeric: The desired number of characters that should separate consecutive line breaks.
`max_n`	DEPRECATED: If provided will currently overwrite `n`; otherwise synonymous with `n` and will be removed from future versions.
`html`	Logical: Should the line breaks be specified in html?
`max_time`	DEPRECATED: Previously the maximum amount of time (in seconds) allowed to adjust groups until character thresholds are reached. Ignored.

Details

Line breaks are only added between words, so the value of n is actually a threshold value rather than being matched exactly.

Value

Returns the input vector unaltered except for the addition of line breaks.

Examples

add_line_breaks(c("On the Origin of Species"), n = 10)
add_line_breaks(c("On the Origin of Species"), n = 10)

bibliography-class

Description

This is a small number of standard methods for interacting with class 'bibliography'. More may be added later.

Usage

## S3 method for class 'bibliography'
summary(object, ...)

## S3 method for class 'bibliography'
print(x, n, ...)

## S3 method for class 'bibliography'
x[n]

## S3 method for class 'bibliography'
c(...)

## S3 method for class 'bibliography'
as.data.frame(x, ...)

as.bibliography(x, ...)

## S3 method for class 'bibliography'
as_tibble(x, ..., .rows, .name_repair, rownames)
## S3 method for class 'bibliography'
summary(object, ...)

## S3 method for class 'bibliography'
print(x, n, ...)

## S3 method for class 'bibliography'
x[n]

## S3 method for class 'bibliography'
c(...)

## S3 method for class 'bibliography'
as.data.frame(x, ...)

as.bibliography(x, ...)

## S3 method for class 'bibliography'
as_tibble(x, ..., .rows, .name_repair, rownames)

Arguments

`object`	An object of class 'bibliography'
`...`	Any further information
`x`	An object of class 'bibliography'
`n`	Number of items to select/print
`.rows`	currently ignored
`.name_repair`	currently ignored
`rownames`	currently ignored

Details

Methods for class bibliography

Clean a `tibble` or vector

Description

Cleans column and author names

Usage

clean_df(data)

clean_authors(x)

clean_colnames(x)
clean_df(data)

clean_authors(x)

clean_colnames(x)

Arguments

`data`	A `tibble` with bibliographic information.
`x`	A vector of strings

Value

Returns the input, but cleaner.

Examples

df <-  data.frame(
  X..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",
    "Reproducible, flexible and high-throughput data extraction from primary literature"),
  YEAR = c("2019", "2019", "2019", "2019"),
  authors = c(
    "Haddaway et al",
    "Westgate",
    "EM Grames AND AN Stillman  & MW Tingley and CS Elphick",
    "Pick et al")
)

clean_df(df)

# or use sub-functions
colnames(df) <- clean_colnames(df)
# colnames(df) <- clean_colnames(colnames(df)) # also works
df$author <- clean_authors(df$author)

df <-  data.frame(
  X..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",
    "Reproducible, flexible and high-throughput data extraction from primary literature"),
  YEAR = c("2019", "2019", "2019", "2019"),
  authors = c(
    "Haddaway et al",
    "Westgate",
    "EM Grames AND AN Stillman  & MW Tingley and CS Elphick",
    "Pick et al")
)

clean_df(df)

# or use sub-functions
colnames(df) <- clean_colnames(df)
# colnames(df) <- clean_colnames(colnames(df)) # also works
df$author <- clean_authors(df$author)

Bibliographic code lookup for search results assembly

Description

A data frame that can be used to look up common codes for different bibliographic fields across databases and merge them to a common format.

Usage

code_lookup
code_lookup

Format

A data.frame with 226 obs of 12 variables

code: code used in search results
order: the order in which to rank fields in assembled results
category_description: type of bibliographic data
entry_description: description of field
field: bibliographic field that codes correspond to
ris_generic: logical: If the code is used in generic ris files
ris_wos: logical: If the code is used in Web of Science ris files
ris_pubmed: logical: If the code is used in PubMed ris files
ris_scopus: logical: If the code is used in Scopus ris files
ris_asp: logical: If the code is used in Academic Search Premier ris files
ris_ovid: logical: If the code is used in Ovid ris files
ris_synthesisr: logical: If the code used in synthesisr imports & exports

Remove duplicates from a bibliographic data set

Description

Removes duplicates using sensible defaults

Usage

deduplicate(data, match_by, method, type = "merge", ...)
deduplicate(data, match_by, method, type = "merge", ...)

Arguments

`data`	A `data.frame` containing bibliographic information.
`match_by`	Name of the column in `data` where duplicates should be sought.
`method`	The duplicate detection function to use; see `link{string_}` or `link{fuzz_}` for examples. Passed to `find_duplicates()`.
`type`	How should entries be selected? Default is `"merge"` which selects the entries with the largest number of characters in each column. Alternatively `"select"` returns the row with the highest total number of characters.
`...`	Arguments passed to `find_duplicates()`.

Details

This is a wrapper function to find_duplicates() and extract_unique_references(), which tries to choose some sensible defaults. Use with care.

Value

A data.frame containing data identified as unique.

Examples

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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)
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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)

Detect file formatting information

Description

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 tibble.

Usage

detect_parser(x)

detect_delimiter(x)

detect_lookup(tags)

detect_year(df)
detect_parser(x)

detect_delimiter(x)

detect_lookup(tags)

detect_year(df)

Arguments

`x`	A character vector containing bibliographic data
`tags`	A character vector containing RIS tags.
`df`	a data.frame containing bibliographic data

Value

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.

Examples

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)
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)

Remove duplicates from a bibliographic data set

Description

Given a list of duplicate entries and a data set, this function extracts only unique references.

Usage

extract_unique_references(data, matches, type = "merge")
extract_unique_references(data, matches, type = "merge")

Arguments

`data`	A `data.frame` containing bibliographic information.
`matches`	A vector showing which entries in `data` are duplicates.
`type`	How should entries be selected to retain? Default is `"merge"`, which selects the entries with the largest number of characters in each column. Alternatively, `"select"` returns the row with the highest total number of characters.

Value

Returns a data.frame of unique references.

Examples

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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)
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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)

Detect duplicate values

Description

Identifies duplicate bibliographic entries using different duplicate detection methods.

Usage

find_duplicates(
  data,
  method = "exact",
  group_by,
  threshold,
  to_lower = FALSE,
  rm_punctuation = FALSE
)
find_duplicates(
  data,
  method = "exact",
  group_by,
  threshold,
  to_lower = FALSE,
  rm_punctuation = FALSE
)

Arguments

`data`	A character vector containing duplicate bibliographic entries.
`method`	A string indicating how matching should be calculated. Either `"exact"` for exact matching (the default), or the name of a function for calculating string distance.
`group_by`	An optional vector, data.frame or list containing data to use as 'grouping' variables; that is, categories within which duplicates should be sought. Defaults to NULL, in which case all entries are compared against all others. Ignored if `method = "exact"`.
`threshold`	Numeric: the cutoff threshold for deciding if two strings are duplicates. Sensible values depend on the `method` chosen. Defaults to 5 if `method = "string_osa"` and must be specified in all other instances except `method = "exact"` (where no threshold is required).
`to_lower`	Logical: Should all entries be converted to lower case before calculating string distance? Defaults to `FALSE.`
`rm_punctuation`	Logical: Should punctuation should be removed before calculating string distance? Defaults to `FALSE.`

Value

Returns a vector of duplicate matches, with attributes listing methods used.

Examples

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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)
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",
    "Reproducible, flexible and high-throughput data extraction from primary literature",
    "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
)

extract_unique_references(my_df, matches = dups)

# or, in one line:
deduplicate(my_df, "title",
  method = "string_osa",
  rm_punctuation = TRUE,
  to_lower = TRUE)

Format a citation

Description

This function takes an object of class data.frame, list, or bibliography and returns a formatted citation.

Usage

format_citation(
  data,
  details = TRUE,
  abstract = FALSE,
  add_html = FALSE,
  line_breaks = FALSE,
  ...
)
format_citation(
  data,
  details = TRUE,
  abstract = FALSE,
  add_html = FALSE,
  line_breaks = FALSE,
  ...
)

Arguments

`data`	An object of class `data.frame`, `list`, or `bibliography.`
`details`	Logical: Should identifying information such as author names & journal titles be displayed? Defaults to `TRUE`.
`abstract`	Logical: Should the abstract be shown (if available)? Defaults to `FALSE.`
`add_html`	Logical: Should the journal title be italicized using html codes? Defaults to `FALSE`.
`line_breaks`	Either logical, stating whether line breaks should be added, or numeric stating how many characters should separate consecutive line breaks. Defaults to `FALSE`.
`...`	any other arguments.

Value

Returns a string of length equal to length(data) that contains formatted citations.

Examples

roses <- c("@article{haddaway2018,
  title={ROSES RepOrting standards for Systematic Evidence Syntheses:
  pro forma, flow-diagram and descriptive summary of the plan and
  conduct of environmental systematic reviews and systematic maps},
  author={Haddaway, Neal R and Macura, Biljana and Whaley, Paul and Pullin, Andrew S},
  journal={Environmental Evidence},
  volume={7},
  number={1},
  pages={7},
  year={2018},
  publisher={Springer}
}")

tmp <- tempfile()
writeLines(roses, tmp)

citation <- read_ref(tmp)
format_citation(citation)
roses <- c("@article{haddaway2018,
  title={ROSES RepOrting standards for Systematic Evidence Syntheses:
  pro forma, flow-diagram and descriptive summary of the plan and
  conduct of environmental systematic reviews and systematic maps},
  author={Haddaway, Neal R and Macura, Biljana and Whaley, Paul and Pullin, Andrew S},
  journal={Environmental Evidence},
  volume={7},
  number={1},
  pages={7},
  year={2018},
  publisher={Springer}
}")

tmp <- tempfile()
writeLines(roses, tmp)

citation <- read_ref(tmp)
format_citation(citation)

Calculate similarity between two strings

Description

These functions duplicate the approach of the 'fuzzywuzzy' Python library for calculating string similarity.

Usage

fuzzdist(
  a,
  b,
  method = c("fuzz_m_ratio", "fuzz_partial_ratio", "fuzz_token_sort_ratio",
    "fuzz_token_set_ratio")
)

fuzz_m_ratio(a, b)

fuzz_partial_ratio(a, b)

fuzz_token_sort_ratio(a, b)

fuzz_token_set_ratio(a, b)
fuzzdist(
  a,
  b,
  method = c("fuzz_m_ratio", "fuzz_partial_ratio", "fuzz_token_sort_ratio",
    "fuzz_token_set_ratio")
)

fuzz_m_ratio(a, b)

fuzz_partial_ratio(a, b)

fuzz_token_sort_ratio(a, b)

fuzz_token_set_ratio(a, b)

Arguments

`a`	A character vector of items to match to b.
`b`	A character vector of items to match to a.
`method`	The method to use for fuzzy matching.

Value

Returns a score of same length as b, giving the proportional dissimilarity between a and b.

Note

fuzz_m_ratio() is a measure of the number of letters that match between two strings. It is calculated as one minus two times the number of matched characters, divided by the number of characters in both strings.

fuzz_partial_ratio() calculates the extent to which one string is a subset of the other. If one string is a perfect subset, then this will be zero.

fuzz_token_sort_ratio() sorts the words in both strings into alphabetical order, and checks their similarity using fuzz_m_ratio().

fuzz_token_set_ratio() is similar to fuzz_token_sort_ratio(), but compares both sorted strings to each other, and to a third group made of words common to both strings. It then returns the maximum value of fuzz_m_ratio() from these comparisons.

fuzzdist() is a wrapper function, for compatability with stringdist.

Examples

fuzzdist("On the Origin of Species",
         "Of the Original Specs",
         method = "fuzz_m_ratio")
fuzzdist("On the Origin of Species",
         "Of the Original Specs",
         method = "fuzz_m_ratio")

Bind two or more data frames with different columns

Description

Takes two or more data.frames with different column names or different column orders and binds them to a single data.frame. This function is maintained for backwards compatibility, but it is synonymous with dplyr::bind_rows() and will be depracated in future.

Usage

merge_columns(x, y)
merge_columns(x, y)

Arguments

`x`	Either a data.frame or a list of data.frames.
`y`	A data.frame, optional if x is a list.

Value

Returns a single data.frame with all the input data frames merged.

Examples

df_1 <-  data.frame(
  title = c(
    "EviAtlas: a tool for visualising evidence synthesis databases",
    "revtools: An R package to support article screening for evidence synthesis"
  ),
  year = c("2019", "2019")
)

df_2 <-  data.frame(
  title = c(
    "An automated approach to identifying search terms for systematic reviews",
    "Reproducible, flexible and high-throughput data extraction from primary literature"
  ),
  authors = c("Grames et al", "Pick et al")
)

merge_columns(df_1, df_2)
df_1 <-  data.frame(
  title = c(
    "EviAtlas: a tool for visualising evidence synthesis databases",
    "revtools: An R package to support article screening for evidence synthesis"
  ),
  year = c("2019", "2019")
)

df_2 <-  data.frame(
  title = c(
    "An automated approach to identifying search terms for systematic reviews",
    "Reproducible, flexible and high-throughput data extraction from primary literature"
  ),
  authors = c("Grames et al", "Pick et al")
)

merge_columns(df_1, df_2)

Manually override duplicates

Description

Re-assign group numbers to text that was classified as duplicated but is unique.

Usage

override_duplicates(matches, overrides)
override_duplicates(matches, overrides)

Arguments

`matches`	Numeric: a vector of group numbers for texts that indicates duplicates and unique values returned by the `find_duplicates` function.
`overrides`	Numeric: a vector of group numbers that are not true duplicates.

Value

The input matches vector with unique group numbers for members of groups that the user overrides.

Parse bibliographic text in a variety of formats

Description

Text in standard formats - such as imported via base::readLines() - can be parsed using a variety of standard formats. Use detect_parser() to determine which is the most appropriate parser for your situation. Note that parse_tsv() and parse_csv() are maintained for backwards compatability only; within read_ref these have been replaced by vroom::vroom().

Usage

parse_bibtex(x)

parse_csv(x)

parse_tsv(x)

parse_pubmed(x)

parse_ris(x, tag_naming = "best_guess")
parse_bibtex(x)

parse_csv(x)

parse_tsv(x)

parse_pubmed(x)

parse_ris(x, tag_naming = "best_guess")

Arguments

`x`	A character vector containing bibliographic information in ris format.
`tag_naming`	What format are ris tags in? Defaults to `"best_guess"` See `read_refs()` for a list of accepted arguments.

Value

Returns an object of class bibliography (ris, bib, or pubmed formats) or data.frame (csv or tsv).

Examples

eviatlas <- c(
  "TY  - JOUR",
  "AU  - Haddaway, Neal R.",
  "AU  - Feierman, Andrew",
  "AU  - Grainger, Matthew J.",
  "AU  - Gray, Charles T.",
  "AU  - Tanriver-Ayder, Ezgi",
  "AU  - Dhaubanjar, Sanita",
  "AU  - Westgate, Martin J.",
  "PY  - 2019",
  "DA  - 2019/06/04",
  "TI  - EviAtlas: a tool for visualising evidence synthesis databases",
  "JO  - Environmental Evidence",
  "SP  - 22",
  "VL  - 8",
  "IS  - 1",
  "SN  - 2047-2382",
  "UR  - https://doi.org/10.1186/s13750-019-0167-1",
  "DO  - 10.1186/s13750-019-0167-1",
  "ID  - Haddaway2019",
  "ER  - "
)

detect_parser(eviatlas) # = "parse_ris"
df <- as.data.frame(parse_ris(eviatlas))
ris_out <- write_refs(df, format = "ris", file = FALSE)
eviatlas <- c(
  "TY  - JOUR",
  "AU  - Haddaway, Neal R.",
  "AU  - Feierman, Andrew",
  "AU  - Grainger, Matthew J.",
  "AU  - Gray, Charles T.",
  "AU  - Tanriver-Ayder, Ezgi",
  "AU  - Dhaubanjar, Sanita",
  "AU  - Westgate, Martin J.",
  "PY  - 2019",
  "DA  - 2019/06/04",
  "TI  - EviAtlas: a tool for visualising evidence synthesis databases",
  "JO  - Environmental Evidence",
  "SP  - 22",
  "VL  - 8",
  "IS  - 1",
  "SN  - 2047-2382",
  "UR  - https://doi.org/10.1186/s13750-019-0167-1",
  "DO  - 10.1186/s13750-019-0167-1",
  "ID  - Haddaway2019",
  "ER  - "
)

detect_parser(eviatlas) # = "parse_ris"
df <- as.data.frame(parse_ris(eviatlas))
ris_out <- write_refs(df, format = "ris", file = FALSE)

Import bibliographic search results

Description

Imports common bibliographic reference formats (i.e. .bib, .ris, or .txt).

Usage

read_refs(
  filename,
  tag_naming = "best_guess",
  return_df = TRUE,
  verbose = FALSE,
  locale = default_locale()
)
read_refs(
  filename,
  tag_naming = "best_guess",
  return_df = TRUE,
  verbose = FALSE,
  locale = default_locale()
)

Arguments

`filename`	A path to a filename or vector of filenames containing search results to import.
`tag_naming`	Either a length-1 character stating how should ris tags be replaced (see details for a list of options), or an object inheriting from class `data.frame` containing user-defined replacement tags.
`return_df`	If `TRUE` (default), returns a `data.frame`; if `FALSE`, returns a list.
`verbose`	If `TRUE`, prints status updates (defaults to `FALSE`).

Details

The default for argument tag_naming is "best_guess", which estimates what database has been used for ris tag replacement, then fills any gaps with generic tags. Any tags missing from the database (i.e. code_lookup) are passed unchanged. Other options are to use tags from Web of Science ("wos"), Scopus ("scopus"), Ovid ("ovid") or Academic Search Premier ("asp"). If a data.frame is given, then it must contain two columns: "code" listing the original tags in the source document, and "field" listing the replacement column/tag names. The data.frame may optionally include a third column named "order", which specifies the order of columns in the resulting data.frame; otherwise this will be taken as the row order. Finally, passing "none" to replace_tags suppresses tag replacement.

Value

Returns a data.frame or list of assembled search results.

Examples

litsearchr <- c(
  "@article{grames2019,
  title={An automated approach to identifying search terms for
  systematic reviews using keyword co-occurrence networks},
  author={Grames, Eliza M and Stillman, Andrew N and Tingley, Morgan W and Elphick, Chris S},
  journal={Methods in Ecology and Evolution},
  volume={10},
  number={10},
  pages={1645--1654},
  year={2019},
  publisher={Wiley Online Library}
}"
)

tmp <- tempfile()

writeLines(litsearchr, tmp)

df <- read_refs(tmp, return_df = TRUE, verbose = TRUE)
litsearchr <- c(
  "@article{grames2019,
  title={An automated approach to identifying search terms for
  systematic reviews using keyword co-occurrence networks},
  author={Grames, Eliza M and Stillman, Andrew N and Tingley, Morgan W and Elphick, Chris S},
  journal={Methods in Ecology and Evolution},
  volume={10},
  number={10},
  pages={1645--1654},
  year={2019},
  publisher={Wiley Online Library}
}"
)

tmp <- tempfile()

writeLines(litsearchr, tmp)

df <- read_refs(tmp, return_df = TRUE, verbose = TRUE)

Manually review potential duplicates

Description

Allows users to manually review articles classified as duplicates.

Usage

review_duplicates(text, matches)
review_duplicates(text, matches)

Arguments

`text`	A character vector of the text that was used to identify potential duplicates.
`matches`	Numeric: a vector of group numbers for texts that indicates duplicates and unique values returned by the `find_duplicates` function.

Value

A data.frame of potential duplicates grouped together.

Calculate similarity between two strings

Description

These functions each access a specific "methods" argument provided by stringdist, and are provided for convenient calling by find_duplicates(). They do not include any new functionality beyond that given by stringdist, which you should use for your own analyses.

Usage

string_osa(a, b)

string_lv(a, b)

string_dl(a, b)

string_hamming(a, b)

string_lcs(a, b)

string_qgram(a, b)

string_cosine(a, b)

string_jaccard(a, b)

string_jw(a, b)

string_soundex(a, b)
string_osa(a, b)

string_lv(a, b)

string_dl(a, b)

string_hamming(a, b)

string_lcs(a, b)

string_qgram(a, b)

string_cosine(a, b)

string_jaccard(a, b)

string_jw(a, b)

string_soundex(a, b)

Arguments

`a`	A character vector of items to match to b.
`b`	A character vector of items to match to a.

Value

Returns a score of same length as b, giving the dissimilarity between a and b.

Export data to a bibliographic format

Description

This function exports data.frames containing bibliographic information to either a .ris or .bib file.

Usage

write_refs(x, file, format = "ris", tag_naming = "synthesisr", write = TRUE)

write_bib(x)

write_ris(x, tag_naming = "synthesisr")
write_refs(x, file, format = "ris", tag_naming = "synthesisr", write = TRUE)

write_bib(x)

write_ris(x, tag_naming = "synthesisr")

Arguments

`x`	Either a data.frame containing bibliographic information or an object of class bibliography.
`file`	filename to save to.
`format`	What format should the data be exported as? Options are ris or bib.
`tag_naming`	what naming convention should be used to write RIS files? See details for options.
`write`	Logical should a file should be written? If FALSE returns a `list`.

Value

This function is typically called for it's side effect of writing a file in the specified location and format. If write is FALSE, returns a character vector containing bibliographic information in the specified format.

Examples

eviatlas <- c(
  "TY  - JOUR",
  "AU  - Haddaway, Neal R.",
  "AU  - Feierman, Andrew",
  "AU  - Grainger, Matthew J.",
  "AU  - Gray, Charles T.",
  "AU  - Tanriver-Ayder, Ezgi",
  "AU  - Dhaubanjar, Sanita",
  "AU  - Westgate, Martin J.",
  "PY  - 2019",
  "DA  - 2019/06/04",
  "TI  - EviAtlas: a tool for visualising evidence synthesis databases",
  "JO  - Environmental Evidence",
  "SP  - 22",
  "VL  - 8",
  "IS  - 1",
  "SN  - 2047-2382",
  "UR  - https://doi.org/10.1186/s13750-019-0167-1",
  "DO  - 10.1186/s13750-019-0167-1",
  "ID  - Haddaway2019",
  "ER  - "
)

detect_parser(eviatlas) # = "parse_ris"
df <- as.data.frame(parse_ris(eviatlas))
ris_out <- write_refs(df, format = "ris", file = FALSE)
eviatlas <- c(
  "TY  - JOUR",
  "AU  - Haddaway, Neal R.",
  "AU  - Feierman, Andrew",
  "AU  - Grainger, Matthew J.",
  "AU  - Gray, Charles T.",
  "AU  - Tanriver-Ayder, Ezgi",
  "AU  - Dhaubanjar, Sanita",
  "AU  - Westgate, Martin J.",
  "PY  - 2019",
  "DA  - 2019/06/04",
  "TI  - EviAtlas: a tool for visualising evidence synthesis databases",
  "JO  - Environmental Evidence",
  "SP  - 22",
  "VL  - 8",
  "IS  - 1",
  "SN  - 2047-2382",
  "UR  - https://doi.org/10.1186/s13750-019-0167-1",
  "DO  - 10.1186/s13750-019-0167-1",
  "ID  - Haddaway2019",
  "ER  - "
)

detect_parser(eviatlas) # = "parse_ris"
df <- as.data.frame(parse_ris(eviatlas))
ris_out <- write_refs(df, format = "ris", file = FALSE)

Package 'synthesisr'

Help Index

synthesisr: Import, assemble, and deduplicate bibiliographic datasets

Description

Import & Export

Formatting

Deduplication

Deprecated

Author(s)

See Also

Add line breaks to one or more strings

Description

Usage

Arguments

Details

Value

Examples

bibliography-class

Description

Usage

Arguments

Details

Clean a tibble or vector

Description

Usage

Arguments

Value

Examples

Bibliographic code lookup for search results assembly

Description

Usage

Format

Remove duplicates from a bibliographic data set

Description

Usage

Arguments

Details

Value

See Also

Examples

Detect file formatting information

Description

Usage

Arguments

Value

Examples

Remove duplicates from a bibliographic data set

Description

Usage

Arguments

Value

See Also

Examples

Detect duplicate values

Description

Usage

Arguments

Value

See Also

Examples

Format a citation

Description

Usage

Arguments

Value

Examples

Calculate similarity between two strings

Description

Usage

Arguments

Value

Note

Examples

Bind two or more data frames with different columns

Description

Usage

Arguments

Value

Examples

Manually override duplicates

Clean a `tibble` or vector