Crossref_members_abstracts.R

# this script uses the rcrossref package to interface with the Crossref API
# to get information on the abstract coverage per member ID
# Crossref REST API information: https://github.com/CrossRef/rest-api-doc

# use rcrossref
# vignette: https://cran.r-project.org/web/packages/rcrossref/rcrossref.pdf

# install.packages("tidyverse")
# install.packages("rcrossref")
library(tidyverse)
library(rcrossref)

#when error message `content-type` == "application/json;charset=UTF-8" is not TRUE, reinstall dev version of rcrossref
# detach("package:rcrossref", unload = TRUE)
# remotes::install_github("ropensci/rcrossref")
# library(rcrossref)

# set email in Renviron
# file.edit("~/.Renviron")
# add email address to be shared with Crossref:
# crossref_email = name@example.com
# save the file and restart your R session

# Use low level API as this includes abstract coverage per type
# set parse = FALSE to get JSON, parse = TRUE to get list output
getCrossref_low <- function(offset) {
  res <- cr_members_(
    offset = offset,
    limit = 1000,
    parse = TRUE
  ) %>%
    .$message %>%
    .$items

  return(res)
}

# add progress bar to function
getCrossref_low_progress <- function(offset) {
  pb$tick()$print()
  result <- getCrossref_low(offset)

  return(result)
}

# extract relevant variables with pluck
# also consider (from ?pluck):
# The map() functions use pluck() by default to retrieve multiple values from a list:
# map(x, list(2, "elt")) is equivalent to map(x, pluck, 2, "elt")

extractData_all <- function(x) {
  data <- tibble(
    id = map_dbl(x, "id"),
    primary_name = map_chr(x, "primary-name"),
    count_current_type = map_dbl(x,
      list(
        "counts",
        "current-dois"
      ),
      .default = 0
    ),
    deposits_abstracts_current = map_lgl(x,
      list(
        "flags",
        "deposits-abstracts-current"
      ),
      .default = NA
    ),
    abstracts_current_type = map(x,
      list(
        "coverage",
        "abstracts-current"
      ),
      .default = 0
    )
  ) %>%
    # keep only members with (current) output of type
    filter(count_current_type > 0) %>%
    # convert abstract coverage into numerical, then percentage
    mutate(
      abstracts_current_type = as.double(abstracts_current_type),
      abstracts_current_type = round(abstracts_current_type, 3)
    ) %>%
    # arrange in descending order of count
    arrange(desc(count_current_type))

  return(data)
}



extractData_type <- function(x, type) {
  data <- tibble(
    id = map_dbl(x, "id"),
    primary_name = map_chr(x, "primary-name"),
    count_current_type = map_dbl(x,
      list(
        "counts-type",
        "current",
        type
      ),
      .default = 0
    ),
    deposits_abstracts_current = map_lgl(x,
      list(
        "flags",
        "deposits-abstracts-current"
      ),
      .default = NA
    ),
    abstracts_current_type = map(x,
      list(
        "coverage-type",
        "current",
        type,
        "abstracts"
      ),
      .default = 0
    )
  ) %>%
    # keep only members with (current) output of type
    filter(count_current_type > 0) %>%
    # convert abstract coverage into numerical
    mutate(abstracts_current_type = as.double(abstracts_current_type)) %>%
    # arrange in descending order of count
    arrange(desc(count_current_type))

  return(data)
}

# define function to write to csv
toFile <- function(type, data, path) {
  filename <- paste0(path, "/crossref_member_abstracts_current_", type, "_", date, ".csv")
  write_csv(data, filename)
}


#------------------------------------------------------------------------------

# set date
date <- Sys.Date()
# create output directory
path <- file.path("data", date)
dir.create(path)


# get number of members
res <- cr_members(limit = 0)
total <- res$meta$total_results

# set vector of offset values
c <- seq(0, total, by = 1000)

# set parameter for progress bar
pb <- progress_estimated(length(c))

# get API results, flatten into 1 list
res <- map(c, getCrossref_low_progress) %>%
  flatten()

# extract data for different types, write each to file
type <- "all"
data <- extractData_all(res)
toFile(type, data, path)

type <- "journal-article"
data <- extractData_type(res, type)
toFile(type, data, path)

type <- "posted-content"
data <- extractData_type(res, type)
toFile(type, data, path)

type <- "book-chapter"
data <- extractData_type(res, type)
toFile(type, data, path)

type <- "monograph"
data <- extractData_type(res, type)
toFile(type, data, path)