R/rt_fund.R

In serghiou/rtransparent: Identifies indicators of transparency

# TODO: First find the paragraphs that contain intersting words and then
#    apply the obliteration and rest of functions and test if this saves
#    time!



#' Identify mentions of support
#'
#' Identifies mentions of "This work was funded by ..." and of "This work was
#'     completed using funds from ..."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_1 <- function(article) {

  # synonyms <- .create_synonyms()
  # words <- c("This", "research", "is_have", "funded", "by")
  #
  # this_research <-
  #   synonyms %>%
  #   magrittr::extract(words[1:2]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   paste(collapse = " ")
  #
  # synonyms %>%
  #   magrittr::extract(words[3:5]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   # lapply(.max_words) %>%
  #   paste(collapse = synonyms$txt) %>%
  #   paste(this_research, .) %>%
  #   grep(article, perl = T)


  synonyms <- .create_synonyms()
  words <- c("This_singular", "research_singular", "is_singular", "funded_funding", "by")

  this_research <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = ".{0,15}")

  was_funded_by <-
    synonyms %>%
    magrittr::extract(words[3:5]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  singular <-
    c(this_research, was_funded_by) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

  if (!length(singular)) {

    words <- c("These", "researches", "are", "funded_funding", "by")

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.bound, location = "end") %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste(collapse = synonyms$txt) %>%
      grep(article, perl = T)

  } else {

    return(singular)

  }
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funded_funding", "this_singular", "research_singular")

  singular <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

  if (!length(singular)) {

    words <- c("funded", "these", "researches")

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.bound, location = "end") %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste(collapse = synonyms$txt) %>%
      grep(article, perl = T)

  } else {

    return(singular)

  }
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_3 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("research", "is_have", "funded", "by")

  research_is <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    paste(research_is, ., sep = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_4 <- function(article) {

  # TODO: I have now made this function much more general than before, by
  #    removing the requirement for "is" from the start. It seems that this now
  #    became more sensitive with no loss in specificity. If it remains so in
  #    further testing, remove all previews functions, which are basically more
  #    specific versions of this!

  synonyms <- .create_synonyms()
  words <- c("funded", "by", "award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


get_support_5 <- function(article) {

  # TODO: This is trying to capture some phrases missed by get_support_4 b/c of
  #    the requirement for is, e.g. "Project supported by X". I am introducing
  #    a 3 word limit at the start to make it stricter - upon tests, there were
  #    very very few mistakes (1/200 FP) without introducing this restriction.

  synonyms <- .create_synonyms()
  words <- c("funded", "by", "foundation")

  funded_by_award <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  start_of_sentence <- "(^\\s*|(:|\\.)\\s*|[A-Z][a-zA-Z]+\\s*)"
  .max_words(start_of_sentence, n_max = 4, space_first = F) %>%
    paste0(funded_by_award) %>%
    grep(article, perl = T)
}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We gratefully acknowledge
#'     support from the UK Engineering and Physical Sciences Research Council.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_6 <- function(article) {

  synonyms <- .create_synonyms()
  words_1 <- c("acknowledge", "support_only", "foundation_award")
  words_2 <- c("support_only", "foundation_award", "acknowledged")

  .max_words(c("acknowledge", "support_only"), 2)

  acknowledge <-
    synonyms %>%
    magrittr::extract(words_1[1]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    lapply(.max_words)

  support_foundation <-
    synonyms %>%
    magrittr::extract(words_1[2:3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  a <-
    c(acknowledge, support_foundation) %>%
    paste(collapse = " ") %>%
    grep(article, perl = T)

  b <-
    synonyms %>%
    magrittr::extract(words_2) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

  return(unique(c(a, b)))
}


#' Identify mentions of support
#'
#' Return the index of support statements such as: Support was provided by the
#'     U.S. Department of Agriculture (USDA) Forest Service.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_7 <- function(article) {

  synonyms <- .create_synonyms()

  a <- "Support"
  b <- .encase(synonyms$received)
  d <- .encase(synonyms$by)
  grep(paste(a, b, d, sep = synonyms$txt), article, perl = T)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We are grateful to the
#'     National Institute of Mental Health (MH091070: PI’s Dante Cicchetti and
#'     Sheree L. Toth) for support of this work.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_8 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("foundation", "provide", "funding_financial", "for_of", "research")
  max_words <- .max_words(" ", n_max = 3, space_first = F)

  # foundation <-
  #   synonyms %>%
  #   magrittr::extract(words[1]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase)
  #
  # funding_for_research <-
  #   synonyms %>%
  #   magrittr::extract(words[2:4]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   paste(collapse = max_words)
#
#   for_research <-
#     synonyms %>%
#     magrittr::extract(words[4:5]) %>%
#     lapply(.bound) %>%
#     lapply(.encase) %>%
#     paste(collapse = max_words)

  # c(foundation, funding_for_research) %>%
  #   paste(collapse = synonyms$txt) %>%
  #   grep(article, perl = T)

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: The US Environmental
#'     Protection Agency’s (USEPA) Office of Research and Development funded and
#'     managed the research described in the present study.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_9 <- function(article) {

  # Only done for words other than "supported" b/c this is very generic

  synonyms <- .create_synonyms()
  words <- c("foundation", "funded", "research")

  foundation <-
    synonyms %>%
    magrittr::extract(words[1]) %>%
    lapply(.bound) %>%
    lapply(.encase)

  funding <-
    synonyms %>%
    magrittr::extract(words[2]) %>%
    lapply(grep, pattern = "upport", value = T, invert = T) %>%
    lapply(.bound) %>%
    lapply(.encase)

  research <-
    synonyms %>%
    magrittr::extract(words[3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste0(".{0,20}\\.")  # only match to words at the end of the sentence.

  c(foundation, funding, research) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We are grateful to the
#'     National Institute of Mental Health (MH091070: PI’s Dante Cicchetti and
#'     Sheree L. Toth) for support of this work.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_support_10 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("thank", "foundation", "funding_financial", "research")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Identify mentions of developed
#'
#' Return the index of statements such as: This publication was developed
#'     under Assistance Agreement No. 83563701-0 awarded by the U.S.
#'     Environmental Protection Agency to the University of Michigan.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_developed_1 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("This", "research", "is", "developed", "by", "foundation")

  this_research_is <-
    synonyms %>%
    magrittr::extract(words[1:3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  developed <- words[4]

  by_foundation <-
    synonyms %>%
    magrittr::extract(words[5:6]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  c(this_research_is, developed, by_foundation) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}

#' Identify mentions of received
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_received_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("received", "funds_award_financial", "by")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ") %>%
    grep(article, perl = T)
}


#' Identify mentions of received
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_received_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("received", "funding_financial", "by", "foundation_award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of recepient
#'
#' Returns the index of mentions such as: "Recipient of National Institutes of
#'     Health Grants AG044552, AI121621, and DK112365.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_recipient_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("recipient", "award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of "the authors ... financial support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_authors_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("This", "author", "funds_award_financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)


  # "[Ss]upport",
  # "[Ff]inancial assistance",
  # "\\b[Aa]id\\b",

  # The authors thank X for funding
  # The authors received no/did not receive financial support
  # The authors acknowledge the/disclosed receipt of support\\b/financial support of
  # The authors are supported by

  # The authors received no funds
  # The authors have no support or funding to report
}


#' Identify mentions of "the authors have no funding ..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_authors_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("This", "author", "have", "no", "funding_financial_award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of "thank ... financial support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_thank_1 <- function(article) {

  synonyms <- .create_synonyms()

  synonyms$financial <- c(synonyms$financial, "for supporting")
  words <- c("We", "thank", "financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of thank you statements
#'
#' Returns the index with the elements related to "The authors acknowledge
#'     Worldwide Cancer Research (AICR) 15-1002; Blueprint 282510; ..."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_thank_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("thank")
  txt <- "[a-zA-Z0-9\\s,()/:;-]*"

  thank <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  thank %>%
    paste("[0-9]{5}", sep = txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of "funding for this study was..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_fund_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funding_financial_award", "for", "research", "received")

  funding_for <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    paste(funding_for, ., sep = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Identify mentions of Funding titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_fund_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funding_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grep(article, perl = T)

  if (length(a) > 0) {

    if (nchar(article[a + 1]) == 0) {
      return(c(a, a + 2))
    } else {
      return(c(a, a + 1))
    }

  } else {

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.title, within_text = T) %>%
      lapply(.encase) %>%
      paste() %>%
      grep(article, perl = T)

  }
}


#' Identify mentions of Funding titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_fund_3 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("any_title")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.encase) %>%
    paste("[A-Z]") %>%
    grep(article, perl = T)

}


#' Identify mentions of funds in acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_fund_acknow <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funded", "funds_award_financial")

  funded_synonyms <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    unlist()

  c(funded_synonyms, "NIH (|\\()(R|P)[0-9]{2}", "awarded by") %>%
    .encase %>%
    grep(article, perl = T, ignore.case = T)

}


#' Identify mentions of funds in acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_fund_acknow_new <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("acknowledge", "support_only", "grant|foundation|insititute|organization")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    unlist() %>%
    .encase %>%
    grep(article, perl = T, ignore.case = T)

}


# get_fund_acknow_new <- function(article) {
#
#   synonyms <- .create_synonyms()
#   words <- c("acknowledge")
#
#   a <- synonyms %>%
#     magrittr::extract(words) %>%
#     lapply(.bound) %>%
#     lapply(.encase)
#
#   grep(paste0(a, synonyms$txt, "[0-9]{3,10}"), article, perl = T)
#
# }


#' Identify mentions of "Supported by ..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_supported_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("Supported", "by")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ") %>%
    paste("[a-zA-Z]+") %>%
    grep(article, perl = T)

}

#' Identify mentions of Financial support titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_financial_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("financial_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste() %>%
    grep(article, perl = T)


  if (length(a) > 0) {

    if (nchar(article[a + 1]) == 0) {
      return(c(a, a + 2))
    } else {
      return(c(a, a + 1))
    }

  } else {

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.title, within_text = T) %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste() %>%
      grep(article, perl = T)

  }
}


#' Identify mentions of Financial support titles followed by specific text
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_financial_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("financial_title", "No")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = " ") %>%
    grep(article, perl = T)

}


#' Identify mentions of Financial support titles followed by specific text
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_financial_3 <- function(article) {


  synonyms <- .create_synonyms()
  words <- c("financial_title", "this", "research")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Identify mentions of Disclosure statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste(collapse = "|") %>%
    grep(article, perl = T)

  out <- integer()
  if (!!length(a)) {

    for (i in 1:length(a)) {

      if (nchar(article[a[i] + 1]) == 0) {
        b <- a[i] + 2
      } else {
        b <- a[i] + 1
      }

      d <-
        synonyms$funding_financial_award %>%
        lapply(.bound) %>%
        paste0(collapse = "|") %>%
        grep(article[b], perl = T)

      if (!!length(d)) out <- c(out, a[i], b)
    }
  }
  return(out)
}


#' Identify mentions of Disclosure statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_disclosure_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title", "funding_financial_award")

  disclosure <-
    synonyms %>%
    magrittr::extract(words[1]) %>%
    lapply(.title, within_text = T)

  funding <-
    synonyms %>%
    magrittr::extract(words[2]) %>%
    lapply(.bound)

  c(disclosure, funding) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Identify mentions of Financial support titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_grant_1 <- function(article) {

  # TODO: This whole function takes a LOT of time to run, but it is ONLY
  #    activated for "(Grant [Ss]ponsor(|s):)|)Contract [Gg]rant
  #    [Ss]ponsor(|s):). Consider replacing it with just this!!!

  synonyms <- .create_synonyms()
  words <- c("grant_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grep(article, perl = T)


  if (!!length(a)) {

    if (nchar(article[a + 1]) == 0) {
      return(c(a, a + 2))
    } else {
      return(c(a, a + 1))
    }

  } else {

    # This is done to avoid mentions such as "Grant AK, Brown AZ, ..."
    grant <- c("G(?i)rant ", "^[A-Z](?i)\\w+ grant ", "Contract grant ")

    support <-
      synonyms %>%
      magrittr::extract(c("support", "funder")) %>%
      lapply(paste0, "(?-i)") %>%
      lapply(.title, within_text = T) %>%
      unlist()

    grant %>%
      lapply(paste0, support) %>%
      unlist() %>%
      .encase() %>%
      grep(article, perl = T)

  }
}


#' Identify mentions of Grant numbers in the Funding/Acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_french_1 <- function(article) {

  # This study was financed by... - avoiding specifics b/c of UTF-8 characters
  grep("Cette.*tude.*financ.*par", article, perl = T, ignore.case = T)

}


#' Identify mentions of project numbers.
#'
#' Returns the index of mentions such as: "NIH Project no. AB123."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_project_acknow <- function(article) {

  grep("project (no|num)", article, perl = T, ignore.case = T)

}


#' Get common phrases
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_common_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "funding_financial_award", "is", "received")

  no_funding <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  was_received <-
    synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  no_funding %>%
    paste(was_received, sep = synonyms$txt) %>%
    grep(article, perl = T)
}



#' Get common phrases
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_common_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("No", "funding_financial_award", "received")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)
}


#' Get common phrases
#'
#' Identify statements of the following type: "All authors are required to
#'     disclose all affiliations, funding sources and financial or management
#'     relationships that could be perceived as potential sources of bias. The
#'     authors disclosed none.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_common_3 <- function(article) {

  grep("required to disclose.*disclosed none", article)

}


#' Get common phrases
#'
#' Identify statements of the following type: "There were no external funding
#'     sources for this study"
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_common_4 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "funding_financial_award", "for", "this", "research")

  no_funding <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  for_this <-
    synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  research <-
    synonyms %>%
    magrittr::extract(words[5]) %>%
    lapply(.bound) %>%
    lapply(.encase)

  c(no_funding, for_this, research) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = T)

}


#' Get common phrases
#'
#' Identify statements of the following type: "No specific sources of funding."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_common_5 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "sources", "funding_financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = .max_words(" ", space_first = F)) %>%
    grep(article, perl = T)

}


#' Identify mentions of "Acknowledgement and"
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_acknow_1 <- function(article) {

  txt <- "[a-zA-Z0-9\\s,()-]*"  # order matters

  txt_0 <- "^Acknowledg(|e)ment(|s)"
  txt_1 <- "(of|and)"
  txt_2 <- "([Ss]upport |\\b[Ff]unding|\\b[Ff]inancial)"

  total_txt <- c(txt_0, txt_1, txt_2)
  indicator_regex <- paste0(total_txt, collapse = " ")

  a <- grep(indicator_regex, article, perl = T, ignore.case = T)

  if (length(a) > 0) {
    if (!is.na(article[a + 1])) {
      # TODO: Change in all functions
      if (nchar(article[a + 1]) == 0) {
        return(c(a, a + 2))
      } else {
        return(c(a, a + 1))
      }
    } else {
      return(a)
    }
  } else {
    return(a)
  }
}


#' Identify acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
get_acknow_2 <- function(article) {

  txt_0 <- "(^A(?i)cknowledg(|e)ment(|s)(?-i))"

  txt_1 <- "(^Acknowledg(|e)ment(|s)"
  txt_2 <- "(of|and)"
  txt_3 <- "([Ss]upport |\\b[Ff]unding|\\b[Ff]inancial))"

  total_txt <- c(txt_1, txt_2, txt_3)
  indicator_regex <- paste0(total_txt, collapse = " ")
  indicator_regex <- paste(txt_0, indicator_regex, sep = "|")

  grep(indicator_regex, article, perl = T)
}


#' Avoid disclosures that are in fact COI statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
negate_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()

  txt <- "[a-zA-Z0-9\\s,()-]*"  # order matters

  disclose_synonyms <- c(
    "[Dd]isclose(|s)(|:|\\.)",
    "[Dd]isclosure(|s)(|:|\\.)"
  )

  conflict_synonyms <- c(
    "conflict(|s) of interest",
    "conflicting interest",
    "conflicting financial interest",
    "conflicting of interest",
    "conflits d'int",
    "conflictos de Inter"
  )

  compete_synonyms <- c(
    "competing interest",
    "competing of interest",
    "competing financial interest"
  )

  and_synonyms <- c(
    "and",
    "&",
    "or"
  )

  not_synonyms <- c(
    "not"
  )

  funded_synonyms <- c(
    "\\bfunded",
    "\\bfinanced",
    "\\bsupported",
    "\\bsponsored",
    "\\bresourced"
  )

  disclose <- .encase(disclose_synonyms)
  conflict <- .encase(c(conflict_synonyms, compete_synonyms))
  and <- .encase(and_synonyms)
  not <- .encase(not_synonyms)
  funded <- .encase(funded_synonyms)

  regex <- paste(disclose, conflict, and, not, funded, sep = txt)
  a <- grepl(regex, article, perl = T)

  if (any(a)) {

    return(a)

  } else {

    funded <- .encase(c(funded_synonyms, synonyms$funding))
    regex <- paste(disclose, funded, conflict, sep = txt)
    grepl(regex, article, perl = T)
  }
}


#' Identify "Funding disclosure. Nothing to declare" statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
negate_disclosure_2 <- function(article) {

  synonyms <- .create_synonyms()

  Disclosure_synonyms <- c(
    "F(?i)inancial disclosure(|s)(?-i)(|:|\\.)",
    "F(?i)inancial declaration(|s)(?-i)(|:|\\.)",
    "Disclosure(|:|\\.)",
    "Declaration(|:|\\.)"
  )

  disclosure_synonyms <- c(
    "financial disclosure(|s)",
    "financial declaration(|s)",
    "disclosure",
    "declaration"
  )

  disclose_synonyms <- c(
    "to disclose",
    "to declare",
    "to report"
  )

  Disclosure <- .encase(Disclosure_synonyms)
  disclosure <- .encase(disclosure_synonyms)
  disclose   <- .encase(disclose_synonyms)
  no <- .encase(synonyms$No)
  no_1 <- "(no|not have any)"
  no_2 <- "(nil|nothing)"

  regex_1 <-  # Financial disclosure: Nothing to declare
    paste(Disclosure, no) %>%
    .encase()
  regex_2 <-  # Financial disclosure: The authors have no financial disclosures
    paste(Disclosure, paste(no_1, disclosure), sep = synonyms$txt) %>%
    .encase()
  regex_3 <-  # Financial disclosure: The author has nothing to declare
    paste(Disclosure, paste(no_2, disclose), sep = synonyms$txt) %>%
    .encase()

  regex <- paste(regex_1, regex_2, regex_3, sep = "|")
  grepl(regex, article, perl = T)

}


#' Avoid financial that is part of COI statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
negate_conflict_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("conflict_title")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grepl(article, perl = T)
}


#' Avoid mentions that no funding information was provided
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
negate_absence_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("No", "info", "of", "funding_financial_award", "is", "received")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grepl(article, perl = T)

}


#' Remove mentions of COIs that may cause FPs
#'
#' Returns the text without potentially misleading mentions of COIs.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
obliterate_conflict_1 <- function(article) {

  # Good for finding, but not for substituting b/c it's  a lookahead
  # words <- c(
  #   # positive lookahead makes these phrases interchangeable
  #   "(?=[a-zA-Z0-9\\s,()-]*(financial|support))",
  #   "(?=[a-zA-Z0-9\\s,()-]*(conflict|competing))"
  # )

  synonyms <- .create_synonyms()

  financial_1 <- "(funding|financial|support)"
  financial_2 <- "(financial|support)"
  relationship <- synonyms$relationship %>% .bound() %>% .encase()
  conflict <- synonyms$conflict %>% .bound() %>% .encase()
  financial_interest <- "(financial(?:\\s+\\w+){0,3} interest)"

  regex_1 <- paste(financial_1, relationship, conflict, sep = synonyms$txt)
  regex_2 <- paste(conflict, relationship, financial_2, sep = synonyms$txt)
  regex_3 <- paste(relationship, financial_interest,  sep = synonyms$txt)

  c(regex_1, regex_2, regex_3) %>%
    lapply(.encase) %>%
    paste(collapse = "|") %>%
    gsub("", article, perl = T)

}


#' Remove disclosures with inappropriate sentences
#'
#' Returns the text without potentially misleading disclsoures mentions. This
#'     is intended to solve problems with disclosures, such as: Disclosure.
#'     Authors have no conflict of interests, and the work was not supported
#'     or funded by any drug company. This project was funded by the Deanship
#'     of Scientific Research, King Abdulaziz University, Jeddah, Saudi Arabia
#'     (Grant No. 4/165/1431);
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
obliterate_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title", "conflict", "and", "not", "funded")

  # disclosure_title <-
  #   synonyms %>%
  #   magrittr::extract(words[1]) %>%
  #   lapply(.title) %>%
  #   lapply(.encase)

  synonyms %>%
    magrittr::extract(words[2:5]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    paste0(synonyms$txt, ., synonyms$txt, "($|.)") %>%
    gsub("", article, perl = T)

  # disclosure_title %>%
  #   paste0("(", synonyms$txt, conflict_funded, synonyms$txt, ")") %>%
  #   gsub("\\1", article, perl = T)


  # if (any(a)) {
  #
  #   return(a)
  #
  # } else {
  #
  #   funded <- .encase(c(funded_synonyms, synonyms$funding))
  #   regex <- paste(disclose, funded, conflict, sep = txt)
  #   grepl(regex, article, perl = T)
  # }

}


#' Remove fullstops that are unlikely to represent end of sentence
#'
#' Returns the list of paragraphs without potentially misleading fullstops.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without misleading fullstops.
obliterate_fullstop_1 <- function(article) {

  j_p_a_i <- "([A-Z])(\\.)\\s*([A-Z])(\\.)\\s*([A-Z])(\\.)"

  article %>%
    stringr::str_replace_all(j_p_a_i, "\\1 \\3 \\5") %>%
    stringr::str_replace_all("([A-Z])(\\.)\\s*([A-Z])(\\.)", "\\1 \\3") %>%
    stringr::str_replace_all("(\\s[A-Z])(\\.) ([A-Z][a-z]+)", "\\1 \\3") %>%
    stringr::str_replace_all("\\.\\s*([a-z0-9])", " \\1") %>%
    stringr::str_replace_all("\\.([A-Z])", " \\1") %>%
    stringr::str_replace_all("\\.\\s*([A-Z]+[0-9])", " \\1") %>%
    stringr::str_replace_all("\\.([^\\s0-9\\[])", "\\1") %>%
    stringr::str_replace_all("([0-9])\\.([0-9])", "\\1\\2")

}


#' Remove references
#'
#' Returns the list of paragraphs without references.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without misleading fullstops.
obliterate_refs_1 <- function(article) {

  # Built like this to avoid distabilizing the algorithm
  article <- gsub("^.*\\([0-9]{4}\\).*$", "References", article)
  article <- gsub("^.* et al\\..*$", "References", article)

  return(article)
}


#' Identify and extract Funding statements in TXT files.
#'
#' Takes a TXT file and returns data related to the presence of a Funding
#'     statement, including whether a Funding statement exists. If a Funding
#'     statement exists, it extracts it.
#'
#' @param filename The name of the TXT file as a string.
#' @return A dataframe of results. It returns the PMID (if this was part of the
#'     filename), whether a funding statement was found, what this statement
#'     was and the name of the function that identified this text. The functions
#'     are returned to add flexibility in how this package is used, such as
#'     future definitions of COI that may differ from the one we used.
#' @examples
#' \dontrun{
#' # Path to PMC XML.
#' filepath <- "../inst/extdata/00003-PMID26637448-PMC4737611.txt"
#'
#' # Identify and extract meta-data and indicators of transparency.
#' results_table <- rt_fund(filepath)
#' }
#' @export
rt_fund <- function(filename) {

  # TODO Update to match format of rt_coi_pmc - careful as this change will
  # duplicate names of functions.

  index <- integer()
  disclosure <- integer()
  diff <- integer()

  # TODO: MOVE THIS TO THE pdf2text FUNCTION AND ENCODE AS UTF-8
  # Fix PDF to txt bugs
  broken_1 <- "([a-z]+)-\n*([a-z]+)"
  broken_2 <- "([a-z]+)(|,|;)\n*([a-z]+)"
  paragraphs <-
    readr::read_file(filename) %>%
    purrr::map(gsub, pattern = broken_1, replacement = "\\1\\2") %>%
    purrr::map(gsub, pattern = broken_2, replacement = "\\1\\3") %>%
    purrr::map(strsplit, "\n| \\*") %>%
    unlist() %>%
    utf8::utf8_encode()


  # TODO: MOVE UP TO obliterate_fullstop_1 TO pdf2text FUNCTION
  # Remove potentially misleading sequences
  utf_1 <- "(\\\\[a-z0-9]{3})"   # remove \\xfc\\xbe etc
  utf_2 <- "(\\\\[a-z])"   # \\f or
  paragraphs_pruned <-
    paragraphs %>%
    purrr::map_chr(gsub, pattern = utf_1, replacement = " ", perl = T) %>%
    purrr::map_chr(gsub, pattern = utf_2, replacement = "",  perl = T) %>%
    obliterate_fullstop_1() %>%
    obliterate_conflict_1() %>%
    obliterate_disclosure_1()  # Adds 30s overhead! TODO: Place elsehwere
  # paragraphs_pruned <- obliterate_refs_1(paragraphs_pruned)
  # to <- .where_refs_txt(paragraphs_pruned)
  # if (!length(to)) to <- length(paragraphs_pruned)  # TODO: prevent early mention


  # Identify sequences of interest
  index_any <- list()
  index_any[['support_1']] <- get_support_1(paragraphs_pruned)
  # index_any[['support_2']] <- get_support_2(paragraphs_pruned)
  index_any[['support_3']] <- get_support_3(paragraphs_pruned)
  index_any[['support_4']] <- get_support_4(paragraphs_pruned)
  index_any[['support_5']] <- get_support_5(paragraphs_pruned)
  index_any[['support_6']] <- get_support_6(paragraphs_pruned)
  index_any[['support_7']] <- get_support_7(paragraphs_pruned)
  index_any[['support_8']] <- get_support_8(paragraphs_pruned)
  index_any[['support_9']] <- get_support_9(paragraphs_pruned)
  index_any[['support_10']] <- get_support_10(paragraphs_pruned)
  index_any[['developed_1']] <- get_developed_1(paragraphs_pruned)
  index_any[['received_1']] <- get_received_1(paragraphs_pruned)
  index_any[['received_2']] <- get_received_2(paragraphs_pruned)
  index_any[['recipient_1']] <- get_recipient_1(paragraphs_pruned)
  index_any[['authors_1']] <- get_authors_1(paragraphs_pruned)
  index_any[['authors_2']] <- get_authors_2(paragraphs_pruned)
  index_any[['thank_1']] <- get_thank_1(paragraphs_pruned)
  index_any[['thank_2']] <- get_thank_2(paragraphs_pruned)
  index_any[['fund_1']] <- get_fund_1(paragraphs_pruned)
  index_any[['fund_2']] <- get_fund_2(paragraphs_pruned)
  index_any[['fund_3']] <- get_fund_3(paragraphs_pruned)
  index_any[['supported_1']] <- get_supported_1(paragraphs_pruned)
  index_any[['financial_1']] <- get_financial_1(paragraphs_pruned)
  index_any[['financial_2']] <- get_financial_2(paragraphs_pruned)
  index_any[['financial_3']] <- get_financial_3(paragraphs_pruned)
  index_any[['grant_1']] <- get_grant_1(paragraphs_pruned)
  index_any[['french_1']] <- get_french_1(paragraphs_pruned)
  index_any[['common_1']] <- get_common_1(paragraphs_pruned)
  index_any[['common_2']] <- get_common_2(paragraphs_pruned)
  index_any[['common_3']] <- get_common_3(paragraphs_pruned)
  index_any[['common_4']] <- get_common_4(paragraphs_pruned)
  index_any[['common_5']] <- get_common_5(paragraphs_pruned)
  index_any[['acknow_1']] <- get_acknow_1(paragraphs_pruned)
  index_any[['disclosure_1']] <- get_disclosure_1(paragraphs_pruned)
  index_any[['disclosure_2']] <- get_disclosure_2(paragraphs_pruned)
  index <- unlist(index_any) %>% unique() %>% sort()

  # Remove potential mistakes
  if (!!length(index)) {

    # Funding info can be within COI statements, as per Ioannidis
    # Comment out until problems arise
    # if (length(unlist(index_any[c("authors_2")]))) {
    #   is_coi <- negate_conflict_1(paragraphs_pruned[min(index) - 1])
    #   index <- index[!is_coi]
    # }


# Difficult to make it work properly because it does not
    # disclosures <- c("disclosure_1", "disclosure_2")
    # if (!!length(unlist(index_any[disclosures]))) {
    #
    #   for (i in 1:seq_along(disclosures)) {
    #
    #     ind <- index_any[[disclosures[i]]]
    #     is_coi_disclosure <- negate_disclosure_1(paragraphs_pruned[ind])
    #     index_any[[disclosures[i]]] <- ind[!is_coi_disclosure]
    #
    #   }
    #
    # }

    is_absent <- negate_absence_1(paragraphs_pruned[index])
    index <- index[!is_absent]

    # Currently removed b/c I made the disclosure functions more robust to
    #     statements like "Financial disclosure. Nothing to disclose.
    # disclosures <- unique(unlist(index_any[c("disclosure_1", "disclosure_2")]))
    # if (!!length(disclosures)) {
    #
    #   if (length(disclosures) == 1) {
    #
    #     is_disclosure <- negate_disclosure_2(paragraphs[index])
    #     index <- index[!is_disclosure]
    #
    #   } else {
    #
    #     disclosure_text <- paste(paragraphs_pruned[disclosures], collapse = " ")
    #     is_disclosure <- negate_disclosure_2(disclosure_text)
    #     index <- setdiff(index, disclosures)
    #
    #   }
    # }
  }


  # Identify potentially missed signals
  index_fund <- list()
  if (!length(index)) {

    from <- .where_acknows_txt(paragraphs_pruned)
    to <- .where_refs_txt(paragraphs_pruned) - 1

    if (!!length(from) & !!length(to)) {

      diff <- to - from

      if (diff < 0) {
        to <- min(length(paragraphs_pruned), from + 100)
        diff <- to - from
      }

      if (diff <= 100) {

        index_fund[['fund']] <- get_fund_acknow(paragraphs_pruned[from:to])
        index_fund[['project']] <- get_project_acknow(paragraphs_pruned[from:to])
        index <- unlist(index_fund) %>% magrittr::add(from - 1)
      }
    }
  }

  index <- sort(unique(index))
  is_funded_pred <- !!length(index)
  funding_text <- paragraphs[index] %>% paste(collapse = " ")

  index_any %<>% purrr::map(function(x) !!length(x))
  index_fund %<>% purrr::map(function(x) !!length(x))

  article <- basename(filename) %>% stringr::word(sep = "\\.")
  pmid <- gsub("^.*PMID([0-9]+).*$", "\\1", filename)

  results <- tibble::tibble(article, pmid, is_funded_pred, funding_text)
  tibble::as_tibble(c(results, index_any, index_fund))
}