#' Generates dfm from ElasticSearch output
#'
#' Generates dfm from ElasticSearch output
#' @param out The elasticizer-generated data frame
#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
#' @param text String indicating whether the "merged" field will contain the "full" text, old-style "lemmas" (will be deprecated), new-style "ud", or ud_upos combining lemmas with upos tags
#' @param clean Boolean indicating whether the results should be cleaned by removing words matching regex (see code).
#' @param tolower Boolean indicating whether dfm features should be lowercased
#' @param binary Boolean indicating whether or not to generate a binary dfm (only indicating term presence, not count). Defaults to FALSE
#' @param ngrams Numeric, if higher than 1, generates ngrams of the given size. Defaults to 1
#' @return A Quanteda dfm
#' @export
#' @examples
#' dfm_gen(out, words = '999')


#################################################################################################
#################################### DFM generator #############################
#################################################################################################

# filter(`_source.codes.timeSpent` != -1) %>% ### Exclude Norwegian summer sample hack

dfm_gen <- function(out, words = '999', text = "lemmas", clean, tolower = T, binary=F, ngrams=1) {
  # Create subset with just ids, codes and text
  out <- out %>%
    select(`_id`, matches("_source.*")) ### Keep only the id and anything belonging to the source field
  fields <- length(names(out))
  if (text == "lemmas" || text == 'ud' || text == 'ud_upos') {
    out <- left_join(out, merger(out, text=text, clean=clean), by = "_id")
  }
  if (text == "full") {
    out <- mamlr:::out_parser(out, field = '_source' , clean = clean)
  }
  if ('_source.codes.majorTopic' %in% colnames(out)) {
    out <- out %>%
      mutate(codes = `_source.codes.majorTopic`) %>%
      mutate(junk = case_when(
          .$codes == 92 ~ 1,
          .$codes == 91 ~ 1,
          .$codes == 93 ~ 1,
          .$codes == 2301 ~ 1,
          .$codes == 3101 ~ 1,
          .$codes == 34 ~ 1,
          TRUE ~ 0
        ),
        aggregate = .$codes %>%
          str_pad(4, side="right", pad="a") %>%
          str_match("([0-9]{1,2})?[0|a][1-9|a]") %>%
          .[,2] %>%
          as.numeric(),
        nondomestic = as.numeric(`_source.codes.nonDomestic`)
      ) %>%
      mutate(
      )
   vardoc <- select(out, codes, junk, aggregate, nondomestic)
  } else {
    vardoc <- NULL
  }
  if (words != "999") {
    ### Former word count regex, includes words up until the next sentence boundary, instead of cutting to the last sentence boundary
    # out$merged2 <- str_extract(lemmas, str_c("^(([\\s\\S]*? ){0,",words,"}[\\s\\S]*?[.!?])\\s+?"))
    out <- out %>% rowwise() %>% mutate(merged = paste0(str_split(merged, '\\s')[[1]][1:words], collapse = ' '))

    if(text != 'ud_upos') {
      out$merged <- str_extract(out$merged,'.*[.?!]')
    }
  }
  dfm <- corpus(out$merged, docnames = out$`_id`, docvars = vardoc) %>%
    tokens(remove_punct = T) %>%
    tokens_ngrams(n = ngrams, skip = 0, concatenator = '_') %>%
    dfm(tolower = tolower, stem = F, valuetype = "regex")
  if (binary) {
    dfm <- dfm_weight(dfm, scheme = 'boolean')
  }
  return(dfm)
}