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69 lines
3.0 KiB
69 lines
3.0 KiB
#' Generates dfm from ElasticSearch output
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#'
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#' Generates dfm from ElasticSearch output
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#' @param out The elasticizer-generated data frame
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#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
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#' @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
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#' @param clean Boolean indicating whether the results should be cleaned by removing words matching regex (see code).
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#' @param cores Number of cores to use for parallel processing, defaults to cores (all cores available)
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#' @param tolower Boolean indicating whether dfm features should be lowercased
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#' @return A Quanteda dfm
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#' @export
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#' @examples
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#' dfm_gen(out, words = '999')
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#################################################################################################
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#################################### DFM generator #############################
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#################################################################################################
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# filter(`_source.codes.timeSpent` != -1) %>% ### Exclude Norwegian summer sample hack
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dfm_gen <- function(out, words = '999', text = "lemmas", clean, cores = 1, tolower = T) {
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# Create subset with just ids, codes and text
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out <- out %>%
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select(`_id`, matches("_source.*")) ### Keep only the id and anything belonging to the source field
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fields <- length(names(out))
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if (text == "lemmas" || text == 'ud' || text == 'ud_upos') {
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out$merged <- unlist(mclapply(seq(1,length(out[[1]]),1),merger, out = out, text = text, clean = clean, mc.cores = cores))
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}
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if (text == "full") {
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out <- mamlr:::out_parser(out, field = '_source' , clean = clean, cores = cores)
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}
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if ('_source.codes.majorTopic' %in% colnames(out)) {
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out <- out %>%
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mutate(codes = `_source.codes.majorTopic`) %>%
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mutate(junk = case_when(
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.$codes == 92 ~ 1,
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.$codes == 91 ~ 1,
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.$codes == 93 ~ 1,
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.$codes == 2301 ~ 1,
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.$codes == 3101 ~ 1,
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.$codes == 34 ~ 1,
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TRUE ~ 0
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)
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) %>%
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mutate(aggregate = .$codes %>%
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str_pad(4, side="right", pad="a") %>%
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str_match("([0-9]{1,2})?[0|a][1-9|a]") %>%
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.[,2] %>%
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as.numeric()
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)
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vardoc <- out[,-seq(1,(length(names(out))-3),1)]
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} else {
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vardoc <- NULL
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}
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if (words != "999") {
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### Former word count regex, includes words up until the next sentence boundary, instead of cutting to the last sentence boundary
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# out$merged2 <- str_extract(lemmas, str_c("^(([\\s\\S]*? ){0,",words,"}[\\s\\S]*?[.!?])\\s+?"))
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out <- out %>% rowwise() %>% mutate(merged = paste0(str_split(merged, '\\s')[[1]][1:words], collapse = ' '))
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if(text != 'ud_upos') {
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out$merged <- str_extract(out$merged,'.*[.?!]')
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}
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}
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dfm <- corpus(out$merged, docnames = out$`_id`, docvars = vardoc) %>%
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dfm(tolower = tolower, stem = F, remove_punct = T, valuetype = "regex")
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return(dfm)
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}
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