#' Transform the documents stored in a dataframe in a bag of words.
#' @param x character. Content to transform in a bag of words.
#' @param language character string. Language for the stopwords to remove.
#' @param rm_words character vector. List of words (or n-tokens) to remove.
#' @param min_nchar integer. Number of characters below which the word is removed.
#' @param match_term dataframe. Table with two variables: "word" for the string in the content and "term" for its categorization. Match words to force desired completions.
#' @return A tibble with the id of the document, words, stems, lemmas, terms, counts, and proportions.
#' @importFrom dplyr select
#' @importFrom dplyr filter
#' @importFrom dplyr mutate
#' @importFrom dplyr group_by
#' @importFrom dplyr ungroup
#' @importFrom dplyr rename
#' @importFrom dplyr left_join
#' @importFrom dplyr summarise_all
#' @importFrom dplyr sample_n
#' @importFrom dplyr case_when
#' @importFrom dplyr %>%
#' @importFrom tidyr nest
#' @importFrom tidyr unnest
#' @importFrom tm stopwords
#' @importFrom tm removeNumbers
#' @importFrom tm removePunctuation
#' @importFrom tm removeWords
#' @importFrom tm stripWhitespace
#' @importFrom tm stemDocument
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_split
#' @importFrom purrr map
#' @importFrom stats median
#' @export
bow_from_doclist <- function(x,
language = "english",
rm_words = NULL,
min_nchar = 3,
match_term = NULL) {
stopifnot(is.character(language), is.numeric(min_nchar))
# bind variables
stem <- NULL
Freq <- NULL
term <- NULL
char_nbr <- NULL
lemma <- NULL
word <- NULL
count <- NULL
bow <- as.character(x) %>%
str_replace_all(pattern = "-", replacement = " ") %>%
str_replace_all(pattern = "[^[:alnum:]]", replacement = " ") %>%
tolower() %>%
removeNumbers() %>%
removePunctuation() %>%
removeWords(words = stopwords(language)) %>%
stripWhitespace()
# Remove specified additional words
if (!is.null(rm_words)) bow <- removeWords(bow, words = rm_words)
# Tidy the data
bow <- bow %>%
stripWhitespace() %>%
str_split(pattern = " ") %>%
unlist() %>%
table() %>%
as.data.frame() %>%
rename(word = ".", count = Freq) %>%
mutate(word = as.character(word), count = as.integer(count)) %>%
unnest() %>%
ungroup() %>%
filter(nchar(word) >= min_nchar) %>%
mutate(stem = stemDocument(word))
# Match words with desired terms
if (!is.null(match_term)) {
bow <- bow %>% left_join(match_term, by = "word")
} else {
bow <- bow %>% mutate(term = NA) %>% mutate(term = as.character(term))
}
# Prepare automatic completion for the rest
for_completion <- bow %>%
select(word, stem, count) %>%
group_by(word, stem) %>%
summarise_all(sum, na.rm = TRUE) %>%
ungroup() %>%
group_by(stem) %>%
filter(count >= median(count)) %>%
mutate(char_nbr = nchar(word)) %>%
filter(char_nbr == min(char_nbr)) %>%
sample_n(1) %>%
ungroup() %>%
select(stem, term2 = word)
bow <- bow %>%
left_join(for_completion, by = "stem") %>%
mutate(
term = case_when(
!is.na(term) ~ term,
TRUE ~ term2
)
) %>%
select(word, stem, lemma, term, count)
return(bow)
}
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