R/unigram.R

In saotd: Sentiment Analysis of Twitter Data

#' @title Twitter Uni-Grams
#'
#' @description Determines and displays the text Uni-Grams within the Twitter 
#'   data in sequence from the most used to the least used.  A Uni-Gram is a 
#'   single word.
#'
#' @param DataFrame Data Frame of Twitter Data.
#'
#' @importFrom dplyr count mutate filter quo
#' @importFrom stringr str_replace_all
#' @importFrom tidytext unnest_tokens 
#'
#' @return A tibble.
#'
#' @examples
#' \dontrun{
#' library(saotd)
#' data <- raw_tweets
#' TD_Unigram <- unigram(DataFrame = data)
#' TD_Unigram
#' }             
#' @export

unigram <- function(DataFrame) {

  # input checking
  if (!is.data.frame(DataFrame)) {
    stop("The input for this function is a data frame.")
  }

  # configure defusing operators for packages checking
  text <- dplyr::quo(text)
  word <- dplyr::quo(word)

  # web url
  wu <- "https://t.co/[A-Za-z\\d]+|http://[A-Za-z\\d]+|&amp;|&lt;|&gt;|RT|https"

  # function main body
  TD_Unigram <- DataFrame %>%
    dplyr::mutate(
      text = stringr::str_replace_all(
        string = text,
        pattern = "RT",
        replacement = ""), # Remove retweet note
      text = stringr::str_replace_all(
        string = text,
        pattern = "&amp",
        replacement = ""), # Remove Accelerated Mobile Pages (AMP) note
      text = stringr::str_replace_all(
        string = text,
        pattern = wu,
        replacement = ""),
      text = stringr::str_replace_all(
        string = text,
        pattern = "#",
        replacement = ""),
      text = stringr::str_replace_all(
        string = text,
        pattern = "[:punct:]",
        replacement = ""),
      text = stringr::str_replace_all(
        string = text,
        pattern = "[^[:alnum:]///' ]",
        replacement = "")) %>%  # Remove Emojis
    tidytext::unnest_tokens(
      output = word,
      input = text) %>%
    dplyr::filter(!word %in% c(tidytext::stop_words$word, "[0-9]+")) %>%
    dplyr::count(word, sort = TRUE)

  return(TD_Unigram)

}