R/tweet_tidy.R

In evan-l-munson/SAoTD: Sentiment Analysis of Twitter Data

#' @title Tidy Twitter Data
#'
#' @description Function to Tidy Twitter Data.  This function will remove a 
#'   significant amount of the original twitter metadata, as it is not needed 
#'   to determine the sentiment of the tweets. This function will remove all 
#'   emoticons, punctuation, weblinks while maintaining actual Tweet text.
#'
#' @param DataFrame Data Frame of Twitter Data.
#'
#' @importFrom dplyr mutate filter quo rename
#' @importFrom stringr str_replace_all
#' @importFrom tidytext unnest_tokens
#'
#' @return A Tidy tibble.
#'
#' @examples
#' \dontrun{
#' library(saotd)
#'
#' data <- raw_tweets
#' tidy_data <- tweet_tidy(DataFrame = data)
#' tidy_data
#'
#' }
#' @export

tweet_tidy <- function(DataFrame) {

  # input checks
  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)
  cleantext <- dplyr::quo(cleantext)
  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_Tidy <- DataFrame %>%
    dplyr::mutate(
      cleantext = stringr::str_replace_all(
        string = text,
        pattern = wu,
        replacement = ""),
      cleantext = stringr::str_replace_all(
        string = cleantext,
        pattern = "#",
        replacement = ""),
      cleantext = stringr::str_replace_all(
        string = cleantext,
        pattern = "http",
        replacement = ""),
      cleantext = stringr::str_replace_all(
        string = cleantext,
        pattern = "RT",
        replacement = ""), # Remove retweet note
      cleantext = stringr::str_replace_all(
        string = cleantext,
        pattern = "[:punct:]",
        replacement = ""),
      cleantext = stringr::str_replace_all(
        string = cleantext,
        pattern = "[^[:alnum:]///' ]",
        replacement = "")) %>%  # Remove Emojis
    tidytext::unnest_tokens(
      output = word,
      input = cleantext,
      token = "words",
      drop = TRUE) %>%
    dplyr::filter(!word %in% tidytext::stop_words$word) %>%
    dplyr::rename(Token = word)

  return(TD_Tidy)

}