#' @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]+|&|<|>|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 = "&",
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)
}
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