R/tweet_corpus_distribution.R

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

#' @title Twitter Corpus Distribution
#'
#' @description Determines the scores distribution for the entire Twitter 
#'   data corpus.
#'
#' @param DataFrameTidyScores DataFrame of Twitter Data that has been tidy'd 
#'   and scored.
#' @param binwidth The width of the bins.  Default is 1.
#' @param color The user selected color to highlight the bins.
#' @param fill The interior color of the bins.
#'
#' @importFrom dplyr quo group_by count
#' @import ggplot2
#'
#' @return A ggplot.
#'
#' @examples
#' \dontrun{
#' library(saotd)
#' data <- raw_tweets
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "hashtag")
#' Corp_Dist <- tweet_corpus_distribution(DataFrameTidyScores = score_data,
#'                                        binwidth = 1,
#'                                        color = "black",
#'                                        fill = "white")
#' Corp_Dist
#' }
#' @export

tweet_corpus_distribution <- function(DataFrameTidyScores, 
                                      binwidth = 1,
                                      color = "black",
                                      fill = "grey") {

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

  # configure defusing operators for packages checking
  TweetSentimentScore <- dplyr::quo(TweetSentimentScore)
  n <- dplyr::quo(n)

  # function main body
    TD_Corpus_Distribution <- DataFrameTidyScores %>%
    dplyr::group_by(TweetSentimentScore) %>%
    dplyr::count() %>%
    ggplot2::ggplot(ggplot2::aes(x = TweetSentimentScore, y = n)) +
    ggplot2::geom_col(colour = color, fill = fill) +
    ggplot2::theme(legend.position = "none") +
    ggplot2::ggtitle("Sentiment Score Distribution") +
    ggplot2::xlab("Sentiment") +
    ggplot2::ylab("Count") +
    ggplot2::theme_bw()

  return(TD_Corpus_Distribution)

}