R/tweet_distribution.R

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

#' @title Twitter Hashtag or Topic Distribution
#'
#' @description Determines the scores distribution by hashtag or topic for 
#'   Twitter data.
#'
#' @param DataFrameTidyScores DataFrame of Twitter Data that has been tidy'd 
#'   and scored.
#' @param HT_Topic If using hashtag data select:  "hashtag".  If using topic 
#'   data select:  "topic".
#' @param bin_width 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 ggplot2 ggplot geom_histogram facet_wrap theme ggtitle xlab ylab theme_bw
#'
#' @return A facet wrap ggplot.
#'
#' @examples
#' \dontrun{
#' library(saotd)
#' data <- raw_tweets
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "hashtag")
#' Dist <- tweet_distribution(DataFrameTidyScores = score_data,
#'                      HT_Topic = "hashtag",
#'                      bin_width = 1,
#'                      color = "black",
#'                      fill = "white")
#' Dist
#' }
#' @export

tweet_distribution <- function(DataFrameTidyScores,
                               HT_Topic,
                               bin_width = 1,
                               color = "black",
                               fill = "black") {
  
  # input checks
  if (!is.data.frame(DataFrameTidyScores)) {
    stop("The input for this function is a data frame.")
  }
  
  if (!(("hashtag" %in% HT_Topic) | ("topic" %in% HT_Topic))) {
    stop("HT_Topic requires an input of either hashtag for analysis using 
         hashtags, or topic for analysis looking at topics.")
  }

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

  # function main body
  if (HT_Topic == "hashtag") {

    TD_HT_Distribution <- DataFrameTidyScores %>%
      tidyr::unnest(
        cols = hashtags,
        keep_empty = FALSE) %>%
      ggplot2::ggplot(ggplot2::aes(TweetSentimentScore)) +
      ggplot2::geom_bar(
        stat = "count",
        # binwidth = bin_width,
        colour = color,
        fill = fill) +
      ggplot2::facet_wrap(~hashtags, ncol = 2) +
      ggplot2::theme(legend.position = "none") +
      ggplot2::ggtitle("Sentiment Score Distribution Across all #Hashtags") +
      ggplot2::xlab('Sentiment') +
      ggplot2::ylab('Count') +
      ggplot2::theme_bw()

    return(TD_HT_Distribution)

  } else {

    TD_Topic_Distribution <- DataFrameTidyScores %>%
      ggplot2::ggplot(ggplot2::aes(TweetSentimentScore)) +
      ggplot2::geom_bar(
        stat = "count",
        # binwidth = bin_width,
        colour = color,
        fill = fill) +
      ggplot2::facet_wrap(~Topic, ncol = 2) +
      ggplot2::theme(legend.position = "none") +
      ggplot2::ggtitle("Sentiment Score Distribution Across all Topics") +
      ggplot2::xlab('Sentiment') +
      ggplot2::ylab('Count') +
      ggplot2::theme_bw()

    return(TD_Topic_Distribution)

  }

}