R/tweet_time.R

In saotd: Sentiment Analysis of Twitter Data

#' @title Twitter Data Timeseries Plot.
#'
#' @description Displays the Twitter data sentiment scores through time.  The 
#'   sentiment scores by hashtag or topic are summed per day and plotted to 
#'   show the change in sentiment through time.
#'
#' @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".
#'
#' @importFrom dplyr summarize group_by quo
#' @import ggplot2
#'
#' @return A ggplot plot.
#'
#' @examples
#' \dontrun{
#' library(saotd)
#' data <- raw_tweets
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "hashtag")
#' ht_time <- tweet_time(DataFrameTidyScores = score_data,
#'                       HT_Topic = "hashtag")
#' ht_time
#'
#' data <- raw_tweets
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "topic")
#' topic_time <- tweet_time(DataFrameTidyScores = score_data,
#'                          HT_Topic = "topic")
#' topic_time                    
#' }
#' @export

tweet_time <- function(DataFrameTidyScores, 
                       HT_Topic) {

  # input checking
  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
  hashtags <- dplyr::quo(hashtags)
  TweetSentimentScore <- dplyr::quo(TweetSentimentScore)
  DayScore <- dplyr::quo(DayScore)
  Topic <- dplyr::quo(Topic)

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

    TD_HT_TimeScale <- DataFrameTidyScores %>%
      tidyr::unnest(
        cols = hashtags,
        keep_empty = FALSE) %>%
      dplyr::mutate(
        hashtags = tolower(hashtags)) %>%
      dplyr::group_by(hashtags, date) %>%
      dplyr::summarise(
        DayScore = sum(TweetSentimentScore)) %>%
      ggplot2::ggplot(
        ggplot2::aes(
          x = factor(date),
          y = DayScore,
          colour = hashtags)) +
      ggplot2::geom_point() +
      ggplot2::geom_path(ggplot2::aes(group = 1)) +
      ggplot2::geom_hline(yintercept = 0, color = "black") +
      ggplot2::facet_wrap(~hashtags, ncol = 2, scales = "free_y") +
      ggplot2::theme(legend.position = "none") +
      ggplot2::ggtitle("Sentiment Scores Across all #Hashtags") +
      ggplot2::xlab("Day") +
      ggplot2::ylab("Daily Sentiment Score") +
      ggplot2::theme(
        axis.text.x = element_text(angle = 45, hjust = 1))

    return(TD_HT_TimeScale)

  } else {

    TD_Topic_TimeScale <- DataFrameTidyScores %>%
      dplyr::group_by(Topic, date) %>%
      dplyr::summarise(
        DayScore = sum(TweetSentimentScore)) %>%
      ggplot2::ggplot(
        ggplot2::aes(
          x = factor(date),
          y = DayScore,
          colour = Topic)) +
      ggplot2::geom_point() +
      ggplot2::geom_path(ggplot2::aes(group = 1)) +
      ggplot2::geom_hline(yintercept = 0, color = "black") +
      ggplot2::facet_wrap(~Topic, ncol = 2, scales = "free_y") +
      ggplot2::theme_bw() +
      ggplot2::theme(legend.position = "none") +
      ggplot2::ggtitle("Sentiment Scores Across all Topics") +
      ggplot2::xlab("Day") +
      ggplot2::ylab("Daily Sentiment Score") +
      ggplot2::theme(
        axis.text.x = element_text(angle = 45, hjust = 1))

    return(TD_Topic_TimeScale)

  }

}