R/tweet_max_scores.R

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

#' @title Twitter Data Maximum Scores
#'
#' @description Determines the Maximum scores for either the entire dataset or 
#'   the Maximum scores associated with a hashtag or topic analysis.
#'
#' @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 HT_Topic_Selection The hashtag or topic to be investigated.  NULL will 
#'   find min across entire data frame.
#'
#' @importFrom dplyr arrange filter quo desc
#' @importFrom tidyr unnest
#' @importFrom utils head
#'
#' @return A Tibble.
#'
#' @examples 
#' \dontrun{
#' library(saotd)
#' data <- raw_tweets
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "hashtag")
#' min_scores <- tweet_max_scores(DataFrameTidyScores = score_data,
#'                                HT_Topic = "hashtag")
#'
#' data <- twitter_data
#' tidy_data <- Tidy(DataFrame = data)
#' score_data <- tweet_scores(DataFrameTidy = tidy_data,
#'                            HT_Topic = "hashtag")
#' min_scores <- tweet_max_scores(DataFrameTidyScores = score_data,
#'                                HT_Topic = "hashtag",
#'                                HT_Topic_Selection = "icecream")
#' }
#' @export

tweet_max_scores <- function(DataFrameTidyScores,
                             HT_Topic,
                             HT_Topic_Selection = NULL) {

  # 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
  hashtags <- dplyr::quo(hashtags)
  Topic <- dplyr::quo(Topic)
  TweetSentimentScore <- dplyr::quo(TweetSentimentScore)

  # function main body
  if (HT_Topic == "hashtag" & is.null(HT_Topic_Selection)) {
    
    TD_HT_noSel_Max_Scores <- DataFrameTidyScores %>% 
      dplyr::arrange(dplyr::desc(TweetSentimentScore)) %>%
      utils::head()

    return(TD_HT_noSel_Max_Scores)

  } else if (HT_Topic == "hashtag" & !is.null(HT_Topic_Selection)) {

    TD_HT_Sel_Max_Scores <- DataFrameTidyScores %>%
      tidyr::unnest(
        cols = hashtags,
        keep_empty = FALSE) %>%
      dplyr::filter(hashtags == HT_Topic_Selection) %>%
      dplyr::arrange(dplyr::desc(TweetSentimentScore)) %>%
      utils::head()

    return(TD_HT_Sel_Max_Scores)

  } else if (HT_Topic == "topic" & is.null(HT_Topic_Selection)) {

    TD_Topic_noSel_Max_Scores <- DataFrameTidyScores %>%
      dplyr::arrange(dplyr::desc(TweetSentimentScore)) %>%
      utils::head()

    return(TD_Topic_noSel_Max_Scores)

  } else {

    TD_Topic_Sel_Max_Scores <- DataFrameTidyScores %>%
      dplyr::filter(Topic == HT_Topic_Selection) %>%
      dplyr::arrange(dplyr::desc(TweetSentimentScore)) %>%
      utils::head()

    return(TD_Topic_Sel_Max_Scores)
  }

}