R/parse_fanduel_data.R

In jimtheflash/betfinder: Find Bets

parse_fanduel_data <- function(fanduel_data, sport, prop = FALSE, game_lines = FALSE, exclude_live = TRUE, exclude_alts = FALSE, game_part = "full") {

  # extract the events as a data.frame for merging later
  events <- dplyr::bind_rows(fanduel_data$main$attachments$events) %>%
    dplyr::select(eventId, name, openDate) %>%
    dplyr::rename(
      matchup = name,
      tipoff = openDate
    )

  # if there's only one element in fanduel_data and its name is main, then do things differently
  if (length(fanduel_data) == 1 && names(fanduel_data) == 'main') {

    # extract the markets as a data.frame
    markets <- dplyr::bind_rows(fanduel_data$main$attachments$markets)

    # subset selected markets based on game lines or prop
    if (game_lines == TRUE) {
      market_names <- c("Total Points", "Spread Betting", "Moneyline")
      }
    else {
      return()
    }
    selected_markets <- markets[markets$marketName %in% market_names, ]

    # merge the events and markets
    output_df <- selected_markets %>%
      dplyr::inner_join(events, by='eventId')

    return(output_df)
  }

  # if theres more than one element in fanduel_data, that means there are individual events to parse. nbd, just a little extra effort
  if (length(fanduel_data) > 1) {
    # loop through the events element and get what is needed
    output_list <- list()
    for (e in names(fanduel_data$events)) {
      # extract the tabs for that event
      game_event <- fanduel_data$events[[e]]
      # parse based on the prop
      if (prop == 'fpts by team') market_names <- "First Team Basket Scorer"
      else if (prop == 'fpts shot type') market_names <- "Method of First Basket"
      else if (prop == 'fpts') market_names <- "First Basket"
      # loop through the tabs in the event and extract the correct markets
      tab_list <- list()
      for (tab in names(game_event)) {
        tab_data <- game_event[[tab]]
        markets_df <- dplyr::bind_rows(tab_data$attachments$markets)
        if (!market_names %in% markets_df$marketName) next
        selected_markets <- markets_df[markets_df$marketName %in% market_names, ]
        if (nrow(selected_markets) == 0 || length(selected_markets) == 0) next
        tab_list[[length(tab_list) + 1]] <- selected_markets
      }
      # turn the tabs into a df and merged into the big output list
      tab_output_df <- dplyr::bind_rows(tab_list)
      output_list[[length(output_list) + 1]] <- tab_output_df
    }
    # stick all the elements into a df and merged with events, then distinctify
    output_df <- dplyr::bind_rows(output_list) %>%
      dplyr::inner_join(events, by='eventId') %>%
      dplyr::distinct()

    return(output_df)
  }

}