R/get_player_data.R

In p0bs/premPredictor: Calculations for a Football Prediction Competition

#' @title Getting Player Predictions
#'
#' @description This function retrieves each player's predictions for my PremPredict competition.
#' @param url_value A character string for the URL on Dropbox of the csv file that stores these predictions.
#' @keywords import
#' @export
#' @examples
#' \dontrun{
#' get_player_data(url_value = "https://www.abcdef.com/abcdefg123.csv")
#' }

get_player_data <- function(url_value){

  myConnection <-  url(
    paste0(url_value, "?raw=1")
    )

  data_early <- readr::read_csv(myConnection) %>%
    dplyr::select(-Timestamp, -`If you want email updates on the competition, please provide your email address`) %>%
    dplyr::arrange(`Who is making this prediction?`) %>%
    t

  data_rowname <- c("Club", row.names(data_early)[-1])
  data_full <- cbind(data_rowname, data_early)
  colnames(data_full) <- data_full[1,]

  # I might need to augment the recode elements over the seasons
  data_input <- data_full %>%
    dplyr::as_tibble() %>%
    dplyr::slice(-1) %>%
    dplyr::mutate(Club = stringr::str_sub(Club, start = 30, end = -2)) %>%
    dplyr::mutate_at(
      .vars = dplyr::vars(-Club),
      .funs = dplyr::funs(as.integer(stringr::str_extract(., "([0-9]+)")))
    ) %>%
    dplyr::mutate(Club = dplyr::recode(
      .x = Club,
      `Bournemouth` = "AFC Bournemouth",
      `Brighton and Hove Albion` = "Brighton",
      `Cardiff City` = "Cardiff",
      `Crystal Palace` = "C Palace",
      `Huddersfield Town` = "Huddersfield",
      `Leicester City` = "Leicester",
      `Manchester City` = "Man City",
      `Manchester United` = "Man Utd",
      `Newcastle United` = "Newcastle",
      `Norwich City` = "Norwich",
      `Sheffield United` = "Sheff Utd",
      `Tottenham Hotspur` = "Spurs",
      `West Ham United` = "West Ham",
      `Wolverhampton Wanderers` = "Wolves")
    ) %>%
    dplyr::arrange(Club)
}