R/create_test_dataset.R

In zeee1/Tashu_Rpackage: Analysis and Prediction of Bicycle Rental Amount

#' Create training dataset on specific station for prediction
#'
#' A function to create training dataset on 'station_number' bicycle station by preprocessing bicycle rental history and weather data from 2013 to 2014.
#'
#' @param station_number number that means the number of each station.(1 ~ 144)
#' @return a dataset containing feature and rental count data on 'station_number' station, 2013 ~ 2014
#' @export
#' @importFrom lubridate hour month wday year floor_date as_datetime
#' @importFrom dplyr summarise group_by left_join
#' @examples
#' \dontrun{test_dataset <- create_test_dataset(1)}
#'

create_test_dataset <- function(station_number) {
  check_data()
  if (station_number < 1 || station_number > 144){
    stop("Station number should be from 1 to 144. Please re-Input station number.", call. = FALSE)
  }

  test_dataset <- data.frame(
    datetime = seq(as_datetime("2015/01/01 00:00:00", tz = "EST"),
                   as_datetime("2015/12/31 23:00:00", tz = "EST"),
                   "hours"))

  # Add feature columns('hour', 'month', 'weekday', 'season') to test_dataset
  test_dataset["hour"] <- as.character(lubridate::hour(test_dataset$datetime))
  test_dataset["month"] <- as.character(lubridate::month(test_dataset$datetime))
  test_dataset["weekday"] <- as.character(lubridate::wday(test_dataset$datetime))
  test_dataset["season"] <- as.character()
  test_dataset[test_dataset$month >= 3 & test_dataset$month <= 5, "season"] <- "1"
  test_dataset[test_dataset$month >= 6 & test_dataset$month <= 8, "season"] <- "2"
  test_dataset[test_dataset$month >= 9 & test_dataset$month <= 11, "season"] <- "3"
  test_dataset[test_dataset$month == 12 | test_dataset$month <= 2, "season"] <- "4"

  # Collect feature data('Temperature', 'Windspeed', 'humidity', 'Rainfall') from weather dataset(2013.01.01 ~ 2014.12.31)
  weather2015 <- tashudata::weather[lubridate::year(tashudata::weather$Datetime) == 2015, ]
  feature_weather <- weather2015[, c("Datetime", "Temperature", "WindSpeed", "Humidity", "Rainfall")]
  colnames(feature_weather) <- c("datetime", "Temperature", "Windspeed", "Humidity", "Rainfall")
  feature_weather$datetime <- as_datetime(feature_weather$datetime, tz = "EST")

  # Collect rental history on 'station_number' station.
  tashu2015 <- tashudata::tashu[lubridate::year(tashudata::tashu$RENT_DATE) == 2015, ]
  rental_history <- tashu2015[tashu2015$RENT_STATION == station_number, ]

  # Compute hourly rental count
  rental_history <- data.frame(table(rental_history$RENT_DATE))
  colnames(rental_history) <- c("datetime", "rentcount")
  rental_history$datetime <- as.POSIXct(rental_history$datetime, tz = "EST")
  rental_history$datetime <- lubridate::floor_date(rental_history$datetime, "hour")
  rental_history <- rental_history %>% dplyr::count(datetime) %>% dplyr::rename(rentcount = n)


  # Add test_dataset to weather data and rental count
  test_dataset <- dplyr::left_join(test_dataset, feature_weather)
  test_dataset <- dplyr::left_join(test_dataset, rental_history)

  # replace NA on Rainfall, Snowfall to 0
  test_dataset[is.na(test_dataset)] <- 0

  return(test_dataset)
}