#' Simple Training/Test Set Splitting

#'

#' `initial_split` creates a single binary split of the data into a training

#'  set and testing set. `initial_time_split` does the same, but takes the

#'  _first_ `prop` samples for training, instead of a random selection.

#'  `training` and `testing` are used to extract the resulting data.

#' @details The `strata` argument causes the random sampling to be conducted

#'  *within the stratification variable*. This can help ensure that the number

#'  of data points in the training data is equivalent to the proportions in the

#'  original data set. (Strata below 10% of the total are pooled together.)

#' @inheritParams vfold_cv

#' @param prop The proportion of data to be retained for modeling/analysis.

#' @param strata A variable that is used to conduct stratified sampling to

#'  create the resamples. This could be a single character value or a variable

#'  name that corresponds to a variable that exists in the data frame.

#' @param breaks A single number giving the number of bins desired to stratify

#'  a numeric stratification variable.

#' @export

#' @return An `rsplit` object that can be used with the `training` and `testing`

#'  functions to extract the data in each split.

#' @examples

#' set.seed(1353)

#' car_split <- initial_split(mtcars)

#' train_data <- training(car_split)

#' test_data <- testing(car_split)

#'

#' data(drinks, package = "modeldata")

#' drinks_split <- initial_time_split(drinks)

#' train_data <- training(drinks_split)

#' test_data <- testing(drinks_split)

#' c(max(train_data$date), min(test_data$date))  # no lag

#'

#' # With 12 period lag

#' drinks_lag_split <- initial_time_split(drinks, lag = 12)

#' train_data <- training(drinks_lag_split)

#' test_data <- testing(drinks_lag_split)

#' c(max(train_data$date), min(test_data$date))  # 12 period lag

#'

#' @export

#'

initial_split <- function(data, prop = 3/4, strata = NULL, breaks = 4, ...) {

    }

  }

      ...

    )

}

#' @rdname initial_split

#' @param lag A value to include a lag between the assessment

#'  and analysis set. This is useful if lagged predictors will be used

#'  during training and testing.

#' @export

initial_time_split <- function(data, prop = 3/4, lag = 0, ...) {

  }

  }

  }

}

#' @rdname initial_split

#' @export

#' @param x An `rsplit` object produced by `initial_split`

#' @rdname initial_split

#' @export