# initial_split: Simple Training/Test Set Splitting In rsample: General Resampling Infrastructure

 initial_split R Documentation

## Simple Training/Test Set Splitting

### Description

`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. `group_initial_split` creates splits of the data based on some grouping variable, so that all data in a "group" is assigned to the same split. `training` and `testing` are used to extract the resulting data.

### Usage

```initial_split(data, prop = 3/4, strata = NULL, breaks = 4, pool = 0.1, ...)

initial_time_split(data, prop = 3/4, lag = 0, ...)

training(x)

testing(x)

group_initial_split(data, group, prop = 3/4, ...)
```

### Arguments

 `data` A data frame. `prop` The proportion of data to be retained for modeling/analysis. `strata` A variable in `data` (single character or name) used to conduct stratified sampling. When not `NULL`, each resample is created within the stratification variable. Numeric `strata` are binned into quartiles. `breaks` A single number giving the number of bins desired to stratify a numeric stratification variable. `pool` A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small. `...` Not currently used. `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. `x` An `rsplit` object produced by `initial_split()` or `initial_time_split()`. `group` A variable in `data` (single character or name) used for grouping observations with the same value to either the analysis or assessment set within a fold.

### Details

With a `strata` argument, the random sampling is conducted within the stratification variable. This can help ensure that the resamples have equivalent proportions as the original data set. For a categorical variable, sampling is conducted separately within each class. For a numeric stratification variable, `strata` is binned into quartiles, which are then used to stratify. Strata below 10% of the total are pooled together; see `make_strata()` for more details.

### Value

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

set.seed(1353)
car_split <- group_initial_split(mtcars, cyl)
train_data <- training(car_split)
test_data <- testing(car_split)

```

rsample documentation built on Aug. 8, 2022, 9:06 a.m.
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