Split data into train and test sets

Usage

tl_split(data, prop = 0.8, stratify = NULL, seed = NULL)

data: A data frame
prop: Proportion for training set (default: 0.8)
stratify: Column name for stratified splitting. Each stratum is split at prop. A numeric column with more than five distinct values is stratified by its quartiles, and rows missing the value form a stratum of their own. A stratum of a single row goes to the training set, unless such strata together hold more than a tenth of the rows, as in an ID column; then they are pooled into one stratum and split together.
seed: Random seed for reproducibility

A list with two elements:

A split that leaves the test set empty, as a single row does, warns.

# \donttest{
split_data <- tl_split(iris, prop = 0.7, stratify = "Species")
train <- split_data$train
test <- split_data$test
# }