data_partition {datawizard}

Partition data

Description

Creates data partitions (for instance, a training and a test set) based on a data frame that can also be stratified (i.e., evenly spread a given factor) using the group argument.

Usage

data_partition(
  data,
  proportion = 0.7,
  group = NULL,
  seed = NULL,
  row_id = ".row_id",
  verbose = TRUE,
  ...
)

Arguments

data

A data frame, or an object that can be coerced to a data frame.

proportion

Scalar (between 0 and 1) or numeric vector, indicating the proportion(s) of the training set(s). The sum of proportion must not be greater than 1. The remaining part will be used for the test set.

group

A character vector indicating the name(s) of the column(s) used for stratified partitioning.

seed

A random number generator seed. Enter an integer (e.g. 123) so that the random sampling will be the same each time you run the function.

row_id

Character string, indicating the name of the column that contains the row-id's.

verbose

Toggle messages and warnings.

...

Other arguments passed to or from other functions.

Value

A list of data frames. The list includes one training set per given proportion and the remaining data as test set. List elements of training sets are named after the given proportions (e.g., ⁠$p_0.7⁠), the test set is named ⁠$test⁠.

See Also

• Functions to rename stuff: data_rename(), data_rename_rows(), data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), data_find()

• Functions to filter rows: data_match(), data_filter()

Examples

data(iris)
out <- data_partition(iris, proportion = 0.9)
out$test
nrow(out$p_0.9)

# Stratify by group (equal proportions of each species)
out <- data_partition(iris, proportion = 0.9, group = "Species")
out$test

# Create multiple partitions
out <- data_partition(iris, proportion = c(0.3, 0.3))
lapply(out, head)

# Create multiple partitions, stratified by group - 30% equally sampled
# from species in first training set, 50% in second training set and
# remaining 20% equally sampled from each species in test set.
out <- data_partition(iris, proportion = c(0.3, 0.5), group = "Species")
lapply(out, function(i) table(i$Species))