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 by argument.
Usage
data_partition(
data,
proportion = 0.7,
by = NULL,
seed = NULL,
row_id = ".row_id",
verbose = TRUE,
...
)Arguments
- data
A data frame.
- proportion
Scalar (between 0 and 1) or numeric vector, indicating the proportion(s) of the training set(s). The sum of
proportionmust not be greater than 1. The remaining part will be used for the test set.- by
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 $test.
See also
Add a prefix or suffix to column names:
data_addprefix(),data_addsuffix()Functions to reorder or remove columns:
data_reorder(),data_relocate(),data_remove()Functions to reshape, pivot or rotate data frames:
data_to_long(),data_to_wide(),data_rotate()Functions to recode data:
rescale(),reverse(),categorize(),recode_values(),slide()Functions to standardize, normalize, rank-transform:
center(),standardize(),normalize(),ranktransform(),winsorize()Split and merge data frames:
data_partition(),data_merge()Functions to find or select columns:
data_select(),extract_column_names()Functions to filter rows:
data_match(),data_filter()
Examples
data(iris)
out <- data_partition(iris, proportion = 0.9)
out$test
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 4.7 3.2 1.3 0.2 setosa 3
#> 2 4.6 3.1 1.5 0.2 setosa 4
#> 3 4.8 3.4 1.6 0.2 setosa 12
#> 4 5.8 4.0 1.2 0.2 setosa 15
#> 5 5.2 3.4 1.4 0.2 setosa 29
#> 6 5.5 4.2 1.4 0.2 setosa 34
#> 7 5.1 3.8 1.6 0.2 setosa 47
#> 8 4.9 2.4 3.3 1.0 versicolor 58
#> 9 6.7 3.0 5.0 1.7 versicolor 78
#> 10 5.5 2.4 3.7 1.0 versicolor 82
#> 11 7.1 3.0 5.9 2.1 virginica 103
#> 12 6.7 3.3 5.7 2.1 virginica 125
#> 13 6.1 3.0 4.9 1.8 virginica 128
#> 14 7.2 3.0 5.8 1.6 virginica 130
#> 15 6.0 3.0 4.8 1.8 virginica 139
nrow(out$p_0.9)
#> [1] 135
# Stratify by group (equal proportions of each species)
out <- data_partition(iris, proportion = 0.9, by = "Species")
out$test
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 5.4 3.7 1.5 0.2 setosa 11
#> 2 5.7 4.4 1.5 0.4 setosa 16
#> 3 5.2 4.1 1.5 0.1 setosa 33
#> 4 5.5 3.5 1.3 0.2 setosa 37
#> 5 5.1 3.4 1.5 0.2 setosa 40
#> 6 4.9 2.4 3.3 1.0 versicolor 58
#> 7 5.2 2.7 3.9 1.4 versicolor 60
#> 8 6.7 3.0 5.0 1.7 versicolor 78
#> 9 6.0 3.4 4.5 1.6 versicolor 86
#> 10 6.2 2.9 4.3 1.3 versicolor 98
#> 11 6.9 3.2 5.7 2.3 virginica 121
#> 12 7.7 2.8 6.7 2.0 virginica 123
#> 13 6.3 2.7 4.9 1.8 virginica 124
#> 14 6.1 3.0 4.9 1.8 virginica 128
#> 15 6.0 3.0 4.8 1.8 virginica 139
# Create multiple partitions
out <- data_partition(iris, proportion = c(0.3, 0.3))
lapply(out, head)
#> $p_0.3
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 4.7 3.2 1.3 0.2 setosa 3
#> 2 5.0 3.6 1.4 0.2 setosa 5
#> 3 5.4 3.9 1.7 0.4 setosa 6
#> 4 5.0 3.4 1.5 0.2 setosa 8
#> 5 4.9 3.1 1.5 0.1 setosa 10
#> 6 5.4 3.7 1.5 0.2 setosa 11
#>
#> $p_0.3
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 4.6 3.4 1.4 0.3 setosa 7
#> 2 4.8 3.0 1.4 0.1 setosa 13
#> 3 5.7 4.4 1.5 0.4 setosa 16
#> 4 5.4 3.9 1.3 0.4 setosa 17
#> 5 5.1 3.5 1.4 0.3 setosa 18
#> 6 4.6 3.6 1.0 0.2 setosa 23
#>
#> $test
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3.0 1.4 0.2 setosa 2
#> 3 4.6 3.1 1.5 0.2 setosa 4
#> 4 4.4 2.9 1.4 0.2 setosa 9
#> 5 4.3 3.0 1.1 0.1 setosa 14
#> 6 5.8 4.0 1.2 0.2 setosa 15
#>
# Create multiple partitions, stratified by group - 30% equally sampled
# from species in first training set, 50% in second training set and
# remaining 20% equally sampled from each species in test set.
out <- data_partition(iris, proportion = c(0.3, 0.5), by = "Species")
lapply(out, function(i) table(i$Species))
#> $p_0.3
#>
#> setosa versicolor virginica
#> 15 15 15
#>
#> $p_0.5
#>
#> setosa versicolor virginica
#> 25 25 25
#>
#> $test
#>
#> setosa versicolor virginica
#> 10 10 10
#>