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,
training_proportion = proportion,
...
)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
proportionmust 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.
- training_proportion
Deprecated, please use
proportion.- ...
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
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 4.8 3.4 1.6 0.2 setosa 12
#> 2 5.8 4.0 1.2 0.2 setosa 15
#> 3 4.8 3.1 1.6 0.2 setosa 31
#> 4 5.0 3.5 1.3 0.3 setosa 41
#> 5 6.0 2.2 4.0 1.0 versicolor 63
#> 6 5.6 2.9 3.6 1.3 versicolor 65
#> 7 6.7 3.1 4.4 1.4 versicolor 66
#> 8 6.3 2.5 4.9 1.5 versicolor 73
#> 9 5.5 2.4 3.8 1.1 versicolor 81
#> 10 5.7 2.9 4.2 1.3 versicolor 97
#> 11 6.3 3.3 6.0 2.5 virginica 101
#> 12 6.3 2.9 5.6 1.8 virginica 104
#> 13 6.5 3.0 5.8 2.2 virginica 105
#> 14 5.7 2.5 5.0 2.0 virginica 114
#> 15 6.9 3.1 5.1 2.3 virginica 142
nrow(out$p_0.9)
#> [1] 135
# Stratify by group (equal proportions of each species)
out <- data_partition(iris, proportion = 0.9, group = "Species")
out$test
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 5.8 4.0 1.2 0.2 setosa 15
#> 2 5.7 4.4 1.5 0.4 setosa 16
#> 3 5.7 3.8 1.7 0.3 setosa 19
#> 4 5.1 3.7 1.5 0.4 setosa 22
#> 5 4.4 3.0 1.3 0.2 setosa 39
#> 6 7.0 3.2 4.7 1.4 versicolor 51
#> 7 6.6 2.9 4.6 1.3 versicolor 59
#> 8 5.6 2.9 3.6 1.3 versicolor 65
#> 9 6.2 2.2 4.5 1.5 versicolor 69
#> 10 6.6 3.0 4.4 1.4 versicolor 76
#> 11 6.3 3.3 6.0 2.5 virginica 101
#> 12 6.5 3.0 5.8 2.2 virginica 105
#> 13 6.3 2.7 4.9 1.8 virginica 124
#> 14 7.2 3.2 6.0 1.8 virginica 126
#> 15 6.7 3.0 5.2 2.3 virginica 146
# 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 3.4 1.5 0.2 setosa 8
#> 3 4.4 2.9 1.4 0.2 setosa 9
#> 4 4.9 3.1 1.5 0.1 setosa 10
#> 5 5.4 3.7 1.5 0.2 setosa 11
#> 6 4.8 3.4 1.6 0.2 setosa 12
#>
#> $p_0.3
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 5.0 3.6 1.4 0.2 setosa 5
#> 2 5.4 3.9 1.7 0.4 setosa 6
#> 3 4.6 3.4 1.4 0.3 setosa 7
#> 4 4.3 3.0 1.1 0.1 setosa 14
#> 5 5.4 3.9 1.3 0.4 setosa 17
#> 6 5.7 3.8 1.7 0.3 setosa 19
#>
#> $test
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .row_id
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3.0 1.4 0.2 setosa 2
#> 3 4.6 3.1 1.5 0.2 setosa 4
#> 4 4.8 3.0 1.4 0.1 setosa 13
#> 5 5.8 4.0 1.2 0.2 setosa 15
#> 6 5.1 3.8 1.5 0.3 setosa 20
#>
# Create multiple partitions, stratified by group - 30% equally sampled
# from species in first training set, 50% in second training set and
# remaining 20% equally sampled from each species in test set.
out <- data_partition(iris, proportion = c(0.3, 0.5), group = "Species")
lapply(out, function(i) table(i$Species))
#> $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
#>