MarcusKlik
commented
4 years ago You can use an existing table a template now:
library(synthetic)
# use iris dataset as template
synth_iris <- synthetic_table(iris, id = "synthetic iris")
# generate a 1e6 rows synthetic 'iris' dataset
generate(synth_iris, 1e6)
#> # A tibble: 1,000,000 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 6.3 2.8 5.1 1.5 virginica
#> 2 6.7 3.3 5.7 2.1 virginica
#> 3 4.7 3.2 1.6 0.2 setosa
#> 4 5.8 2.8 5.1 2.4 virginica
#> 5 5.1 3.5 1.4 0.2 setosa
#> 6 4.9 3.1 1.5 0.1 setosa
#> 7 6 2.2 4 1 versicolor
#> 8 6.4 3.2 4.5 1.5 versicolor
#> 9 4.6 3.4 1.4 0.3 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ... with 999,990 more rows
# each call to generate creates a unique dataset
generate(synth_iris, 1e6)
#> # A tibble: 1,000,000 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 6.7 3.3 5.7 2.5 virginica
#> 2 4.8 3 1.4 0.3 setosa
#> 3 5.2 3.5 1.5 0.2 setosa
#> 4 6.4 2.8 5.6 2.2 virginica
#> 5 5 3.4 1.5 0.2 setosa
#> 6 5.2 4.1 1.5 0.1 setosa
#> 7 7.7 3 6.1 2.3 virginica
#> 8 6.5 3 5.8 2.2 virginica
#> 9 7 3.2 4.7 1.4 versicolor
#> 10 4.9 3.6 1.4 0.1 setosa
#> # ... with 999,990 more rows
# column selection
generate(synth_iris, 1e6, c("Sepal.Length", "Species"))
#> # A tibble: 1,000,000 x 2
#> Sepal.Length Species
#> <dbl> <fct>
#> 1 5.5 versicolor
#> 2 6 versicolor
#> 3 5 setosa
#> 4 5.7 setosa
#> 5 5.8 versicolor
#> 6 5.7 versicolor
#> 7 5.5 setosa
#> 8 5.5 versicolor
#> 9 4.6 setosa
#> 10 6.3 virginica
#> # ... with 999,990 more rows
Like:
This will automatically generate a table definition that samples the actual data to generate new datasets (by storing original dataset and sampling that data)