This repository contains the tidysynthesis R package for generating synthetic data. Complete documentation is available is available at the tidysynthesis documentation website.
# The easiest way to get tidysynthesis is from CRAN:
install.packages("tidysynthesis")
# Or the development version from GitHub:
# install.packages("pak")
pak::pak("UrbanInstitute/tidysynthesis")tidysynthesis is a “metapackage” for creating synthetic
data sets for statistical disclosure limitation that shares the
underlying design philosophy, grammar, and data structures of the tidyverse and tidymodels.
tidysynthesis flexibly supports sequential synthesis
modeling and sampling specifications with different formal and empirical
privacy properties.
Note that the privacy and security properties of tidysynthesis’s outputs rely on many different technical assumptions. Our goal is to make the package agnostic to many of these assumptions, which places greater responsibility on users to evaluate synthetic data prior to dissemination. For more information, see our notes on security principles from our documentation.
presynthSyntheses ultimately depend on a presynth object with
two main components: a roadmap that outlines the
macroscopic workflow (i.e., what order to synthesize variables, how
variables are defined and relate to one another, etc.) and a
synth_spec that details how individual variables are
synthesized (i.e. how are specific output variable modeling workflows
specified, how are new synthetic samples generated, etc.). Here is the
general workflow, with required objects in blue and optional objects in
magenta.
flowchart TD
A[conf_data]:::required --> B[roadmap]:::required
C[start_data]:::required --> B
D[start_method]:::optional --> B
E[schema]:::optional --> B
F[visit_sequence]:::optional --> B
G[replicates]:::optional --> B
H[constraints]:::optional --> B
I[models]:::required --> J[synth_spec]:::required
K[samplers]:::required --> J
L[steps]:::optional --> J
M[noise]:::optional --> J
N[tuners]:::optional --> J
O[extractors]:::optional --> J
B --> P[presynth]:::required
J --> P
P --> Q[postsynth]:::required
classDef required fill:#1696d2,stroke:#1696d2;
classDef optional fill:#ec008b,stroke:#ec008b;roadmap Componentsroadmap objects require data inputs. All other inputs
can be optionally supplied as S3 objects or constructed by default.
roadmaps also have a tidymodels-style API that
lets you update objects, for example:
# create an example roadmap
roadmap(conf_data = example_conf_data,
start_data = example_start_data) |>
# add an example visit_sequence using the API
add_sequence_manual(var1, var2) |>
# update the example schema using the API
update_schema(col_schema = list("var1" = list("dtype" = "fct")))See the documentation website for more API examples.
Required roadmap objects:
conf_data: the data.frame of confidential
data to synthesize.start_data the data.frame of starting data
to initialize sequential models.Optional roadmap objects:
start_method(): S3 object that specifies randomized
transformations on the start_data, such as resampling,
noise infusion, or joint modeling. Defaults to no transformation.schema(): S3 column schema with specifications
describing variables, such as data types and NA values.
Defaults to inferred types from the provided
conf_data.visit_sequence(): S3 object that specifies the order in
which variables get synthesized. Defaults to the same order they appear
in the confidential data.contraints(): S3 object that controls imposed
constraints during the synthesis process, such as maxima and minima for
numeric variables. Defaults to no constraints.replicates(): S3 object controlling the synthesis
component repetition for producing multiple synthetic datasets. Defaults
to one synthetic dataset.synth_spec Componentssynth_spec S3 objects allow you to specify different
components using default versions for regression and classification
models, or custom models mapping individual variables to components.
Here is an example:
synth_spec(
default_regression_sampler = tidysynthesis::sample_lm,
default_classification_sampler = tidysynthesis::sample_rpart,
custom_samplers = list(
list("vars" = c("var1", "var2"), "sampler" = sample_ranger)
),
...
)These components also support updating via a
tidymodels-style API; see the documentation website for
more API examples.
Required synth_spec components:
models: parsnip model specificationssamplers: sampler functions (many provided in
tidysynthesis)Optional synth_spec components:
steps: functions that transform predictors using
recipe::steps_* functions.noise: S3 object for specifying additive noise for
synthesis outputs.tuners: list specifications for cross-validating
hyperparameter tuningextractors: parsnip functions for
extracting fit model information.Code in the following set of examples synthesizes the palmerpenguins data set with missing values removed.
library(palmerpenguins)
library(tidyverse)
library(tidysynthesis)
penguins_complete <- penguins |>
select(-year) |>
drop_na() |>
mutate(
flipper_length_mm = as.numeric(flipper_length_mm),
body_mass_g = as.numeric(body_mass_g)
)All of the examples use the same starting data and visit sequence, as specified by the roadmap below.
set.seed(20220218)
# create "starting data"
starting_data <- penguins_complete |>
group_by(island) |>
slice_sample(n = 5) |>
select(species, island, sex) |>
ungroup()
# create roadmap
rm <- roadmap(
conf_data = penguins_complete,
start_data = starting_data
) |>
add_sequence_numeric(
dplyr::where(is.numeric),
method = "correlation",
cor_var = "bill_length_mm"
)Example 1 uses linear regression to synthesize the numeric data in
the penguins data set. sample_lm() samples from normal
distributions centered on the regression line with standard deviation
equal to the residual standard error.
# synth_spec
lm_mod <- parsnip::linear_reg() |>
parsnip::set_engine(engine = "lm") |>
parsnip::set_mode(mode = "regression")
synth_spec1 <- synth_spec(
default_regression_model = lm_mod,
default_regression_sampler = tidysynthesis::sample_lm
)
# create a presynth object
# use defaults for noise, constraints, and replicates
presynth1 <- presynth(
roadmap = rm,
synth_spec = synth_spec1
)
# synthesize!
set.seed(1)
postsynth1 <- synthesize(presynth = presynth1)#> Synthesizing 1/4 bill_length_mm...
#> Synthesizing 2/4 flipper_length_mm...
#> Synthesizing 3/4 body_mass_g...
#> Synthesizing 4/4 bill_depth_mm...postsynth1$synthetic_data#> # A tibble: 15 × 7
#> species island sex bill_length_mm flipper_length_mm body_mass_g
#> <fct> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 Gentoo Biscoe male 47.9 219. 5764.
#> 2 Gentoo Biscoe male 49.8 221. 5400.
#> 3 Gentoo Biscoe female 43.7 218. 4895.
#> 4 Gentoo Biscoe male 53.1 227. 5605.
#> 5 Gentoo Biscoe female 46.4 217. 4432.
#> 6 Chinstrap Dream female 45.1 196. 3308.
#> 7 Chinstrap Dream female 48.1 197. 3399.
#> 8 Adelie Dream male 42.1 195. 4075.
#> 9 Chinstrap Dream female 48.3 182. 3565.
#> 10 Adelie Dream female 35.9 189. 3624.
#> 11 Adelie Torgersen male 44.4 197. 4110.
#> 12 Adelie Torgersen female 38.1 188. 3322.
#> 13 Adelie Torgersen female 35.8 179. 3391.
#> 14 Adelie Torgersen male 35.8 189. 3997.
#> 15 Adelie Torgersen female 39.8 192. 3307.
#> # ℹ 1 more variable: bill_depth_mm <dbl>synth_spec() can accept different model types. The
example below is a regression tree model. Notice how all of the other
objects from example 1 can be reused.
dt_mod <- parsnip::decision_tree() |>
parsnip::set_engine(engine = "rpart") |>
parsnip::set_mode(mode = "regression")
synth_spec2 <- synth_spec(
default_regression_model = dt_mod,
default_regression_sampler = tidysynthesis::sample_rpart
)
# create a presynth object
presynth2 <- presynth(
roadmap = rm,
synth_spec = synth_spec2
)
# synthesize!
set.seed(1)
postsynth2 <- synthesize(presynth = presynth2)#> Synthesizing 1/4 bill_length_mm...
#> Synthesizing 2/4 flipper_length_mm...
#> Synthesizing 3/4 body_mass_g...
#> Synthesizing 4/4 bill_depth_mm...postsynth2$synthetic_data#> # A tibble: 15 × 7
#> species island sex bill_length_mm flipper_length_mm body_mass_g
#> <fct> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 Gentoo Biscoe male 49.6 220 5350
#> 2 Gentoo Biscoe male 50 215 5550
#> 3 Gentoo Biscoe female 45.2 222 4700
#> 4 Gentoo Biscoe male 59.6 216 5500
#> 5 Gentoo Biscoe female 42.8 218 5000
#> 6 Chinstrap Dream female 46.6 195 3550
#> 7 Chinstrap Dream female 40.9 185 3700
#> 8 Adelie Dream male 39.2 186 3800
#> 9 Chinstrap Dream female 47.7 190 2900
#> 10 Adelie Dream female 36 176 3850
#> 11 Adelie Torgersen male 41.4 178 4050
#> 12 Adelie Torgersen female 37 174 3350
#> 13 Adelie Torgersen female 37 180 3775
#> 14 Adelie Torgersen male 41.6 195 3550
#> 15 Adelie Torgersen female 36.8 191 3200
#> # ℹ 1 more variable: bill_depth_mm <dbl>Sometimes prediction error is not enough and additional noise is
added to predictions. noise controls adding additional
noise to predicted values. add_noise toggles on/off which
variables will receive additional noise. Here, we use the function
add_noise_kde() with additional arguments
exclusions and n_ntiles passed to this
function (see the documentation ?add_noise_kde for
details.)
# noise
# this turns on noise for all variables and adds 0 as an exclusion for body_mass_g
noise_spec <- noise(
add_noise = TRUE,
noise_func = add_noise_kde,
exclusions = 0,
n_ntiles = 20
)
synth_spec3 <- synth_spec2 |>
update_synth_spec(
default_regression_noise = noise_spec
)
presynth3 <- presynth(
roadmap = rm,
synth_spec = synth_spec3
)
# synthesize!
set.seed(1)
postsynth3 <- synthesize(presynth = presynth3)#> Synthesizing 1/4 bill_length_mm...
#> Synthesizing 2/4 flipper_length_mm...
#> Synthesizing 3/4 body_mass_g...
#> Synthesizing 4/4 bill_depth_mm...postsynth3$synthetic_data#> # A tibble: 15 × 7
#> species island sex bill_length_mm flipper_length_mm body_mass_g
#> <fct> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 Gentoo Biscoe male 49.5 217. 5283.
#> 2 Gentoo Biscoe male 49.8 218. 5254.
#> 3 Gentoo Biscoe female 45.1 208. 4420.
#> 4 Gentoo Biscoe male 59.9 215. 5881.
#> 5 Gentoo Biscoe female 42.4 210. 4467.
#> 6 Chinstrap Dream female 47.4 195. 3321.
#> 7 Chinstrap Dream female 41.0 208. 3289.
#> 8 Adelie Dream male 39.9 195. 3914.
#> 9 Chinstrap Dream female 47.4 198. 3463.
#> 10 Adelie Dream female 36.5 184. 3072.
#> 11 Adelie Torgersen male 40.8 199. 3548.
#> 12 Adelie Torgersen female 37.6 196. 3841.
#> 13 Adelie Torgersen female 37.3 170. 3582.
#> 14 Adelie Torgersen male 41.4 189. 3461.
#> 15 Adelie Torgersen female 37.7 182. 3400.
#> # ℹ 1 more variable: bill_depth_mm <dbl>tidysynthesis contains a system for specifying
constraints during this synthesis process. This means constraints
imposed on earlier variables are realized before later variables are
synthesized. The constraints can be unconditional (e.g. penguin weight
must be positive) or conditional (e.g. a Gentoo penguin must weigh at
least 6,000 grams).
Constraints can be specified for numeric and/or categorical
variables. Depending on the variable type, two different
constraint_df_* can be specified, either
constraints_df_num or constraints_df_cat (see
the documentation ?constraints for examples.)
max_z_num and max_z_cat controls the number of
times a value should be resampled if it violates a constraint before
enforcing the constraints by modifying synthesized values.
Below is an example using constraints_df_num and
max_df_num.
# create a tibble of constraints
# skipped variables will have a minimum of -Inf and a maximum of Inf
constraints_df_num <-
tibble::tribble(
~var, ~min, ~max, ~conditions,
"bill_length_mm", 0, Inf, "TRUE",
"bill_length_mm", 0, Inf, "TRUE",
"flipper_length_mm", 0, Inf, "TRUE",
"body_mass_g", 0, Inf, "TRUE",
"body_mass_g", 4000, 10000, "flipper_length_mm > 190",
"body_mass_g", 6000, Inf, "species == 'Gentoo'"
)
# create a constraints object
constraints4 <- constraints(
schema = rm$schema,
constraints_df_num = constraints_df_num,
max_z_num = list(0, 1, 2, 3)
)
presynth4 <- presynth(
roadmap = rm |>
add_constraints(constraints4),
synth_spec = synth_spec3
)
# synthesize!
set.seed(1)
postsynth4 <- synthesize(presynth = presynth4)#> Synthesizing 1/4 bill_length_mm...
#> Synthesizing 2/4 flipper_length_mm...
#> Synthesizing 3/4 body_mass_g...
#> Synthesizing 4/4 bill_depth_mm...postsynth4$synthetic_data#> # A tibble: 15 × 7
#> species island sex bill_length_mm flipper_length_mm body_mass_g
#> <fct> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 Gentoo Biscoe male 49.5 217. 6001.
#> 2 Gentoo Biscoe male 49.8 218. 6309.
#> 3 Gentoo Biscoe female 45.1 208. 6000
#> 4 Gentoo Biscoe male 59.9 215. 6000
#> 5 Gentoo Biscoe female 42.4 210. 6000
#> 6 Chinstrap Dream female 47.4 195. 4000
#> 7 Chinstrap Dream female 41.0 208. 4000
#> 8 Adelie Dream male 39.9 195. 4000
#> 9 Chinstrap Dream female 47.4 198. 4000
#> 10 Adelie Dream female 36.5 184. 3072.
#> 11 Adelie Torgersen male 40.8 199. 4000
#> 12 Adelie Torgersen female 37.6 196. 4000
#> 13 Adelie Torgersen female 37.3 170. 3582.
#> 14 Adelie Torgersen male 41.4 189. 3461.
#> 15 Adelie Torgersen female 37.7 182. 3400.
#> # ℹ 1 more variable: bill_depth_mm <dbl>tidysynthesis can generate multiple replicates. This
means that all input conditions are the same, but, due to random
sampling, the syntheses themselves differ. The replicates()
functionality allows for the creation of these replicates (see the
documentation ?replicates() for different kinds of
replicate specification).
replicates5 <- replicates(model_sample_replicates = 5)
presynth5 <- presynth(
roadmap = rm |>
add_replicates(replicates5),
synth_spec = synth_spec2
)
# synthesize!
set.seed(1)
suppressMessages(synth5 <- synthesize(presynth = presynth5))
glimpse(synth5[[1]]$synthetic_data)#> Rows: 15
#> Columns: 7
#> $ species <fct> Gentoo, Gentoo, Gentoo, Gentoo, Gentoo, Chinstrap, C…
#> $ island <fct> Biscoe, Biscoe, Biscoe, Biscoe, Biscoe, Dream, Dream…
#> $ sex <fct> male, male, female, male, female, female, female, ma…
#> $ bill_length_mm <dbl> 49.6, 50.0, 45.2, 59.6, 42.8, 46.6, 40.9, 39.2, 47.7…
#> $ flipper_length_mm <dbl> 220, 215, 222, 216, 218, 195, 185, 186, 190, 176, 17…
#> $ body_mass_g <dbl> 5350, 5550, 4700, 5500, 5000, 3550, 3700, 3800, 2900…
#> $ bill_depth_mm <dbl> 15.3, 15.9, 14.5, 15.9, 13.7, 18.0, 16.6, 17.2, 18.7…glimpse(synth5[[2]]$synthetic_data)#> Rows: 15
#> Columns: 7
#> $ species <fct> Gentoo, Gentoo, Gentoo, Gentoo, Gentoo, Chinstrap, C…
#> $ island <fct> Biscoe, Biscoe, Biscoe, Biscoe, Biscoe, Dream, Dream…
#> $ sex <fct> male, male, female, male, female, female, female, ma…
#> $ bill_length_mm <dbl> 50.4, 51.4, 44.9, 51.9, 47.5, 46.5, 48.4, 37.6, 50.1…
#> $ flipper_length_mm <dbl> 228, 220, 208, 230, 209, 189, 198, 187, 202, 190, 18…
#> $ body_mass_g <dbl> 6000, 5550, 4850, 5700, 5200, 3200, 3500, 4500, 3700…
#> $ bill_depth_mm <dbl> 16.4, 15.7, 13.8, 15.3, 14.3, 19.4, 16.8, 19.5, 17.3…glimpse(synth5[[3]]$synthetic_data)#> Rows: 15
#> Columns: 7
#> $ species <fct> Gentoo, Gentoo, Gentoo, Gentoo, Gentoo, Chinstrap, C…
#> $ island <fct> Biscoe, Biscoe, Biscoe, Biscoe, Biscoe, Dream, Dream…
#> $ sex <fct> male, male, female, male, female, female, female, ma…
#> $ bill_length_mm <dbl> 51.3, 48.5, 46.0, 50.0, 48.1, 45.2, 46.4, 41.4, 45.7…
#> $ flipper_length_mm <dbl> 220, 225, 210, 230, 220, 200, 192, 189, 200, 195, 17…
#> $ body_mass_g <dbl> 5800, 5050, 4950, 5600, 3950, 3000, 3500, 4100, 2700…
#> $ bill_depth_mm <dbl> 15.6, 16.3, 13.1, 17.0, 14.0, 18.9, 17.3, 17.5, 16.9…Please share bugs and feature requests on GitHub. Minimal reproducible examples are appreciated.