write_data

<code>write_data()</code> writes a data frame to any format listed by
<code>supported_formats()</code>. The format is inferred from the file extension
unless <code>format</code> is given; text formats are compressed when the file name
ends in <code>.gz</code>, <code>.bz2</code> or <code>.xz</code>.
HDF5 files are written with one dataset per column inside a group
(<code>dataset</code>, default <code>"data"</code>), with attributes that record column order,
factor levels, <code>Date</code>/<code>POSIXct</code> classes, time zones and missing strings,
so that <code>read_data()</code> restores the original column types.
Date-times (<code>POSIXct</code>) keep their instant in every format. Text formats
(CSV, TSV, JSON, NDJSON) store them as ISO 8601 strings in UTC; Excel,
SPSS, Stata and SAS files, which store clock time without a time zone,
receive the UTC clock time; Parquet, Feather, fst and HDF5 store the
instant together with the time zone. Pass the original data as <code>template</code>
to <code>read_data()</code> to restore the original time zone.

Generates synthetic versions of numeric, categorical, mixed-type
and time-series data. Marginal distributions are modelled with
interpolated empirical quantile functions and dependence with Gaussian or
Student-t copulas (Sklar, 1959). Discrete and categorical variables enter
the copula through the distributional transform (Ruschendorf, 2009,
<doi:10.1016/j.jspi.2009.05.030>), and their latent correlations are
estimated by maximum likelihood as polychoric (Olsson, 1979,
<doi:10.1007/BF02296207>) and polyserial (Olsson, Drasgow and Dorans,
1982, <doi:10.1007/BF02294164>) correlations. Unordered categories are
placed on their latent coordinate in an order estimated from their
association with the other variables. Missing values are
modelled by missingness indicators inside the copula, so that
missingness may depend on other variables. Supports stratified models,
a closeness parameter with calibration to a target distance or
disclosure-risk level, chunked generation of very large data sets,
optional hybrid copula and conditional CART or GAM synthesis for
one continuous response (requiring 'rpart' or 'mgcv'), training-only
automatic selection of the conditional engine, experimental nominal
conditional synthesis of one unordered categorical response (requiring
'nnet'), copula vector-autoregressive and bootstrap time-series synthesis,
augmentation of small training sets for machine learning, and random
generation from multivariate distributions and elliptical and
Archimedean copulas. Fidelity, utility and disclosure risk are assessed
with marginal distances, association matrices, propensity score
mean-squared error (Snoke et al., 2018, <doi:10.1111/rssa.12358>),
confidence-interval overlap, correct attribution probability and
distance to closest record, with 'ggplot2' visualisations. Data can be
read from and written to CSV, TSV, RDS, RData, Parquet, Feather, HDF5,
JSON, NDJSON, Excel, SPSS, Stata, SAS and fst files. The
empirical-quantile and copula approach builds on ideas of the
'synthesizer' package by Mark van der Loo.

Bogdan Oancea

SynthesizerPlus

Synthetic Data Generation with Copulas, Evaluation and
Multi-Format I/O

write_data function

<dl><dt>x</dt>
<dd>A data frame (matrices are converted).</dd>
<dt>path</dt>
<dd>Output file path.</dd>
<dt>format</dt>
<dd>Optional format name or extension.</dd>
<dt>overwrite</dt>
<dd>Overwrite an existing file?</dd>
<dt>object</dt>
<dd>For <code>rdata</code>: name under which the object is saved.</dd>
<dt>dataset</dt>
<dd>For <code>hdf5</code>: name of the group to write.</dd>
<dt>...</dt>
<dd>Further arguments passed to the backend writer.</dd></dl>

Arguments

Write a data set to a file — write_data

<dl>

<dt>x</dt>
<dd>A data frame (matrices are converted).</dd>


<dt>path</dt>
<dd>Output file path.</dd>


<dt>format</dt>
<dd>Optional format name or extension.</dd>


<dt>overwrite</dt>
<dd>Overwrite an existing file?</dd>


<dt>object</dt>
<dd>For <code>rdata</code>: name under which the object is saved.</dd>


<dt>dataset</dt>
<dd>For <code>hdf5</code>: name of the group to write.</dd>


<dt>...</dt>
<dd>Further arguments passed to the backend writer.</dd>

</dl>

write_data: Write a data set to a file

Description

Usage

Value

Arguments

See Also

Examples