clean_llm_code

Replaces literal '\n' with real line breaks, trims whitespace, and optionally indents.

Data from statistical agencies and other institutions are mostly
confidential. This package, introduced in Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized
(micro)data, i.e. for the creation of public- and scientific-use files.
The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
Various risk estimation and anonymization methods are included. Note that the package
includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this
package.

Matthias Templ

sdcMicro

Statistical Disclosure Control Methods for Anonymization of Data
and Risk Estimation

Bernhard Meindl

Alexander Kowarik

Johannes Gussenbauer

Organisation For Economic Co-Operation And Development 

Statistics Netherlands 

Pascal Heus 

clean_llm_code function

<dl><dt>code_string</dt>
<dd>Character string returned by LLM (with '\n' line breaks).</dd>
<dt>indent</dt>
<dd>Logical, whether to indent lines (for printing only).</dd></dl>

Arguments

Clean LLM-generated R code — clean_llm_code

<dl>

<dt>code_string</dt>
<dd>Character string returned by LLM (with '\n' line breaks).</dd>


<dt>indent</dt>
<dd>Logical, whether to indent lines (for printing only).</dd>

</dl>

clean_llm_code: Clean LLM-generated R code

Description

Usage

Value

Arguments