dcc_missing_states

Returns explicit missing semantics for imported and cleaned cells, including
not administered, respondent omission, import missing, declared missing code,
and cleared by cleaning.

Rule-driven, auditable cleaning of survey and assessment
response data, implementing the WeianData Detect-Execute-Report
workflow. Provides a multi-format, multi-encoding input layer
(CSV, 'Excel', 'SPSS', 'Stata', 'SAS', Parquet, JSON), the
dcc_data container with a provenance chain, level-0 structural
diagnostics, five built-in response-quality detectors (missing
items, straight-lining, response time, trap items, score
anomalies), a declarative YAML rule engine, an execution engine
with a cell-level audit log, answer-key scoring, multi-form to
master item bank mapping, a normalized report model rendered as bilingual
staff workbooks and HTML, complete statistical bundles, and versioned
machine JSON/JSONL with findings-to-changes reconciliation,
cell-level lineage tracing, and
manifest-based one-command reproduction. Includes a protected bilingual
strict project workbook and matching JSON contract with cell-addressed
validation, non-mutating preflight, preview-first execution, and localized
staff guidance. All formally supported input backends install with the
package; PDF is optional rather than a fixed report output.

Kunxiang Ma

Data Cleaning Center for Survey and Assessment Data

WEIAN DATA TECH (Beijing) Co., Ltd. 

dcc_missing_states function

<dl><dt>x</dt>
<dd>A <code>dcc_data</code> object.</dd></dl>

Arguments

Canonical cell-level missing states — dcc_missing_states

<dl>

<dt>x</dt>
<dd>A <code>dcc_data</code> object.</dd>

</dl>

dcc_missing_states: Canonical cell-level missing states

Description

Usage

Value

Arguments