handle_missing_values

Handles missing values (NA) in a data frame using one of several strategies:
exclude rows, replace with a value, fill with column mean, fill with column median, or flag with an indicator column.

internal

A general-purpose toolkit for comparing any two data frames
with optional 'CDISC' (Clinical Data Interchange Standards Consortium)
validation for clinical trial data. Core comparison functions work on
arbitrary datasets: variable-level and observation-level comparison, data
type checking, metadata attribute analysis (types, labels, lengths,
formats), missing value handling, key-based row matching, tolerance-based
numeric comparisons, and group-wise comparisons. Optional z-score
outlier detection is available when enabled. When working with clinical
data, the package additionally
validates 'SDTM' (Study Data Tabulation Model) and 'ADaM' (Analysis Data
Model) datasets against CDISC standards (SDTM IG 3.3/3.4, ADaM IG
1.1/1.2/1.3), automatically detecting domains and flagging non-conformant
variables. Generates unified comparison reports in text or HTML format
with interactive dashboards. For CDISC standards, see
<https://www.cdisc.org/standards>.

Siddharth Lokineni

clinCompare

Dataset Comparison with 'CDISC' Validation for Clinical Trial
Data

handle_missing_values function

<dl><dt>df</dt>
<dd>A data frame with potential missing values.</dd>
<dt>method</dt>
<dd>Method for handling missing values ('exclude', 'replace', 'mean', 'median', 'flag').</dd>
<dt>replace_with</dt>
<dd>Optional; a value or named list to replace missing values with (used with 'replace' method).</dd></dl>

Arguments

Handle Missing Values in Dataset — handle_missing_values

<dl>

<dt>df</dt>
<dd>A data frame with potential missing values.</dd>


<dt>method</dt>
<dd>Method for handling missing values ('exclude', 'replace', 'mean', 'median', 'flag').</dd>


<dt>replace_with</dt>
<dd>Optional; a value or named list to replace missing values with (used with 'replace' method).</dd>

</dl>

handle_missing_values: Handle Missing Values in Dataset

Description

Usage

Value

Arguments