impute_missing

This function performs missing value imputation in the input data using various methods.
 The available imputation methods are: 
- "mean": Imputes missing values with the mean of the variable.
 - "median": Imputes missing values with the median of the variable.
 - "mode": Imputes missing values with the mode of the variable (for categorical data).
 - "locf": Imputes missing values using the Last Observation Carried Forward method.
 - "knn": Imputes missing values using the k-Nearest Neighbors algorithm (specify k).

A quick and effective data exploration toolkit. It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis.

Zhiwei Lin

AnalysisLin

Exploratory Data Analysis

impute_missing function

<dl><dt>data</dt>
<dd>Input data.</dd>
<dt>method</dt>
<dd>Method of handling missing values: "mean," "median," "mode," "locf," or "knn."</dd>
<dt>k</dt>
<dd>Value of the number of neighbors to be checked (only for knn method). Default is NULL.</dd></dl>

Arguments

Missing Value Imputation — impute_missing

<dl>

<dt>data</dt>
<dd>Input data.</dd>


<dt>method</dt>
<dd>Method of handling missing values: "mean," "median," "mode," "locf," or "knn."</dd>


<dt>k</dt>
<dd>Value of the number of neighbors to be checked (only for knn method). Default is NULL.</dd>

</dl>

impute_missing: Missing Value Imputation

Description

Usage

Value

Arguments

Examples