Generalized detection of outlier values in a database, based on contingency tables (tac)

A set of concise and efficient tools for statistical production. Can also be used for data management.
In statistical production, you deal with complex data and need to control your process at each step of your work.
Concise functions are very helpful, because you do not hesitate to use them.
The following functions are included in the package.
'dup'  checks duplicates.
'miss' checks missing values.
'tac' computes contingency table of all columns.
'toc' compares two tables, spotting significant deviations.
'chi2_find' compares columns within a data.frame, spotting related categories of (a more complex function).

Vincent Reduron

ProduceR

Concise and Efficient Tools for Everyday Statistical Production

toc function

<dl><dt>df1</dt>
<dd>Input data.frame (to compare with df2)</dd>
<dt>df2</dt>
<dd>Input data.frame (to compare with df1)</dd>
<dt>values</dt>
<dd>Vector of columns that serve as measures (amounts, counts, etc.)</dd>
<dt>a</dt>
<dd>Allowed absolute variation</dd>
<dt>r</dt>
<dd>Allowed relative variation</dd>
<dt>sample_rate</dt>
<dd>Sampling rate, if df is a remote table</dd>
<dt>num_but_discrete</dt>
<dd>Numeric variables to be treated as discrete modal variables. If 'all', all numeric variables are treated as discrete modal variables.</dd></dl>

Arguments

TAC-based Outlier Control (TOC) — toc

<dl>

<dt>df1</dt>
<dd>Input data.frame (to compare with df2)</dd>


<dt>df2</dt>
<dd>Input data.frame (to compare with df1)</dd>


<dt>values</dt>
<dd>Vector of columns that serve as measures (amounts, counts, etc.)</dd>


<dt>a</dt>
<dd>Allowed absolute variation</dd>


<dt>r</dt>
<dd>Allowed relative variation</dd>


<dt>sample_rate</dt>
<dd>Sampling rate, if df is a remote table</dd>


<dt>num_but_discrete</dt>
<dd>Numeric variables to be treated as discrete modal variables. If 'all', all numeric variables are treated as discrete modal variables.</dd>

</dl>

toc: TAC-based Outlier Control (TOC)

Description

Usage

Value

Arguments

Details