Data quality diagnosis

In dlookr: Tools for Data Diagnosis, Exploration, Transformation

Preface

After you have acquired the data, you should do the following:

• Diagnose data quality.
  • If there is a problem with data quality,
  • The data must be corrected or re-acquired.
• Explore data to understand the data and find scenarios for performing the analysis.
• Derive new variables or perform variable transformations.

The dlookr package makes these steps fast and easy:

• Performs a data diagnosis or automatically generates a data diagnosis report.
• Discover data in a variety of ways, and automatically generate EDA(exploratory data analysis) report.
• Impute missing values and outliers, resolve skewed data, and categorize continuous variables into categorical variables. And generates an automated report to support it.

This document introduces Data Quality Diagnosis methods provided by the dlookr package. You will learn how to diagnose the quality of tbl_df data that inherits from data.frame and data.frame with functions provided by dlookr.

dlookr increases synergy with dplyr. Particularly in data exploration and data wrangle, it increases the efficiency of the tidyverse package group.

Supported data structures

Data diagnosis supports the following data structures.

• data frame : data.frame class.
• data table : tbl_df class.
• table of DBMS : table of the DBMS through tbl_dbi.
• Use dplyr as the back-end interface for any DBI-compatible database.

How to perform data diagnosis

For information on how to perform data diagnosis, refer to the following website.

• Data quality diagnosis

dlookr documentation built on July 9, 2023, 6:31 p.m.