The Landscape of R Packages for Automated Exploratory Data Analysis

Authors

Mateusz Staniak, Warsaw University of Technology
Przemysław Biecek, University of Warsaw

Date of this Version

12-2019

Document Type

Article

Citation

The R Journal (December 2019) 11(2); Editor: Michael J. Kane

Comments

Copyright 2019, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering

There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of fifteen popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.