# The Landscape of R Packages for Automated Exploratory Data Analysis

## TL;DR

This paper systematically reviews twelve R packages for automated exploratory data analysis, highlighting current capabilities and identifying future development directions to enhance data analysis automation.

## Contribution

It provides a comprehensive analysis of existing autoEDA tools in R and suggests new avenues for improving automation in data analysis workflows.

## Key findings

- Identifies key features of 12 popular R autoEDA packages.
- Highlights automation gaps in current autoEDA tools.
- Suggests directions for future autoEDA development.

## 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 twelve 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.