  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.
