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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-033.zip
cranlogs, radiant, visdat, archivist, xtable, arsenal, DataExplorer, dataMaid, dlookr, ExPanDaR, explore, shiny, exploreR, funModeling, inspectdf, RtutoR, SmartEDA, data.table, summarytools, knitr, ggplot2, xray, tableone, describer, skimr, prettyR, Hmisc, ggfortify, autoplotly, gpairs, GGally, survminer, cr17, DALEX, iml
ReproducibleResearch, TeachingStatistics, MissingData, WebTechnologies, Bayesian, ClinicalTrials, Econometrics, Finance, Graphics, HighPerformanceComputing, Multivariate, OfficialStatistics, Phylogenetics, SocialSciences, Survival, TimeSeries
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For attribution, please cite this work as
Staniak & Biecek, "The Landscape of R Packages for Automated Exploratory Data Analysis", The R Journal, 2019
BibTeX citation
@article{RJ-2019-033,
author = {Staniak, Mateusz and Biecek, Przemysław},
title = {The Landscape of R Packages for Automated Exploratory Data Analysis},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-033},
doi = {10.32614/RJ-2019-033},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {347-369}
}