The Best Exploratory Data Analysis Libraries and AI Agents for R Programming

Exploratory Data Analysis is a general framework, yet data scientists have many libraries and AI Assistants to use. Here’s an overview using the gtcars dataset.

When it comes to data analysis, exploratory data analysis (EDA) provides a bevy of benefits to an analysis workflow. EDA sets up a framework for assessing a given dataset for oddities, like null values or skewed data distributions. The results cause a preview of the functions you need in your data model, be it a regression, a machine learning model, or a predictive analytics model.

Conducting EDA in R programming leads to a variety of libraries for determining how to call columns and data values. Many of the libraries provide a summary function to help you visualize your data characteristics quickly and make decisions on how to best clean your given dataset.

R is designed for larger datasets than that in an Excel spreadsheet or CSV file, so the functions can be handy for certain instances that occur when working with datasets.

If you have a few rows and columns, many of the functions will feel a bit redundant to use. But for long columns of data or many columns where the…