Exploratory Data Analysis Basics

An iterative cycle of questioning and visualization is essential in EDA.

A goal of EDA is to understand relationships and patterns in data without fitting a statistical model.

One purpose of EDA is to detect missing values and data quality issues.

EDA focuses solely on numerical data, ignoring categorical data.

Data frames in R allow each row to represent an observation and each column to represent a variable.

The dim() function in R returns the dimensions of a data frame in terms of rows and columns.

The str() function provides an overview of the contents and structure of a data frame.

A data frame must only contain numerical data for analysis in R.

The summary() function in R provides a quick overview of summary statistics for each column in a data frame.

The dplyr package is a part of the tidyverse collection of R packages.