How can you perform exploratory data analysis in R? Discuss the techniques and tools available for data exploration and visualization.

Performing exploratory data analysis (EDA) in R involves a variety of techniques and tools that help you understand and gain insights from your data. R provides a rich ecosystem of packages specifically designed for data exploration and visualization. Let's discuss some of the key techniques and tools available in R for EDA: 1. Summary Statistics: Summary statistics provide an overview of the data, allowing you to understand its distribution, central tendency, variability, and other key characteristics. R offers functions like summary(), mean(), median(), min(), max(), sd(), var(), and quantile() to calculate various summary statistics. 2. Data Visualization: Visualization plays a crucial role in EDA as it helps uncover patterns, trends, and relationships in the data. R provides numerous packages for creating a wide range of visualizations, including: * Base R graphics: R's base graphics system offers functions like plot(), hist(), boxplot(), and barplot() to create basic visualizations. * ggplot2: ggplot2 is a popular data visualization package that follo....