Course Content

R Introduction: Part I

## R Introduction: Part I

# Why R?

Hello and welcome! If you're here, you're likely curious about **R** and why it's such a big deal in the world of **data science**, so let's discuss why it is a fantastic tool for analyzing and visualizing data.

But what makes R stand out in a sea of programming languages?

## Key Features of R

**Statistical Powerhouse**: R was designed by statisticians, for statisticians. Its rich library of statistical and graphical methods includes linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more;**Free and Open Source**: R is free and open-source software, meaning you can use it without any cost. Additionally, it has a vibrant community that contributes to its extensive package ecosystem, constantly expanding R's capabilities;**Highly Extensible**: The R environment can be extended via packages. There are over 15,000 packages available on CRAN (the Comprehensive R Archive Network), catering to a wide range of statistical, graphical, and machine learning tasks;**Data Manipulation and Cleaning**: R excels at data manipulation and cleaning providing intuitive and powerful tools for transforming and organizing data;**Data Visualization**: One of R's standout features is its data visualization capabilities. In particular, it is renowned for creating complex and aesthetically pleasing visualizations with minimal code.

Everything was clear?