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?

- **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.

R is one of the most popular programming languages among data analysts and data scientists. R has a wide community that will always help you with any problem. Also, R has tons of different packages designed to solve various problems. The language can be easily applied to statistics and data analytics problems. In the first part, you will be introduced to the very basics of R and will deal with single 1-D values of different types.

In the initial section, you’ll dive into the foundational elements of R, such as generating output and performing elementary operations. This section will conclude with an exploration of the theory behind variables.

You’ll become familiar with the vector class in R, which also handles individual values. Here, you’ll delve into the primary data types for singular values and learn how to manage multiple values of the same type simultaneously.

Factors, also known as categorical variables, are prevalent in the real world. Examples include blood types, currencies, nationalities, and brands. Unlike numerical variables, factors have a limited set of possible values.

R Introduction: Part I

Why R?

Key Features of R