R is a programming language and open-source software environment used for statistical computing, data analysis, and visualization. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, in the mid-1990s. R is widely used in both academia and industry for a variety of data analysis tasks, from simple data cleaning and exploration to advanced statistical modeling and machine learning.

The main strengths of R is its flexibility and ease of use. R has a rich set of built-in functions and packages, but users can also write their own functions and packages, making it possible to customize and extend the language to suit their needs. R has a large and active community of users and developers, who contribute new packages and functionality to the language on a regular basis. This means that R is constantly evolving, and new tools and techniques are being added all the time. It also means that if you are having trouble with R, finding the answer is often only a Google search away.

R is particularly well-suited for statistical analysis and data visualization. It has a wide range of built-in statistical functions for data analysis, including basic descriptive statistics, hypothesis testing, regression analysis, and time series analysis. R also has powerful data visualization tools, with a variety of built-in plotting functions and packages for creating charts, graphs, and interactive visualizations. These visualization tools allow users to explore data and communicate their findings in a clear and effective way.

Another key feature of R is its ability to handle large datasets. R can easily handle datasets with millions of observations and hundreds of variables, making it a popular choice for big data analysis. R also has tools for working with messy or incomplete data, including functions for data cleaning, imputation, and transformation.

This book is a compilation of videos that will help you learn R. We will walk you through everything from installing R and RStudio to installing packages to basic statistics and some deeper data science algorithms. We will spend time discussing how to use R for statistical calculations and what the output that R generates means. Hopefully, when you are finished with this book, you will have a deeper understanding of statistics and data science and how these techniques can be applied to real life.