This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools.
About This Book
Get a firm hold on the fundamentals of R through practical hands-on examples
Get started with good R programming fundamentals for data science
Exploit the different libraries of R to build interesting applications in R
Who This Book Is For
This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed.
What You Will Learn
Discover techniques to leverage R's features, and work with packages
Perform a descriptive analysis and work with statistical models using R
Work efficiently with objects without using loops
Create diverse visualizations to gain better understanding of the data
Understand ways to produce good visualizations and create reports for the results
Read and write data from relational databases and REST APIs, both packaged and unpackaged
Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel
In Detail
R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.
We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.
By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
Style and Approach
This is an easy-to-understand guide filled with real-world examples, giving you a holistic view of R and practical, hands-on experience.
