Learning R programming : become an efficient data scientist with R / Kun Ren.

Format:

Book

Author/Creator:

Ren, Kun, author.

Language:

English

Subjects (All):

R (Computer program language).

Open source software.

Physical Description:

1 online resource (576 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2016.

System Details:

text file

Biography/History:

Ren Kun: Kun Ren has used R for nearly 4 years in quantitative trading, along with C++ and C#, and he has worked very intensively (more than 8-10 hours every day) on useful R packages that the community does not offer yet. He contributes to packages developed by other authors and reports issues to make things work better. He is also a frequent speaker at R conferences in China and has given multiple talks. Kun also has a great social media presence. Additionally, he has substantially contributed to various projects, which is evident from his GitHub account: https: //github. com/renkun-ken https: //cn. linkedin. com/in/kun-ren-76027530 http: //renkun. me http: //renkun. me/formattable/ http: //renkun. me/pipeR/ http: //renkun. me/rlist/

Summary:

Become an efficient data scientist with R About This Book Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming techniques Learn how to address the core problems of programming in R and leverage the most popular packages for common tasks Who This Book Is For This is the perfect tutorial for anyone who is new to statistical programming and modeling. Anyone with basic programming and data processing skills can pick this book up to systematically learn the R programming language and crucial techniques. What You Will Learn Explore the basic functions in R and familiarize yourself with common data structures Work with data in R using basic functions of statistics, data mining, data visualization, root solving, and optimization Get acquainted with R’s evaluation model with environments and meta-programming techniques with symbol, call, formula, and expression Get to grips with object-oriented programming in R: including the S3, S4, RC, and R6 systems Access relational databases such as SQLite and non-relational databases such as MongoDB and Redis Get to know high performance computing techniques such as parallel computing and Rcpp Use web scraping techniques to extract information Create RMarkdown, an interactive app with Shiny, DiagramR, interactive charts, ggvis, and more In Detail R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techn...

Contents:

Learning R programming: become an efficient data scientist with R

Credits

About the Author

About the Reviewer

www.PacktPub.com

Table of Contents

Preface

1. Quick Start

2. Basic Objects

3. Managing Your Workspace

4. Basic Expressions

5. Working with Basic Objects

6. Working with Strings

7. Working with Data

8. Inside R

9. Metaprogramming

10. Object-Oriented Programming

11. Working with Databases

12. Data Manipulation

13. High-Performance Computing

14. Web Scraping

15. Boosting Productivity

Index.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed March 1, 2017).

ISBN:

9781785880629

1785880624

OCLC:

962419840