Key Features
- 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
Book Description
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 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
About the Author
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/
Table of Contents
- Quick Start
- Basic Objects
- Managing Your Workspace
- Basic Expressions
- Working with Basic Objects
- Working with Strings
- Working with Data
- Inside R
- Metaprogramming
- Object-Oriented Programming
- Working with Databases
- Data Manipulation
- High-Performance Computing
- Web Scraping
- Boosting Productivity
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/
