Book Image

Learning R Programming

By : Kun Ren
Book Image

Learning R Programming

By: Kun Ren

Overview of this book

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.
Table of Contents (21 chapters)
Learning R Programming
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Summary


In this chapter, you learned a number of basic functions and various packages for data manipulation. Using built-in functions to manipulate data can be redundant. Several packages are tailored for filtering and aggregating data based on different techniques and philosophies. The sqldf packages use embedded SQLite databases so that we can directly write SQL statements to query data frame in our working environment. On the other hand, data.table provides an enhanced version of data.frame and a powerful syntax, and dplyr defines a grammar of data manipulation by providing a set of pipeline friendly verb functions. The rlist class provides a set of pipeline friendly functions for non-tabular data manipulation. No single package is best for all situations. Each of them represents a way of thinking, and which best fits a certain problem depends on how you understand the problem and your experience of working with data.

Processing data and doing simulation require considerable computing...