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

Understanding the copy-on-modify mechanism


In the previous section, we showed how lazy evaluation works and how it may help save computing time and working memory by avoiding unnecessary evaluation of function arguments. In this section, I will show you an important feature of R that makes it safer to work with data. Suppose we create a simple numeric vector x1:

x1 <- c(1, 2, 3) 

Then, we assign the value of x1 to x2:

x2 <- x1 

Now, x1 and x2 have exactly the same value. What if we modify an element in one of the two vectors? Will both vectors change?

x1[1] <- 0
x1
## [1] 0 2 3
x2
## [1] 1 2 3 

The output shows that when x1 is changed, x2 will remain unchanged. You may guess that the assignment automatically copies the value and makes the new variable point to the copy of the data instead of the original data. Let's use tracemem() to track the footprint of the data in memory.

Let's reset the vectors and conduct an experiment by tracing the memory addresses...