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

Using built-in functions to manipulate data frames


Previously, you learned the basics of data frames. Here, we will review the built-in functions used to filter a data frame. Although a data frame is essentially a list of vectors, we can access it like a matrix since all column vectors are of the same length. To select rows that meet certain conditions, we will supply a logical vector as the first argument of [], while the second is left empty.

In R, these operations can be done with built-in functions. In this section, we will introduce some built-in functions that are most helpful to manipulate data into the form we need as model input or for presentation. Some of the functions or techniques are already presented in the previous chapters.

Most of the code in this section and subsequent sections are based on a group of fictitious data about some products. We will use the readr package to load the data for better handling of column types. If you don't have this package installed, run install...