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

Working with relational databases


In the previous chapters, we used a family of built-in functions such as read.csv and read.table to import data from separator-delimited files, such as those in the csv format. Using text formats to store data is handy and portable. When the data file is large, however, such a storage method may not be the best way.

There are three main reasons why text formats can no longer be easy to use. They are as follows:

  1. Functions such as read.csv() are mostly used to load the whole file into memory, that is, a data frame in R. If the data is too large to fit into the computer memory, we simply cannot do it.

  2. Even if the dataset is large, we usually don't have to load the whole dataset into memory when we work on a task. Instead, we often need to extract a subset of the dataset that meets a certain condition. The built-in data-importer functions simply do not support querying a csv file.

  3. The dataset is still updating, that is, we need to insert records into the dataset...