Book Image

R Programming By Example

By : Omar Trejo Navarro
Book Image

R Programming By Example

By: Omar Trejo Navarro

Overview of this book

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
Table of Contents (12 chapters)

How fast is fast enough?

Let's assume that you have chosen a good algorithm and implemented it without too much regard for optimization, as is commonly the case with first attempts. Should you invest the time to optimize it? Performance optimization can be a very costly activity. You must not try to optimize your code unless you must. Your time is valuable, and it's probably better spent doing something else.

Let's say that for some reason, you must make your implementation faster. The first thing you must decide on is how fast is fast enough. Is your algorithm required to simply finish within a couple of hours instead of a couple of days, or do you need to come down to microsecond levels? Is this an absolute requirement or should you simply do the best job you can within a specific time frame? These are important questions that you must consider before optimizing...