Book Image

Modern R Programming Cookbook

By : Jaynal Abedin
Book Image

Modern R Programming Cookbook

By: Jaynal Abedin

Overview of this book

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. The objective of this book is to show how to work with different programming aspects of R. The emerging R developers and data science could have very good programming knowledge but might have limited understanding about R syntax and semantics. Our book will be a platform develop practical solution out of real world problem in scalable fashion and with very good understanding. You will work with various versions of R libraries that are essential for scalable data science solutions. You will learn to work with Input / Output issues when working with relatively larger dataset. At the end of this book readers will also learn how to work with databases from within R and also what and how meta programming helps in developing applications.
Table of Contents (10 chapters)

Introduction

As a data scientist or data analyst, you often need to repeat certain computations or series of computations many times. To complete repetitive tasks you could easily use a for loop in R. But if you need a larger number of repetitions or a very complex computation, the for loop could a be time consuming. To overcome slow computation problems, you could use multiple computation cores that are available in any recent computer. You can easily spread your task to multiple computing cores in your computer to simplify complex and repetitive tasks.

Suppose you want to predict airline delay (departure delay) time for each destination, you could do this using simple regression, but for larger data and for each destination, this could be a computational problem and consume huge memory. You can tackle this computational issue by using parallel computing facilities available...