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Big Data Analytics with R
Big Data Analytics with R

Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data

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Big Data Analytics with R

Chapter 2. Introduction to R Programming Language and Statistical Environment

In Chapter 1, The era of "Big Data", you have become familiar with the most useful Big Data terminology, and a small selection of typical tools applied to unusually large or complex data sets. You have also gained essential insights into how R was developed and how it became the leading statistical computing environment and programming language favored by technology giants and the best universities in the world. In this chapter you will have the opportunity to learn some most important R functions from base R installation and well-known third party packages used for data crunching, transformation, and analysis. More specifically in this chapter you will:

  • Understand the landscape of available R data structures
  • Be guided through a number of R operations allowing you to import data from standard and proprietary data formats
  • Carry out essential data cleaning and processing activities such as subsetting...

Learning R

This book assumes that you have been previously exposed to R programming language, and this chapter will serve more as a revision, and an overview, of the most essential operations, rather than a very thorough handbook on R. The goal of this work is to present you with specific R applications related to Big Data and the way you can combine R with your existing Big Data analytics workflows instead of teaching you basics of data processing in R. There is a substantial number of great introductory and beginner-level books on R available at IT specialized bookstores or online, directly from Packt Publishing, and other respected publishers, as well as on the Amazon store. Some recommendations include the following:

  • R in Action: Data Analysis and Graphics with R by Robert Kabacoff (2015), 2nd edition, Manning Publications
  • R Cookbook by Paul Teetor (2011), O'Reilly
  • Discovering Statistics Using R by Andy Field, Jeremy Miles, and Zoe Field (2012), SAGE Publications
  • R for Data Science...

Revisiting R basics

In the following section we will present a short revision of the most useful and frequently applied R functions and statements. We will start from a quick R and RStudio installation guide and then proceed to creating R data structures, data manipulation, and transformation techniques, and basic methods used in Exploratory Data Analysis (EDA). Although the R codes listed in this book have been tested extensively, as always in such cases, please make sure that your equipment is not faulty and note that you will be running all the following scripts at your own risk.

Getting R and RStudio ready

Depending on your operating system (whether Mac OS X, Windows, or Linux) you can download and install specific base R files directly from https://cran.r-project.org/ . If you prefer to use RStudio IDE you still need to install the R core available from CRAN website first and then download and run installers of the most recent version of RStudio IDE specific for your platform from...

Applied data science with R

Applied data science covers all the activities and processes data analysts must typically undertake to deliver evidence-based results of their analyses. This includes data collection, preprocessing data that may contain some basic but frequently time-consuming data transformations, and manipulations, EDA to describe the data under investigation, research methods, and statistical models applicable to the data and related to the research questions, and finally, data visualizations and reporting the insights. Data science is an enormous field, covering a great number of specific disciplines, techniques, and tools, and there are hundreds of very good printed and online resources explaining the particulars of each method or application.

In this section, we will merely focus on a small fraction of selected topics in data science using the R language. From this moment on, we will also be using real data sets from socio-economic domains. These data sets, however...

Summary

In this chapter we've revisited many concepts related to data management, data processing, transformations, and data analysis, using R programming language and a statistical environment. Our target was to enable you to familiarize yourself with major functions and R packages, which facilitate manipulation of data and hypothesis testing. Finally, we have mentioned a few words on the topic of static and interactive data visualizations and their nearly limitless applications.

This chapter was by no means inclusive of all available methods and techniques. We have merely scratched the surface of what's possible in R, but we also believe that the information provided in this chapter enabled you to either revise your existing R skills, or to identify potential gaps.

In the following chapters we will be building on these skills with almost exclusive focus on Big Data. In Chapter 3, Unleashing the Power of R from Within you will be exposed to a number of packages which allow R users...

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Key benefits

  • Perform computational analyses on Big Data to generate meaningful results
  • Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,
  • Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market

Description

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.

Who is this book for?

This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.

What you will learn

  • Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities
  • Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner
  • Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage
  • Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform

Product Details

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Publication date : Jul 29, 2016
Length: 506 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466457
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Product Details

Publication date : Jul 29, 2016
Length: 506 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466457
Vendor :
Apache
Category :
Languages :
Concepts :
Tools :

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Table of Contents

9 Chapters
1. The Era of Big Data Chevron down icon Chevron up icon
2. Introduction to R Programming Language and Statistical Environment Chevron down icon Chevron up icon
3. Unleashing the Power of R from Within Chevron down icon Chevron up icon
4. Hadoop and MapReduce Framework for R Chevron down icon Chevron up icon
5. R with Relational Database Management Systems (RDBMSs) Chevron down icon Chevron up icon
6. R with Non-Relational (NoSQL) Databases Chevron down icon Chevron up icon
7. Faster than Hadoop - Spark with R Chevron down icon Chevron up icon
8. Machine Learning Methods for Big Data in R Chevron down icon Chevron up icon
9. The Future of R - Big, Fast, and Smart Data Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(8 Ratings)
5 star 75%
4 star 12.5%
3 star 0%
2 star 0%
1 star 12.5%
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Sungho Hwang Feb 24, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Big data analysis has been a trend in Korea too! This book solved my curiosity of big data analysis and the practice with R.
Amazon Verified review Amazon
f.janvier Feb 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It was exactly what I was looking for: a pedagogical read guiding me towards the next step with R. I'm an intermediate-level R user (not my first programming language), working mainly with relational databases and non-big data volumes. This book gave me a clear view of what is possible with R once the data become larger, analysis is moved to the cloud and a big-data environment (Hadoop & co) comes into play.
Amazon Verified review Amazon
sbeltran Dec 03, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really enjoyed this book. Every concept is thoroughly explained, and the comparisons with other programs and platforms are really helpful. I don’t think there’s a better resource to learn R for data analytics (I’ve definitely looked and have been disappointed many, many times), so I can absolutely recommend this book.
Amazon Verified review Amazon
Z.V. Aug 14, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for anyone wishing to hone their data engineering skills, having the R platform as their basis. Although this book is intended for the intermediate data science professional, some of the concepts it covers are fairly advanced, yet explained in such a way that they seem elementary. Some experience with the Linux OS (particularly the CLI aspect of it) would be very useful as the author goes into the nitty-gritty often, in order to perform low-level operations that enable the data engineering tasks he describes. He also provides a plethora of meticulously illustrated examples that make all the concepts he introduces hands-on and comprehensible.R is not my platform of choice (in fact I rarely use it nowadays), but I still enjoyed reading this book at least twice and I would recommend it to anyone who wishes to advance their data science expertise, using this particular platform. This is not a book you would read on your commute though. For best results I would recommend you treat it like a textbook and follow all of the examples on your computer.Disclaimer: I have been given a copy of this book for free in order to provide the Amazon community with an unbiased review of it. So, even though I'm not a verified purchaser, trust me when I say it, I know this book inside-out!
Amazon Verified review Amazon
Wolf Aug 02, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is an excellent book. It is so from the purely technical point of view but it is also full of interesting facts and historical notes that make it entertaining as well. Well written and pedagogical, I definitely recommend it for anyone also looking for the R- Big Data connection.
Amazon Verified review Amazon
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