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Mike McGrath - R for Data Analysis in easy steps: R Programming Essentials

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Mike McGrath R for Data Analysis in easy steps: R Programming Essentials
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The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.
R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data.
R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in vectors from which simple graphs can be plotted.
Next, the book describes how to create matrices to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create data frames from imported data sets, and how to employ the Grammar of Graphics to produce advanced visualizations that can best illustrate useful insights from your data.
R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding.
By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so its ideal for the newcomer to computer programming.
Contents:
1.Getting started
2.Storing values
3.Performing operations
4.Testing conditions
5.Employing functions
6.Building matrices
7.Constructing data frames
8.Producing quick plots
9.Telling stories with data
10.Plotting perfection

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Mike McGrath - R for Data Analysis in easy steps: R Programming Essentials
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R for Data Analysis in easy steps: R Programming Essentials

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In easy steps is an imprint of In Easy Steps Limited 16 Hamilton Terrace - photo 1
In easy steps is an imprint of In Easy Steps Limited16 Hamilton Terrace Holly Walk Leamington SpaWarwickshire CV32 4LYwww.ineasysteps.comCopyright 2018 by In Easy Steps Limited. All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without prior written permission from the publisher.Notice of LiabilityEvery effort has been made to ensure that this book contains accurate and current information. However, In Easy Steps Limited and the author shall not be liable for any loss or damage suffered by readers as a result of any information contained herein.TrademarksAll trademarks are acknowledged as belonging to their respective companies.
Contents
PrefaceThe creation of this book has been for me, Mike McGrath, an exciting personal journey in discovering how the R programming language can be used today for data analysis and the production of beautiful data visualizations. Example code listed in this book describes how to produce R Scripts in easy steps and the screenshots illustrate the actual results. Components and keywords of the R programming language are colored blue, programmer-specified names are colored red, literal numeric values and literal character string values are colored black, and comments are colored green, like this:# Write the traditional greeting.greeting = Hello World!print( greeting )Additionally, non-literal values are colored gray like this: color= RedIn order to readily identify each source code file described in the steps a file icon and file name appears in the margin alongside the steps:ScriptR For convenience I have placed source code files from the examples - photo 2Script.RFor convenience I have placed source code files from the examples featured in this book into a single ZIP archive. You can obtain the complete archive by following these easy steps:Picture 3Browse to www.ineasysteps.comthen navigate to Free Resources and choose the Downloads sectionPicture 4Find R for Data Analysis in easy steps in the list, then click on the hyperlink entitled All Code Examples to download the archivePicture 5Next, extract the MyRScripts folder to a convenient location on your systemPicture 6Now, follow the steps to call upon the R program interpreter and see the output
1Getting startedWelcome to the exciting world of R programming. This chapter describes how to set up an R environment and demonstrates how to create a first R program.Understanding dataThe term data refers to items of information that describe a (qualitative) status or a (quantitative) measure of magnitude. Various types of data is collected from a huge range of sources and reported for analysis to reveal pattern and trend insights:R for Data Analysis in easy steps R Programming Essentials - image 7R for Data Analysis in easy steps R Programming Essentials - image 8This illustration depicts only some of the many data types that can be reported for analysis.Data is increasingly being collected by devices that are able to report measurements for analysis via the internet (The Cloud). For example, devices that have temperature and humidity sensors can report measurements for instant analysis of climate conditions. The recent rapid decline in the cost of device sensors has given rise to the Internet of Things (IoT) that can easily and cheaply report vast amounts of data this is often referred to as big data. Big data consists of extremely large data sets that can best be analyzed by computer to reveal pattern and trend insights.
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