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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
About This Book
- Proficiently analyze data and apply machine learning techniques
- Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R
- Construct a predictive model by using a variety of machine learning packages
Who This Book Is For
This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R – it will help you increase your R expertise, regardless of your level of experience.
What You Will Learn
- Get data into your R environment and prepare it for analysis
- Perform exploratory data analyses and generate meaningful visualizations of the data
- Generate various plots in R using the basic R plotting techniques
- Create presentations and learn the basics of creating apps in R for your audience
- Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
- Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm
- Build, tune, and evaluate predictive models with different machine learning packages
- Incorporate R and Hadoop to solve machine learning problems on big data
In Detail
The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
- R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
- R Data Visualization Cookbook by Atmajitsinh Gohil
- Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
Style and approach
This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.
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Seitenzahl: 1062
Veröffentlichungsjahr: 2016
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Table of Contents
R: Recipes for Analysis, Visualization and Machine Learning
R: Recipes for Analysis, Visualization and Machine Learning
Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
A course in three modules
BIRMINGHAM - MUMBAI
R: Recipes for Analysis, Visualization and Machine Learning
Copyright © 2016 Packt Publishing
All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Published on: November 2016
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78728-959-8
www.packtpub.com
Credits
Authors
Viswa Viswanathan
Shanthi Viswanathan
Atmajitsinh Gohil
Yu-Wei, Chiu (David Chiu)
Reviewers
Kenneth D. Graves
Jithin S L
Dipanjan Sarkar
Hang (Harvey) Yu
Sharan Kumar Ravindran
Kannan Kalidasan
Erik M. Rodríguez Pacheco
Arun Padmanabhan
Juan Pablo Zamora
Patric Zhao
Tarek Amr
Abir Datta (data scientist)
Saibal Dutta
Ratanlal Mahanta
(senior quantitative analyst)
Ricky Shi
Jithin S.L
Content Development Editor
Mayur Pawanikar
Production Coordinator
Nilesh Mohite
Preface
Since the release of version 1.0 in 2000, R's popularity as an environment for statistical computing, data analytics, and graphing has grown exponentially. People who have been using spreadsheets and need to perform things that spreadsheet packages cannot readily do, or need to handle larger data volumes than what a spreadsheet program can comfortably handle, are looking to R. Analogously, people using powerful commercial analytics packages are also intrigued by this free and powerful option. As a result, a large number of people are now looking to quickly get things done in R. Being an extensible system, R's functionality is divided across numerous packages with each one exposing large numbers of functions. Even experienced users cannot expect to remember all the details off the top of their head.
Our ability to generate data has improved tremendously with the advent of technology. The data generated has become more complex with the passage of time. The complexity in data forces us to develop new tools and methods to analyze it, interpret it, and communicate with the data. Data visualization empowers us with the necessary skills required to convey the meaning of underlying data. Data visualization is a remarkable intersection of data, science, and art, and this makes it hard to define visualization in a formal way; a simple Google search will prove me right. The Merriam-Webster dictionary defines visualization as "formation of mental visual images”.
Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and much more challenging.
Traditionally, most researchers perform statistical analysis using historical samples of data. The main downside of this process is that conclusions drawn from statistical analysis are limited. In fact, researchers usually struggle to uncover hidden patterns and unknown correlations from target data. Aside from applying statistical analysis, machine learning has emerged as an alternative. This process yields a more accurate predictive model with the data inserted into a learning algorithm. Through machine learning, the analysis of business operations and processes is not limited to human-scale thinking. Machine-scale analysis enables businesses to discover hidden values in big data.
What this learning path covers
Module 1, R Data Analysis Cookbook, this module, aimed at users who are already exposed to the fundamentals of R, provides ready recipes to perform many important data analytics tasks. Instead of having to search the Web or delve into numerous books when faced with a specific task, people can find the appropriate recipe and get going in a matter of minutes.
Module 2, R Data Visualization Cookbook, in this module you will learn how to generate basic visualizations, understand the limitations and advantages of using certain visualizations, develop interactive visualizations and applications, understand various data exploratory functions in R, and finally learn ways of presenting the data to our audience. This module is aimed at beginners and intermediate users of R who would like to go a step further in using their complex data to convey a very convincing story to their audience.
Module 3, Machine Learning with R Cookbook, this module covers how to perform statistical analysis with machine learning analysis and assessing created models, which are covered in detail later on in the book. The module includes content on learning how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects.
What you need for this learning path
Module 1:
We have tested all the code in this module for R versions 3.0.2 (Frisbee Sailing) and 3.1.0 (Spring Dance). When you install or load some of the packages, you may get a warning message to the effect that the code was compiled for a different version, but this will not impact any of the code in this module.
Module 2:
You need to download R to generate the visualizations. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the recipes were written using RStudio. RStudio is an integrated development environment (IDE) for R and can be downloaded from http://www.rstudio.com/products/rstudio/. Many of the visualizations are created using R packages and they are discussed in their respective recipes.
In few of the recipes, I have introduced users to some other open source platforms such as ScapeToad, ArcGIS, and Mapbox. Their installation procedures are outlined in their respective recipes.
Module 3:
To follow the course's examples, you will need a computer with access to the Internet and the ability to install the R environment. You can download R from http://www.cran.r-project.org/. Detailed installation instructions are available in the first chapter.
The examples provided in this book were coded and tested with R Version 3.1.2 on a computer with Microsoft Windows installed on it. These examples should also work with any recent version of R installed on either MAC OSX or a Unix-like OS.
Who this learning path is for
This Learning Path is ideal for those who are already exposed to R, but have not yet used it extensively. This Learning Path will set you up with an extensive insight into professional techniques for analysis, visualization and machine learning with R. Regardless of your level of experience, this course also covers the basics of using R and it is written keeping in mind new and intermediate R users interested in learning.
Reader feedback
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To send us general feedback, simply e-mail <[email protected]>, and mention the course's title in the subject of your message.
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Customer support
Now that you are the proud owner of a Packt course, we have a number of things to help you to get the most from your purchase.
Downloading the example code
You can download the example code files for this course from your account at http://www.packtpub.com. If you purchased this course elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.
You can download the code files by following these steps:
You can also download the code files by clicking on the Code Files button on the course's webpage at the Packt Publishing website. This page can be accessed by entering the course's name in the Search box. Please note that you need to be logged in to your Packt account.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
The code bundle for the course is also hosted on GitHub at https://github.com/PacktPublishing/R-Recipes-for-Analysis-Visualization-and-Machine-Learning. We also have other code bundles from our rich catalog of books, videos and courses available at https://github.com/PacktPublishing/. Check them out!
Errata
Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this course. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your course, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.
To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
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We appreciate your help in protecting our authors and our ability to bring you valuable content.
Questions
If you have a problem with any aspect of this course, you can contact us at <[email protected]>, and we will do our best to address the problem.
Part 1. Module 1
R Data Analysis Cookbook
Over 80 recipes to help you breeze through your data analysis projects using R
Chapter 1. A Simple Guide to R
In this chapter, we will cover the following recipes:
Installing packages and getting help in R
If you are a new user and have never launched R, you must definitely start the learning process by understanding the use of install.packages(), library(), and getting help in R. R comes loaded with some basic packages, but the R community is rapidly growing and active R users are constantly developing new packages for R.
As you read through this cookbook, you will observe that we have used a lot of packages to create different visualizations. So the question now is, how do we know what packages are available in R? In order to keep myself up-to-date with all the changes that are happening in the R community, I diligently follow these blogs:
There are many blogs, websites, and posts that I will refer to as we go through the book. We can view a list of all the packages available in R by going to http://cran.r-project.org/, and also http://www.inside-r.org/packages provides a list as well as a short description of all the packages.
Getting ready
We can start by powering up our R studio, which is an Integrated Development Environment (IDE) for R. If you have not downloaded Rstudio, then I would highly recommend going to http://www.rstudio.com/ and downloading it.
How to do it…
To install a package in R, we will use the install.packages() function. Once we install a package, we will have to load the package in our active R session; if not, we will get an error. The library() function allows us to load the package in R.
How it works…
The install.packages() function comes with some additional arguments but, for the purpose of this book, we will only use the first argument, that is, the name of the package. We can also load multiple packages by using install.packages(c("plotrix", "RColorBrewer")). The name of the package is the only argument we will use in the library() function. Note that you can only load one package at a time with the library() function unlike the install.packages() function.
There's more…
It is hard to remember all the functions and their arguments in R, unless we use them all the time, and we are bound to get errors and warning messages. The best way to learn R is to use the active R community and the help manual available in R.
To understand any function in R or to learn about the various arguments, we can type ?<name of the function>. For example, I can learn about all the arguments related to the plot() function by simply typing ?plot or ?plot() in the R console window. You will now view the help page on the right side of the screen. We can also learn more about the behavior of the function using some of the examples at the bottom of the help page.
If we are still unable to understand the function or its use and implementation, we could go to Google and type the question or use the Stack Overflow website. I am always able to resolve my errors by searching on the Internet. Remember, every problem has a solution, and the possibilities with R are endless.
