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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 281
Veröffentlichungsjahr: 2015
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Table of Contents
R Data Visualization Cookbook
R Data Visualization Cookbook
Copyright © 2015 Packt Publishing
All rights reserved. No part of this book 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 book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, 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 book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: January 2015
Production reference: 1240115
Published by Packt Publishing Ltd.
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ISBN 978-1-78398-950-8
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Credits
Author
Atmajitsinh Gohil
Reviewers
Sharan Kumar Ravindran
Kannan Kalidasan
Erik M. Rodríguez Pacheco
Arun Padmanabhan
Juan Pablo Zamora
Patric Zhao
Commissioning Editor
Kartikey Pandey
Acquisition Editor
Neha Nagwekar
Content Development Editor
Arun Nadar
Technical Editors
Rohit Kumar Singh
Mitali Somaiya
Copy Editors
Nithya P
Shambhavi Pai
Rashmi Sawant
Project Coordinator
Neha Bhatnagar
Proofreaders
Simran Bhogal
Stephen Copestake
Paul Hindle
Joanna McMahon
Indexer
Priya Sane
Graphics
Disha Haria
Abhinash Sahu
Production Coordinator
Nilesh R. Mohite
Cover Work
Nilesh R. Mohite
About the Author
Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog at http://datavisualizationineconomics.blogspot.com.
He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a master of arts degree in economics from University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.
This book would not have been possible without the help from numerous data visualizers and data scientists around the globe who bring into existence new and innovative ways to transform data into beautiful stories. I would like to sincerely thank the developers of R and R packages who have contributed so generously to the growing R open source community.
I would like to thank Jer Thorpe and Hans Rosling for their inspiring Ted videos on data visualization.
I would also like to thank all the economists and statisticians who have so often inspired me.
I would like to thank my publisher, Packt Publishing, for giving me the opportunity to work on this book. I would also like to thank all the technical reviewers and content development editors at Packt Publishing for their informative comments and suggestions.
Finally, I would like to thank my amazing family and magnificent friends for always encouraging and supporting me.
About the Reviewers
Sharan Kumar Ravindran is a lead data scientist in the fastest growing big data start-up based in Bangalore. His primary interests lie in statistics and machine learning. He has over 4 years of experience and has worked in the domains of e-commerce and IoT.
He has solved several problems on Kaggle and is among the top 10 percent of experts on Kaggle. His blog and social profiles can be found at www.rsharankumar.com.
He works for Flutura, which is ranked among the top 20 most promising big data companies across the globe by the leading analyst magazine, CIO Review. Flutura also featured on Gigaom reports on big data and M2M in the energy sector. Flutura was also the winner at TechSparks, where 800 innovative start-ups were evaluated.
Kannan Kalidasan is a software developer by profession, an autodidact, and open source evangelist. He has a decade's experience in database computing, data management, open source, and distributed computing. He holds a bachelor's of technology degree in computer science from Pondicherry University. He has played different roles in his career, such as a developer, an architect, a team lead, and a DBA. He currently holds the position of a BI Engineer at Orbitz Worldwide.
He started his own start-up back in 2005 on a part-time basis during his college days, worked with other companies in different open source projects, and provided training. He is passionate about technology and an entrepreneur at heart, and he likes to mentor fellow enthusiasts. His inherent curiosity keeps him occupied with learning new technologies and trying new things. He always believes that "our dreams can be delayed but will never fail if we work hard."
He blogs at www.kannandreams.wordpress.com and you can follow him on Twitter at @kannanpoem. He loves to take long walks alone, write Tamil poems, paint, and read books.
A big thank you to all who believed in me and supported me. I would like to thank my strong soul for pushing me to achieve my dreams. I would like to express my deepest gratitude to Packt Publishing for giving me this opportunity.
Erik M. Rodríguez Pacheco works as a manager in the Business Intelligence Unit at Banco Improsa in San José, Costa Rica. He has 11 years of experience in the financial industry. He is currently a professor of the Business Intelligence Specialization Program at the Instituto Tecnológico de Costa Rica's Continuing Education Program. Erik is an enthusiast of new technologies, particularly those related to business intelligence, data mining, and data science. He holds a Bachelor's degree in business administration from Universidad de Costa Rica, and has specialized in business intelligence from the Instituto Tecnológico de Costa Rica, data mining from Promidat (Programa Iberoamericano de Formación en Minería de Datos), and business intelligence and data mining from Universidad del Bosque, Colombia. He is currently enrolled in a specialization program in data science from Johns Hopkins University. He can be reached at cr.linkedin.com/in/erikrodriguez.
Arun Padmanabhan has about 4 years of experience in developing products including mobile, enterprise, statistical, and data mining applications. He graduated with a master's degree in computer applications in 2010. Currently, he is a data scientist at Flutura Decision Science and Analytics, where he is working at saving the world, one data product at a time.
Juan Pablo Zamora holds a bachelor's degree in statistics from the University of Costa Rica (UCR) in 2007. He is currently working on his dissertation in the field of predictive analytics and will obtain an MSc degree in statistics from the University of Costa Rica.
He enjoys teaching and was a tutor of statistics courses at the Business School of UNED of Costa Rica during 2010-2012. He also mentored others in the areas of data processing and analytics as well as the use of statistical analysis tools, to name a few.
Juan has over 7 years of experience in the banking industry, primarily in the credit card business for Central America and Mexico. He began as an analyst, eventually becoming the leader of a team of analysts for Central America's largest credit card issuer and acquirer. During this period, he participated in several predictive analytics projects related to credit risk, account retention, and profitability.
Juan recently joined a large multinational company in the retail sector with the task of building an analytics program to identify and prevent high-risk issues and/or threats to the business in Latin America.
His current interests are R, data visualization, business intelligence, predictive modeling, and big data. He can be reached at cr.linkedin.com/in/datasciencezamora or data.<[email protected]>.
Patric Zhao is a senior GPU architect in the High Performance Computing (HPC) field at Nvidia. He has experience in developing scientific and engineering applications and focuses on parallelization, performance modeling, and architecture-specific tuning. Patric is currently working on big data and machine learning areas, including regression, neural network, recommending system design, and implementation in CPU and GPU architectures. Patric has also contributed to accelerate R's applications by CUDA in the GPU ecosystem. You can find related articles on Nvidia's blog at http://devblogs.nvidia.com/parallelforall/author/patricz/ or write to him at <[email protected]>.
I would like to really thank my wife Yan Li J for always supporting and encouraging me.
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Preface
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". In reality, the term visualization goes beyond the limits of providing visual images by assisting humans in data recording, revealing pattern, exploration of data, and spreading information in a meaningful way.
Jer Thorpe in an interview with Mashable.com (http://mashable.com/2012/12/11/data-visualization-jer-thorp/) introduces the idea of humanizing data:
"…And I think that there's a huge possibility for humans, society as a whole—if we could share that data more usefully, for science and for the construction of cities, and for all these kinds of things, then it becomes much more useful. So in my work, I'm really thinking about how we can give people glimpses into that type of future. Giving people an opportunity to think about data ownership or giving people a visualization so that they can see the kinds of things that can be done with data".
R is an open source platform used to analyze data. It has been widely used as a statistical tool in the past. An individual does not necessarily have to be a programmer to use R. A beginner can use basic R functionalities to manipulate and extract data and create very simple and quick visualizations using the basic graphic tools. An intermediate R user can implement interactive visualizations, perform predictive modeling, or even create animated applications using packages developed by the R community. R will present you with the tools you need to process, manipulate, and communicate with your data, and it is not just limited to statistical analysis.
In this book, 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 book 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.
What this book covers
Chapter 1, A Simple Guide to R, is a quick tutorial on getting started with R. You will learn how to install packages, access help on R, construct and edit matrices, create and manipulate data frames, and write and save plots.
Chapter 2, Basic and Interactive Plots, introduces some of the basic R plots, such as scatter, line, and bar charts. We will also discuss the basic elements of interactive plots using the googleVis package in R. This chapter is a great resource for understanding the basic R plotting techniques.
Chapter 3, Heat Maps and Dendrograms, starts with a simple introduction to dendrograms and further introduces the concept of clustering techniques. The second half of this chapter discusses heat maps and integrating heat maps with dendrograms to get a more complete picture.
Chapter 4, Maps, discusses the importance of spatial data and various techniques used to visualize geographic data in R. You will learn how to generate static as well as interactive maps in R. The chapter discusses the topic of shape files and how to use them to generate a cartogram.
Chapter 5, The Pie Chart and Its Alternatives, is a detailed discussion on how to generate pie charts in R. You will also learn about the various criticisms of pie charts and how the pie chart is transformed to overcome them. The chapter also provides you with various alternatives used by data scientists and visualization artists to overcome the limitation of a pie chart.
Chapter 6, Adding the Third Dimension, dives into constructing 3D plots. This chapter also introduces packages such as rgl and animation, which are used to create interactive 3D plots.
Chapter 7, Data in Higher Dimensions, demonstrates the use of visualizations that are used to display data in higher dimension. You will learn the techniques to generate sunflower plots, hexbin plots, Chernoff faces, and so on. This chapter also discusses the usefulness of network, radial, and coxcomb plots, which have been widely used in news.
Chapter 8, Visualizing Continuous Data, illustrates the use of visualizations to display time series data. The chapter also discusses some general concepts related to visualizing correlations, the shape of the distribution, and detection of outliers using box and whisker plots.
Chapter 9, Visualizing Text and XKCD-style Plots, illustrates the use of text in creating effective visualizations. This chapter focuses mainly on techniques to create word clouds, phase tree, and comparison clouds in R. You will also learn how to use the XKCD package to introduce humor in visualizations.
Chapter 10, Creating Applications in R, shows you the techniques to create presentations and R markdown documents for publishing on a blog or a website. The chapter further discusses the XML package used to extract and visualize data as well as using shiny package used to create interactive applications.
What you need for this book
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.
Who this book is for
Having studied economics, I am not a software programmer myself and have written this book for readers new to R and visualization. This book does not delvento complex R code or complex data manipulating techniques, and it is written keeping in mind new and intermediate R users interested in learning about data visualization and data exploration techniques.
The book aims at teaching you the implementation of interactive and animated data visualizations and not just the basic R techniques. However, I have introduced some basic functionalities in Chapter 1, A Simple Guide to R and Chapter 2, Basic and Interactive Plots.
Wherever possible, I have provided references to websites, blogs, and journals, which can be explored to learn more about specific functions, graphics, animations, or even basic functionalities in R.
Sections
In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections:
Getting ready
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
How to do it…
This section contains the steps required to follow the recipe.
How it works…
This section usually consists of a detailed explanation of what happened in the previous section.
There's more…
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or may have disliked. Reader feedback is important for us to develop titles that you really get the most out of.
To send us general feedback, simply send an e-mail to <[email protected]>, and mention the book title via the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide on www.packtpub.com/authors.
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Downloading the example code
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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 would report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the erratasubmissionform link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded on our website, or added to any list of existing errata, under the Errata section of that title. Any existing errata can be viewed by selecting your title from http://www.packtpub.com/support.
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Questions
You can contact us at <[email protected]> if you are having a problem with any aspect of the book, and we will do our best to address it.
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.
