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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature
About This Book
- 54 useful and practical tasks to improve working systems
- Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on
- Offers 10-15 real-life, practical improvements for each user type
Who This Book Is For
This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable.
What You Will Learn
- Familiarize yourself with the latest advanced R console features
- Create advanced and interactive graphics
- Manage your R project and project files effectively
- Perform reproducible statistical analyses in your R projects
- Use RStudio to design predictive models for a specific domain-based application
- Use RStudio to effectively communicate your analyses results and even publish them to a blog
- Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product
In Detail
The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment.
This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Style and approach
RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment.
This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are:
- Accessing an API with R
- Substituting missing values by interpolation
- Performing data filtering activities
- R Statistical implementation for Geospatial data
- Developing shiny add-ins to expand RStudio functionalities
- Using GitHub with RStudio
- Modelling a recommendation engine with R
- Using R Markdown for static and dynamic reporting
- Curating a blog through RStudio
- Advanced statistical modelling with R and RStudio
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Seitenzahl: 254
Veröffentlichungsjahr: 2016
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Table of Contents
RStudio for R Statistical Computing Cookbook
RStudio for R Statistical Computing Cookbook
Copyright © 2016 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: April 2016
Production reference: 1250416
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78439-103-4
www.packtpub.com
Credits
Author
Andrea Cirillo
Reviewer
Mark van der Loo
Commissioning Editor
Kartikey Pandey
Acquisition Editor
Vinay Argekar
Content Development Editor
Deepti Thore
Technical Editor
Madhunikita Sunil Chindarkar
Copy Editor
Karuna Narayan
Project Coordinator
Shweta H Birwatkar
Proofreader
Safis Editing
Indexer
Rekha Nair
Graphics
Disha Haria
Production Coordinator
Aparna Bhagat
Cover Work
Aparna Bhagat
About the Author
Andrea Cirillo is currently working as an internal auditor at Intesa Sanpaolo banking group. He gained a lot of financial and external audit experience at Deloitte Touche Tohmatsu and internal audit experience at FNM, a listed Italian company.
His current main responsibilities involve evaluation of credit risk management models and their enhancement mainly within the field of the Basel III capital agreement.
He is married to Francesca and is the father of Tommaso, Gianna, and Zaccaria.
Andrea has written and contributed to a few useful R packages and regularly shares insightful advice and tutorials about R programming.
His research and work mainly focuses on the use of R in the fields of risk management and fraud detection, mainly through modeling custom algorithms and developing interactive applications.
This book is the result of a lot of patience by my wife and sons, which left me with the time to write this book, the time that I should have spend with them.
By Deepti Thore, my content developer editor at Packt Publishing, who was so clement with me when, and it happened a lot of time, I missed my writing deadlines.
By my colleagues who endured my talks about the book every three hours and when I asked for their opinions about almost every recipe.
To all of you, I would like to say a sincere thank you.
About the Reviewer
Mark van der Loo is a statistical researcher who specializes in data cleaning methodology and likes to program in R and C. He is the author and coauthor of several R packages published on CRAN, including stringdist, validate, deductive, lintools, and several others. In 2012, he authored Learning RStudio for R Statistical Computing, Packt Publishing, with Edwin de Jonge.
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Preface
Why should you read RStudio for R Statistical Computing Cookbook?
Well, even if there are plenty of books and blog posts about R and RStudio out there, this cookbook can be an unbeatable friend through your journey from being an average R and RStudio user to becoming an advanced and effective R programmer.
I have collected more than 50 recipes here, covering the full spectrum of data analysis activities, from data acquisition and treatment to results reporting.
All of them come from my direct experience as an auditor and data analyst and from knowledge sharing with the really dynamic and always growing R community.
I took great care selecting and highlighting those packages and practices that have proven to be the best for a given particular task, sometimes choosing between different packages designed for the same purpose.
You can therefore be sure that what you will learn here is the cutting edge of the R language and will place you on the right track of your learning path to R's mastery.
What this book covers
Chapter 1, Acquiring Data for Your Project, shows you how to import data into the R environment, taking you through web scraping and the process of connecting to an API.
Chapter 2, Preparing for Analysis – Data Cleansing and Manipulation, teaches you how to get your data ready for analysis, leveraging the latest data-handling packages and advanced statistical techniques for missing values and outlier treatments.
Chapter 3, Basic Visualization Techniques, lets you get the first sense of your data, highlighting its structure and discovering patterns within it.
Chapter 4, Advanced and Interactive Visualization, shows you how to produce advanced visualizations ranging from 3D graphs to animated plots.
Chapter 5, Power Programming with R, discusses how to write efficient R code, making use of the R objective-oriented systems and advanced tools for code performance evaluation.
Chapter 6, Domain-specific Applications, shows you how to apply the R language to a wide range of problems related to different domains, from financial portfolio optimization to e-commerce fraud detection.
Chapter 7, Developing Static Reports, helps you discover the reporting tools available within the RStudio IDE and how to make the most of them to produce static reports for sharing results of your work.
Chapter 8, Dynamic Reporting and Web Application Development, displays the collected recipes designed to make use of the latest features introduced in RStudio from shiny web applications with dynamic UIs to RStudio add-ons.
What you need for this book
The basic requirements for this book are the latest versions of R and RStudio, which you can download from the following URLs:
More software will be needed for a few specific recipes, which will be highlighted in the Getting Ready section of the respective recipe.
Just a closing note: all the software employed in this book is available for free for personal use, and the greatest advantage of them is that they are open source and powered by the R community.
Who this book is for
This book was developed and written keeping in mind an average R and RStudio user who would like to make the move from good to great in the field of their programming skills on the language.
If you think you are quite good at R and RStudio but you are still missing something in order to be great, this book is exactly what you need to read.
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 as follows:
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.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The plot() function is one of most powerful functions in base R."
A block of code is set as follows:
Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to embed your Sankey diagram, you can leverage the RStudio Save as Web Page control from the Export menu."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
Reader feedback
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To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.
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You can download the code files by following these steps:
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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 could 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 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 book, you can contact us at <[email protected]>, and we will do our best to address the problem.
Chapter 1. Acquiring Data for Your Project
In this chapter, we will cover the following recipes:
Introduction
The American statistician Edward Deming once said:
"Without data you are just another man with an opinion."
I think this great quote is enough to highlight the importance of the data acquisition phase of every data analysis project. This phase is exactly where we are going to start from. This chapter will give you tools for scraping the Web, accessing data via web APIs, and importing nearly every kind of file you will probably have to work with quickly, thanks to the magic package rio.
All the recipes in this book are based on the great and popular packages developed and maintained by the members of the R community.
After reading this section, you will be able to get all your data into R to start your data analysis project, no matter where it comes from.
Before starting the data acquisition process, you should gain a clear understanding of your data needs. In other words, what data do you need in order to get solutions to your problems?
A rule of thumb to solve this problem is to look at the process that you are investigating—from input to output—and outline all the data that will go in and out during its development.
In this data, you will surely have that chunk of data that is needed to solve your problem.
In particular, for each type of data you are going to acquire, you should define the following:
After covering these points for each set of data, you will have a clear vision of future data acquisition activities. This will let you plan ahead the activities needed to clearly define resources, steps, and expected results.
Acquiring data from the Web – web scraping tasks
Given the advances in the Internet of Things (IoT) and the progress of cloud computing, we can quietly affirm that in future, a huge part of our data will be available through the Internet, which on the other hand doesn't mean it will be public.
It is, therefore, crucial to know how to take that data from the Web and load it into your analytical environment.
You can find data on the Web either in the form of data statically stored on websites (that is, tables on Wikipedia or similar websites) or in the form of data stored on the cloud, which is accessible via APIs.
For API recipes, we will go through all the steps you need to get data statically exposed on websites in the form of tabular and nontabular data.
This specific example will show you how to get data from a specific Wikipedia page, the one about the R programming language: https://en.wikipedia.org/wiki/R_(programming_language).
Getting ready
Data statically exposed on web pages is actually pieces of web page code. Getting them from the Web to our R environment requires us to read that code and find where exactly the data is.
Dealing with complex web pages can become a really challenging task, but luckily, SelectorGadget was developed to help you with this job. SelectorGadget is a bookmarklet, developed by Andrew Cantino and Kyle Maxwell, that lets you easily figure out the CSS selector of your data on the web page you are looking at. Basically, the CSS selector can be seen as the address of your data on the web page, and you will need it within the R code that you are going to write to scrape your data from the Web (refer to the next paragraph).
Note
The CSS selector is the token that is used within the CSS code to identify elements of the HTML code based on their name.
CSS selectors are used within the CSS code to identify which elements are to be styled using a given piece of CSS code. For instance, the following script will align all elements (CSS selector *) with 0 margin and 0 padding:
SelectorGadget is currently employable only via the Chrome browser, so you will need to install the browser before carrying on with this recipe. You can download and install the last version of Chrome from https://www.google.com/chrome/.
SelectorGadget is available as a Chrome extension; navigate to the following URL while already on the page showing the data you need:
This long URL shows that the CSS selector is provided as JavaScript; you can make this out from the :javascript: token at the very beginning.
We can further analyze the URL by decomposing it into three main parts, which are as follows:
The .js file is where the CSS selector's core functionalities are actually defined and the place where they are taken to make them available to users.
That being said, I'm not suggesting that you try to use this link to employ SelectorGadget for your web scraping purposes, but I would rather suggest that you look for the Chrome extension or at the official SelectorGadget page, http://selectorgadget.com. Once you find the link on the official page, save it as a bookmark so that it is easily available when you need it.
The other tool we are going to use in this recipe is the rvest package, which offers great web scraping functionalities within the R environment.
To make it available, you first have to install and load it in the global environment that runs the following:
How to do it...
In both cases, after activating the gadget, a Loading… message will appear, and then, you will find a bar on the bottom-right corner of your web browser, as shown in the following screenshot:
You are now ready to select the data you are interested in.
Select the data you are interested in. After clicking on the data you are going to scrape, you will note that beside the data you've selected, there are some other parts on the page that will turn yellow:This is because SelectorGadget is trying to guess what you are looking at by highlighting all the elements included in the CSS selector that it considers to be most useful for you.
If it is guessing wrong, you just have to click on the wrongly highlighted parts and those will turn red:
When you are done with this fine-tuning process, SelectorGadget will have correctly identified a proper selector, and you can move on to the next step.
Find your data location on the page. To do this, all you have to do is copy the CSS selector that you will find in the bar at the bottom-right corner:This piece of text will be all you need in order to scrape the web page from R.
The next step is to read data from the Web with the rvest package. The rvest package by Hadley Wickham is one of the most comprehensive packages for web scraping activities in R. Take a look at the There's more... section for further information on package objectives and functionalities.For now, it is enough to know that the rvest package lets you download HTML code and read the data stored within the code easily.
Now, we need to import the HTML code from the web page. First of all, we need to define an object storing all the html code of the web page you are looking at:
This code leverages read_html function(), which retrieves the source code that resides at the written URL directly from the Web.
Next, we will select the defined blocks. Once you have got your HTML code, it is time to extract the part of the code you are interested in. This is done using the html_nodes() function, which is passed as an argument in the CSS selector and retrieved using SelectorGadget. This will result in a line of code similar to the following:As you can imagine, this code extracts all the content of the selected nodes, including HTML tags.
Note
The HTML language
HyperText Markup Language (HTML) is a markup language that is used to define the format of web pages.
The basic idea behind HTML is to structure the web page into a format with a head and body, each of which contains a variable number of tags, which can be considered as subcomponents of the structure.
The head is used to store information and components that will not be seen by the user but will affect the web page's behavior, for instance, in a Google Analytics script used for tracking page visits, the body contains all the contents which will be showed to the reader.
Since the HTML code is composed of a nested structure, it is common to compare this structure to a tree, and here, different components are also referred to as nodes.
Printing out the version_blockobject, you will obtain a result similar to the following:
This result is not exactly what you are looking for if you are going to work with this data. However, you don't have to worry about that since we are going to give your text a better shape in the very next step.
In order to obtain a readable and actionable format, we need one more step: extracting text from HTML tags.This can be done using the html_text()
