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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Make the most of R's extensive toolset R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R's graphics, interactive, and machine learning tools, you'll learn to apply R's extensive capabilities in an array of scenarios. The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too! R is a free tool, and it's the basis of a huge amount of work in data science. It's taking the place of costly statistical software that sometimes takes a long time to learn. One reason is that you can use just a few R commands to create sophisticated analyses. Another is that easy-to-learn R graphics enable you make the results of those analyses available to a wide audience. This book will help you sharpen your skills by applying them in the context of projects with R, including dashboards, image processing, data reduction, mapping, and more. * Appropriate for R users at all levels * Helps R programmers plan and complete their own projects * Focuses on R functions and packages * Shows how to carry out complex analyses by just entering a few commands If you're brand new to R or just want to brush up on your skills, R Projects For Dummies will help you complete your projects with ease.
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Veröffentlichungsjahr: 2018
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R Projects For Dummies®
Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com
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Published simultaneously in Canada
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Library of Congress Control Number: 2017964027
ISBN: 978-1-119-44618-7; 978-1-119-44617-0 (ebk); 978-1-119-44616-3 (ebk)
R Projects For Dummies®
To view this book's Cheat Sheet, simply go to www.dummies.com and search for “R Projects For Dummies Cheat Sheet” in the Search box.
Table of Contents
Cover
Introduction
About This Book
What You Can Safely Skip
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: The Tools of the Trade
Chapter 1: R: What It Does and How It Does It
Getting R
Getting RStudio
A Session with R
R Functions
User-Defined Functions
Comments
R Structures
Of for Loops and if Statements
Chapter 2: Working with Packages
Installing Packages
Examining Data
R Formulas
More Packages
Exploring the tidyverse
Chapter 3: Getting Graphic
Touching Base
Graduating to ggplot2
Part 2: Interacting with a User
Chapter 4: Working with a Browser
Getting Your Shine On
Creating Your First shiny Project
Working with ggplot
Another shiny Project
Suggested Project
Chapter 5: Dashboards — How Dashing!
The shinydashboard Package
Exploring Dashboard Layouts
Working with the Sidebar
Interacting with Graphics
Part 3: Machine Learning
Chapter 6: Tools and Data for Machine Learning Projects
The UCI (University of California-Irvine) ML Repository
Introducing the
Rattle
package
Using
Rattle
with
iris
Chapter 7: Decisions, Decisions, Decisions
Decision Tree Components
Decision Trees in R
Decision Trees in
Rattle
Project: A More Complex Decision Tree
Suggested Project: Titanic
Chapter 8: Into the Forest, Randomly
Growing a Random Forest
Random Forests in R
Project: Identifying Glass
Suggested Project: Identifying Mushrooms
Chapter 9: Support Your Local Vector
Some Data to Work With
Separability: It's Usually Nonlinear
Support Vector Machines in R
Project: House Parties
Suggested Project: Titanic Again
Chapter 10: K-Means Clustering
How It Works
K-Means Clustering in R
Project: Glass Clusters
Suggested Project: A Few Quick Ones
Chapter 11: Neural Networks
Networks in the Nervous System
Artificial Neural Networks
Neural Networks in R
Project: Banknotes
Suggested Projects: Rattling Around
Part 4: Large(ish) Data Sets
Chapter 12: Exploring Marketing
Project: Analyzing Retail Data
Enter Machine Learning
Suggested Project: Another Data Set
Chapter 13: From the City That Never Sleeps
Examining the Data Set
Warming Up
Project: Departure Delays
Suggested Project: Delay and Weather
Part 5: Maps and Images
Chapter 14: All Over the Map
Project: The Airports of Wisconsin
Suggested Project 1: Map Your State
Suggested Project 2: Map the Country
Chapter 15: Fun with Pictures
Polishing a Picture: It's magick!
Project: Two Legends in Search of a Legend
Suggested Project: Combine an Animation with a Plot
Part 6: The Part of Tens
Chapter 16: More Than Ten Packages for Your R Projects
Machine Learning
Databases
Maps
Image Processing
Text Analysis
Chapter 17: More than Ten Useful Resources
Interacting with Users
Machine Learning
Databases
Maps and Images
About the Author
Connect with Dummies
End User License Agreement
Guide
Cover
Table of Contents
Begin Reading
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Introduction
If you’re like me, you think the best way to learn is by doing. Don’t just read about something — practice it! If you want to be a builder, then build. If you want to be a writer, then write. If you want to be a carpenter, then carpenter. (Yes, that noun and verb are the same. Carpent is not a word.)
I based this book on that learning-by-doing philosophy. My objective is for you to expand your R skill set by using R to complete projects in a variety of areas, and to learn something about those areas, too.
Even with those noble intentions, a book like this one can fall into a trap. It can quickly become a cookbook: Use this package, use these functions, create a graphic — and presto, you’ve finished a project and it’s time to move on.
I didn’t want to write that book. Instead, beginning in Part 2 (which is where the projects start), each chapter does more than just walk you through a project. First, I show you some background material about the subject area, and then (in most chapters) you work through a scaled-down project in that area to get your feet wet, and then you complete a larger project.
But a chapter doesn’t end there. At the end of each chapter, you’ll find a Suggested Project that challenges you to apply your newly minted skills. For each of those, I supply just enough information to get you started. (Wherever necessary, I include tips about potential pitfalls.)
Along the way, you’ll also encounter Quick Suggested Projects. These are based on tweaks to projects you’ve already completed, and they present additional challenges to your growing skill set.
One more thing: Every subject area could be the basis for an entire book, so I can only scratch the surface of each one. Chapter 17 directs you toward resources that provide more information.
About This Book
I’ve organized this book into six parts.
Part 1: The Tools of the Trade
Part 1 is all about R and RStudio. I discuss R functions, structures, and packages, and I show you how to create a variety of graphics.
Part 2: Interacting with a User
The projects begin in Part 2, where you learn to create applications that respond to users. I discuss the shiny package for working with web browsers, and the shinydashboard package for creating dashboards.
Part 3: Machine Learning
This is the longest part of the book. I begin by telling you about the University of California–Irvine Machine Learning Repository, which provides the data sets for the projects. I also discuss the rattle package for creating machine learning applications. The projects cover decision trees, random forests, support vector machines, k-means clustering, and neural networks.
Part 4: Large(ish) Data Sets
The two projects in Part 4 deal with larger data sets than you encounter earlier in the book. The first project is a customer segmentation analysis of over 300,000 customers of an online retailer. A follow-up analysis applies machine learning. The second project analyzes a data set of more than 500,000 airline flights.
Part 5: Maps and Images
Two projects are in Part 5. The first is to plot the location (along with other information) of airports in one of the US states. The second shows you how to combine an animated image with a stationary one.
Part 6: The Part of Tens
The first chapter in Part 6 provides information about useful packages that can help you with future projects. The second tells you where to learn more about the subject areas of this book.
What You Can Safely Skip
Any reference book throws a lot of information at you, and this one is no exception. I intended it all to be useful, but I didn't aim it all at the same level. So if you're not deeply into the subject matter, you can avoid paragraphs marked with the Technical Stuff icon, and you can also skip the sidebars.
Foolish Assumptions
I'm assuming that you
Know how to work with Windows or the Mac. I don't spell out the details of pointing, clicking, selecting, and other actions.
Can install R and RStudio (I show you how in
Chapter 1
), and follow along with the examples. I use the Windows version of RStudio, but you should have no problem if you’re working on a Mac.
Icons Used in This Book
You’ll find icons in all For Dummies books, and this one is no exception. Each one is a little picture in the margin that lets you know something special about the paragraph it sits next to.
This icon points out a hint or a shortcut that helps you in your work and makes you an all-around better person.
This one points out timeless wisdom to take with you as you continue on the path to enlightenment.
Pay attention to this icon. It's a reminder to avoid something that might gum up the works for you.
As I mention in the earlier section “What You Can Safely Skip,” this icon indicates material you can blow past if it’s just too technical. (I’ve kept this information to a minimum.)
Beyond the Book
In addition to what you’re reading right now, this product comes with a free access-anywhere Cheat Sheet that presents a selected list of R functions and describes what they do. To get this Cheat Sheet, visit www.dummies.com and type R Projects For Dummies Cheat Sheet in the Search box.
Where to Go from Here
You can start the book anywhere, but here are a couple of hints. Want to introduce yourself to R and packages? You’ll find the info in Chapters 1 and 2. Want to start with graphics? Hit Chapter 3. For anything else, find it in the table of contents or in the index and go for it.
If you’re a cover-to-cover reader, turn the page… .
Part 1
The Tools of the Trade
IN THIS PART …
Learn about R and RStudio
Understand R Functions and Structures
Create your own R Functions
Examine data
Use base R graphics
Graduate to ggplot2 graphics
Chapter 1
R: What It Does and How It Does It
IN THIS CHAPTER
Getting R and RStudio on your computer
Plunging into a session with R
Working with R functions
Working with R structures
So you’re ready to journey into the wonderful world of R! Designed by and for statisticians and data scientists, R has a short but illustrious history.
In the 1990s, Ross Ihaka and Robert Gentleman developed R at the University of Auckland, New Zealand. The Foundation for Statistical Computing supports R, which is growing more popular by the day.
Getting R
If you don’t already have R on your computer, the first thing to do is to download R and install it.
You’ll find the appropriate software on the website of the Comprehensive R Archive Network (CRAN). In your browser, type this web address if you work in Windows:
cran.r-project.org/bin/windows/base
Type this one if you work on the Mac:
cran.r-project.org/bin/macosx
Click the link to download R. This puts a win.exe file in your Windows computer or a pkg file in your Mac. In either case, follow the usual installation procedures. When installation is complete, Windows users see two R icons on their desktop, one for 32-bit processors and one for 64-bit processors (pick the one that's right for you). Mac users see an R icon in their Application folder.
Both addresses provide helpful links to FAQs. The windows-related one also has the link Installation and Other Instructions.
Getting RStudio
Working with R is a lot easier if you do it through an application called RStudio. Computer honchos refer to RStudio as an IDE (Integrated Development Environment). Think of it as a tool that helps you write, edit, run, and keep track of your R code, and as an environment that connects you to a world of helpful hints about R.
Here’s the web address for this terrific tool:
www.rstudio.com/products/rstudio/download
Click the link for the installer for your computer’s operating system — Windows, Mac, or a flavor of Linux — and again follow the usual installation procedures.
In this book, I work with R version 3.4.0 and RStudio version 1.0.143. By the time you read this, later versions of both might be available.
After you finish installing R and RStudio, click on your brand-new RStudio icon to open the window shown in Figure 1-1.
FIGURE 1-1: RStudio, immediately after you install it and click on its icon.
The large Console pane on the left runs R code. One way to run R code is to type it directly into the Console pane. I show you another in a moment.
The other two panes provide helpful information as you work with R. The Environment/History pane is in the upper right. The Environment tab keeps track of the things you create (which R calls objects) as you work with R. The History tab tracks R code that you enter.
Get used to the word object. Everything in R is an object.The Files/Plots/Packages/Help pane is in the lower right. The Files tab shows files you create. The Plots tab holds graphs you create from your data. The Packages tab shows add-ons (called packages) that have downloaded with R. Bear in mind that downloaded doesn’t mean “ready to use.” To use a package’s capabilities, one more step is necessary, and trust me — you’ll want to use packages.
Figure 1-2 shows the Packages tab. I discuss packages later in this chapter.
FIGURE 1-2: The RStudio Packages tab.
The Help tab, shown in Figure 1-3, links you to a wealth of information about R and RStudio.
FIGURE 1-3: The RStudio Help tab.
To tap into the full power of RStudio as an IDE, click the icon in the upper right corner of the Console pane. That changes the appearance of RStudio so that it looks like Figure 1-4.
FIGURE 1-4: RStudio after you click the icon in the upper right corner of the Console pane.
The Console pane relocates to the lower left. The new pane in the upper left is the Scripts pane. You type and edit code in the Scripts pane by pressing Ctrl+R (Command+Enter on the Mac), and then the code executes in the Console pane.
Ctrl+Enter works just like Ctrl+R. You can also highlight lines of code in the Scripts pane and select Code ⇒ Run Selected Line(s) from RStudio’s main menu.
A Session with R
Before you start working, select File ⇒ Save As from the main menu and then save your work file as My First R Session. This relabels the tab in the Scripts pane with the name of the file and adds the .R extension. This also causes the filename (along with the .R extension) to appear on the Files tab.
The working directory
What exactly does R save, and where does R save it? What R saves is called the workspace, which is the environment you're working in. R saves the workspace in the working directory. In Windows, the default working directory is
C:\Users\<User Name>\Documents
On a Mac, it’s
/Users/<User Name>
If you ever forget the path to your working directory, type
> getwd()
in the Console pane, and R returns the path onscreen.
In the Console pane, you don’t type the right-pointing arrowhead at the beginning of the line. That’s a prompt.
My working directory looks like this:
> getwd()
[1] "C:/Users/Joseph Schmuller/Documents
Note the direction the slashes are slanted. They’re opposite to what you typically see in Windows file paths. This is because R uses \ as an escape character — whatever follows the \ means something different from what it usually means. For example, \t in R means Tab key.
You can also write a Windows file path in R as
C:\\Users\\<User Name>\\Documents
If you like, you can change the working directory:
> setwd(<file path>)
Another way to change the working directory is to select Session ⇒ Set Working Directory ⇒ Choose Directory from the main menu.
Getting started
Let's get down to business and start writing R code. In the Scripts pane, type
x <- c(5,10,15,20,25,30,35,40)
and then press Ctrl+R.
That puts this line into the Console pane:
> x <- c(5,10,15,20,25,30,35,40)
As I say in an earlier Tip paragraph, the right-pointing arrowhead (the greater-than sign) is a prompt that R puts in the Console pane. You don’t see it in the Scripts pane.
Here’s what R just did: The arrow-sign says that x gets assigned whatever is to the right of the arrow-sign. Think of the arrow-sign as R’s assignment operator. So the set of numbers 5, 10, 15, 20 … 40 is now assigned to x.
In R-speak, a set of numbers like this is a vector. I tell you more about this concept in the later section “R Structures.”
You can read that line of code as “x gets the vector 5, 10, 15, 20.”
Type x into the Scripts pane and press Ctrl+R, and here’s what you see in the Console pane:
> x
[1] 5 10 15 20 25 30 35 40
The 1 in square brackets is the label for the first line of output. So this signifies that 5 is the first value.
Here you have only one line, of course. What happens when R outputs many values over many lines? Each line gets a bracketed numeric label, and the number corresponds to the first value in the line. For example, if the output consists of 23 values and the eighteenth value is the first one on the second line, the second line begins with [18].
Creating the vector x causes the Environment tab to look like Figure 1-5.
FIGURE 1-5: The RStudio Environment tab after creating the vector x.
Another way to see the objects in the environment is to type ls() into the Scripts pane and then press Ctrl+R. Or you can type > ls() directly into the Console pane and press Enter. Either way, the result in the Console pane is
[1] "x"
Now you can work with x. First, add all numbers in the vector. Typing sum(x) in the Scripts pane (be sure to follow with Ctrl+R) executes the following line in the Console pane:
> sum(x)
[1] 180
How about the average of the numbers in vector x?
That would involve typing mean(x) in the Scripts pane, which (when followed by Ctrl+R) executes
> mean(x)
[1] 22.5
in the Console pane.
As you type in the Scripts pane or in the Console pane, you see that helpful information pops up. As you become experienced with RStudio, you learn how to use that information.
Variance is a measure of how much a set of numbers differ from their mean. Here's how to use R to calculate variance:
> var(x)
[1] 150
What, exactly, is variance and what does it mean? (Shameless plug alert.) For the answers to these and numerous other questions about statistics and analysis, read one of the most classic works in the English language: Statistical Analysis with R For Dummies (written by yours truly and published by Wiley).
After R executes all these commands, the History tab looks like Figure 1-6.
FIGURE 1-6: The History tab, after creating and working with a vector.
To end a session, select File ⇒ Quit Session from the main menu or press Ctrl+Q. As Figure 1-7 shows, a dialog box opens and asks what you want to save from the session. Saving the selections enables you to reopen the session where you left off the next time you open RStudio (although the Console pane doesn’t save your work).
FIGURE 1-7: The Quit R Session dialog box.
Moving forward, most of the time I don’t say “Type this code into the Scripts pane and press Ctrl+Enter” whenever I take you through an example. I just show you the code and its output, as in the var() example.
Also, sometimes I show code with the > prompt, and sometimes without. Generally, I show the prompt when I want you to see R code and its results. I don’t show the prompt when I just want you to see R code that I create in the Scripts pane.
R Functions
The examples in the preceding section use c(), sum(), and var(). These are three functions built into R. Each one consists of a function name immediately followed by parentheses. Inside the parentheses are arguments. In the context of a function, argument doesn't mean “debate” or “disagreement” or anything like that. It’s the math name for whatever a function operates on.
Sometimes a function takes no arguments (as is the case with ls()). You still include the parentheses.
The functions in the examples I showed you are pretty simple: Supply an argument, and each one gives you a result. Some R functions, however, take more than one argument.
R has a couple of ways for you to deal with multi-argument functions. One way is to list the arguments in the order that they appear in the function’s definition. R calls this positional mapping.
Here’s an example. Remember when I created the vector x?
x <- c(5,10,15,20,25,30,35,40)
Another way to create a vector of those numbers is with the function seq():
> y <- seq(5,40,5)
> y
[1] 5 10 15 20 25 30 35 40
Think of seq() as creating a “sequence.” The first argument to seq() is the number to start the sequence from (5). The second argument is the number that ends the sequence — the number the sequence goes to (40). The third argument is the increment of the sequence — the amount the sequence increases by (5).
If you name the arguments, it doesn't matter how you order them:
> z <- seq(to=40,by=5,from=5)
> z
[1] 5 10 15 20 25 30 35 40
So when you use a function, you can place its arguments out of order, if you name them. R calls this keyword matching. This comes in handy when you use an R function that has many arguments. If you can’t remember their order, use their names, and the function works.
For help on a particular function — seq(), for example — type ?seq. When you run that code, helpful information appears on the Help tab and useful information pops up in a little window right next to where you’re typing.
User-Defined Functions
R enables you to create your own functions, and here are the fundamentals on how to do it.
The form of an R function is
myfunction <- function(argument1, argument2, …){
statements
return(object)
}
Here’s a function for dealing with right triangles. Remember them? A right triangle has two sides that form a right angle, and a third side called a hypotenuse. You might also remember that a guy named Pythagoras showed that if one side has length a and the other side has length b, the length of the hypotenuse, c, is
So here’s a simple function called hypotenuse() that takes two numbers a and b, (the lengths of the two sides of a right triangle) and returns c, the length of the hypotenuse:
hypotenuse <- function(a,b){
hyp <- sqrt(a^2+b^2)
return(hyp)
}
Type that code snippet into the Scripts pane and highlight it. Then press Ctrl+Enter. Here's what appears in the Console pane:
> hypotenuse <- function(a,b){
+ hyp <- sqrt(a^2+b^2)
+ return(hyp)
+ }
Each plus sign is a continuation prompt. It just indicates that a line continues from the preceding line.
And here’s how to use the function:
> hypotenuse(3,4)
[1] 5
Writing R functions can encompass way more than I’ve shown you here. To learn more, take a look at R For Dummies, by Andrie de Vries and Joris Meys (Wiley).
Comments
A comment is a way of annotating code. Begin a comment with the # symbol, which, as everyone knows, is called an octothorpe. (Wait. What? “Hashtag?” Get atta here!) This symbol tells R to ignore everything to the right of it.
Comments help someone who has to read the code you’ve written. For example:
hypotenuse <- function(a,b){ # list the arguments
hyp <- sqrt(a^2+b^2) # perform the computation
return(hyp) # return the value
}
Here’s a heads-up: I don’t typically add comments to lines of code in this book. Instead, I provide detailed descriptions. In a book like this, I feel it’s the best way to get the message across.
Of for Loops and if Statements
Like many programming languages, R provides a way to iterate through its structures to get things done. R’s way is called the for loop. And, like many languages, R gives you a way to test against a criterion: the if statement.
The general format of a for loop is
for counter in start:end{
statement 1
statement n
}
As you might imagine, counter tracks the iterations.
The simplest general format of an if statement is
if(test){statement to execute if test is TRUE}
