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Herausgeber: John Wiley & Sons
Kategorie: Wissenschaft und neue Technologien
Sprache: Englisch

Beschreibung

A deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You'll find coverage of statistical analysis, machine learning, and data management with R. * Grasp the basics of the R programming language and write your first lines of code * Understand how R programmers use code to analyze data and perform statistical analysis * Use R to create data visualizations and machine learning programs * Work through sample projects to hone your R coding skill This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.

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R All-in-One For Dummies®

Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com

Published simultaneously in Canada

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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Library of Congress Control Number: 2022950749

ISBN: 978-1-119-98369-9 (pbk); 978-1-119-98370-5 (ebk); 978-1-119-98371-2 (ebk)

R All-in-One For Dummies®

To view this book's Cheat Sheet, simply go to www.dummies.com and search for “R All-in-One For Dummies Cheat Sheet” in the Search box.

Cover

Title Page

Introduction

About This All-in-One

What You Can Safely Skip

Foolish Assumptions

Icons Used in This Book

Beyond This Book

Where to Go from Here

Book 1: Introducing R

Chapter 1: R: What It Does and How It Does It

The Statistical (and Related) Ideas You Just Have to Know

Getting R

Getting RStudio

A Session with R

R Functions

User-Defined Functions

Comments

R Structures

for Loops and if Statements

Chapter 2: Working with Packages, Importing, and Exporting

Installing Packages

Examining Data

R Formulas

More Packages

Exploring the tidyverse

Importing and Exporting

Book 2: Describing Data

Chapter 1: Getting Graphic

Finding Patterns

Doing the Basics: Base R Graphics, That Is

Kicking It Up a Notch to ggplot2

Putting a Bow On It

Chapter 2: Finding Your Center

Means: The Lure of Averages

Calculating the Mean

The Average in R: mean()

Medians: Caught in the Middle

The Median in R:

median()

Statistics à la Mode

The Mode in R

Chapter 3: Deviating from the Average

Measuring Variation

Back to the Roots: Standard Deviation

Standard Deviation in R

Chapter 4: Meeting Standards and Standings

Catching Some Zs

Standard Scores in R

Where Do You Stand?

Summarizing

Chapter 5: Summarizing It All

How Many?

The High and the Low

Living in the Moments

Tuning in the Frequency

Summarizing a Data Frame

Chapter 6: What’s Normal?

Hitting the Curve

Working with Normal Distributions

Meeting a Distinguished Member of the Family

Book 3: Analyzing Data

Chapter 1: The Confidence Game: Estimation

Understanding Sampling Distributions

An EXTREMELY Important Idea: The Central Limit Theorem

Confidence: It Has Its Limits!

Fit to a t

Chapter 2: One-Sample Hypothesis Testing

Hypotheses, Tests, and Errors

Hypothesis Tests and Sampling Distributions

Catching Some Z’s Again

Z Testing in R

t for One

t Testing in R

Working with t-Distributions

Visualizing t-Distributions

Testing a Variance

Working with Chi-Square Distributions

Visualizing Chi-Square Distributions

Chapter 3: Two-Sample Hypothesis Testing

Hypotheses Built for Two

Sampling Distributions Revisited

for Two

Like Peas in a Pod: Equal Variances

-Testing in

A Matched Set: Hypothesis Testing for Paired Samples

Paired Sample t-testing in R

Testing Two Variances

Working with

Distributions

Visualizing

Distributions

Chapter 4: Testing More than Two Samples

Testing More than Two

ANOVA in R

Another Kind of Hypothesis, Another Kind of Test

Getting Trendy

Trend Analysis in R

Chapter 5: More Complicated Testing

Cracking the Combinations

Two-Way ANOVA in R

Two Kinds of Variables … at Once

After the Analysis

Multivariate Analysis of Variance

Chapter 6: Regression: Linear, Multiple, and the General Linear Model

The Plot of Scatter

Graphing Lines

Regression: What a Line!

Linear Regression in R

Juggling Many Relationships at Once: Multiple Regression

ANOVA: Another Look

Analysis of Covariance: The Final Component of the GLM

But Wait — There’s More

Chapter 7: Correlation: The Rise and Fall of Relationships

Understanding Correlation

Correlation and Regression

Testing Hypotheses about Correlation

Correlation in

Multiple Correlation

Partial Correlation

Partial Correlation in

Semipartial Correlation

Semipartial Correlation in

Chapter 8: Curvilinear Regression: When Relationships Get Complicated

What Is a Logarithm?

What Is e?

Power Regression

Exponential Regression

Logarithmic Regression

Polynomial Regression: A Higher Power

Which Model Should You Use?

Chapter 9: In Due Time

A Time Series and Its Components

Forecasting: A Moving Experience

Forecasting: Another Way

Working with Real Data

Chapter 10: Non-Parametric Statistics

Independent Samples

Matched Samples

Correlation: Spearman’s

Correlation: Kendall’s Tau

A Heads-Up

Chapter 11: Introducing Probability

What Is Probability?

Compound Events

Conditional Probability

Large Sample Spaces

R Functions for Counting Rules

Random Variables: Discrete and Continuous

Probability Distributions and Density Functions

The Binomial Distribution

The Binomial and Negative Binomial in R

Hypothesis Testing with the Binomial Distribution

More on Hypothesis Testing: R versus Tradition

Chapter 12: Probability Meets Regression: Logistic Regression

Getting the Data

Doing the Analysis

Visualizing the Results

Book 4: Learning from Data

Chapter 1: Tools and Data for Machine Learning Projects

The UCI (University of California-Irvine) ML Repository

Introducing the

Rattle

package

Using

Rattle

with

iris

Chapter 2: Decisions, Decisions, Decisions

Decision Tree Components

Decision Trees in R

Decision Trees in

Rattle

Project: A More Complex Decision Tree

Suggested Project: Titanic

Chapter 3: Into the Forest, Randomly

Growing a Random Forest

Random Forests in R

Project: Identifying Glass

Suggested Project: Identifying Mushrooms

Chapter 4: Support Your Local Vector

Some Data to Work With

Separability: It’s Usually Nonlinear

Support Vector Machines in R

Project: House Parties

Chapter 5: K-Means Clustering

How It Works

K-Means Clustering in R

Project: Glass Clusters

Chapter 6: Neural Networks

Networks in the Nervous System

Artificial Neural Networks

Neural Networks in R

Project: Banknotes

Suggested Projects: Rattling Around

Chapter 7: Exploring Marketing

Analyzing Retail Data

Enter Machine Learning

Suggested Project: Another Data Set

Chapter 8: From the City That Never Sleeps

Examining the Data Set

Warming Up

Quick Suggested Project: Airline Names

Suggested Project: Departure Delays

Quick Suggested Project: Analyze Weekday Differences

Suggested Project: Delay and Weather

Book 5: Harnessing R: Some Projects to Keep You Busy

Chapter 1: Working with a Browser

Getting Your Shine On

Creating Your First shiny Project

Working with ggplot

Another shiny Project

Suggested Project

Chapter 2: Dashboards — How Dashing!

The shinydashboard Package

Exploring Dashboard Layouts

Working with the Sidebar

Interacting with Graphics

Index

About the Author

Connect with Dummies

End User License Agreement

Guide

Cover

Title Page

Table of Contents

Begin Reading

Index

About the Author

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Introduction

In this book, I’ve brought together all the information you need to hit the ground running with R. It’s heavy on statistics, of course, because R’s creators built this language to analyze data.

So it’s necessary that you learn the foundations of statistics. Let me tell you at the outset: This All-in-One is not a cookbook. I’ve never taught statistics that way and I never will. Before I show you how to use R to work with a statistical concept, I give you a strong grounding in what that concept is all about.

In fact, Books 2 and 3 of this 5-book compendium are something like an introductory statistics text that happens to use R as a way of explaining statistical ideas.

Book 4 follows that path by teaching the ideas behind machine learning before you learn how to use R to implement them. Book 5 gives you a set of projects that give you a chance to exercise your newly minted R skill set.

Want some more details? Read on.

About This All-in-One

The volume you’re holding (or the e-book you’re viewing) consists of five books that cover a lot of the length and breadth of R.

Book 1: Introducing R

As I said earlier in this introduction, R is a language that deals with statistics. Accordingly, Book 1 introduces you to the fundamental concepts of statistics that you just have to know in order to progress with R.

You then learn about R and RStudio, a widely used development environment for working with R. I begin by describing the rudiments of R code, and I discuss R functions and structures.

R truly comes alive when you use its specialized packages, which you learn about early on.

Book 2: Describing Data

Part of working with statistics is to summarize data in meaningful ways. In Book 2, you find out how to do just that.

Most people know about averages and how to compute them. But that’s not the whole story. In Book 2, I tell you about additional descriptive statistics that fill in the gaps, and I show you how to use R to calculate and work with those statistics. You also learn to create graphics that visualize the data descriptions and analyses you encounter in Books 2 and 3.

Book 3: Analyzing Data

Book 3 addresses the fundamental aim of statistical analysis: to go beyond the data and help you make decisions. Usually, the data are measurements of a sample taken from a large population. The goal is to use these data to figure out what’s going on in the population.

This opens a wide range of questions: What does an average mean? What does the difference between two averages mean? Are two things associated? These are only a few of the questions I address in Book 3, and you learn to use the R tools that help you answer them.

Book 4: Learning from Data

Effective machine learning model creation comes with experience. Accordingly, in Book 4 you gain experience by completing machine learning projects. In addition to the projects you complete along with me, I suggest additional projects for you to try on your own.

I begin by telling you about the University of California-Irvine Machine Learning Repository, which provides the data sets for most of the projects you encounter in Book 4.

To give you a gentle on-ramp into the field, I show you the Rattle package for creating machine learning applications. It’s a friendly interface to R’s machine learning functionality. I like Rattle a lot, and I think you will, too. You use it to learn about and work with decision trees, random forests, support vector machines, k-means clustering, and neural networks.

You also work with fairly large data sets — not the terabytes and petabytes data scientists work with, but large enough to get you started. In one project, you analyze a data set of more than 500,000 airline flights. In another, you complete a customer segmentation analysis of over 300,000 customers of an online retailer.

Book 5: Harnessing R: Some Projects to Keep You Busy

As its title suggests, Book 5 is also organized around projects.

In these projects, you create applications that respond to users. I show you the shiny package for working with web browsers and the shinydashboard package for creating dashboards.

All this is a little far afield from R’s original mission in life, but you get an idea of R’s potential to expand in new directions.

After you’ve worked with R for a while, maybe you can discover some of those new directions!

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 go through the details of pointing, clicking, selecting, and so forth.

Can install R and RStudio (I show you how in

Book 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

As is the case in all For Dummies books, icons help guide you through your journey. Each one is a little picture in the margin that lets you know something special about the paragraph it’s 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 “What You Can Safely Skip,” this icon indicates material you can blow past if it’s just too technical. (I’ve kept this content to a minimum.)

Beyond This Book

In addition to what you’re reading right now, this book comes with a free, access-anywhere Cheat Sheet that will help you quickly use the tools I discuss. To find this Cheat Sheet, visit www.dummies.com and search for R All-in-One For Dummies Cheat Sheet in the Search box.

If you’ve read any of my earlier books, welcome back!

Where to Go from Here

Time to hit the books! You can start from anywhere, but here are a couple of hints. Want to introduce yourself to R and packages? Book 1 is for you. Has it been a while (or maybe never?) since your last statistics course? Hit Book 2. For anything else, find it in the table of contents or in the index and go for it.

If you prefer to read from cover to cover, just turn the page… .

Book 1