A deep dive into the programming language of choice for statistics and data
WithR 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 codeUnderstand how R programmers use code to analyze data and perform statistical analysisUse R to create data visualizations and machine learning programsWork 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.
Introduction 1
Book 1: Introducing R 5
Chapter 1: R: What It Does and How It Does It 7
Chapter 2: Working with Packages, Importing, and Exporting 37
Book 2: Describing Data 51
Chapter 1: Getting Graphic 53
Chapter 2: Finding Your Center 93
Chapter 3: Deviating from the Average 103
Chapter 4: Meeting Standards and Standings 113
Chapter 5: Summarizing It All 125
Chapter 6: Whats Normal? 145
Book 3: Analyzing Data 163
Chapter 1: The Confidence Game: Estimation 165
Chapter 2: One-Sample Hypothesis Testing 181
Chapter 3: Two-Sample Hypothesis Testing 207
Chapter 4: Testing More than Two Samples 233
Chapter 5: More Complicated Testing 257
Chapter 6: Regression: Linear, Multiple, and the General Linear Model 279
Chapter 7: Correlation: The Rise and Fall of Relationships 315
Chapter 8: Curvilinear Regression: When Relationships Get Complicated 335
Chapter 9: In Due Time 359
Chapter 10: Non-Parametric Statistics 371
Chapter 11: Introducing Probability 393
Chapter 12: Probability Meets Regression: Logistic Regression 415
Book 4: Learning from Data 423
Chapter 1: Tools and Data for Machine Learning Projects 425
Chapter 2: Decisions, Decisions, Decisions 449
Chapter 3: Into the Forest, Randomly 467
Chapter 4: Support Your Local Vector 483
Chapter 5: K-Means Clustering 503
Chapter 6: Neural Networks 519
Chapter 7: Exploring Marketing 537
Chapter 8: From the City That Never Sleeps 557
Book 5: Harnessing R: Some Projects to Keep You Busy 573
Chapter 1: Working with a Browser 575
Chapter 2: Dashboards How Dashing! 603
Index 639
