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R All–in–One For Dummies

Paperback Engels 2023 9781119983699
Verkooppositie 5487Hoogste positie: 5487
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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.


Trefwoorden:Programmeren, R
Aantal pagina's:688
Hoofdrubriek:IT-management / ICT


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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: What’s 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

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