R All-in-One For Dummies

  • 7h 55m
  • Joseph Schmuller
  • John Wiley & Sons (US)
  • 2023

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.

About the Author

Joseph Schmuller is a cognitive scientist and statistical analyst. His recent work in the For Dummies series includes the 5th edition of Statistical Analysis with Excel For Dummies along with Statistical Analysis with R For Dummies and R Projects For Dummies.

In this Book

  • Introduction
  • R—What it Does and How it Does It
  • Working with Packages, Importing, and Exporting
  • Getting Graphic
  • Finding Your Center
  • Deviating from the Average
  • Meeting Standards and Standings
  • Summarizing it All
  • What’s Normal?
  • The Confidence Game—Estimation
  • One-Sample Hypothesis Testing
  • Two-Sample Hypothesis Testing
  • Testing More Than Two Samples
  • More Complicated Testing
  • Regression—Linear, Multiple, and the General Linear Model
  • Correlation—The Rise and Fall of Relationships
  • Curvilinear Regression—When Relationships Get Complicated
  • In Due Time
  • Non-Parametric Statistics
  • Introducing Probability
  • Probability Meets Regression—Logistic Regression
  • Tools and Data for Machine Learning Projects
  • Decisions, Decisions, Decisions
  • Into the Forest, Randomly
  • Support Your Local Vector
  • K-Means Clustering
  • Neural Networks
  • Exploring Marketing
  • From the City That Never Sleeps
  • Working with a Browser
  • Dashboards—How Dashing!
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