R All-in-One For Dummies

R All-in-One For Dummies

by Joseph Schmuller
Released February 2023
Publisher(s): For Dummies
ISBN: 9781119983699

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Book description

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.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Introduction
    1. About This All-in-One
    2. What You Can Safely Skip
    3. Foolish Assumptions
    4. Icons Used in This Book
    5. Beyond This Book
    6. Where to Go from Here
  5. Book 1: Introducing R
    1. Chapter 1: R: What It Does and How It Does It
      1. The Statistical (and Related) Ideas You Just Have to Know
      2. Getting R
      3. Getting RStudio
      4. A Session with R
      5. R Functions
      6. User-Defined Functions
      7. Comments
      8. R Structures
      9. for Loops and if Statements
    2. Chapter 2: Working with Packages, Importing, and Exporting
      1. Installing Packages
      2. Examining Data
      3. R Formulas
      4. More Packages
      5. Exploring the tidyverse
      6. Importing and Exporting
  6. Book 2: Describing Data
    1. Chapter 1: Getting Graphic
      1. Finding Patterns
      2. Doing the Basics: Base R Graphics, That Is
      3. Kicking It Up a Notch to ggplot2
      4. Putting a Bow On It
    2. Chapter 2: Finding Your Center
      1. Means: The Lure of Averages
      2. Calculating the Mean
      3. The Average in R: mean()
      4. Medians: Caught in the Middle
      5. The Median in R: median()
      6. Statistics à la Mode
      7. The Mode in R
    3. Chapter 3: Deviating from the Average
      1. Measuring Variation
      2. Back to the Roots: Standard Deviation
      3. Standard Deviation in R
    4. Chapter 4: Meeting Standards and Standings
      1. Catching Some Zs
      2. Standard Scores in R
      3. Where Do You Stand?
      4. Summarizing
    5. Chapter 5: Summarizing It All
      1. How Many?
      2. The High and the Low
      3. Living in the Moments
      4. Tuning in the Frequency
      5. Summarizing a Data Frame
    6. Chapter 6: What’s Normal?
      1. Hitting the Curve
      2. Working with Normal Distributions
      3. Meeting a Distinguished Member of the Family
  7. Book 3: Analyzing Data
    1. Chapter 1: The Confidence Game: Estimation
      1. Understanding Sampling Distributions
      2. An EXTREMELY Important Idea: The Central Limit Theorem
      3. Confidence: It Has Its Limits!
      4. Fit to a t
    2. Chapter 2: One-Sample Hypothesis Testing
      1. Hypotheses, Tests, and Errors
      2. Hypothesis Tests and Sampling Distributions
      3. Catching Some Z’s Again
      4. Z Testing in R
      5. t for One
      6. t Testing in R
      7. Working with t-Distributions
      8. Visualizing t-Distributions
      9. Testing a Variance
      10. Working with Chi-Square Distributions
      11. Visualizing Chi-Square Distributions
    3. Chapter 3: Two-Sample Hypothesis Testing
      1. Hypotheses Built for Two
      2. Sampling Distributions Revisited
      3. t for Two
      4. Like Peas in a Pod: Equal Variances
      5. t-Testing in R
      6. A Matched Set: Hypothesis Testing for Paired Samples
      7. Paired Sample t-testing in R
      8. Testing Two Variances
      9. Working with F Distributions
      10. Visualizing F Distributions
    4. Chapter 4: Testing More than Two Samples
      1. Testing More than Two
      2. ANOVA in R
      3. Another Kind of Hypothesis, Another Kind of Test
      4. Getting Trendy
      5. Trend Analysis in R
    5. Chapter 5: More Complicated Testing
      1. Cracking the Combinations
      2. Two-Way ANOVA in R
      3. Two Kinds of Variables … at Once
      4. After the Analysis
      5. Multivariate Analysis of Variance
    6. Chapter 6: Regression: Linear, Multiple, and the General Linear Model
      1. The Plot of Scatter
      2. Graphing Lines
      3. Regression: What a Line!
      4. Linear Regression in R
      5. Juggling Many Relationships at Once: Multiple Regression
      6. ANOVA: Another Look
      7. Analysis of Covariance: The Final Component of the GLM
      8. But Wait — There’s More
    7. Chapter 7: Correlation: The Rise and Fall of Relationships
      1. Understanding Correlation
      2. Correlation and Regression
      3. Testing Hypotheses about Correlation
      4. Correlation in R
      5. Multiple Correlation
      6. Partial Correlation
      7. Partial Correlation in R
      8. Semipartial Correlation
      9. Semipartial Correlation in R
    8. Chapter 8: Curvilinear Regression: When Relationships Get Complicated
      1. What Is a Logarithm?
      2. What Is e?
      3. Power Regression
      4. Exponential Regression
      5. Logarithmic Regression
      6. Polynomial Regression: A Higher Power
      7. Which Model Should You Use?
    9. Chapter 9: In Due Time
      1. A Time Series and Its Components
      2. Forecasting: A Moving Experience
      3. Forecasting: Another Way
      4. Working with Real Data
    10. Chapter 10: Non-Parametric Statistics
      1. Independent Samples
      2. Matched Samples
      3. Correlation: Spearman’s rS
      4. Correlation: Kendall’s Tau
      5. A Heads-Up
    11. Chapter 11: Introducing Probability
      1. What Is Probability?
      2. Compound Events
      3. Conditional Probability
      4. Large Sample Spaces
      5. R Functions for Counting Rules
      6. Random Variables: Discrete and Continuous
      7. Probability Distributions and Density Functions
      8. The Binomial Distribution
      9. The Binomial and Negative Binomial in R
      10. Hypothesis Testing with the Binomial Distribution
      11. More on Hypothesis Testing: R versus Tradition
    12. Chapter 12: Probability Meets Regression: Logistic Regression
      1. Getting the Data
      2. Doing the Analysis
      3. Visualizing the Results
  8. Book 4: Learning from Data
    1. Chapter 1: Tools and Data for Machine Learning Projects
      1. The UCI (University of California-Irvine) ML Repository
      2. Introducing the Rattle package
      3. Using Rattle with iris
    2. Chapter 2: Decisions, Decisions, Decisions
      1. Decision Tree Components
      2. Decision Trees in R
      3. Decision Trees in Rattle
      4. Project: A More Complex Decision Tree
      5. Suggested Project: Titanic
    3. Chapter 3: Into the Forest, Randomly
      1. Growing a Random Forest
      2. Random Forests in R
      3. Project: Identifying Glass
      4. Suggested Project: Identifying Mushrooms
    4. Chapter 4: Support Your Local Vector
      1. Some Data to Work With
      2. Separability: It’s Usually Nonlinear
      3. Support Vector Machines in R
      4. Project: House Parties
    5. Chapter 5: K-Means Clustering
      1. How It Works
      2. K-Means Clustering in R
      3. Project: Glass Clusters
    6. Chapter 6: Neural Networks
      1. Networks in the Nervous System
      2. Artificial Neural Networks
      3. Neural Networks in R
      4. Project: Banknotes
      5. Suggested Projects: Rattling Around
    7. Chapter 7: Exploring Marketing
      1. Analyzing Retail Data
      2. Enter Machine Learning
      3. Suggested Project: Another Data Set
    8. Chapter 8: From the City That Never Sleeps
      1. Examining the Data Set
      2. Warming Up
      3. Quick Suggested Project: Airline Names
      4. Suggested Project: Departure Delays
      5. Quick Suggested Project: Analyze Weekday Differences
      6. Suggested Project: Delay and Weather
  9. Book 5: Harnessing R: Some Projects to Keep You Busy
    1. Chapter 1: Working with a Browser
      1. Getting Your Shine On
      2. Creating Your First shiny Project
      3. Working with ggplot
      4. Another shiny Project
      5. Suggested Project
    2. Chapter 2: Dashboards — How Dashing!
      1. The shinydashboard Package
      2. Exploring Dashboard Layouts
      3. Working with the Sidebar
      4. Interacting with Graphics
  10. Index
  11. About the Author
  12. Connect with Dummies
  13. End User License Agreement

Product information

  • Title: R All-in-One For Dummies
  • Author(s): Joseph Schmuller
  • Release date: February 2023
  • Publisher(s): For Dummies
  • ISBN: 9781119983699