R All-in-One For Dummies
- Length: 688 pages
- Edition: 1
- Language: English
- Publisher: For Dummies
- Publication Date: 2023-02-07
- ISBN-10: 111998369X
- ISBN-13: 9781119983699
- Sales Rank: #986370 (See Top 100 Books)
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.
Cover Title Page Copyright 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 t for Two Like Peas in a Pod: Equal Variances t-Testing in R A Matched Set: Hypothesis Testing for Paired Samples Paired Sample t-testing in R Testing Two Variances Working with F Distributions Visualizing F 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 R Multiple Correlation Partial Correlation Partial Correlation in R Semipartial Correlation Semipartial Correlation in R 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 rS 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
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