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Statistics With R
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Statistics With R
Solving Problems Using Real-World Data

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January 2020 | 784 pages | SAGE Publications, Inc
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“Statistics with R is easily the most accessible and almost fun introduction to statistics and R that I have read. Even the most hesitant student is likely to embrace the material with this text.”

—David A.M. Peterson, Department of Political Science, Iowa State University

Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the “R Team” characters. Inspired by the programming group “R Ladies,” the R Team works together to master the skills of statistical analysis and data visualization to untangle real-world, messy data using R. The storylines draw students into investigating contemporary issues such as marijuana legalization, voter registration, and the opioid epidemic, and lead them step-by-step through full-color illustrations of R statistics and interactive exercises. 


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Chapter 1. Preparing data for analysis and visualization in R: The R-team and the pot policy problem
Choosing and Learning R

 
Learning R with Publicly-Available Data

 
Achievements to Unlock

 
The Tricky Weed Problem

 
Achievement 1: Observations and Variables

 
Achievement 2: Using Reproducible Research Practices

 
Achievement 3: Understanding and Changing Data Types

 
Achievement 4: Entering or Loading Data into R

 
Achievement 5: Identifying and Treating Missing Values

 
Achievement 6: Building a Basic Bar Graph

 
Chapter Summary

 
 
Chapter 2: Computing and Reporting Descriptive Statistics: The R-team and the Troubling Transgender Healthcare Problem
Achievements to Unlock

 
The Transgender Healthcare Problem

 
Data, Codebook, and R Packages for Learning About Descriptive Statistics

 
Achievement 1: Understanding Variable Types and Data Types

 
Achievement 2: Choosing and Conducting Descriptive Analyses for Categorical (Factor) Values

 
Achievement 3: Choosing and Conducting Descriptive Analyses for Continuous (Numeric) Variables

 
Achievement 4: Developing Clear Tables for Reporting Descriptive Statistics

 
Chapter Summary

 
 
Chapter 3: Data Visualization: The R-Team and the Tricky Trigger Problem
Achievements to Unlock

 
The Tricky Trigger Problem

 
Data, Codebook, and R Packages for Graphs

 
Achievement 1: Graphs for a Single Categorical Variable

 
Achievement 2: Graphs for a Single Continuous Variable

 
Achievement 3: Choosing and Creating Graphs for Two Variables at Once

 
Achievement 4: Ensuring Graphs are Well-Formatted with Appropriate and Clear Titles, Labels, Colors, and Other Features

 
Chapter Summary

 
 
Chapter 4: Probability Distributions and Inference: The R-Team and the Opioid Overdose Problem
Achievements to Unlock

 
The Awful Opioid Overdose Problem

 
Data, Codebook, and R Packages for Learning About Distributions

 
Achievement 1: Defining and Using the Probability Distributions to Infer From A Sample

 
Achievement 2: Understanding the Characteristics and Uses of a Binomial Distribution of a Binary Variable

 
Achievement 3: Understanding the Characteristics and Uses of the Normal Distribution of a Continuous Variable

 
Achievement 4: Computing and Interpreting z-scores to Compare Observations to Groups

 
Achievement 5: Estimating Population Means from Sample Means Using the Normal Distribution

 
Achievement 6: Computing and Interpreting Confidence Intervals around Means and Proportions

 
Chapter Summary

 
 
Chapter 5: Computing and Interpreting Chi-Squared: The R-Team and the Vexing Voter Fraud Problem
Achievements to Unlock

 
The Voter Fraud Problem

 
Data, Codebook, and R Packages for Learning About Chi-Squared

 
Achievement 1: Understanding the Relationship Between Two Categorical Variables using Bar Graphs, Frequencies, and Percentages

 
Achievement 2: Computing and Comparing Observed and Expected Values for the Groups

 
Achievement 3: Calculating the Chi-Squared Statistics for the Test of Indepedence

 
Achievement 4: Intepreting the Chi-Squared Statistics and Making a Conclusion about Whether or Not There is A Relationship

 
Achievement 5: Using Null Hypothesis Significance Testing to Organize Statistical Testing

 
Achievement 6: Using Standardized Residuals to Understand Which Groups Contributed to Significant Relationship

 
Achievement 7: Computing and Interpreting Effect Sizes to Understand the Strength of a Significant Chi-Squared Relationship

 
Achievement 8: Understanding the Options for Failed Chi-Squared Assumptions

 
Chapter Summary

 
 
Chapter 6: Conducting and Interpreting t-tests: The R-Team and the Blood Pressure Predicament
Achievements to Unlock

 
The Blood Pressure Predicament

 
Data, Codebook, and R Packages for Learning about t-tests

 
Achievement 1: Understanding the Relationship between One Categorical Variable and One Continuous Variable Using Graphs, Frequencies, and Percentages

 
Achievement 2: Comparing a Sample Mean to a Population Mean with One Sample t-test

 
Achievement 3: Comparing Two Unrelated Sample Means with an Independent Samples t-test

 
Achievement 4: Comparing Two Related Sample Means with a Dependent Samples Test

 
Achievement 5: Computing and Interpreting an Effect Size for Significant t-tests

 
Achievement 6: Examining and Checking the Underlying Assumptions for Using the t-test

 
Achievement 7: Identifying and Using Alternate tests for when t-test Assumptions are Not Met

 
Chapter Summary

 
 
Chapter 7: Analysis of Variance (ANOVA): The R-Team and the Technical Difficulties Problem
The Technical Difficulties Problem

 
Data, Codebook, and R Packages for Learning about ANOVA

 
Achievement 1: Exploring the Data Using Graphics and Descriptive Statistics

 
Achievement 2: Understanding and Conducting One-Way Analysis of Variance (ANOVA)

 
Achievement 3: Choosing and Using Post-Hoc Tests and Contrasts

 
Achievement 4: Computing and Interpreting Effect Sizes for ANOVA

 
Achievement 5: Testing ANOVA Assumptions

 
Achievement 6: Choosing and Using Alternative Tests when ANOVA Assumptions are Not Met

 
Achievement 7: Understanding and Conducting Two-Way ANOVA

 
Chapter Summary

 
 
Chapter 8: Correlation Coefficients: The R-team and the Clean Water Conundrum
Achievements to Unlock

 
The Clean Water Conundrum

 
Data and R Packages for Learning about Correlation

 
Achievement 1: Exploring the Data Using Graphics and Descriptive Statistics

 
Achievement 2: Computing and Interpreting Pearson's r Correlation Coefficient

 
Achievement 3: Conducting an Inferential Statistical Test for Pearson's r Correlation Coefficient

 
Achievement 4: Examining Effect Size for Pearson's r with the Coefficient of Determination

 
Achievement 5: Checking Assumptions for Pearson's r Correlation Analyses

 
Achievement 6: Transforming the Variables as an Alternative as an Alternative when Pearso's r Correlation Assumptions are Not Met

 
Achievement 7: Using Spearman's rho as an Alternative When Pearson's r Correlation Assumptions are Not Met

 
Achievment 8: Introducing Partial Correlations

 
Chapter Summary

 
 
Chapter 9: Linear Regression: The R-Team and the Needle Exchange Examination
Achievements to Unlock

 
The Needle Exchange Examination

 
Data, Codebook, and R Packages for Linear Regression Practice

 
Achievement 1: Using Exploratory Data Analysis to Learn about the Data Before Developing a Linear Regression Model

 
Achievement 2: Exploring the Statistical Model for a Line

 
Achievement 3: Computing the Slope and Intercept in a Simple Linear Regression

 
Achievement 4: Slope Interpretation and Significance (b, p-value, CI)

 
Achievement 5: Model Significance and Model Fit

 
Achievement 6: Checking Assumptions and Conducting Diagnoses

 
Achievement 7: Adding Variables to the Model and Using Transformation

 
 
Chapter 10: Binary Logistic Regression: The R-Team Examines the Perplexing Libraries Problem
Achievements to Unlock

 
The Perplexing Libraries Problem

 
Data, Codebook, and R Packages for Logistics Regression Practice

 
Achivement 1: Using Exploratory Data Analysis before Developing a Logistic Regression Model

 
Achievement 2: Understanding the Binary Logistic Regression Statistical Model

 
Achievement 3: Estimating a Simple Logistic Regression Model and Interpreting Predictor Significance and Interpretation

 
Achievement 4: Computing and Interpreting Two Measures of Model Fit

 
Achievement 5: Estimating a Larger Logistic Regression Model with Categorical and Continuous Predictors

 
Achievement 6: Interpreting the results of a Larger Logistic Regression Model

 
Achievement 7: Checking Logistic Regression Assumptions and Using Diagnostics to Identify Outliers and Influential Values

 
Achievement 8: Using the Model to Predict Probabilities for Observations that are Outside the Data Set

 
Achievement 9: Adding and Interpreting Interaction Terms in Logistic Regression

 
Achievement 10: Using the Likelihood Ratio (LR) Test to Compare

 
Chapter Summary

 
 
Chapter 11: Multinational and Ordinal Logistic Regression: The R-Team Examines the Diversity Dilemma in STEM
Achievements to Unlock

 
The Diversity Dilemma in STEM

 
Data, Codebook, and R Packages for Multinomial and Ordinal Regression Practice

 
Achievement 1: Exploratory Data Analysis for the Multinomial Model

 
Achievement 2: Estimating and Interpreting a Multinomial Logistic Regression Model

 
Achievement 3: Checking Assumptions for Multinomial Logistic Regression

 
Achievement 4: Exploratory Data Analysis for Ordinal Regression

 
Achievement 5: Estimate an Ordinal Regression Model

 
Achievement 6: Check Assumptions for Ordinal Regression

 
Chapter Summary

 
 
References

Supplements

Instructor Teaching Site
SAGE Edge for instructors supports your teaching by making it easy to integrate quality content and create a rich learning environment for students with:
  • a password-protected site for complete and protected access to all text-specific instructor resources;  
  • test banks that provide a diverse range of ready-to-use options that save you time. You can also easily edit any question and/or insert your own personalized questions;
  • tutorial videos produced exclusively for this text that demonstrate how to use R to conduct key statistical tests using real-world data; 
  • editable, chapter-specific PowerPoint® slides that offer complete flexibility for creating a multimedia presentation for your course;
  • downloadable Coder (beginner/intermediate) and Hacker (advanced) exercises from the book can be used as homework or labs. Students can take the multiple-choice pre-test questions electronically first to check their level; 
  • downloadable data files and R code are available for use with the book and exercises
  • solutions to selected in-text exercises;
  • Instructor Ideas for Gamification compiled by the author are offered for those who want to gamify their course; and
  • full-color figures from the book available for download.
Student Study Site
SAGE Edge for students enhances learning, it’s easy to use, and offers:
  • an open-access site that makes it easy for students to maximize their study time, anywhere, anytime;
  • tutorial videos produced exclusively for this text that demonstrate how to use R to conduct key statistical tests using real-world data; 
  • downloadable Coder (beginner/intermediate) and Hacker (advanced) exercises from the book. Students can take the multiple-choice pre-test questions electronically first to check their level; and
  • downloadable data files and R code are available for use with the book and exercises.
 

“Statistics With R is easily the most accessible and almost fun introduction to statistics and R that I have read. Even the most hesitant student is likely to embrace the material with this text.”

David A.M. Peterson
Department of Political Science, Iowa State University

“This is an entertaining and unorthodox text that explains statistical concepts in a way that engages students and rewards them for achievements. As useful to instructors as it is to their students.”

Matthew Phillips
Department of Criminal Justice, University of North Carolina, Charlotte

“This text makes the R statistics software accessible to all students by providing excellent examples and step-by-step processes. The student gains mastery over statistical analysis that can be applied to the real world.”

Mary A. Moore
Department of Healthcare Administration, Colorado State University

“This is an engaging textbook for learning statistics and R at the same time.”

Yi Shao
Department of Psychology, Oklahoma State University

“Using a simple but engaging style, this textbook relies on three friendly characters to introduce and explore the most common statistical problems students will face in their career. And, as a bonus, students learn how to use and master R for analyzing and illustrating simple and complex data sets.”

Sylvain Fiset
Department of Psychology, Université de Moncton, Canada

“There are many good statistics textbooks on the market- and there are equally good books that teach R; there are very few that do both. This book fills this gap. Students who use this text will benefit not only from having a top-notch stats textbook, but a great resource for how to conduct their analysis in R.”

Jonathan Hack
Harvard Law School

“Allowing students to see how statistics is actually relevant to them through guided stories is a priceless experience. This text provides cross-cutting skills in R programming that students can take away with them for their CVs/résumés and career development."

Benjamin Becerra
Allied Health Studies, Loma Linda University

“A unique introduction to statistics using characters in a storyline who are themselves learning how to solve real case studies using the R programming language. The first statistics textbook of its kind!”

Patrick Bolger
Department of Psychology, Texas A&M University

“This is a wonderful, innovative statistics text that integrates R coding into learning about quantitative methods. The highly engaging lessons walk students through each stage of the analytical process and teach students how to perform a statistical analysis, including the presentation of results in graphical form, using code.”

Jennifer Bachner
Center for Advanced Governmental Studies, Johns Hopkins University
Key features
KEY FEATURES:
  • A narrative approach with three characters working together to learn statistics provides an accessible and relatable way to introduce statistical topics.
  • Publicly available data sets for each chapter offer topics of interest with data management and analysis tasks that are more realistic than pre-cleaned or synthesized data sets.
  • Each chapter addresses a different social problem that demonstrates the utility of the methods versus studying synthetic data on fake topics.
    • Topics include pot legalization, voter suppression, opioid overdoses, diversity in the STEM fields, transgender healthcare, gun deaths, and more.
  • The inclusion of diverse characters and artwork portray women, people of color, and other demographics that are generally underrepresented in statistics to students.
 

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