Statistics and Data Visualization Using R

David S. Brown - University of Colorado, Boulder, USA

September 2021 | 616 pages | SAGE Publications, Inc

Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.

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Preface

Acknowledgments

About the Author

Chapter 1: Getting Started

Learning Objectives

Overview

R, RStudio, and R Markdown

Objects and Functions

Getting Started in RStudio

Navigating RStudio With R Markdown

Using R Markdown Files Versus R-Scripts

A Little Practice

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 2: An Introduction to Data Analysis

Learning Objectives

Overview

Motivating Data Analysis

The Main Components of Data Analysis

Developing Hypotheses by Describing Data

Model Building and Estimation

Diagnostics

Next Questions

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 3: Describing Data

Learning Objectives

Overview

Data Sets and Variables

Different Kinds of Variables

Describing Data Saves Time and Effort

Measurement

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 4: Central Tendency and Dispersion

Learning Objectives

Overview

Measures of Central Tendency: The Mode, Mean, and Median

Mean Versus Median

Measures of Dispersion: The Range, Interquartile Range, and Standard Deviation

Interquartile Range Versus Standard Deviation

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 5: Univariate and Bivariate Descriptions of Data

Learning Objectives

Overview

The Good, the Bad, and the Outlier

Five Views of Univariate Data

Are They in a Relationship?

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 6: Transforming Data

Learning Objectives

Overview

Theoretical Reasons for Transforming Data

Transforming Data for Practical Reasons

Transforming Data—Continuous to Categorical Variables

Transforming Data—Changing Categories

Box-Cox Transformations

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 7: Some Principles of Displaying Data

Learning Objectives

Overview

Some Elements of Style

The Basic Elements of a Story

Documentation (Establishing Credibility as a Storyteller)

Build an Intuition (Setting the Context)

Show Causation (The Journey)

From Causation to Action (The Resolution)

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 8: The Essentials of Probability Theory

Overview

Learning Objectives

Populations and Samples

Sample Bias and Random Samples

The Law of Large Numbers

The Central Limit Theorem

The Standard Normal Distribution

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 9: Confidence Intervals and Testing Hypotheses

Learning Objectives

Overview

Confidence Intervals With Large Samples

Small Samples and the t-Distribution

Comparing Two Sample Means

Confidence Levels

A Brief Note on Statistical Inference and Causation

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 10: Making Comparisons

Overview

Learning Objectives

Why Do We Make Comparisons?

Questions That Beg Comparisons

Comparing Two Categorical Variables

Comparing Continuous and Categorical Variables

Comparing Two Continuous Variables

Exploratory Data Analysis: Investigating Abortion Rates in the United States

Good Analysis Generates Additional Questions

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 11: Controlled Comparisons

Learning Objectives

Overview

What Is a Controlled Comparison?

Comparing Two Categorical Variables, Controlling for a Third

Comparing Two Continuous Variables, Controlling for a Third

Arguments and Controlled Comparisons

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Practice on Analysis and Visualization

Chapter 12: Linear Regression

Learning Objectives

Overview

The Advantages of Linear Regression

The Slope and Intercept in Linear Regression

Goodness of Fit (R2 Statistic)

Statistical Significance

Examples of Bivariate Regressions

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 13: Multiple Regression

Learning Objectives

Overview

What Is Multiple Regression?

Regression Models and Arguments

Regression Models, Theory, and Evidence

Interpreting Estimates in Multiple Regression

Example: Homicide Rate and Education

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Practice on Analysis and Visualization

Chapter 14: Dummies and Interactions

Learning Objectives

Overview

What Is a Dummy Variable?

Additive Models and Interactive Models

Bivariate Dummy Variable Regression

Multiple Regression and Dummy Variables

Interactions in Multiple Regression

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 15: Diagnostics I: Is Ordinary Least Squares Appropriate?

Learning Objectives

Overview

Diagnostics in Regression Analysis

Properties of Statistics and Estimators

The Gauss-Markov Assumptions

The Residual Plot

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 16: Diagnostics II: Residuals, Leverages, and Measures of Influence

Learning Objectives

Overview

Outliers

Leverages

Measures of Influence

Added Variable Plots

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Chapter 17: Logistic Regression

Learning Objectives

Overview

Questions and Problems That Require Logistic Regression

Logistic Regression Violates Gauss-Markov Assumptions

Working With Logged Odds

Working With Predicted Probabilities

Model Fit With Logistic Regression

Summary

Common Problems

Review Questions

Practice on Analysis and Visualization

Annotated R Functions

Answers

Appendix: Developing Empirical Implications

Overview

Developing Empirical Implications

Testing Additional Dependent Variables

Testing Additional Independent Variables

Using Information on Cases

Causal Mechanisms

The Rabbit Hole

Glossary

References

Index

Supplements

Instructor Website
edge.sagepub.com/brownstats1e

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LMS cartridge included with this title for use in Blackboard, Canvas, Brightspace by Desire2Learn (D2L), and Moodle

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Student Website

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The open-access Student Study Site makes it easy for students to maximize their study time, anywhere, anytime. It offers datasets and code for use in R.

This book provides a well-written approach to beginning to intermediate-level statistical principles using the R statistical language. It provides some mathematical formulas to help students understand the underlying principles of statistics. It has many excellent social science examples. It provides the statistical understanding with a practical approach to using the most valuable statistical tool—R. Please consider it. I have been looking for a good social science textbook using R—this may be the best so far.

Jeffrey D. Stone

California State University Los Angeles

This text successfully presents an introduction to data analysis using R in a highly approachable manner. The use of easy-to-follow examples and conceptual linkage across chapters makes this an outstanding option for undergraduate and graduate stats courses in the social sciences.

Joseph Nedelec

University of Cincinnati

A great text with in-depth coverage of statistics concepts with helpful R code segments. Great installation directions and rationale for use of R programming versus others.

Esther Pearson

Lasell University

This text takes students on a journey through introductory and intermediate statistical methods along with R programming to accomplish the descriptive and inferential statistics. Images of RStudio and samples of R code are woven throughout the text to help students follow along.

Galen I. Papkov

Florida Gulf Coast University

An accessible book for any student to learn data analysis, even without a strong math background. It is a student-friendly book that is easy to read, with knowledge checks as the student reads along, and there are great code examples and visualizations that will greatly engage the student.

Catherine Garcia

University of Nebraska - Lincoln

Very good book for explain R for students!

Professor vanessa bertoni

Gatton College of Business & Economics, University Of Kentucky

December 2, 2022

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The book is an essential reading recommendation for the module as it details the application of R programming language in data visualisation and provides the fundamental statistical knowledge needed for a new student to the world of data analytics. Using this book in the module will help students develop the necessary data analytics knowledge and skills in a friendly and incremental manner.

Mr Ganiyu Otukogbe

Cardiff School of Management, Cardiff Metropolitan University

July 11, 2023

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Key features

KEY FEATURES:

A focus on data visualization helps students develop a more intuitive understanding of statistics as a way to interpret numbers, not as formulas to memorize or tests to perform.
Exercises throughout and at the end of each chapter give students the practice they need to learn and cement their knowledge of statistics.
A wide variety of examples from social science research help students appreciate the value of statistics.
A copy-and-paste approach to R keeps the focus on the statistics and visualization components, emphasizing that students can easily adjust the code for their own work rather than starting from scratch each time.
“Art and Practice of Visualization” boxes delve into the process of constructing charts and graphs and provide tips, tricks, and best practices for data displays to help students produce the appropriate charts for their data.
By using the free and popular R programming language, students learn skills they can take with them through research or into the workplace.
Each figure has its own accompanying Code Chunk so students can copy and paste the code to manipulate the data, keeping the focus on statistics, not the software.
Social science data from across the U.S. and around the world, from demographic data to opinion research on major social issues, demonstrate the value of statistics.