The book focuses on providing students with the real-world R skills that are often hard to get to in statistics classes: basic data management and manipulation, and working with R graphics. The book is designed to get students with little or no background in statistics or programming started on R within the context of a statistics class, and to ensure that they have acquired functional R skills that they can continue to use as they move on to their own projects.
Kurt Taylor Gaubatz, PhD, is an associate professor in the Graduate Program in International Studies at Old Dominion University. In addition to courses in international relations and international law, he regularly teaches research methods and advanced statistics. He has previously taught methodology and formal modeling as a faculty member at Stanford University and at Oxford University (Nuffield College), where he was the visiting John G. Winant Lecturer in American Foreign Policy. He has also served as the Susan Luise Dyer Peace Fellow at the Hoover Institution at Stanford University, and received a Pew Faculty Fellowship from the Kennedy School of Government at Harvard University. Professor Gaubatz's most recent book is A Survivor's Guide to R (SAGE 2015), which is a broad and cross-disciplinary introduction to the R language for statistical programming. He is also the author of Elections and War (Stanford University Press, 1999), which is a study of the electoral politics of military conflict. His work on international law and on the relationship between domestic politics and international relations has appeared in a number of leading journals. His work on political modeling has received funding from the US Department of Defense. Professor Gaubatz earned an AB in economics from U.C. Berkeley, an MALD in international law from the Fletcher School of Law and Diplomacy, an MDiv in theology from Princeton Theological Seminary, and a PhD in political science from Stanford University. More information can be found at kktg.net/kurt.
Chapter 1: Getting Started Things Your Statistics Class Probably Won't Teach You Why R? Statistical Modeling A Few R Basics Saving Your Work R Packages Help with R Help Organization of this Book Chapter 2: A Sample Session Reviewing Your Data Data Visualization Hypothesis Testing for Fun and Profit A Regression Model A Nonlinear Model Chapter 3: Object Types in R R Objects And Their Names How to Think about Data Objects in R R Object Storage Modes R Data Object Types The Basic Data Objects: Vectors The Basic Data Objects: Matrices and Their Indices The Basic Data Objects: Data Frames The Basic Data Objects: Lists A Few Things about Working with Objects Object Attributes Objects and Environments R Object Classes The Pseudo Storage Modes Date and Time as a Storage Modes Factors Coercing Storage Modes The Curse of Number-Character-Factor Confusion Conclusions Chapter 4: Getting Your Data Into R Entering Data Creating Data Importing Data The Read Command: Overview The Read Command: Reading from the Clipboard The Read Command: Blank Delimited Tables The Read Command: Comma Separated Values The Read Command: Tab Separated Data The Read Command: Fixed-Width Data Importing Foreign File Types Integrating SQL with R Extracting Data from Complex Data Sources Web Scraping Dealing with Multi-Dimensional Data Importing Problematic Characters More Resources Chapter 5: Reviewing and Summarizing Data Summary Functions Checking A Sample Of Your Data Reviewing Data By Categories Displaying Data With A Histogram Displaying Data With A Scatter Plot Scatter Plot Matrices Chapter 6: Sorting and Selecting Data Using Index Values to Select Data Using Conditional Values for Selecting Using Subset( ) with Variable or Row Names to Select Data Splitting a Dataset into Groups Splitting Up Continuous Numeric Data Sorting And Ordering Data Chapter 7: Transforming Data Creating New Variables Editing Data Basic Math with R R Functions Math and Logical Functions in R Truncation and Rounding Functions The Apply( ) Family of Functions Changing Variable Values Conditionally Creating New Functions Additional R Programming Character Strings as Program Elements and Program Elements as Character Strings Chapter 8: Text Operations Some Useful Text Functions Finding Things Regular Expressions Processing Raw Text Data Scraping the Web for Fun And Profit Chapter 9: Working With Date And Time DataDates in R Dates in R Formatting Dates for R Working with POSIX Dates Special Date Operations Formatting Dates for Output Time Series Data Creating Moving Averages in Time-Series Data Lagged Variables in Time-Series Data Differencing Variables in Time-Series Data The Limitations of ts Data Chapter 10: Data Merging And Aggregation Dataset Concatenation Match Merging Keyed Table Look-up Merging Aggregating Data Transposing and Rotating Datasets Chapter 11: Dealing with Missing Data Reading Data with Missing Values Summarizing Missing Values The Missing Values Functions Recoding Missing Values Missing Values And Regression Modeling Visualizing Missing Data Chapter 12: R Graphics I: The Built-in Plots Scatter Plots Pairs Plots Line Plots Box Plots Histograms, Density Plots, and Bar Charts Dot Charts Pie Charts Mosaic Plots Conclusions Chapter 13: R Graphics II: The Boring Stuff The Graphics Device Graphics Parameters The Plot Layout Graphic Coordinates in R Overlaying Plots Multiple Plots Conclusions Chapter 14: R Graphics III: The Fun Stuff--Text Adding Text Setting up a Font Titles and Subtitles Creating a Legend Simple Axes and Axis Labels Building More Complex Axes Ad-hoc Text Chapter 15: R Graphics IV: The Fun Stuff--Shapes Doing Colors Custom Points Adding Lines Shapes Incorporating Images into Plots A Final Word about Aesthetics Chapter 16 from Here to Where?