A Survivor's Guide to R

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 StartedThings Your Statistics Class Probably Won′t Teach YouWhy R?Statistical ModelingA Few R BasicsSaving Your WorkR PackagesHelp with R HelpOrganization of this BookChapter 2: A Sample SessionReviewing Your DataData VisualizationHypothesis Testing for Fun and ProfitA Regression ModelA Nonlinear ModelChapter 3: Object Types in RR Objects And Their NamesHow to Think about Data Objects in RR Object Storage ModesR Data Object TypesThe Basic Data Objects: VectorsThe Basic Data Objects: Matrices and Their IndicesThe Basic Data Objects: Data FramesThe Basic Data Objects: ListsA Few Things about Working with ObjectsObject AttributesObjects and EnvironmentsR Object ClassesThe Pseudo Storage ModesDate and Time as a Storage ModesFactorsCoercing Storage ModesThe Curse of Number-Character-Factor ConfusionConclusionsChapter 4: Getting Your Data Into REntering DataCreating DataImporting DataThe Read Command: OverviewThe Read Command: Reading from the ClipboardThe Read Command: Blank Delimited TablesThe Read Command: Comma Separated ValuesThe Read Command: Tab Separated DataThe Read Command: Fixed-Width DataImporting Foreign File TypesIntegrating SQL with RExtracting Data from Complex Data SourcesWeb ScrapingDealing with Multi-Dimensional DataImporting Problematic CharactersMore ResourcesChapter 5: Reviewing and Summarizing DataSummary FunctionsChecking A Sample Of Your DataReviewing Data By CategoriesDisplaying Data With A HistogramDisplaying Data With A Scatter PlotScatter Plot MatricesChapter 6: Sorting and Selecting DataUsing Index Values to Select DataUsing Conditional Values for SelectingUsing Subset( ) with Variable or Row Names to Select DataSplitting a Dataset into GroupsSplitting Up Continuous Numeric DataSorting And Ordering DataChapter 7: Transforming DataCreating New VariablesEditing DataBasic Math with RR FunctionsMath and Logical Functions in RTruncation and Rounding FunctionsThe Apply( ) Family of FunctionsChanging Variable Values ConditionallyCreating New FunctionsAdditional R ProgrammingCharacter Strings as Program Elements and Program Elements as Character StringsChapter 8: Text OperationsSome Useful Text FunctionsFinding ThingsRegular ExpressionsProcessing Raw Text DataScraping the Web for Fun And ProfitChapter 9: Working With Date And Time DataDates in RDates in RFormatting Dates for RWorking with POSIX DatesSpecial Date OperationsFormatting Dates for OutputTime Series DataCreating Moving Averages in Time-Series DataLagged Variables in Time-Series DataDifferencing Variables in Time-Series DataThe Limitations of ts DataChapter 10: Data Merging And AggregationDataset ConcatenationMatch MergingKeyed Table Look-up MergingAggregating DataTransposing and Rotating DatasetsChapter 11: Dealing with Missing DataReading Data with Missing ValuesSummarizing Missing ValuesThe Missing Values FunctionsRecoding Missing ValuesMissing Values And Regression ModelingVisualizing Missing DataChapter 12: R Graphics I: The Built-in PlotsScatter PlotsPairs PlotsLine PlotsBox PlotsHistograms, Density Plots, and Bar ChartsDot ChartsPie ChartsMosaic PlotsConclusionsChapter 13: R Graphics II: The Boring StuffThe Graphics DeviceGraphics ParametersThe Plot LayoutGraphic Coordinates in ROverlaying PlotsMultiple PlotsConclusionsChapter 14: R Graphics III: The Fun Stuff--TextAdding TextSetting up a FontTitles and SubtitlesCreating a LegendSimple Axes and Axis LabelsBuilding More Complex AxesAd-hoc TextChapter 15: R Graphics IV: The Fun Stuff--ShapesDoing ColorsCustom PointsAdding LinesShapesIncorporating Images into PlotsA Final Word about AestheticsChapter 16 from Here to Where?

"The guide is detailed enough that students could practice these operations outside the classroom until they mastered them, which means that more class time can be spent discussing the conceptual issues in statistics."