Contents Preface Introduction Installing and Updating R Running R

Transcription

1 Contents Preface... v 1 Introduction Overview Similarities Between R and Stata WhyLearnR? Is R Accurate? What About Tech Support? Getting Started Quickly Programming Conventions Typographic Conventions Installing and Updating R Installing Add-on Packages Loading an Add-on Package Updating Your Installation Uninstalling R Choosing Repositories AccessingDatainPackages Running R Running R Interactively on Windows Running R Interactively on Macintosh Running R Interactively on Linux or UNIX Running Programs That Include Other Programs Running R in Batch Mode GraphicalUserInterfaces R Commander Rattle for Data Mining JGR Java GUI for R ix

2 x Contents 4 Help and Documentation Introduction HelpFiles StartingHelp HelpExamples Help for Functions That Call Other Functions HelpforPackages HelpforDataSets BooksandManuals Lists Searching the Web Vignettes Programming Language Basics Introduction Simple Calculations DataStructures Vectors Factors Data Frames Matrices Arrays Lists SavingYourWork Comments to Document Your Programs Controlling Functions (Commands) Controlling Functions with Arguments Controlling Functions with Formulas Controlling Functions with an Object s Class Controlling Functions with Extractor Functions HowMuchOutputisThere? Writing Your Own Functions (Macros) R Program Demonstrating Programming Basics Data Acquisition The R Data Editor Reading Delimited Text Files Reading Comma-Delimited Text Files Reading Tab-Delimited Text Files Missing Values for Character Variables Trouble with Tabs Skipping Variables in Delimited Files Example Programs for Reading Delimited Text Files Reading Text Data Within a Program

3 Contents xi The Easy Approach The More General Approach Example Programs for Reading Text Data Within a Program Reading Fixed-Width Text Files, One Record per Case Macro Substitution Example Programs for Reading Fixed-Width Text Files,OneRecordPerCase Reading Fixed-Width Text Files, Two or More Records per Case Example Programs to Read Fixed-Width Text Files with Two Records per Case Importing Data from Stata into R R Program to Import Data from Stata Writing Data to a Comma-Delimited Text File Example Programs for Writing a Comma-Delimited File Exporting Data from R to Stata Selecting Variables Selecting Variables in Stata SelectingAllVariables Selecting Variables Using Index Numbers Selecting Variables Using Column Names Selecting Variables Using Logic SelectingVariablesUsingStringSearch Selecting Variables Using $ Notation Selecting Variables Using Component Names The attach Function The with Function Using Component Names in Formulas Selecting Variables with the subset Function Selecting Variables Using List Index Generating Indexes A to Z from Two Variable Names Saving Selected Variables to a New Dataset Example Programs for Variable Selection Stata Program to Select Variables R Program to Select Variables Selecting Observations Selecting Observations in Stata Selecting All Observations Selecting Observations Using Index Numbers Selecting Observations Using Row Names Selecting Observations Using Logic

4 xii Contents 8.6 Selecting Observations Using String Search Selecting Observations Using the subset Function Generating Indexes A to Z from Two Row Names Variable Selection Methods with No Counterpart for Selecting Observations Saving Selected Observations to a New Data Frame Example Programs for Selecting Observations Stata Program to Select Observations R Program to Select Observations Selecting Variables and Observations The subset Function Selecting Observations by Logic and Variables by Name Using Names to Select Both Observations and Variables Using Numeric Index Values to Select Both Observations and Variables Using Logic to Select Both Observations and Variables Saving and Loading Subsets Example Programs for Selecting Variables and Observations Stata Program for Selecting Variables and Observations R Program for Selecting Variables and Observations Data Management Transforming Variables Example Programs for Transforming Variables Functions or Commands? The apply Function Decides Applying the mean Function Finding N or NVALID Example Programs for Applying Statistical Functions Conditional Transformations Example Programs for Conditional Transformations Multiple Conditional Transformations Example Programs for Multiple Conditional Transformations Missing Values Substituting Means for Missing Values Finding Complete Observations When 99 Has Meaning Example Programs to Assign Missing Values Renaming Variables (and Observations) Renaming Variables Advanced Examples Renaming by Index Renaming by Column Name

5 Contents xiii Renaming Many Sequentially Numbered Variable Names Renaming Observations Example Programs for Renaming Variables Recoding Variables Recoding a Few Variables Recoding Many Variables Example Programs for Recoding Variables Keeping and Dropping Variables Example Programs for Keeping and Dropping Variables Stacking/Appending Data Sets Example Programs for Stacking/Appending DataSets Joining/Merging Data Sets Example Programs for Joining/Merging Data Sets Creating Collapsed or Aggregated Data Sets The aggregate Function The tapply Function Merging Aggregates with Original Data Tabular Aggregation The reshape Package Example Programs for Collapsing/Aggregating Data By or Split-File Processing Comparing Summarization Methods Example Programs for By or Split-file Processing Removing Duplicate Observations Example Programs for Removing Duplicate Observations Selecting First or Last Observations per Group Example Programs for Selecting Last Observation pergroup Reshaping Variables to Observations and Back Example Programs for Reshaping Variables to Observations and Back Sorting Data Frames Example Programs for Sorting Data Sets Converting Data Structures Converting from Logical to Numeric Index andback Enhancing Your Output Value Labels or Formats (and Measurement Level) Character Factors Numeric Factors

6 xiv Contents Making Factors of Many Variables Converting Factors into Numeric or Character Variables Dropping Factor Levels Example Programs for Value Labels or Formats Variable Labels Variable Labels in The Hmisc Package Long Variable Names as Labels Other Packages That Support Variable Labels Example Programs for Variable Labels Output for Word Processing and Web Pages The xtable Package Other Options for Formatting Output Example Programs for Formatting Output Generating Data Generating Numeric Sequences Generating Factors Generating Repetitious Patterns (Not Factors) Generating Integer Measures Generating Continuous Measures Generating a Data Frame Example Programs for Generating Data Stata Program for Generating Data R Program for Generating Data Managing Your Files and Workspace Loading and Listing Objects Understanding Your Search Path Attaching Data Frames Attaching Files Removing Objects from Your Workspace Minimizing Your Workspace Setting Your Working Directory Saving Your Workspace Saving Your Workspace Manually Saving Your Workspace Automatically Getting Operating Systems to Show You.RData Files Organizing Projects with Windows Shortcuts Saving Your Programs and Output Saving Your History Large Data Set Considerations Example R Program for Managing Files andworkspace...307

7 Contents xv 14 Graphics Overview Stata Graphics R Graphics The Grammar of Graphics Other Graphics Packages Graphics Procedures and Graphics Systems Graphics Devices Practice Data: mydata Traditional Graphics Bar Plots Bar Plots of Counts Bar Plots for Subgroups of Counts Bar Plots of Means Adding Titles, Labels, Colors, and Legends Graphics Parameters and Multiple Plots on a Page Pie Charts Dot Charts Histograms Basic Histograms Histograms Stacked Histograms Overlaid Normal QQ Plots Strip Charts Scatter Plots and Line Plots Scatter plots with Jitter Scatter plots with Large Data Sets Scatter plots with Lines Scatter plots with Linear Fit by Group Scatter plots by Group or Level (Coplots) Scatter plots with Confidence Ellipse Scatter plots with Confidence and Prediction Intervals Plotting Labels Instead of Points Scatter plot Matrices Dual-Axes Plots Box Plots Error Bar Plots Interaction Plots Adding Equations and Symbols to Graphs Summary of Graphics Elements and Parameters Plot Demonstrating Many Modifications Example Program for Traditional Graphics Stata Program for Traditional Graphics R Program for Traditional Graphics

8 xvi Contents 16 Graphics with ggplot Introduction Overview qplot and ggplot Missing Values Typographic Conventions Bar Plots Pie Charts Bar Charts for Groups Plots by Group or Level Presummarized Data Dot Charts Adding Titles and Labels Histograms and Density Plots Histograms Density Plots Histograms with Density Overlaid Histograms for Groups, Stacked Histograms for Groups, Overlaid Normal QQ Plots Strip Plots Scatter Plots and Line Plots Scatter Plots with Jitter Scatter Plots for Large Data Sets Hexbin Plots Scatter Plots with Fit Lines Scatter Plots with Reference Lines Scatter Plots with Labels Instead of Points Changing Plot Symbols Scatter Plot with Linear Fits by Group Scatter Plots Faceted for Groups Scatter Plot Matrix Box Plots Error Bar Plots Logarithmic Axes Aspect Ratio Multiple Plots on a Page Saving ggplot2 GraphstoaFile An Example Specifying All Defaults Summary of Graphic Elements and Parameters Example Programs for ggplot Statistics Scientific Notation Descriptive Statistics The Hmisc describe Function The summary Function

9 Contents xvii The table Function and Its Relatives The mean Function and Its Relatives Cross-Tabulation The CrossTable Function The tables and chisq.test Functions Correlation The cor Function Linear Regression Plotting Diagnostics Comparing Models Making Predictions with New Data t-test: Independent Groups Equality of Variance t-test: Paired or Repeated Measures Wilcoxon Mann-Whitney Rank Sum Test: Independent Groups Wilcoxon Signed-Rank Test: Paired Groups Analysis of Variance Sums of Squares The Kruskal Wallis Test Example Programs for Statistical Tests Stata Program for Statistical Tests R Program for Statistical Tests Conclusion Glossary of R jargon Comparison of Stata commands and R functions Automating Your R Setup C.1 Setting Options C.2 Creating Objects C.3 LoadingPackages C.4 Running Functions C.5 Example.Rprofile Example Simulation D.1 Stata Example Simulation D.2 R Example Simulation References Index...517

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