R for Stata Users

von: Robert A. Muenchen, Joseph M. Hilbe

Springer-Verlag, 2010

ISBN: 9781441913180

Sprache: Englisch

549 Seiten, Download: 7459 KB

Format: PDF, auch als Online-Lesen

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R for Stata Users

	Preface	6
	Contents	10
	List of Tables	20
	List of Figures	22
	1 Introduction	26
	1.1 Overview	26
	1.2 Similarities Between R and Stata	27
	1.3 Why Learn R?	28
	1.4 Is R Accurate?	29
	1.5 What About Tech Support?	29
	1.6 Getting Started Quickly	30
	1.7 Programming Conventions	30
	1.8 Typographic Conventions	31
	2 Installing and Updating R	33
	2.1 Installing Add-on Packages	34
	2.2 Loading an Add-on Package	34
	2.3 Updating Your Installation	38
	2.4 Uninstalling R	39
	2.5 Choosing Repositories	39
	2.6 Accessing Data in Packages	41
	3 Running R	43
	3.1 Running R Interactively on Windows	43
	3.2 Running R Interactively on Macintosh	45
	3.3 Running R Interactively on Linux or UNIX	47
	3.4 Running Programs That Include Other Programs	49
	3.5 Running R in Batch Mode	49
	3.6 Graphical User Interfaces	50
	3.6.1 R Commander	50
	3.6.2 Rattle for Data Mining	53
	3.6.3 JGR Java GUI for R	54
	4 Help and Documentation	60
	4.1 Introduction	60
	4.2 Help Files	60
	4.3 Starting Help	60
	4.4 Help Examples	62
	4.5 Help for Functions That Call Other Functions	63
	4.6 Help for Packages	64
	4.7 Help for Data Sets	65
	4.8 Books and Manuals	65
	4.9 E-mail Lists	65
	4.10 Searching the Web	66
	4.11 Vignettes	66
	5 Programming Language Basics	68
	5.1 Introduction	68
	5.2 Simple Calculations	69
	5.3 Data Structures	70
	5.3.1 Vectors	70
	5.3.2 Factors	74
	5.3.3 Data Frames	79
	5.3.4 Matrices	83
	5.3.5 Arrays	86
	5.3.6 Lists	86
	5.4 Saving Your Work	90
	5.5 Comments to Document Your Programs	92
	5.6 Controlling Functions (Commands)	93
	5.6.1 Controlling Functions with Arguments	93
	5.6.2 Controlling Functions with Formulas	95
	5.6.3 Controlling Functions with an Object's Class	96
	5.6.4 Controlling Functions with Extractor Functions	98
	5.7 How Much Output is There?	100
	5.8 Writing Your Own Functions (Macros)	104
	5.9 R Program Demonstrating Programming Basics	107
	6 Data Acquisition	114
	6.1 The R Data Editor	114
	6.2 Reading Delimited Text Files	116
	6.2.1 Reading Comma-Delimited Text Files	117
	6.2.2 Reading Tab-Delimited Text Files	118
	6.2.3 Missing Values for Character Variables	120
	6.2.4 Trouble with Tabs	121
	6.2.5 Skipping Variables in Delimited Files	122
	6.2.6 Example Programs for Reading Delimited TextFiles	123
	6.3 Reading Text Data Within a Program	125
	6.3.1 The Easy Approach	125
	6.3.2 The More General Approach	127
	6.3.3 Example Programs for Reading Text Data Within a Program	127
	6.4 Reading Fixed-Width Text Files, One Record per Case	129
	6.4.1 Macro Substitution	132
	6.4.2 Example Programs for Reading Fixed-Width Text Files, One Record Per Case	133
	6.5 Reading Fixed-Width Text Files, Two or More Records per Case	134
	6.5.1 Example Programs to Read Fixed-Width Text Files with Two Records per Case	135
	6.6 Importing Data from Stata into R	136
	6.6.1 R Program to Import Data from Stata	137
	6.7 Writing Data to a Comma-Delimited Text File	137
	6.7.1 Example Programs for Writing a Comma-Delimited File	138
	6.8 Exporting Data from R to Stata	139
	7 Selecting Variables	141
	7.1 Selecting Variables in Stata	141
	7.2 Selecting All Variables	142
	7.3 Selecting Variables Using Index Numbers	142
	7.4 Selecting Variables Using Column Names	145
	7.5 Selecting Variables Using Logic	146
	7.6 Selecting Variables Using String Search	148
	7.7 Selecting Variables Using $ Notation	150
	7.8 Selecting Variables Using Component Names	151
	7.8.1 The attach Function	151
	7.8.2 The with Function	152
	7.8.3 Using Component Names in Formulas	152
	7.9 Selecting Variables with the subset Function	153
	7.10 Selecting Variables Using List Index	154
	7.11 Generating Indexes A to Z from Two Variable Names	154
	7.12 Saving Selected Variables to a New Dataset	155
	7.13 Example Programs for Variable Selection	156
	7.13.1 Stata Program to Select Variables	156
	7.13.2 R Program to Select Variables	156
	8 Selecting Observations	161
	8.1 Selecting Observations in Stata	161
	8.2 Selecting All Observations	162
	8.3 Selecting Observations Using Index Numbers	162
	8.4 Selecting Observations Using Row Names	165
	8.5 Selecting Observations Using Logic	167
	8.6 Selecting Observations Using String Search	170
	8.7 Selecting Observations Using the subset Function	172
	8.8 Generating Indexes A to Z from Two Row Names	173
	8.9 Variable Selection Methods with No Counterpart for Selecting Observations	174
	8.10 Saving Selected Observations to a New Data Frame	174
	8.11 Example Programs for Selecting Observations	174
	8.11.1 Stata Program to Select Observations	175
	8.11.2 R Program to Select Observations	175
	9 Selecting Variables and Observations	179
	9.1 The subset Function	179
	9.2 Selecting Observations by Logic and Variables by Name	180
	9.3 Using Names to Select Both Observations and Variables	181
	9.4 Using Numeric Index Values to Select Both Observations and Variables	182
	9.5 Using Logic to Select Both Observations and Variables	183
	9.6 Saving and Loading Subsets	184
	9.7 Example Programs for Selecting Variables and Observations	184
	9.7.1 Stata Program for Selecting Variables and Observations	184
	9.7.2 R Program for Selecting Variables and Observations	185
	10 Data Management	189
	10.1 Transforming Variables	189
	10.1.1 Example Programs for Transforming Variables	193
	10.2 Functions or Commands? The apply Function Decides	194
	10.2.1 Applying the mean Function	195
	10.2.2 Finding N or NVALID	198
	10.2.3 Example Programs for Applying StatisticalFunctions	200
	10.3 Conditional Transformations	202
	10.3.1 Example Programs for ConditionalTransformations	204
	10.4 Multiple Conditional Transformations	205
	10.4.1 Example Programs for Multiple Conditional Transformations	207
	10.5 Missing Values	208
	10.5.1 Substituting Means for Missing Values	210
	10.5.2 Finding Complete Observations	211
	10.5.3 When ``99'' Has Meaning	212
	10.5.4 Example Programs to Assign Missing Values	214
	10.6 Renaming Variables (and Observations)	216
	10.6.1 Renaming Variables---Advanced Examples	218
	10.6.2 Renaming by Index	219
	10.6.3 Renaming by Column Name	220
	10.6.4 Renaming Many Sequentially Numbered Variable Names	221
	10.6.5 Renaming Observations	222
	10.6.6 Example Programs for Renaming Variables	222
	10.7 Recoding Variables	226
	10.7.1 Recoding a Few Variables	227
	10.7.2 Recoding Many Variables	227
	10.7.3 Example Programs for Recoding Variables	230
	10.8 Keeping and Dropping Variables	231
	10.8.1 Example Programs for Keeping and Dropping Variables	232
	10.9 Stacking/Appending Data Sets	232
	10.9.1 Example Programs for Stacking/AppendingData Sets	235
	10.10 Joining/Merging Data Sets	236
	10.10.1 Example Programs for Joining/Merging Data Sets	239
	10.11 Creating Collapsed or Aggregated Data Sets	241
	10.11.1 The aggregate Function	241
	10.11.2 The tapply Function	243
	10.11.3 Merging Aggregates with Original Data	244
	10.11.4 Tabular Aggregation	246
	10.11.5 The reshape Package	248
	10.11.6 Example Programs for Collapsing/AggregatingData	248
	10.12 By or Split-File Processing	250
	10.12.1 Comparing Summarization Methods	254
	10.12.2 Example Programs for By or Split-file Processing	255
	10.13 Removing Duplicate Observations	256
	10.13.1 Example Programs for Removing Duplicate Observations	258
	10.14 Selecting First or Last Observations per Group	259
	10.14.1 Example Programs for Selecting Last Observation per Group	261
	10.15 Reshaping Variables to Observations and Back	262
	10.15.1 Example Programs for Reshaping Variables to Observations and Back	264
	10.16 Sorting Data Frames	265
	10.16.1 Example Programs for Sorting Data Sets	268
	10.17 Converting Data Structures	269
	10.17.1 Converting from Logical to Numeric Indexand Back	272
	11 Enhancing Your Output	274
	11.1 Value Labels or Formats (and Measurement Level)	274
	11.1.1 Character Factors	275
	11.1.2 Numeric Factors	277
	11.1.3 Making Factors of Many Variables	279
	11.1.4 Converting Factors into Numeric or Character Variables	281
	11.1.5 Dropping Factor Levels	283
	11.1.6 Example Programs for Value Labels or Formats	284
	11.2 Variable Labels	287
	11.2.1 Variable Labels in The Hmisc Package	287
	11.2.2 Long Variable Names as Labels	288
	11.2.3 Other Packages That Support Variable Labels	291
	11.2.4 Example Programs for Variable Labels	291
	11.3 Output for Word Processing and Web Pages	292
	11.3.1 The xtable Package	293
	11.3.2 Other Options for Formatting Output	295
	11.3.3 Example Programs for Formatting Output	296
	12 Generating Data	298
	12.1 Generating Numeric Sequences	299
	12.2 Generating Factors	300
	12.3 Generating Repetitious Patterns (Not Factors)	301
	12.4 Generating Integer Measures	302
	12.5 Generating Continuous Measures	304
	12.6 Generating a Data Frame	306
	12.7 Example Programs for Generating Data	306
	12.7.1 Stata Program for Generating Data	306
	12.7.2 R Program for Generating Data	307
	13 Managing Your Files and Workspace	312
	13.1 Loading and Listing Objects	312
	13.2 Understanding Your Search Path	315
	13.3 Attaching Data Frames	317
	13.4 Attaching Files	319
	13.5 Removing Objects from Your Workspace	320
	13.6 Minimizing Your Workspace	322
	13.7 Setting Your Working Directory	322
	13.8 Saving Your Workspace	323
	13.8.1 Saving Your Workspace Manually	323
	13.8.2 Saving Your Workspace Automatically	324
	13.9 Getting Operating Systems to Show You ``.RData'' Files	324
	13.10 Organizing Projects with Windows Shortcuts	325
	13.11 Saving Your Programs and Output	325
	13.12 Saving Your History	326
	13.13 Large Data Set Considerations	326
	13.14 Example R Program for Managing Filesand Workspace	328
	14 Graphics Overview	332
	14.1 Stata Graphics	333
	14.2 R Graphics	333
	14.3 The Grammar of Graphics	334
	14.4 Other Graphics Packages	336
	14.5 Graphics Procedures and Graphics Systems	336
	14.6 Graphics Devices	337
	14.7 Practice Data: mydata100	339
	15 Traditional Graphics	340
	15.1 Bar Plots	340
	15.1.1 Bar Plots of Counts	340
	15.1.2 Bar Plots for Subgroups of Counts	345
	15.1.3 Bar Plots of Means	347
	15.2 Adding Titles, Labels, Colors, and Legends	348
	15.3 Graphics Parameters and Multiple Plots on a Page	351
	15.4 Pie Charts	352
	15.5 Dot Charts	354
	15.6 Histograms	354
	15.6.1 Basic Histograms	355
	15.6.2 Histograms Stacked	357
	15.6.3 Histograms Overlaid	358
	15.7 Normal QQ Plots	362
	15.8 Strip Charts	363
	15.9 Scatter Plots and Line Plots	368
	15.9.1 Scatter plots with Jitter	371
	15.9.2 Scatter plots with Large Data Sets	371
	15.9.3 Scatter plots with Lines	373
	15.9.4 Scatter plots with Linear Fit by Group	374
	15.9.5 Scatter plots by Group or Level (Coplots)	375
	15.9.6 Scatter plots with Confidence Ellipse	377
	15.9.7 Scatter plots with Confidence and PredictionIntervals	378
	15.9.8 Plotting Labels Instead of Points	383
	15.9.9 Scatter plot Matrices	385
	15.10 Dual-Axes Plots	387
	15.11 Box Plots	389
	15.12 Error Bar Plots	391
	15.13 Interaction Plots	391
	15.14 Adding Equations and Symbols to Graphs	392
	15.15 Summary of Graphics Elements and Parameters	393
	15.16 Plot Demonstrating Many Modifications	394
	15.17 Example Program for Traditional Graphics	395
	15.17.1 Stata Program for Traditional Graphics	396
	15.17.2 R Program for Traditional Graphics	396
	16 Graphics with ggplot2	406
	16.1 Introduction	406
	16.1.1 Overview qplot and ggplot	407
	16.1.2 Missing Values	408
	16.1.3 Typographic Conventions	409
	16.2 Bar Plots	410
	16.2.1 Pie Charts	413
	16.2.2 Bar Charts for Groups	414
	16.3 Plots by Group or Level	415
	16.4 Presummarized Data	417
	16.5 Dot Charts	418
	16.6 Adding Titles and Labels	420
	16.7 Histograms and Density Plots	421
	16.7.1 Histograms	421
	16.7.2 Density Plots	422
	16.7.3 Histograms with Density Overlaid	422
	16.7.4 Histograms for Groups, Stacked	424
	16.7.5 Histograms for Groups, Overlaid	425
	16.8 Normal QQ Plots	426
	16.9 Strip Plots	426
	16.10 Scatter Plots and Line Plots	429
	16.10.1 Scatter Plots with Jitter	431
	16.10.2 Scatter Plots for Large Data Sets	432
	16.10.3 Hexbin Plots	435
	16.10.4 Scatter Plots with Fit Lines	436
	16.10.5 Scatter Plots with Reference Lines	437
	16.10.6 Scatter Plots with Labels Instead of Points	441
	16.10.7 Changing Plot Symbols	442
	16.10.8 Scatter Plot with Linear Fits by Group	443
	16.10.9 Scatter Plots Faceted for Groups	443
	16.10.10 Scatter Plot Matrix	445
	16.11 Box Plots	446
	16.12 Error Bar Plots	449
	16.13 Logarithmic Axes	451
	16.14 Aspect Ratio	451
	16.15 Multiple Plots on a Page	452
	16.16 Saving ggplot2 Graphs to a File	454
	16.17 An Example Specifying All Defaults	454
	16.18 Summary of Graphic Elements and Parameters	456
	16.19 Example Programs for ggplot2	457
	17 Statistics	474
	17.1 Scientific Notation	474
	17.2 Descriptive Statistics	475
	17.2.1 The Hmisc describe Function	475
	17.2.2 The summary Function	477
	17.2.3 The table Function and Its Relatives	478
	17.2.4 The mean Function and Its Relatives	480
	17.3 Cross-Tabulation	481
	17.3.1 The CrossTable Function	481
	17.3.2 The tables and chisq.test Functions	483
	17.4 Correlation	486
	17.4.1 The cor Function	489
	17.5 Linear Regression	491
	17.5.1 Plotting Diagnostics	494
	17.5.2 Comparing Models	495
	17.5.3 Making Predictions with New Data	496
	17.6 t-Test: Independent Groups	497
	17.7 Equality of Variance	498
	17.8 t-Test: Paired or Repeated Measures	499
	17.9 Wilcoxon Mann-Whitney Rank Sum Test: IndependentGroups	500
	17.10 Wilcoxon Signed-Rank Test: Paired Groups	501
	17.11 Analysis of Variance	502
	17.12 Sums of Squares	507
	17.13 The Kruskal--Wallis Test	508
	17.14 Example Programs for Statistical Tests	510
	17.14.1 Stata Program for Statistical Tests	510
	17.14.2 R Program for Statistical Tests	512
	18 Conclusion	518
	Glossary of R jargon	519
	Comparison of Stata commands and R functions	525
	Automating Your R Setup	527
	C.1 Setting Options	527
	C.2 Creating Objects	528
	C.3 Loading Packages	528
	C.4 Running Functions	528
	C.5 Example .Rprofile	530
	Example Simulation	531
	D.1 Stata Example Simulation	531
	D.2 R Example Simulation	532
	References	533
	Index	537

R for Stata Users

von: Robert A. Muenchen, Joseph M. Hilbe

R for Stata Users

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