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Quick R
Data Types 7
Vectors 7
Matrices 7
Arrays 7
Dataframes 7
Lists 8
Factors 8
Useful Functions 9
Importing Data 9
From A Comma Delimited Text File 9
From Excel 9
From SPSS 10
From SAS 10
From Stata 10
From systat 10
Keyboard Input 10
ccess to Database Management Systems (DBMS) 11
ODBC Interface 11
Other Interfaces 11
Exporting Data 12
To A Tab Delimited Text File 12
To an Excel Spreadsheet 12
To SPSS 12
To SAS 12
To Stata 12
Getting Information on a Dataset 12
Variable Labels 13
Value Labels 13
Missing Data 14
Testing for Missing Values 14
Recoding Values to Missing 14
Excluding Missing Values from Analyses 14
Advanced Handling of Missing Data 15
Date Values 15
Date Conversion 16
Learning More 16
Creating new variables 16
Recoding variables 16
Renaming variables 17
Operators 17
Arithmetic Operators 17
Logical Operators 18
Built-in Functions 18
Numeric Functions 18
Character Functions 19
Statistical Probability Functions 20
Other Statistical Functions 21
Other Useful Functions 22
Control Structures 22
if-else 22
for 22
while 22
switch 22
ifelse 22
Example 23
User-written Functions 23
Sorting Data 24
Merging Data 24
Adding Columns 24
Adding Rows 25
Aggregating Data 25
Reshaping Data 25
Transpose 25
Reshape Package 25
Subsetting Data 27
Selecting (Keeping) Variables 27
Excluding (DROPPING) Variables 27
Selecting Observerations 28
Selection using the Subset Function 28
Random Samples 28
Going Further 28
Data Type Conversion 28
Examples 29
Dates 29
Descriptive Statistics 29
Summary Statistics by Group 30
Frequencies and Crosstabs 30
Generating Frequency Tables 31
Tests of Independence 32
Measures of Association 33
Visualizing results 33
Converting Frequency Tables to an "Original" Flat file 33
Correlations 33
Other Types of Correlations 34
Visualizing Correlations 34
t-tests 35
Visualizing Results 35
Nonparametric Tests of Group Differences 35
Visualizing Results 36
Multiple (Linear) Regression 36
Fitting the Model 36
Diagnostic Plots 36
Comparing Models 37
Cross Validation 37
Variable Selection 38
Relative Importance 39
Graphic Enhancements 40
Going Further 41
Regression Diagnostics 41
Outliers 41
Influential Observations 42
Non-normality 44
Non-constant Error Variance 45
Multi-collinearity 46
Nonlinearity 46
Non-independence of Errors 47
Additional Diagnostic Help 47
Going Further 48
ANOVA 48
Multiple Comparisons 49
Visualizing Results 49
MANOVA 51
Going Further 52
Assessing Classical Test Assumptions 52
Outliers 52
Univariate Normality 53
Multivariate Normality 54
Homogeneity of Variances 55
Homogeneity of Covariance Matrices 57
Resampling Statistics 57
Independent Two- and K-Sample Location Tests 57
symmetry of a response for repeated measurements 57
Independence of Two Numeric Variables 57
Independence in Contingency Tables 58
Power Analysis 58
Overview 58
Power Analysis in R 58
t-tests 59
ANOVA 60
Correlations 60
Linear Models 60
Tests of Proportions 61
Chi-square Tests 61
some Examples 62
Creating Power or Sample Size Plots 62
Using with( ) and by( ) 64
With 64
By 64
Generalized Linear Models 64
Logistic Regression 65
Poisson Regression 66
Survival Analysis 66
Discriminant Function Analysis 67
Linear Discriminant Function 67
Quadratic Discriminant Function 68
Visualizing the Results 68
Test Assumptions 72
Bootstrapping 72
Nonparametric Bootstrapping 72
Going Further 76
Learning More 77
Matrix Algebra 77
Matrix facilites 77
Matlab Emulation 78
Going Further 78
Creating a Graph 79
Saving Graphs 79
Viewing Several Graphs 80
Graphical Parameters 80
Histograms and Density Plots 81
Histograms 81
Kernal Density Plots 82
Comparing Groups VIA Kernal Density 82
Dot Plots 83
Going Further 85
Bar Plots 85
Simple Bar Plot 86
Stacked Bar Plot 86
Grouped Bar Plot 87
Notes 87
Line Charts 87
Pie Charts 92
Simple Pie Chart 92
Pie Chart with Annotated Percentages 92
3D Pie Chart 92
Creating Annotated Pies from a Dataframe 93
Boxplots 93
Other Options 94
Violin Plots 95
Bagplot - A 2D Boxplot Extension 95
Scatterplots 96
Simple Scatterplot 96
Scatterplot Matrices 98
High Density Scatterplots 102
3D Scatterplots 104
Graphical Parameters 109
Text and Symbol Size 110
Plotting Symbols 110
Lines 111
colors 111
fonts 113
Margins and Graph Size 114
Going Further 114
Axes and Text 114
Titles 114
Text Annotations 115
Axes 116
Reference Lines 119
Legend 119
Combining Plots 120
creating a figure arrangement with fine control 122
Trellis Graphs 123
Customizing Trellis Graphs 128
Going Further 129
Probability Plots 129
Probability Plots for Teaching and Demonstration 129
Fitting Distributions 132
Visualizing Categorical Data 134
Mosaic Plots 134
Association Plots 134
Going Further 135
Correlograms 135
Changing the colors in a correlogram 139
Interactive Graphics 140
GGobi 140
iPlots 141
Interacting with Plots (Indentifying Points) 142
Other Interactive Graphs 142
Data Types
R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, dataframes, and lists.
Vectors
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