DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Introduction Getting Started The Business Problem Data Loading Data Exploring Data Interactive Graphics Statistical Tests Models Network Analysis Text Mining Decision Trees Random Forests Boosting Bagging Support Vector Machine Linear Regression Neural Network Naive Bayes Survival Analysis Evaluation and Deployment Transforming Data Deployment Troubleshooting Issues Moving into R R Getting Help Data Graphics in R Understanding Data Preparing Data Issues Evaluating Models Reporting Fraud Analysis Archetype Analysis Algorithms Bayes Classifier K-Nearest Neighbours Linear Models Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Installing Rattle Projects Bibliography Index Preface Goals Organisation Features Audience Typographical Conventions A Note on Languages Currency Acknowledgements Introduction Data Mining The Business Problem Types of Analysis Data Mining Applications A Framework for Modelling Agile Data Mining R Rattle Why R and Rattle? Data Preparation Number of Algorithms Repeatability Performance Open Source Data Mining Business Case Sample Business Case Pros and Cons Books on R Getting Started Initial Interaction with R Quitting Rattle and R First Contact Loading a Dataset Building a Model Understanding Our Data Evaluating the Model Evaluating the Model Interacting with R Interacting with Rattle Projects Toolbar Menus Interacting with Plots Keyboard Navigation Summary Command Summary The Business Problem Solar Panel Efficiency Water Collection Others Other Business Problems Fraud Detection Loan Approval Documenting the Business Problem Summary Resources Exercises Data Data Nomenclature Loading Data into Rattle CSV Data Datasets Reading Direct from URL Play Golf Weather Data Other Data Sources ARFF Data ODBC Sourced Data Setting Up a Data Source Name Netezza Setup Teradata Setup R Data R Dataset Data Entry Data Tab Options in Rattle Sampling Data Variable Roles Automatic Role Identification Weights Calculator Manipulating Data Loading Data CSV Data Locating and Loading Data Loading the File CSV Options Basic Data Summary ARFF Data ODBC Sourced Data R Dataset R Data Library Data Options Sampling Data Variable Roles Automatic Role Identification Weights Calculator Command Summary Exploring Data Summarising Data Summary Describe Basics Kurtosis Skewness Missing Exploring Distributions Box Plot Histogram Cumulative Distribution Plot Benford's Law Other Digits Stratified Benford Plots Bar Plot Dot Plot Mosaic Plot GGobi Scatterplot Data Viewer Brushing Identify Multivariate Outliers Other Options Quality Plots Using R Further GGobi Documentation Correlation Analysis Hierarchical Correlation Principal Components Single Variable Overviews Interactive Graphics Interactive Visualisations Latticist GGobi Scatterplot Multiple Plots Brushing Other Plots Data Viewer Brushing Identify Multivariate Outliers Other Options Quality Plots Using R Further GGobi Documentation Documenting Interactive Explorations Code Review Chapter Exercises Command Summary Statistical Tests Documenting Interactive Explorations Code Review Further Resources Chapter Exercises Command Summary Models A Framework for Modelling Descriptive Analytics Predictive Analytics Documenting Models Summary Code Review Exercises Further Resources Command Summary Cluster Analysis Summary Clusters Basic Clustering Hot Spots Alternative Clustering Other Cluster Examples KMeans Export KMeans Clusters Discriminant Coordinates Plot Number of Clusters Hierarchical Clusters Other Cluster Algorithms Association Analysis Summary Overview Algorithm Usage Read Transactions file format sep cols rm.duplicates Summary Apriori data parameter appearance control Inspect Examples Video Marketing Survey Data Other Examples Resources and Further Reading Basket Analysis General Rules Network Analysis Documenting Interactive Explorations Code Review Chapter Exercises Command Summary Text Mining Application to Text Text Mining with R Decision Trees Knowledge Representation Search Heuristic Measures Tutorial Example Rattle R Tuning Parameters Min Split (Rarg[]minsplit) Min Bucket (minbucket) Priors (prior) Loss Matrix Complexity (cp) Other Options Simple Example Convert Tree to Rules Predicting Salary Group Issues Summary Code Review Iris Wine Exercises Resources Command Summary Random Forests Formalities Tutorial Example Tuning Parameters Number of Trees Sample Size Number of Variables Summary Overview Algorithm Usage Random Forest importance classwt Examples Resources and Further Reading Summary Overview Example Algorithm Resources and Further Reading Boosting Formalities Tutorial Example Tuning Parameters Summary Overview AdaBoost Algorithm Examples Step by Step Using gbm Extensions and Variations Alternating Decision Tree Resources and Further Reading Documenting Code Review Further Resources Chapter Exercises Command Summary Bootstrapping Summary Usage Further Information Summary Overview Example Algorithm Resources and Further Reading Bagging Support Vector Machine Formalities Tutorial Example Tuning parameters Examples Resources and Further Reading Overview Examples Resources and Further Reading Linear Regression Linear Regression Formalities Tutorial Example Tuning parameters Generalized Regression Formalities Tutorial Example Tuning parameters Logistic Regression Formalities Tutorial Example Tuning parameters Discussion Probit Regression Formalities Tutorial Example Tuning Parameters Multinomial Regression Formalities Tutorial Example Tuning Parameters Neural Network Formalities Tutorial Example Tuning parameters Documenting Code Review Further Resources Chapter Exercises Command Summary Naive Bayes Summary Code Review Resources Exercises Command Summary Survival Analysis Sample Data Simple Lung Descriptive Analysis Regression survreg Simple Lung coxph Simple Lung Apply to New Data More Input Variables Decision Tree Example from Singer and Willett Other Approaches Design Package Random Survival Forests Prediction on Test Data Evaluation The Evaluate Tab Confusion Matrix Measures Graphical Measures Risk Charts Cost Curves Lift ROC Curves Area Under Curve Precision versus Recall Sensitivity versus Specificity Predicted versus Observed Scoring Documenting Interactive Explorations Code Review Chapter Exercises Command Summary Transforming Data Rescale Data Recenter Scale [0,1] Rank Median/MAD Peer Relativity Profiling Index Impute Zero/Missing Mean/Median/Mode Constant Remap Binning Indicator Variables Join Categorics Math Transforms Outliers Cleanup Delete Ignored Delete Selected Delete Missing Delete Obs with Missing Other Transformations Removing Duplicates Command Summary Deployment Documenting Deployment Code Review Chapter Exercises Command Summary Troubleshooting Cairo A factor has new levels Issues Model Selection Overfitting Imbalanced Classification Sampling Cost Based Learning Model Deployment and Interoperability SQL PMML XML for Data Bibliographic Notes Documenting Code Review Chapter Exercises Command Summary Moving into R Interacting with R Basic Command Line Windows, Icons, Mouse, Pointer--WIMP The Current Rattle State Samples Projects The Rattle Log Further Tuning Models Emacs and ESS Documenting Code Review Chapter Exercises Command Summary R Evaluation Exercises Assignment Libraries and Packages Searching for Objects Package Management Information About a Package Testing Package Availability Packages and Namespaces Basic Programming in R Principles Folders and Files Flow Control If Statement For Loop Functions Apply Methods Objects System Running System Commands System Parameters Misc Internet Memory Management Memory Usage Garbage Collection Errors Frivolous Sudoku Further Resources Using R Specific Purposes Survey Analysis Getting Help R Documentation Data Data Types Numbers Strings Building Strings Splitting Strings Substitution Trim Whitespace Evaluating Strings Logical Dates and Times Space Data Structures Vectors Arrays Lists Sets Matricies Exercises Data Frames Accessing Columns Removing Columns Exercises General Manipulation Factors Elements Rows and Columns Finding Index of Elements Partitions Head and Tail Reverse a List Sorting Unique Values Loading Data Interactive Responses Interactive Data Entry Available Datasets The Iris Dataset CSV Data Used In The Book The Wine Dataset The Cardiac Arrhythmia Dataset The Adult Survey Dataset Foreign Formats Stata Data Conversions Reading Variable Width Data Saving Data Formatted Output Automatically Generate Filenames Reading a Large File Manipulating Data Manipulating Data As SQL Using SQLite ODBC Data Database Connection Excel Access Clipboard Data Spatial Data Simple Map A Density Map Overlays and Point in Polygon Other Data Formats Fixed Width Data Global Positioning System Documenting a Dataset Common Data Problems Graphics in R Basic Plot Controlling Axes Arrow Axes Legends and Points Tables Within Plots Colour Labels in Plots Axis Labels Legend Labels Within Plots Maths in Labels Multiple Plots MatPlot Multiple Plots Using ggplot2 Using GGPlot Networks Symbols Other Graphic Elements Making an Animation Animated Mandelbrot Adding a Logo to a Graphic Graphics Devices Setup Screen Devices Multiple Devices File Devices Multiple Plots Copy and Print Devices Graphics Parameters Plotting Region Locating Points on a Plot Scientific Notation and Plots Understanding Data Single Variable Overviews Textual Summaries Multiple Line Plots Separate Line Plots Pie Chart Fan Plot Stem and Leaf Plots Histogram Barplot Trellis Histogram Histogram Uneven Distribution Bump Chart Density Plot Basic Histogram Basic Histogram with Density Curve Practical Histogram Multiple Variable Overviews Scatterplot Scatterplot with Marginal Histograms Multi-Dimension Scatterplot Correlation Plot Colourful Correlations Fluctuation Plot Heat Map Projection Pursuit RADVIZ Parallel Coordinates Categoric and Numeric Measuring Data Distributions Textual Summaries Boxplot Multiple Boxplots Boxplot by Class Tuning a Boxplot Boxplot Using Lattice Boxplot Using ggplot Violin Plot What Distribution Miscellaneous Plots Line and Point Plots Matrix Data Multiple Plots Aligned Plots Probability Scale Network Plot Sunflower Plot Stairs Plot Graphing Means and Error Bars Bar Charts With Segments Bar Plot With Means 3d Bar Plot Stacks Versus Lines Multi-Line Title Mathematics Plots for Normality Basic Bar Chart Bar Chart Displays Multiple Dot Plots Alternative Multiple Dot Plots 3D Plot Clustered Box Plot Perspective Plots Star Plot Residuals Plot Waterfall Plots Dates and Times Simple Time Series Multiple Time Series Plot Time Series Plot Time Series with Axis Labels Grouping Time Series for Box Plot Time Series Heatmap Textual Summaries Stem and Leaf Plots Histogram Barplot Density Plot Basic Histogram Basic Histogram with Density Curve Practical Histogram Correlation Plot Colourful Correlations Measuring Data Distributions Textual Summaries Boxplot Multiple Boxplots Boxplot by Class Box and Whisker Plot Box and Whisker Plot Clustered Box Plot Further Resources Map Displays Further Resources Preparing Data Data Selection and Extraction Training and Test Datasets Data Cleaning Review Data Selectively Changing Vector Values Replace Indices By Names Missing Values Remove Levels from a Factor Variable Manipulations Remove Columns Reorder Columns Remove Non-Numeric Columns Remove Variables with no Variance Cleaning the Wine Dataset Cleaning the Cardiac Dataset Cleaning the Survey Dataset Imputation Nearest Neighbours Multiple Imputation Data Linking Simple Linking Record Linkage Data Transformation Aggregation Sum of Columns Pivot Tables Normalising Data Binning Interpolation Variable Selection Classification Classification Classification Issues Incremental or Online Modelling Model Tuning Tuning rpart Unbalanced Classification Building Models Temporal Analysis Evaluation Basics Basic Measures Cross Validation Graphical Performance Measures Lift The ROC Curve Other Examples 10 Fold Cross Validation Area Under Curve Calibration Curves Reporting Generating Open Document Format Getting Started with odfWeave OpenOffice.org Macro Support Generating HTML Generating PDF with LATEX Configuration Figure Sizes Fraud Analysis Archetype Analysis

File Devices

In order to save the graphics to a file the following file devices are provided:

> bitmap("sample.png")		# GhostScript to generate any format
> bmp("sample.bmp")  		# MS/Windows only
> fig("sample.fig")		# Vector based and editable with xfig
> jpeg("sample.jpg")		# Bitmap
> pdf("sample.pdf")		# Vector based Portable Document Format
> png("sample.png")		# Bitmap
> postscript("sample.eps")	# Vector based
> win.metafile("sample.emf") 	# MS/Windows only

Once created, you can start building the elements of the graphic you wish to produce (and there are plenty of examples of building a variety of graphics throughout this book). Once complete, close the specific graphic device with:

> dev.off()

Each device tends to have a collection of options that fine tune the capabilities of the device. Refer to the on-line documentation for details:

> ?pdf

To generate a an older tiff format (now largely superseded by the png format for graphics that include vectors and text), perhaps as 24-bit RGB output (8 bits per component), you can use the bitmap device:

> bitmap("sample.tiff", type="tiff24nc")

Any output device supported by GostScript is available. GhostScript needs to be installed, but once installed you can get a list of supported output devices:

[language=csh]
$ gs --help
[...]
Available devices:
   alc1900 alc2000 alc4000 alc4100 alc8500 alc8600 alc9100 ap3250 appledmp
   atx23 atx24 atx38 bbox bit bitcmyk bitrgb bj10e bj10v bj10vh bj200 bjc600
   bjc800 bjc880j bjccmyk bjccolor bjcgray bjcmono bmp16 bmp16m bmp256
   bmp32b bmpa16 bmpa16m bmpa256 bmpa32b bmpamono bmpasep1 bmpasep8 bmpgray
   bmpmono bmpsep1 bmpsep8 ccr cdeskjet cdj1600 cdj500 cdj550 cdj670 cdj850
   cdj880 cdj890 cdj970 cdjcolor cdjmono cfax cgm24 cgm8 cgmmono chp2200 cif
   cljet5 cljet5c cljet5pr coslw2p coslwxl cp50 cups declj250 deskjet
   devicen dfaxhigh dfaxlow dj505j djet500 djet500c dl2100 dnj650c epl2050
   epl2050p epl2120 epl2500 epl2750 epl5800 epl5900 epl6100 epl6200 eps9high
   eps9mid epson epsonc epswrite escp escpage faxg3 faxg32d faxg4 fmlbp fmpr
   fs600 gdi hl1240 hl1250 hl7x0 hpdj1120c hpdj310 hpdj320 hpdj340 hpdj400
   hpdj500 hpdj500c hpdj510 hpdj520 hpdj540 hpdj550c hpdj560c hpdj600
   hpdj660c hpdj670c hpdj680c hpdj690c hpdj850c hpdj855c hpdj870c hpdj890c
   hpdjplus hpdjportable ibmpro ijs imagen inferno iwhi iwlo iwlq jetp3852
   jj100 jpeg jpeggray la50 la70 la75 la75plus laserjet lbp310 lbp320 lbp8
   lex2050 lex3200 lex5700 lex7000 lips2p lips3 lips4 lips4v lj250 lj3100sw
   lj4dith lj4dithp lj5gray lj5mono ljet2p ljet3 ljet3d ljet4 ljet4d
   ljet4pjl ljetplus ln03 lp1800 lp1900 lp2000 lp2200 lp2400 lp2500 lp2563
   lp3000c lp7500 lp7700 lp7900 lp8000 lp8000c lp8100 lp8200c lp8300c
   lp8300f lp8400f lp8500c lp8600 lp8600f lp8700 lp8800c lp8900 lp9000b
   lp9000c lp9100 lp9200b lp9200c lp9300 lp9400 lp9500c lp9600 lp9600s
   lp9800c lq850 lx5000 lxm3200 lxm5700m m8510 mag16 mag256 md1xMono md2k
   md50Eco md50Mono md5k mgr4 mgr8 mgrgray2 mgrgray4 mgrgray8 mgrmono miff24
   mj500c mj6000c mj700v2c mj8000c ml600 necp6 npdl nullpage oce9050 oki182
   oki4w okiibm omni oprp opvp paintjet pam pbm pbmraw pcl3 pcx16 pcx24b
   pcx256 pcx2up pcxcmyk pcxgray pcxmono pdfwrite pgm pgmraw pgnm pgnmraw
   photoex picty180 pj pjetxl pjxl pjxl300 pkm pkmraw pksm pksmraw plan9bm
   png16 png16 png16m png16m png256 png256 pngalpha pngalpha pnggray pnggray
   pngmono pngmono pnm pnmraw ppm ppmraw pr1000 pr1000_4 pr150 pr201 psdcmyk
   psdrgb psgray psmono psrgb pswrite pxlcolor pxlmono r4081 rpdl samsunggdi
   sgirgb sj48 spotcmyk st800 stcolor sunhmono t4693d2 t4693d4 t4693d8
   tek4696 tiff12nc tiff24nc tiffcrle tiffg3 tiffg32d tiffg4 tifflzw
   tiffpack uniprint x11 x11alpha x11cmyk x11cmyk2 x11cmyk4 x11cmyk8
   x11gray2 x11gray4 x11mono xcf xes
[...]

That's a pretty impressive collection of output devices!

Note that the win.metafile device requires the MS/Windows libraries. On a GNU/Linux system, to generate the MS/Windows Metafile format, you will need to install and run a version of R under, for example, the GNU/Linux wine package. This was discussed in See Section A.7.

The graphics included in this book are generated as pdf using scalable vector graphics. We could have generated png files for inclusion in web pages or even convert pdf files to png using the Debian GNU/Linux convert program:

  $ convert rplot-basic.pdf rplot-basic.png

Support further development through the purchase of the PDF version of the book.
The PDF version is a formatted comprehensive draft book (with over 800 pages).
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