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

Pros and Cons

On 7 January 2009 the New York Times carried a front page technology article on R where a SAS representative is quoted:

``I think it addresses a niche market for high-end data analysts that want free, readily available code,'' said Anne H. Milley, director of technology product marketing at SAS. She adds, ``We have customers who build engines for aircraft. I am happy they are not using Freeware when I get on a jet.''

This is a common misunderstanding put out there by vendors. R is a peer reviewed software product that any number of the worlds top statisticians have reviewed, and over the years, any issues will have been identified and rectified. On the other hand, SAS is a non-peer-reviewed software product with closed source (i.e., hidden) implementations of analytic methods that cannot be reproduced by others. Who would you trust when building aircraft engines!

A common reason for changing to R is to do with the bureaucratic processes that organisations impose upon users wanting software. Often, barriers have to be jumped in order to purchase software, doing due diligence in comparing what is available. With free open source software, we just get the software we want to use and if it doesn't server the purpose, we move on. For commercial purchases, if the software is found not to serve the purpose, we are stuck with it and have to make do. The decision making also turns into seconds, from months.

Let's start with some of the advantages with using R:

• R is the most comprehensive statistical analysis package available. It incorporates all of the standard statistical tests, models, analyses, as well as providing a comprehensive language for managing and manipulating data.
• R is a programming language and environment developed for statistical analysis by practising statisticians and researchers.
• R is developed by a core team of some 10 developers, including some of the worlds leading statisticians.
• The validity of the R software is ensured through openly validated and comprehensive governance as documented for the American Food and Drug Authority in XXXX. Because R is open source, unlike commercial software, R has been reviewed by many internationally renowned statisticians and computational scientists.
• R has over 1400 packages available specialising in topics like from Econometrics, Data Mining, Spatial Analysis, Bio-Informatics.
• R is free and open source software allowing anyone to use and, importantly, to modify it. R is licensed under the GNU General Public License, with Copyright held by The R Foundation for Statistical Computing.
• Anyone can freely download and install the R software and even freely modify the software, or look at the code behind the software to learn how things are done.
• Anyone is welcome to provide bug fixes, code enhancements, and new packages, and the wealth of quality packages available for R is a testament to this approach to software development and sharing.
• R well integrates packages in different languages, including Java (hence the RWeka package), Fortran (hence randomForest), C (hence arules), C++, and Python.
• The R command line is much more powerful than a graphical user interface.
• R is cross platform. R runs on many operating systems and different hardware. It is popularly used on GNU/Linux, Macintosh, and MW/Windows, running on both 32bit and 64bit processors.
• R has active user groups where questions can be asked and are often quickly responded to, and often responded to by the very people who have developed the environment--this support is second to none. Have you ever tried getting support from people who really know SAS or are core developers of SAS?
• New books for R (the Springer Use R! series) are emerging and there will soon be a very good library of books for using R.
• No license restrictions (other than ensuring our freedom to use it at our own discretion) and so you can run R anywhere and at any time.
• R probably has the most complete collection of statistical functions of any statistical or data mining package. New technology and ideas often appear first in R.
• The graphic capabilities of R are outstanding, providing a fully programmable graphics language which surpasses most other statistical and graphical packages.
• A very active email list, with some of the worlds leading statisticians actively responding, is available for anyone to join. Questions are quickly answered and the archive provides a wealth of user solutions and examples. Be sure to read the Posting Guide first.
• Being open source the R source code is peer reviewed, and anyone is welcome to review it and suggest improvements. Bugs are fixed very quickly. Consequently, R is a rock solid product. New packages provided with R do go through a life cycle, often beginning as somewhat less quality tools, but usually quickly evolving into top quality products.
• R plays well with many other tools, importing data, for example, from CSV files, SAS, and SPSS, or directly from MS/Excel, MS/Access, Oracle, MySQL, and SQLite. It can also produce graphics output in PDF, JPG, PNG, and SVG formats, and table output for L^ATEX and HTML.

Whilst the advantages might flow from the pen with a great deal of enthusiasm, it is useful to note some of the disadvantages or weaknesses of R, even if they are perhaps transitory!

• You may need to wear a fire proof jacket if you are looking for help on some of the R mailing lists (but don't let that put you off trying). If you get a nasty response from a particular character on the mailing list, don't lose heart -- he's actually made a lot of significant contributions to R but is well known for lacking on the sensitivity scale. Do go gentle with him -- he does not seem to realise what harm he does and reacts badly to criticism -- not worth spending time worrying about as he'll probably never change.
• R is not so easy to use for the novice. There are several simple to use graphical user interfaces (GUIs) for R that encompass point and click interactions, but they generally do not have the polish of the commercial offerings of Clementine (See Chapter 54) and SAS/Enterprise Miner (See Chapter 59).
• Documentation is sometimes patchy. Whilst there are extensive documents on line and available in books and throughout the Internet, it can sometimes be terse and even impenetrable to the non-statistician. On the other hand, for example, SAS has extensive, self-contained, and often well explained, documentation, readily available to the user. Nonetheless, users do comment that the R documentation is to the point and easy to consult.
• The quality of some packages is less than perfect, although if a package is useful to many people, it will quickly evolve into a very robust product through collaborative efforts.
• There is no one to complain to if something doesn't work - at least no one who has a financial interest in keeping you, the user, as a satisfied customer. Organisations are quite happy to pay major premiums for that apparent peace of mind! Nonetheless, problems are usually dealt with quickly on the mailing list, and bugs disappear with lightning speed.
• R has a steep learning curve--it does take a while to get used to the power of R--but no steeper than, for example, SAS.
• There is no graphical user interface that compares with the SAS/Enterprise Guide or SAS/JMP interfaces which are more comfortable for the new and infrequent users.
• Many R commands give little thought to memory management and so R can very quickly consume all available memory. This can be a restriction when doing data mining. There are various solutions, including using 64bit operating systems that can access much more memory than 32 bit operating systems.

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