Doc/Rossini-RiceU-2009Mar.tex

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  14 \title[CLS]{Common Lisp Statistics}
  15 \subtitle{Using History to design better data analysis environments}
  16 \author[Rossini]{Anthony~(Tony)~Rossini}
  17
  18 \institute[Novartis and University of Washington] % (optional, but mostly needed)
  19 {
  20   Group Head, Modeling and Simulation\\
  21   Novartis Pharma AG, Switzerland
  22   \and
  23   Affiliate Assoc Prof, Biomedical and Health Informatics\\
  24   University of Washington, USA}
  25
  26 \date[Rice 09]{Rice, Mar 2009}
  27 \subject{Statistical Computing Environments}
  28
  29 \begin{document}
  30
  31 \begin{frame}
  32   \titlepage
  33 \end{frame}
  34
  35 \begin{frame}{Outline}
  36   \tableofcontents
  37 \end{frame}
  38
  39 % Structuring a talk is a difficult task and the following structure
  40 % may not be suitable. Here are some rules that apply for this
  41 % solution:
  42
  43 % - Exactly two or three sections (other than the summary).
  44 % - At *most* three subsections per section.
  45 % - Talk about 30s to 2min per frame. So there should be between about
  46 %   15 and 30 frames, all told.
  47
  48 % - A conference audience is likely to know very little of what you
  49 %   are going to talk about. So *simplify*!
  50 % - In a 20min talk, getting the main ideas across is hard
  51 %   enough. Leave out details, even if it means being less precise than
  52 %   you think necessary.
  53 % - If you omit details that are vital to the proof/implementation,
  54 %   just say so once. Everybody will be happy with that.
  55
  56 \section{Preliminaries}
  57
  58 \begin{frame}{Goals}{(define, strategic approach, justify)}
  59
  60   \begin{itemize}
  61   \item To describe the concept of \alert{computable and executable
  62       statistics}.
  63
  64   \item To demonstrate that \alert{there exists a research program}
  65     consisting of simple steps which is adaptable to a practitioner's
  66     work habits, which is feasible and introduces relatively minimal
  67     disruptive changes.
  68
  69   \item To justify that the \alert{approach is worthwhile} and
  70     represents a staged effort towards \alert{increased use of best
  71       practices}.
  72   \end{itemize}
  73   (unfortunately, the last is still incomplete)
  74 \end{frame}
  75
  76 \subsection{Background}
  77
  78 \begin{frame}{Many systems existed concurrently for statistical
  79     computing}
  80
  81   \begin{itemize}
  82   \item LispStat (ViSta, ARC)
  83   \item SPSS (BMDP)
  84   \item MiniTab
  85   \item Stata
  86   \item SAS
  87   \item Quail
  88   \item XGobi (Orca, GGobi, Statistical Reality Engine)
  89   \item DataDesk
  90   \item R
  91   \item Excel
  92   \end{itemize}
  93 \end{frame}
  94
  95 \begin{frame}{Why Lisp?}
  96   \begin{itemize}
  97   \item Lisp as an ancient ``AI'' language; Statistics as ``artificial
  98     intelligence'' (not real intelligence, \alert{humans are too
  99       flawed and inconsistent} for Bayesian work to be anything but
 100     AI).
 101   \item Semantics and context are important: well supported by Lisp
 102     paradigms.
 103   \item Lisp's parentheses describe single, multi-scale,
 104     \alert{complete thought}.  See \#1 for why that could make it
 105     difficult.
 106   \end{itemize}
 107   Aside: Common Lisp is the building block for all my current research
 108   projects.
 109 \end{frame}
 110
 111 \begin{frame}
 112   \frametitle{Semantics and Statistics}
 113   \begin{itemize}
 114   \item
 115     There have been many wonderful talks about the semantic web which \\
 116     \alert{demonstrated its coolness} \\
 117     while completely \\
 118     \alert{failed to demonstrate its usefulness}.\\
 119     This talk follows in the tradition of such giants\ldots{}
 120
 121   \item
 122     Biological informatics support (GO, Entrez) has allowed for
 123     precise definitions of concepts in biology.
 124
 125   \item It is a shame that a field like statistics, requiring such
 126     precision, has less than an imprecise and temporally instable
 127     field such as biology\ldots
 128   \end{itemize}
 129 \end{frame}
 130
 131
 132 \subsection{Context}
 133
 134 \begin{frame}{Context}{(where I'm coming from, my ``priors'')}
 135   \begin{itemize}
 136   \item Pharmaceutical Industry
 137   \item Modeling and Simulation uses mathematical models/constructs to
 138     record beliefs for explication, clinical team alignment, decision
 139     support, and quality management.
 140   \item My major role at Novartis is to work at the intersection of
 141     biomedical informatics, statistics, and mathematical modeling.
 142   \item I need a mix of applications and novel research development to
 143     solve challenges better, faster, more efficiently.
 144   \item Data analysis is a specialized approach to computer
 145     programming, \alert{different} than applications programming or
 146     systems programming.
 147   \item \alert{Nearly all of the research challenges I face today
 148       existed for me in academia, and vice-versa.}
 149   \end{itemize}
 150 \end{frame}
 151
 152 \subsection{Illustrating Computable / Executable Statistics}
 153
 154 \begin{frame}{Can we compute?}
 155
 156   For the following examples, the critical question becomes:
 157   \centerline{\alert{Can we compute with it?}}
 158 \end{frame}
 159
 160
 161 \begin{frame}[fragile]{Example 1: Theory\ldots}
 162   \label{example1}
 163   Let $f(x;\theta)$ describe the likelihood of XX under the following
 164   assumptions.
 165   \begin{enumerate}
 166   \item assumption 1
 167   \item assumption 2
 168   \end{enumerate}
 169   Then if we use the following algorithm:
 170   \begin{enumerate}
 171   \item step 1
 172   \item step 2
 173   \end{enumerate}
 174   then $\hat{\theta}$ should be $N(0,\hat\sigma^2)$ with the following
 175   characteristics\ldots
 176 \end{frame}
 177
 178 \begin{frame}
 179   \frametitle{Can we compute, using this description?}
 180   Given the information at hand:
 181   \begin{itemize}
 182   \item we ought to have a framework for initial coding for the
 183     actual simulations (test-first!)
 184   \item the implementation is somewhat clear
 185   \end{itemize}
 186 \end{frame}
 187
 188
 189 \begin{frame}
 190   \frametitle{Example 2: Practice\ldots}
 191   \label{example2}
 192   The dataset comes from a series of clinical trials.  We model the
 193   primary endpoint, ``relief'', as a binary random variable.  There is a random
 194   trial effect on relief as well as severity due to differences in
 195   recruitment and inclusion/exclusion criteria.
 196 \end{frame}
 197
 198 \begin{frame}
 199   \frametitle{Can we compute, using this description?}
 200   \begin{itemize}
 201   \item With a real such description, it is clear what some of the
 202     potential models might be for this dataset
 203   \item It should be clear how to start thinking of a data dictionary
 204     for this problem.
 205   \end{itemize}
 206 \end{frame}
 207
 208 \begin{frame}{Example 3: The Round-trip\ldots}
 209   \label{example3}
 210   The first examples describe ``ideas $\rightarrow$ code''
 211
 212   Consider the last time you read someone else's implementation of a
 213   statistical procedure (i.e. R package code).  When you read the
 214   code, could you see:
 215   \begin{itemize}
 216   \item the assumptions used?
 217   \item the algorithm implemented?
 218   \item practical guidance for when you might select the algorithm
 219     over others?
 220   \item practical guidance for when you might select the
 221     implementation over others?
 222   \end{itemize}
 223   These are usually components of any reasonable journal article.
 224   \textit{(Q: have you actually read an R package that wasn't yours?)}
 225 \end{frame}
 226
 227 \section{Motivation}
 228
 229 \subsection{IT Can Speed up Deliverables in Statistical Practice}
 230
 231 \begin{frame}{Our Generic Work-flow and Life-cycle}
 232   {describing most data analytic activities}
 233   Workflow:
 234   \begin{enumerate}
 235   \item Scope out the problem
 236   \item Sketch out a potential solution
 237   \item Implement until road-blocks appear
 238   \item Deliver results
 239   \end{enumerate}
 240
 241   Lifecycle:
 242   \begin{enumerate}
 243   \item paper sketch
 244   \item 1st e-draft of text/code/date (iterate to \#1, discarding)
 245   \item cycle through work
 246   \item publish
 247   \item ``throw-away''
 248   \end{enumerate}
 249   but there is valuble information that could enable the next
 250   generation!
 251 \end{frame}
 252
 253 \begin{frame}[fragile]{Paper $\rightarrow$ Computer  $\rightarrow$ Article $\rightarrow$ Computer}{Cut and Paste makes for large errors.}
 254   \begin{itemize}
 255   \item Problems in a regulatory setting
 256   \item Regulatory issues are just ``best practices''
 257   \end{itemize}
 258
 259   Why do we ``copy/paste'', or analogously, restart our work?
 260
 261   pro:
 262   \begin{itemize}
 263   \item every time we repeat, we reinforce the idea in our brain
 264   \item review of ideas can help improve them
 265   \end{itemize}
 266   con:
 267   \begin{itemize}
 268   \item inefficiency
 269   \item introduction of mistakes
 270   \item loss of historical context
 271   \item changes to earlier work (on a different development branch)
 272     can not propagate.
 273   \end{itemize}
 274 \end{frame}
 275
 276 \subsection{Literate Programming is insufficient}
 277
 278 \begin{frame}{Literate Statistical Practice.}
 279   \begin{enumerate}
 280   \item Literate Programming applied to data analysis
 281   \item among the \alert{most annoying} techniques to integrate into
 282     work-flow if one is not perfectly methodological.
 283   \item Some tools:
 284     \begin{itemize}
 285     \item ESS: supports interactive creation of literate programs.
 286     \item Sweave: tool which exemplifies reporting context; odfWeave
 287       primarily simplifies reporting.
 288     \item Roxygen: primarily supports a literate programming
 289       documentation style, not a literate data analysis programming
 290       style.
 291   \end{itemize}
 292   \item ROI demonstrated in specialized cases: BioConductor.
 293   \item \alert{usually done after the fact} (final step of work-flow)
 294     as a documentation/computational reproducibility technique, rarely
 295     integrated into work-flow.
 296   \end{enumerate}
 297   Many contributors to this general theory/approach:
 298   Knuth, Claerbout, de Leeuw, Leisch, Gentleman, Temple-Lang,
 299   \ldots{}
 300 \end{frame}
 301
 302 % \begin{frame}
 303 %   \frametitle{Literate Programming}
 304 %   \framesubtitle{Why is it not enough?}
 305
 306 %   Claim: it isn't
 307 %   \begin{enumerate}
 308 %   \item used for statistics since mid 90s (Emacs/ESS support in 1997)
 309 %   \item active popular use with R  (Leisch, 2001)
 310 %   \end{enumerate}
 311
 312 %   but it provides a work-flow which is difficult and unnatural for many
 313 %   people (no perceived ROI).
 314 % \end{frame}
 315
 316 \begin{frame}{Related work}
 317
 318   Mathematica Workbooks for mathematics concepts
 319   \begin{itemize}
 320   \item Mathematical storage and reproducibility, what bout Statistical
 321     Concepts?
 322   \item Not open, but freely reproducible.
 323   \item Some semantics, hopefully this will improve.
 324   \end{itemize}
 325
 326   Electronic Lab Notebooks for data and the data/data analytics
 327   interaction (but not quantitative methodological development).
 328 \end{frame}
 329
 330 \section{Results/Contribution}
 331
 332 \subsection{Claims}
 333
 334 % \begin{frame}{Semantic Web}{How do we communicate "things"?}
 335 %   Recall Monday evening talk:   What kinds of communication problems can we have?
 336 %   \begin{itemize}
 337 %   \item I say "reinigung", you say "waschen"
 338 %   \item I say "clean", you say "sauber"
 339 %   \end{itemize}
 340 %   In the context of our work, how do we communicate what we've done?
 341 % \end{frame}
 342
 343 \begin{frame}{Communication in Statistical Practice}{\ldots is essential for \ldots}
 344   \begin{itemize}
 345   \item finding
 346   \item explanations
 347   \item agreement
 348   \item receiving information
 349   \end{itemize}
 350   \alert{``machine-readable'' communication/computation lets the
 351     computer help} \\
 352   Semantic Web is about ``machine-enabled computability''.
 353 \end{frame}
 354
 355 \begin{frame}
 356   \frametitle{Literate Programming}
 357   \framesubtitle{Why isn't it enough for Data Analysis?}
 358
 359   Only 2 contexts: (executable) code and documentation.  Fine for
 360   application programming,  but for data analysis, we could benefit
 361   from:
 362   \begin{itemize}
 363   \item classification of statistical procedures
 364   \item descriptions of assumptions
 365   \item pragmatic recommendations
 366   \item inheritance of structure through the work-flow of a
 367     statistical methodology or data analysis project
 368   \item datasets and metadata
 369   \end{itemize}
 370   Concept: ontologies describing mathematical assumptions, applications
 371   of methods, work-flow, and statistical data structures can enable
 372   machine communication.
 373
 374   (i.e. informatics framework ala biology)
 375 \end{frame}
 376
 377 \begin{frame}  \frametitle{Semantics}
 378   \framesubtitle{One definition: description and context}
 379
 380   Interoperability is the key, with respect to
 381   \begin{itemize}
 382   \item ``Finding things''
 383   \item Applications and activities with related functionality
 384     \begin{itemize}
 385     \item moving information from one state to another (paper, journal
 386       article, computer program)
 387     \item computer programs which implement solutions to similar tasks
 388     \end{itemize}
 389   \end{itemize}
 390 \end{frame}
 391
 392 \begin{frame}{Statistical Practice is somewhat restricted}
 393   {...but in a good sense, enabling potential for semantics...}
 394
 395   There is a restrictable set of intended actions for what can be done
 396   -- the critical goal is to be able to make a difference by
 397   accelerating activities that should be ``computable'':
 398   \begin{itemize}
 399   \item restricted natural language processing
 400   \item mathematical translation
 401   \item common description of activities for simpler programming/data
 402     analysis (S approach to objects and methods)
 403   \end{itemize}
 404   R is a good primitive start (model formulation approach, simple
 405   ``programming with data'' paradigm); we should see if we can do
 406   better!
 407 \end{frame}
 408
 409
 410 % \begin{frame}{Semantics}{Capturing Ideas, Concepts, Proposals.}
 411 %   \begin{itemize}
 412 %   \item Capturing the historical state and corresponding decisions is
 413 %     essential for developing improved approaches.  A common problem in
 414 %     ``product development'' (stat research, drug development) is
 415 %     cycling through the same issues repeatedly.
 416 %   \item These should be captured semantically
 417 %   \item Conversion of concepts to computable semantics is sensible
 418 %     when you need it, difficult without a compelling reasons
 419 %   \end{itemize}
 420 % \end{frame}
 421
 422
 423 % \begin{frame}{Lowering the bounds to interactive work.}
 424 %   \begin{enumerate}
 425 %   \item Limitations of object-orientation and information-hiding
 426 %     routines: require context in order to keep the context.
 427 %   \item Statistical and Data analysis: context is central and obvious.
 428 %   \end{enumerate}
 429 % \end{frame}
 430
 431 \subsection{Current Approach / Implementation}
 432
 433 \begin{frame}{Computable and Executable Statistics requires}
 434
 435   \begin{itemize}
 436   \item approaches to describe data and metadata (``data'')
 437     \begin{itemize}
 438     \item semantic WWW
 439     \item metadata management and integration, driving
 440     \item data integration
 441     \end{itemize}
 442   \item approaches to describe data analysis methods (``models'')
 443     \begin{itemize}
 444     \item quantitatively: many ontologies (AMS, etc), few meeting
 445       statistical needs.
 446     \item many substantive fields have implementations
 447       (bioinformatics, etc) but not well focused.
 448     \end{itemize}
 449   \item approaches to describe the specific form of interaction
 450     (``instances of models'')
 451     \begin{itemize}
 452     \item Original idea behind ``Literate Statistical Analysis''.
 453     \item That idea is suboptimal, more structure needed (not
 454       necessarily built upon existing...).
 455     \end{itemize}
 456   \end{itemize}
 457 \end{frame}
 458
 459 \begin{frame}[fragile]{Representation: XML and Lisp}{executing your data}
 460   Many people are familiar with XML:
 461 \begin{verbatim}
 462 <name phone="+41793674557">Tony Rossini</name>
 463 \end{verbatim}
 464   which is shorter in Lisp:
 465 \begin{verbatim}
 466 (name "Tony Rossini" :phone "+41613674557")
 467 \end{verbatim}
 468   \begin{itemize}
 469   \item Lisp ``parens'', universally hated by unbelievers, are
 470     wonderful for denoting when a ``concept is complete''.
 471   \item Why can't your data self-execute?
 472   \end{itemize}
 473 \end{frame}
 474
 475 \begin{frame}{Common Lisp Stat.}
 476   Ross talked about Lisp.   I generally agree.  My current
 477   research program dates back over 3 years, and:
 478   \begin{itemize}
 479   \item Originally based on LispStat (reusability)
 480   \item Re-factored structure (some numerics worked with a 1990-era code base).
 481   \item Current activities:
 482     \begin{enumerate}
 483     \item numerics redone using CFFI-based BLAS/LAPLACK (cl-blapack)
 484     \item matrix interface based on MatLisp
 485     \item design of graphics system on-going; constraint system
 486       (Cells) supporting interactivity.
 487     \item general framework for model specification (regression,
 488       likelihood, ODEs)
 489     \end{enumerate}
 490   \end{itemize}
 491 \end{frame}
 492
 493 \begin{frame}{Common Lisp Stat}
 494
 495   Source code available!
 496
 497   (but it is ugly, works only in 10 cases, and changes with my moods).
 498
 499 \end{frame}
 500 \section*{Summary}
 501
 502 % \begin{frame}{Delivering Better Data Analyses Faster}
 503 %   Industrial settings:
 504 %   \begin{enumerate}
 505 %   \item Pharmaceutical companies
 506 %   \item Academic departments
 507 %   \item Review-centric organizations (Health Authorities, Regulators)
 508 %   \end{enumerate}
 509 % \end{frame}
 510
 511 \begin{frame}{Summary}
 512
 513   \begin{itemize}
 514   \item In the Pharma industry, it is all about getting the right
 515     drugs to the patient faster.  Data analysis systems seriously
 516     impact this process, being potentially an impediment or an
 517     accelerator.
 518
 519     \begin{itemize}
 520     \item \alert{Information technologies can increase the efficiency
 521         of statistical practice}, though innovation change management
 522       must be taking into account.  (i.e. Statistical practice, while
 523       considered by some an ``art form'', can benefit from
 524       industrialization).
 525     \item \alert{Lisp's features match the basic requirements we need}
 526       (dichotomy: programs as data, data as programs).  Sales pitch,
 527       though...
 528     \item Outlook: Lots of work and experimentation to do!
 529     \end{itemize}
 530   \item {\tiny Gratuitous Advert: We are hiring, have student
 531       internships (undergrad, grad students), and a visiting faculty
 532       program.  Talk with me if possibly interested.}
 533   \end{itemize}
 534 \end{frame}
 535
 536 % All of the following is optional and typically not needed.
 537 \appendix
 538
 539
 540 \section<presentation>*{\appendixname}
 541
 542
 543 \begin{frame} \frametitle{Complements and Backup}
 544   No more, stop here.  Questions?  (now or later).
 545 \end{frame}
 546
 547 \begin{frame}{The Industrial Challenge.}{Getting the Consulting Right.}
 548   % - A title should summarize the slide in an understandable fashion
 549   %   for anyone how does not follow everything on the slide itself.
 550
 551   \begin{itemize}
 552   \item Recording assumptions for the next data analyst, reviewer.
 553     Use \texttt{itemize} a lot.
 554   \item
 555     Use very short sentences or short phrases.
 556   \end{itemize}
 557 \end{frame}
 558
 559
 560 \begin{frame}{The Industrial Challenge.}{Getting the Right Research Fast.}
 561   % - A title should summarize the slide in an understandable fashion
 562   %   for anyone how does not follow everything on the slide itself.
 563
 564   \begin{itemize}
 565   \item
 566     Use \texttt{itemize} a lot.
 567   \item
 568     Use very short sentences or short phrases.
 569   \end{itemize}
 570 \end{frame}
 571
 572
 573 \begin{frame}{Explicating the Work-flow}{QA/QC-based improvements.}
 574
 575
 576 \end{frame}
 577
 578 \section{Semantics and Statistical Practice}
 579
 580
 581 \begin{frame}
 582   \frametitle{Statistical Activity Leads to Reports}
 583   \framesubtitle{You read what you know, do you understand it?}
 584
 585   How can we improve the communication of the ideas we have?
 586
 587   Precision of communication?
 588
 589 \end{frame}
 590
 591
 592
 593 \begin{frame}  \frametitle{Communication Requires Context}
 594   \framesubtitle{Intentions imply more than one might like...}
 595
 596   \begin{itemize}
 597   \item Consideration of what we might do
 598   \item Applications with related functionality
 599   \end{itemize}
 600 \end{frame}
 601
 602
 603
 604 \begin{frame}
 605   \frametitle{Design Patterns}
 606   \framesubtitle{Supporting Work-flow Transitions}
 607
 608   (joint work with H Wickham): The point of this research program is
 609   not to describe what to do at any particular stage of work, but to
 610   encourage researchers and practitioners to consider how the
 611   translation and transfer of information between stages so that work
 612   is not lost.
 613
 614   Examples of stages in a work-flow:
 615   \begin{itemize}
 616   \item planning, execution, reporting;
 617   \item scoping, illustrative examples or counter examples, algorithmic construction,
 618     article writing.
 619   \item descriptive statistics, preliminary inferential analysis,
 620     model/assumption checking, final inferential analysis,
 621     communication of scientific results
 622   \end{itemize}
 623   Description of work-flows is essential to initiating discussions on
 624   quality/efficiency of approaches to work.
 625 \end{frame}
 626
 627 \section{Design Challenges}
 628
 629 \begin{frame}
 630   \frametitle{Activities are enhanced by support}
 631
 632   \begin{itemize}
 633   \item Mathematical manipulation can be enhanced by symbolic
 634     computation
 635   \item Statistical programming can be enabled by examples and related
 636     algorithm implementation
 637   \item Datasets, to a limited extent, can self-describe.
 638   \end{itemize}
 639 \end{frame}
 640
 641 \begin{frame}
 642   \frametitle{Executable and Computable Science}
 643
 644   Use of algorithms and construction to describe how things work.
 645
 646   Support for agent-based approaches
 647 \end{frame}
 648
 649
 650 \begin{frame}
 651   \frametitle{What is Data?  Metadata?}
 652
 653   Data: what we've observed
 654
 655   MetaData: context for observations, enables semantics.
 656 \end{frame}
 657
 658
 659 \section{Discussion}
 660
 661 \begin{frame}
 662   \frametitle{Outlook}
 663   \begin{itemize}
 664   \item Semantics and Computability have captured a great deal of
 665     attention in the informatics and business computing R\&D worlds
 666   \item Statistically-driven Decision Making and Knowledge Discovery
 667     is, with high likelihood, the next challenging stage after data
 668     integration.
 669   \item Statistical practice (theory and application) can be enhanced,
 670     made more efficient, providing  increased benefit to organizations
 671     and groups using appropriate methods.
 672 %   \item Lisp as a language, shares characteristics of both Latin
 673 %     (difficult dead language useful for classical training) and German
 674 %     (difficult living language useful for general life).
 675 %  Of course, for some people, they are not difficult.
 676   \end{itemize}
 677
 678   The research program described in this talk is currently driving the
 679   design of CommonLisp Stat, which leverages concepts and approaches
 680   from the dead and moribund XLisp-Stat project.
 681
 682   \url{http://repo.or.cz/w/CommonLispStat.git/}
 683 \end{frame}
 684
 685 \section{Common Lisp Statistics}
 686
 687 \begin{frame}
 688   \frametitle{Interactive Programming}
 689   \framesubtitle{Everything goes back to being Lisp-like}
 690   \begin{itemize}
 691   \item Interactive programming (as originating with Lisp): works
 692     extremely well for data analysis (Lisp being the original
 693     ``programming with data'' language).
 694   \item Theories/methods for how to do this are reflected in styles
 695     for using R.
 696   \end{itemize}
 697 \end{frame}
 698
 699 \begin{frame}[fragile]
 700   \frametitle{Lisp}
 701
 702   Lisp (LISt Processor) is different than most high-level computing
 703   languages, and is very old (1956).  Lisp is built on lists of things
 704   which are evaluatable.
 705 \begin{verbatim}
 706 (functionName data1 data2 data3)
 707 \end{verbatim}
 708   or ``quoted'':
 709 \begin{verbatim}
 710 '(functionName data1 data2 data3)
 711 \end{verbatim}
 712   which is shorthand for
 713 \begin{verbatim}
 714 (list functionName data1 data2 data3)
 715 \end{verbatim}
 716   The difference is important -- lists of data (the second/third) are
 717   not (yet?!) functions applied to (unencapsulated lists of) data (the first).
 718 \end{frame}
 719
 720 \begin{frame}
 721   \frametitle{Features}
 722   \begin{itemize}
 723   \item Data and Functions semantically the same
 724   \item Natural interactive use through functional programming with
 725     side effects
 726   \item Batch is a simplification of interactive -- not a special mode!
 727   \end{itemize}
 728 \end{frame}
 729
 730
 731
 732 % \begin{frame}[fragile]
 733 %   \frametitle{Defining Variables}
 734 %   \framesubtitle{Setting variables}
 735 % \begin{verbatim}
 736 % (setq <variable> <value>)
 737 % \end{verbatim}
 738 %   Example:
 739 % \begin{verbatim}
 740 % (setq ess-source-directory
 741 %       "/home/rossini/R-src")
 742 % \end{verbatim}
 743 % \end{frame}
 744
 745 % \begin{frame}[fragile]
 746 %   \frametitle{Defining on the fly}
 747 % \begin{verbatim}
 748 % (setq ess-source-directory
 749 %    (lambda () (file-name-as-directory
 750 %          (expand-file-name
 751 %            (concat (default-directory)
 752 %                    ess-suffix "-src")))))
 753 % \end{verbatim}
 754 %   (Lambda-expressions are anonymous functions, i.e. ``instant-functions'')
 755 % \end{frame}
 756
 757
 758 % \begin{frame}[fragile]
 759 %   \frametitle{Function Reuse}
 760 %   By naming the function, we could make the previous example reusable
 761 %   (if possible):
 762 % \begin{verbatim}
 763 % (defun my-src-directory ()
 764 %       (file-name-as-directory
 765 %          (expand-file-name
 766 %            (concat (default-directory)
 767 %                    ess-suffix "-src"))))
 768 % \end{verbatim}
 769 %   Example:
 770 % \begin{verbatim}
 771 % (setq ess-source-directory (my-src-directory))
 772 % \end{verbatim}
 773 % \end{frame}
 774
 775
 776 % \begin{frame}
 777 %   \frametitle{Equality Among Packages}
 778 %   \begin{itemize}
 779 %   \item more/less equal can be described specifically through
 780 %     overriding imports.
 781 %   \end{itemize}
 782 % \end{frame}
 783
 784
 785 \subsection<presentation>*{For Further Reading}
 786
 787 \begin{frame}[allowframebreaks]
 788   \frametitle<presentation>{Related Material}
 789
 790   \begin{thebibliography}{10}
 791
 792   \beamertemplatebookbibitems
 793   % Start with overview books.
 794
 795   \bibitem{LispStat1990}
 796     L.~Tierney
 797     \newblock {\em LispStat}.
 798
 799   \beamertemplatearticlebibitems
 800   % Followed by interesting articles. Keep the list short.
 801
 802   \bibitem{Rossini2001}
 803     AJ.~Rossini
 804     \newblock Literate Statistical Practice
 805     \newblock {\em Proceedings of the Conference on Distributed
 806       Statistical Computing}, 2001.
 807
 808   \bibitem{RossiniLeisch2003}
 809     AJ.~Rossini and F.~Leisch
 810     \newblock Literate Statistical Practice
 811     \newblock {\em Technical Report Series, University of Washington
 812       Department of Biostatistics}, 2003.
 813
 814   \beamertemplatearrowbibitems
 815   % Followed by interesting articles. Keep the list short.
 816
 817   \bibitem{CLS}
 818     Common Lisp Stat, 2008.
 819     \newblock \url{http://repo.or.cz/CommonLispStat.git/}
 820
 821   \end{thebibliography}
 822 \end{frame}
 823
 824 \end{document}