From jmc@research.bell-labs.com  Wed Mar 20 15:21:48 2002
From: jmc@research.bell-labs.com (John Chambers)
Date: Wed, 20 Mar 2002 10:21:48 -0500
Subject: [Rd] Question re:S4 classes and design; clashing classes?
References: <Pine.LNX.4.43.0203190943380.28638@hymn08.u.washington.edu>
Message-ID: <3C98A90C.484269EB@research.bell-labs.com>

It wasn't entirely clear what your design goals were, but the general
question of comparing functional method languages (S, CLOS, Dylan) and
OOP languages (Java, etc) is of much interest to me, so here are some
general comments.  Maybe we can then iterate on your example, with or
without r-devel.

The basic difference in approach is easy to state:

 Functional languages define methods based on the function and a
signature (a list matching classes to formal arguments);  OOP languages
attach all methods to a class definition.

The distinction becomes relevant if a function does "multiple dispatch";
that is, defining methods that depend on more than one argument.  (In
that sense, S3 methods made no use of the functional nature of the
language.)

If, say, f(x, y) only has methods that depend on x, then it's just
syntactic sugar, and a matter of organization, that distinguishes the
functional form f(x, y) from an OOP-style form, say x$f(y).  

(That's a slight exaggeration, because the functional language model
thinks that "f" should mean something similar regardless of the class of
x, where in OOP you could define the method in a totally inconsistent
way.)

In that sense, I read your description as essentially OOP.  You talk of
"Orca objects having methods", but in a functional language the question
is how the functions that do the computation depend on the objects that
are their arguments.

Nothing wrong with the OOP view, but it means that your question goes
more to how, and if, the underlying R code uses functions.  If it's just
basically an interface to an OOP definition, then it can stay in that
form.  (And I'd like to discuss in separate mail using the Omegahat OOP
package to formalize the R side.)

Functional methods would become relevant if the R software involved
multiple objects.  Not knowing the innards of how things work, let me
make up an example.

If the current view included, say, an object describing a wireframe that
was displayed around the points, then one might have useful classes of
wireframe objects, then a function that stepped the display to the next
view could have methods that depended on both the current data and the
wireframe:
  step(data, wireframe)
(Definitely not asserting this is a relevant example, but perhaps the
general distinction is clear.)

John


Anthony Rossini wrote:
> 
> Thanks to Duncan's recent work on SJava, I've got a (so far) stable
> platform to put together the R-Orca interface (dynamic graphics,
> including brushing, subseting, etc).  While the first version will be
> looking at installation issues and study basic user interfaces for providing a
> few working examples, it will be important for this particular package to look  at how one integrates a "toolkit" at the user-level with other R libraries.
> 
> The obvious place (to me, at least initially) to look is at how the S4
> classes might be used for this.
> 
> However, I'm having a heck of a time wrapping my head around the
> proper design for this.
> 
> Here is the general issue:
> 
> For "normal data objects" with standard data views (i.e. class of
> grand tours, etc), I can simply setMethod on the standard data views,
> i.e.
> 
> setMethod("grandtour","dataframe", ...)  # standard unstructured grand tour
> setMethod("grandtour","Surv", ... )      # tour, restricting temporal data,
>                                          # handling survival
> 
> etc.  I'm fine on this, and have an acceptable sense of what I'd like
> to do here.  Note that here, I'm talking about visualizations that
> currently exist, not that might be written for such data.  This is in
> fact the first step beyond the package I'm currently finishing up.
> 
> For the third version (beyond the initial package and beyond the initial methods version of the package), I've got a second class of objects,
> i.e. the data views (possibly customized themselves), which I'd like
> to think about.  In particular, I'd like to be able to extend/subclass
> "grandtour", for example, to regular ("nice") longitudinal and
> spatio-temporal data, i.e. classify the methods, not just the data
> being acted on by the methods.
> 
> I think I'm missing something really obvious here, and I suspect it is
> related to using too many object-oriented systems (I'm more used to
> Python's, right now, for example; but even with Java, there is a sense
> of balance between methods and data within a class).  Thinking about
> it more, I guess I'm not clear on the path or approach that S4 classes
> use for inheritance and for contractual negotiation.
> 
> Here's the example that I'm using as a basis for thinking about design:
> 
> I've got a grandtour object which has slots for Data, Colors (a vector
> used for subsetting from brushes -- one can color points in orca or
> from R, this being the first approach for it), as well as Guidance (at
> this point, merely a string, but will be classed for robustness and
> extensibility at somepoint).
> 
> At the first step, this is for a single sample.  Now, I'd like to
> extend this to problems where one variable is fixed (i.e. TIME, in the
> longitudinal or time-series problem, or GROUP, in the correlated data
> setting).  So we've got, in a sense, the multiple inheritance problem.
> I'd like to be able to inherit from both the Orca object as well as
> the data library object.
> 
> In other OO systems, I'd subclass grandtour, probably using multiple
> inheritance, and modify only the methods which change (or to
> paraphrase the Java approach, use interfaces for the orca toolkit, and
> classes for the data libraries).  For example, I might want to try a
> different visualization for survival data, and only want to change the
> display in some way (not the brushing/colors or guidance methods).
> I'd like to be able to "rerun" numerical analyses on potentially
> interesting subsets, or possibly even predict from current fit models,
> and have the required objects be part of the orca object.
> 
> Orca objects could have associated methods: dumpPipeline, buildPipeline, brush/selectData, getData, setData, addPipe, rmPipe.  In particular, the Data methods will need to use S4 data objects.
> 
> Do I simply extend the data classes to add slots for visualization and the requisite parameters and subclass from existing R libraries?  (i.e. add slots for both parameters and dispatched functions).
> 
> Is there a better approach to approaching this problem (which should
> be phrased as: "I've got more than one set of initial classes to build from; what might be the preferred approach to extend"?)
> 
> I think I'm missing something from reading John's Green book.  If
> anyone has pointers to pages/sections I need to read much more
> carefully, I'd be grateful to hear them!
> 
> (note: I know a number of approaches for how I can implement it, but I
> don't have a good enough sense of S4 class design intentions to pick
> any of them as being "reasonably faithful" to good S4 design).
> 
> best,
> -tony
> 
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-- 
John M. Chambers                  jmc@bell-labs.com
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[Rd] Question re:S4 classes and design; clashing classes?
John Chambers