The workflow for statistical analyses is discussed at several places. Often, it is recommended:

• never change the raw data, but transform it,
• keep your analysis reproducible,
• separate functions and data,
• use R package system as organizing structure.

In some recent projects I tried an S4 class approach for this workflow, which I want to present and discuss. It makes use of the package datamart, which I recently submitted to CRAN. Here is a sample session:

> library(datamart)
> library(beeswarm)
> # load one of my datasets
> xp <- expenditures()
> # introspection: what
> # "resources" for this
> # dataset did I once define?
> queries(xp)
Evs#Categories Evs#Elasticities   Evs#Elasticity 
"Categories"   "Elasticities"     "Elasticity" 
InternalData#Raw 
"Raw" 
> # get me a resource
> head(query(xp, "Raw"))
coicop2                                    coicop2de
1      15 Expenditures (exclusive private consumption)
2      15 Expenditures (exclusive private consumption)
3      15 Expenditures (exclusive private consumption)
4      15 Expenditures (exclusive private consumption)
5      15 Expenditures (exclusive private consumption)
6      15 Expenditures (exclusive private consumption)
income               hhtype value
1  (all)                (all)  2539
2  (all)               Single  1462
3  (all)         Single woman  1232
4  (all)           Single man  1866
5  (all)        Single parent  1004
6  (all) Single parent, 1 kid   991

Read on to see how a S4 dataset object is defined and accessed, and what I see in favour and against this approach.