A wrapper for R’s data() function
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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.
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