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Why we wrote wrapr to/unpack

One reason we are developing the wrapr to/unpack methods is the following: we wanted to spruce up the R vtreat interface a bit.

We had recently back-ported a Python sklearn Pipeline step style interface from the Python vtreat to R (announcement here). But that doesn’t mean we are not continuing to make enhancements to the R style interfaces, using features that are unique to R.

With the upcoming wrapr unpacking features we can re-write use of vtreat’s mkCrossFrame*Experiment() cross-frame interfaces from this:

# remotes::install_github("WinVector/wrapr")


cross_frame_experiment <- mkCrossFrameCExperiment(
  varlist = varlist,
  outcomename = outcomename,
  outcometarget = outcometarget)
# unpack the items we want from returned list
treatments <- cross_frame_experiment$treatments
cross_frame <- cross_frame_experiment$crossFrame

To this:

  varlist = varlist,
  outcomename = outcomename,
  outcometarget = outcometarget) %.>% 
    treatments <- treatments,
    cross_frame <- crossFrame

In our first unpack note we mentioned there were R functions that returned named lists. The vtreat mkCrossFrame*Experiment() functions are just that: functions that return named lists. Functions that return named lists/classes really call out for a named multiple assignment operator (for a positional multiple assignment operator see zeallot, and for a different named multiple assignment system see also vadr).

The assignments in a unpacking block (currently written as: to, or unpack) can use any of the R local assignment-like operators (<-, ->, =, or :=). And we have the convention that a name alone such as “a” is shorthand for “a <- a“. These unpack operators now look like a full named multi-assignment system for R.

Below is a small stand-alone example of the assignment notation.

# remotes::install_github("WinVector/wrapr")

  a_variable <- a_value,
  b_variable <- b_value
  ] <- list(a_value = 1, b_value = 2)

#> [1] 1

#> [1] 2

We are working on a new vignette about named multiple assignment here.

Categories: Exciting Techniques Practical Data Science Pragmatic Data Science Pragmatic Machine Learning Tutorials

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Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.

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