In smaakage85/trimmer: Trim an Object

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
library(pryr)

trimmer

trimmer is a lightweight toolkit to trim a (potentially big) R object without breaking the results of a given function call, where the (trimmed) R object is given as argument.

The trim function is the bread and butter of trimmer. It seeks to reduce the size of an R object by recursively removing elements from the object one-by-one. It does so in a 'greedy' fashion - it constantly tries to remove the element that uses the most memory.

The trimming process is constrained by a reference function call. The trimming procedure will not allow elements to be removed from the object, that will cause results from the function call to diverge from the original results of the function call.

Installation

Install the development version of trimmer with:

remotes::install_github("smaakage85/trimmer")

Or install the version released on CRAN:

install.packages("trimmer")

Workflow Example

Get ready by loading the package.

library(trimmer)

Train a model on the famous mtcars data set.

# load training data.
trn <- datasets::mtcars

# estimate model.
mdl <- lm(mpg ~ ., data = trn)

I want to trim the model object mdl as possible without affecting the predictions, computed with function predict(), for the resulting model.

The trimming is then simply conducted by invoking:

mdl_trim <- trim(obj = mdl,
                 obj_arg_name = "object",
                 fun = predict,
                 newdata = trn)

And that's it!

Note, that I provide the trim function with the extra argument newdata, that is passed to the function call with fun. This means, that the trimming is constrained by, that the results of 'fun' (=predict) MUST be exactly the same on these data before and after the trimming.

The trimmed model object now measures r pf_obj_size(object_size(mdl_trim)). The original object measured r pf_obj_size(object_size(mdl)).

For more information about how to use trimmer, please take a look at the vignette: