rsample contains a set of functions to create different types of
resamples and corresponding classes for their analysis. The goal is to
have a modular set of methods that can be used across different R
- traditional resampling techniques for estimating the sampling distribution of a statistic and
- estimating model performance using a holdout set
The scope of
rsample is to provide the basic building blocks for
creating and analyzing resamples of a data set but does not include code
for modeling or calculating statistics. The “Working with Resample Sets”
vignette gives demonstrations of how
rsample tools can be used.
Note that resampled data sets created by
rsample are directly
accessible in a resampling object but do not contain much overhead in
memory. Since the original data is not modified, R does not make an
For example, creating 50 bootstraps of a data set does not create an object that is 50-fold larger in memory:
library(rsample) library(mlbench) data(LetterRecognition) lobstr::obj_size(LetterRecognition) #> 2,644,640 B set.seed(35222) boots <- bootstraps(LetterRecognition, times = 50) lobstr::obj_size(boots) #> 6,686,512 B # Object size per resample lobstr::obj_size(boots)/nrow(boots) #> 133,730.2 B # Fold increase is <<< 50 as.numeric(lobstr::obj_size(boots)/lobstr::obj_size(LetterRecognition)) #>  2.528326
Created on 2020-05-07 by the reprex package (v0.3.0)
The memory usage for 50 bootstrap samples is less than 3-fold more than the original data set.
To install it, use:
And the development version from GitHub with:
# install.packages("devtools") install_dev("rsample")
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