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README.Rmd
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README.Rmd
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---
output: github_document
editor_options:
chunk_output_type: console
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# rsample <a href='https://rsample.tidymodels.org/'><img src='man/figures/logo.png' align="right" height="139" alt="a boot on a green background" /></a>
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[](https://app.codecov.io/gh/tidymodels/rsample?branch=main)
[](https://cran.r-project.org/package=rsample)
[](https://cran.r-project.org/package=rsample)
[](https://lifecycle.r-lib.org/articles/stages.html)
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## Overview
The rsample package provides 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 for:
* resampling for estimating the sampling distribution of a statistic
* 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 this package does not include code for modeling or calculating statistics. The [Working with Resample Sets](https://rsample.tidymodels.org/articles/Working_with_rsets.html) vignette gives a demonstration of how rsample tools can be used when building models.
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 automatic copy.
For example, creating 50 bootstraps of a data set does not create an object that is 50-fold larger in memory:
``` r
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)
#> 6,686,776 B
# Object size per resample
lobstr::obj_size(boots)/nrow(boots)
#> 133,735.5 B
# Fold increase is <<< 50
as.numeric(lobstr::obj_size(boots)/lobstr::obj_size(LetterRecognition))
#> [1] 2.528426
```
<sup>Created on 2022-02-28 by the [reprex package](https://reprex.tidyverse.org) (v2.0.1)</sup>
The memory usage for 50 bootstrap samples is less than 3-fold more than the original data set.
## Installation
To install it, use:
```{r install, eval = FALSE}
install.packages("rsample")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("pak")
pak::pak("rsample")
```
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on Posit Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/rsample/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).