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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" /></a>
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## Overview
`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 packages for:
* 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 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)
#> 6,686,512 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))
#> [1] 2.528326
```
<sup>Created on 2020-05-07 by the [reprex package](https://reprex.tidyverse.org) (v0.3.0)</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("devtools")
install_dev("rsample")
```
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/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 RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/parsnip/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.
- We welcome contributions, including typo corrections, bug fixes, and feature requests! If you have never made a pull request to an R package before, `rsample` is an excellent place to start. Find an [issue](https://github.com/tidymodels/rsample/issues/) with the **help wanted `r emo::ji("heart")`** tag, comment that you'd like to take it on, and we'll help you get started.
- 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/).