Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/davisvaughan/strapgod
"I'm beginning to feel like a strap god." - Eminem
https://github.com/davisvaughan/strapgod
Last synced: 11 days ago
JSON representation
"I'm beginning to feel like a strap god." - Eminem
- Host: GitHub
- URL: https://github.com/davisvaughan/strapgod
- Owner: DavisVaughan
- Created: 2018-09-18T16:18:02.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-08-09T17:52:05.000Z (about 3 years ago)
- Last Synced: 2024-10-09T22:09:29.764Z (28 days ago)
- Language: R
- Homepage: https://davisvaughan.github.io/strapgod/
- Size: 773 KB
- Stars: 65
- Watchers: 4
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# strapgod
[](https://codecov.io/gh/DavisVaughan/strapgod?branch=master)
[](https://travis-ci.org/DavisVaughan/strapgod)
[](https://cran.r-project.org/package=strapgod)
[](https://www.tidyverse.org/lifecycle/#experimental)
## Introduction
The goal of strapgod is to create _virtual groups_ on top of a `tibble` or `grouped_df` as a way of resampling the original data frame. You can then efficiently perform various dplyr operations on this `resampled_df`, like: `summarise()`, `do()`, `group_map()`, and more, to easily compute bootstrapped and resampled statistics.
## Installation
You can install the released version of strapgod from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("strapgod")
```
Install the development version from GitHub with:
``` r
devtools::install_github("DavisVaughan/strapgod")
```
## Learning about strapgod
If you aren't already on the [pkgdown site](https://davisvaughan.github.io/strapgod/), I would encourage starting there. From there, you will be able to click on these two vignettes to learn about working with resampled tibbles.
- `vignette("virtual-bootstraps", "strapgod")`
- `vignette("dplyr-support", "strapgod")`
## Example
Create resampled data frames with `bootstrapify()` or `samplify()`. Notice how we grouped by the _virtual_ column, `.bootstrap` and there are still only 150 rows even though we bootstrapped this dataset 10 times.
```{r first-strap, warning=FALSE, message=FALSE}
library(strapgod)
library(dplyr)
set.seed(123)
bootstrapify(iris, 10)
```
You can feed a `resampled_df` into `summarise()` or `group_modify()` to perform
efficient bootstrapped computations.
```{r summarise}
iris %>%
bootstrapify(10) %>%
summarise(per_strap_mean = mean(Petal.Width))
```
The original data can be grouped as well, and the bootstraps will be created for each group.
```{r summarise-by-group}
iris %>%
group_by(Species) %>%
bootstrapify(10) %>%
summarise(per_strap_per_species_mean = mean(Petal.Width))
```
## Plotting bootstrapped results
A fun example of using strapgod is to create bootstrapped visualizations quickly and easily for hypothetical outcome plots.
```{r non-bootstrap-plots, warning=FALSE, message=FALSE}
set.seed(123)
library(ggplot2)
# without bootstrap
mtcars %>%
ggplot(aes(hp, mpg)) +
geom_smooth(se = FALSE) +
ylim(y = c(0, 40))
```
```{r bootstrap-plots, warning=FALSE, message=FALSE}
# with bootstrap
mtcars %>%
bootstrapify(10) %>%
collect() %>%
ggplot(aes(hp, mpg, group = .bootstrap)) +
geom_smooth(se = FALSE) +
ylim(y = c(0, 40))
```
## In the wild
- Claus Wilke has used strapgod to power some pieces of his [ungeviz](https://github.com/wilkelab/ungeviz) package for visualizing uncertainty.
- You can watch Claus's [rstudio::conf 2019](https://resources.rstudio.com/rstudio-conf-2019/visualizing-uncertainty-with-hypothetical-outcomes-plots) talk to see ungeviz and strapgod in action.