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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# dabestr <img src="man/figures/logo.png" align="right" height="139" /></a>
<!-- badges: start -->
[](https://cran.r-project.org/) [](https://cran.r-project.org/package=dabestr) [](https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D) [](https://spdx.org/licenses/BSD-3-Clause-Clear.html)
[](https://github.com/sunroofgod/dabestr-prototype/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
<!-- ## Overview -->
dabestr is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.
[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics "Estimation Stats on Wikipedia") is a [simple framework](https://thenewstatistics.com/itns/ "Introduction to the New Statistics") that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288 "The fickle P value generates irreproducible results, Halsey et al 2015") of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values.
An estimation plot has two key features.
1. It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.
2. It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.
DABEST powers [estimationstats.com](http://estimationstats.com), allowing everyone access to high-quality estimation plots.
## Installation
```{r, eval = FALSE}
# Install it from CRAN
install.packages("dabestr")
# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github(repo = "ACCLAB/dabestr", ref = "dev")
```
## Usage
```{r, warning = FALSE, message = FALSE}
library(dabestr)
```
```{r, dpi = 500}
data("non_proportional_data")
dabest_obj.mean_diff <- load(
data = non_proportional_data,
x = Group,
y = Measurement,
idx = c("Control 1", "Test 1")
) %>%
mean_diff()
dabest_plot(dabest_obj.mean_diff, TRUE)
```
Please refer to the official [tutorial](https://acclab.github.io/dabestr/) for more useful code snippets.
## Citation
**Moving beyond P values: Everyday data analysis with estimation plots**
*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang*
Nature Methods 2019, 1548-7105. [10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3)
[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view PDF](https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D)
## Contributing
Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/dabestr/issues/new).
All contributions are welcome; please read the [Guidelines for contributing](https://github.com/ACCLAB/dabestr/blob/master/CONTRIBUTING.md) first.
We also have a [Code of Conduct](https://github.com/ACCLAB/dabestr/blob/master/CODE_OF_CONDUCT.md) to foster an inclusive and productive space.
## Acknowledgements
We would like to thank alpha testers from the [Claridge-Chang lab](https://www.claridgechang.net/): [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and Stanislav Ott.
## DABEST in other languages
DABEST is also available in Python ([DABEST-python](https://github.com/ACCLAB/DABEST-python "DABEST-Python on Github")) and Matlab
([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab "DABEST-Matlab on Github")).