# DABEST-Python
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## Recent Version Update
On 22 March 2024, we officially released **DABEST Version Ondeh
(v2024.03.29)**. This new version provides several new features and
includes performance improvements.
1. **New Paired Proportion Plot**: This feature builds upon the
existing proportional analysis capabilities by introducing advanced
aesthetics and clearer visualization of changes in proportions
between different groups, inspired by the informative nature of
Sankey Diagrams. It’s particularly useful for studies that require
detailed examination of how proportions shift in paired
observations.
2. **Customizable Swarm Plot**: Enhancements allow for tailored swarm
plot aesthetics, notably the adjustment of swarm sides to produce
asymmetric swarm plots. This customization enhances data
representation, making visual distinctions more pronounced and
interpretations clearer.
3. **Standardized Delta-delta Effect Size**: We added a new metric akin
to a Hedges’ g for delta-delta effect size, which allows comparisons
between delta-delta effects generated from metrics with different
units.
4. **Miscellaneous Improvements**: This version also encompasses a
broad range of miscellaneous enhancements, including bug fixes,
Bootstrapping speed improvements, new templates for raising issues,
and updated unit tests. These improvements are designed to
streamline the user experience, increase the software’s stability,
and expand its versatility. By addressing user feedback and
identified issues, DABEST continues to refine its functionality and
reliability.
## Contents
<!-- TOC depthFrom:1 depthTo:2 withLinks:1 updateOnSave:1 orderedList:0 -->
- [About](#about)
- [Installation](#installation)
- [Usage](#usage)
- [How to cite](#how-to-cite)
- [Bugs](#bugs)
- [Contributing](#contributing)
- [Acknowledgements](#acknowledgements)
- [Testing](#testing)
- [DABEST in other languages](#dabest-in-other-languages)
<!-- /TOC -->
## About
DABEST is a package for **D**ata **A**nalysis using
**B**ootstrap-Coupled **EST**imation.
[Estimation
statistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a
[simple framework](https://thenewstatistics.com/itns/) that avoids the
[pitfalls](https://www.nature.com/articles/nmeth.3288) of significance
testing. It employs familiar statistical concepts such as means, mean
differences, and error bars. More importantly, it focuses on the effect
size of one’s experiment or intervention, rather than succumbing to a
false dichotomy engendered by *P* values.
An estimation plot comprises two key features.
1. It presents all data points as a swarm plot, ordering each point to
display the underlying distribution.
2. It illustrates the effect size as a **bootstrap 95% confidence
interval** on a **separate but aligned axis**.
DABEST powers [estimationstats.com](https://www.estimationstats.com/),
allowing everyone access to high-quality estimation plots.
## Installation
This package is tested on Python 3.8 and onwards. It is highly
recommended to download the [Anaconda
distribution](https://www.continuum.io/downloads) of Python in order to
obtain the dependencies easily.
You can install this package via `pip`.
To install, at the command line run
``` shell
pip install dabest
```
You can also
[clone](https://help.github.com/articles/cloning-a-repository) this repo
locally.
Then, navigate to the cloned repo in the command line and run
``` shell
pip install .
```
## Usage
``` python3
import pandas as pd
import dabest
# Load the iris dataset. This step requires internet access.
iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
# Load the above data into `dabest`.
iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
idx=("setosa", "versicolor", "virginica"))
# Produce a Cumming estimation plot.
iris_dabest.mean_diff.plot();
```
Please refer to the official
[tutorial](https://acclab.github.io/DABEST-python/) for more useful code
snippets.
## How to cite
**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://rdcu.be/bHhJ4)
## Bugs
Please report any bugs on the [issue
page](https://github.com/ACCLAB/DABEST-python/issues/new).
## Contributing
All contributions are welcome; please read the [Guidelines for
contributing](CONTRIBUTING.md) first.
We also have a [Code of Conduct](CODE_OF_CONDUCT.md) to foster an
inclusive and productive space.
### A wish list for new features
If you have any specific comments and ideas for new features that you
would like to share with us, please read the [Guidelines for
contributing](CONTRIBUTING.md), create a new issue using Feature request
template or create a new post in [our Google
Group](https://groups.google.com/g/estimationstats).
## 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.
## Testing
To test DABEST, you need to install
[pytest](https://docs.pytest.org/en/latest) and
[nbdev](https://nbdev.fast.ai/).
- Run `pytest` in the root directory of the source distribution. This
runs the test suite in the folder `dabest/tests/mpl_image_tests`.
- Run `nbdev_test` in the root directory of the source distribution.
This runs the value assertion tests in the folder `dabest/tests`
The test suite ensures that the bootstrapping functions and the plotting
functions perform as expected.
For detailed information, please refer to the [test
folder](nbs/tests/README.md)
## DABEST in other languages
DABEST is also available in R
([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab
([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)).
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These improvements are designed to\n streamline the user experience, increase the software\u2019s stability,\n and expand its versatility. By addressing user feedback and\n identified issues, DABEST continues to refine its functionality and\n reliability.\n\n## Contents\n\n<!-- TOC depthFrom:1 depthTo:2 withLinks:1 updateOnSave:1 orderedList:0 -->\n\n- [About](#about)\n- [Installation](#installation)\n- [Usage](#usage)\n- [How to cite](#how-to-cite)\n- [Bugs](#bugs)\n- [Contributing](#contributing)\n- [Acknowledgements](#acknowledgements)\n- [Testing](#testing)\n- [DABEST in other languages](#dabest-in-other-languages)\n\n<!-- /TOC -->\n\n## About\n\nDABEST is a package for **D**ata **A**nalysis using\n**B**ootstrap-Coupled **EST**imation.\n\n[Estimation\nstatistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a\n[simple framework](https://thenewstatistics.com/itns/) that avoids the\n[pitfalls](https://www.nature.com/articles/nmeth.3288) of significance\ntesting. 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It is highly\nrecommended to download the [Anaconda\ndistribution](https://www.continuum.io/downloads) of Python in order to\nobtain the dependencies easily.\n\nYou can install this package via `pip`.\n\nTo install, at the command line run\n\n``` shell\npip install dabest\n```\n\nYou can also\n[clone](https://help.github.com/articles/cloning-a-repository) this repo\nlocally.\n\nThen, navigate to the cloned repo in the command line and run\n\n``` shell\npip install .\n```\n\n## Usage\n\n``` python3\nimport pandas as pd\nimport dabest\n\n# Load the iris dataset. 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