# dabestr

## Contents

## About

dabestr is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.

Estimation statistics is a simple framework that avoids the pitfalls 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.

It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.

It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.

## Requirements

Your version of R must be 3.5.0 or higher.

## Installation

```
install.packages("dabestr")
# To install the latest development version on Github,
# use the line below.
devtools::install_github("ACCLAB/dabestr")
```

## Usage

```
library(dabestr)
# Performing unpaired (two independent groups) analysis.
unpaired_mean_diff <- dabest(iris, Species, Petal.Width,
idx = c("setosa", "versicolor", "virginica"),
paired = FALSE) %>%
mean_diff()
# Display the results in a user-friendly format.
unpaired_mean_diff
#> DABEST (Data Analysis with Bootstrap Estimation in R) v0.3.0
#> ============================================================
#>
#> Good morning!
#> The current time is 11:10 AM on Monday July 13, 2020.
#>
#> Dataset : iris
#> X Variable : Species
#> Y Variable : Petal.Width
#>
#> Unpaired mean difference of versicolor (n=50) minus setosa (n=50)
#> 1.08 [95CI 1.01; 1.14]
#>
#> Unpaired mean difference of virginica (n=50) minus setosa (n=50)
#> 1.78 [95CI 1.69; 1.85]
#>
#>
#> 5000 bootstrap resamples.
#> All confidence intervals are bias-corrected and accelerated.
# Produce a Cumming estimation plot.
plot(unpaired_mean_diff)
```

You will find more useful code snippets in this vignette.

## 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

Paywalled publisher site; Free-to-view PDF

## Bugs

Please open a new issue. Include a reproducible example (aka reprex) so anyone can copy-paste your code and move quickly towards helping you out!

## Contributing

All contributions are welcome; please read the Guidelines for contributing first.

We also have a Code of Conduct to foster an inclusive and productive space.

## dabestr in other languages

dabestr is also available in Python and Matlab.