easystats:An R Framework for Easy Statistical Modeling, Visualization, and Reporting

What iseasystats?

easystatsis a collection of R packages, which aims to provide a unifying and consistent framework to tame, discipline, and harness the scary R statistics and their pesky models.

However, there is not (yet) anunique“easystats” way of doing data analysis. Instead, start with one package and, when you’ll face a new challenge, do check if there is aneasystatsanswer for it in other packages. You will slowly uncover how using them together facilitates your life. And, who knows, you might even end up using them all.

Installation

Type	Source	Command
Release	CRAN	install.packages( "easystats" )
Development	r-universe	install.packages( "easystats", repos = "https://easystats.r-universe.dev" )
Development	GitHub	remotes::install_github( "easystats/easystats" )

Finally, aseasystatssometimes depends on some additional packages for specific functions that are not downloaded by default. If you want to benefit from thefulleasystatsexperiencewithout any hiccups, simply run the following:

easystats::install_suggested()

Citation

To cite the package, run the following command:

citation("easystats")
Tociteeasystatsinpublicationsuse:

Lüdecke,Patil,Ben-Shachar,Wiernik,Bacher,Thériault,&Makowski
(2022).easystats:FrameworkforEasyStatisticalModeling,
Visualization,andReporting.CRAN.
doi:10.32614/CRAN.package.easystats
<https://doi.org/10.32614/CRAN.package.easystats>

ABibTeXentryforLaTeXusersis

@Article{,
title={easystats:FrameworkforEasyStatisticalModeling,Visualization,andReporting},
author={DanielLüdeckeandMattanS.Ben-ShacharandIndrajeetPatilandBrentonM.WiernikandEtienneBacherandRémiThériaultandDominiqueMakowski},
journal={CRAN},
doi={10.32614/CRAN.package.easystats},
year={2022},
note={Rpackage},
url={https://easystats.github.io/easystats/},
}

If you want to do this only for certain packages in the ecosystem, have a look at this article on how you can do so! https://easystats.github.io/easystats/articles/citation.html

Getting started

Eacheasystatspackage has a different scope and purpose. This means yourbest way to startis to explore and pick the one(s) that you feel might be useful to you. However, as they are built with a “bigger picture” in mind, you will realize that using more of them creates a smooth workflow, as these packages are meant to work together. Ideally, these packages work in unison to cover all aspects of statistical analysis and data visualization.

• report:📜 🎉 Automated statistical reporting of objects in R
• correlation:🔗 Your all-in-one package to run correlations
• modelbased:📈 Estimate effects, group averages and contrasts between groups based on statistical models
• bayestestR:👻 Great for beginners or experts of Bayesian statistics
• effectsize:🐉 Compute, convert, interpret and work with indices of effect size and standardized parameters
• see:🎨 The plotting companion to create beautiful results visualizations
• parameters:📊 Obtain a table containing all information about the parameters of your models
• performance:💪 Models’ quality and performance metrics (R2, ICC, LOO, AIC, BF,…)
• insight:🔮 For developers, a package to help you work with different models and packages
• datawizard:🧙 Magic potions to clean and transform your data

Frequently Asked Questions

How iseasystatsdifferent from thetidyverse?

You’ve probably already heard about the tidyverse,another very popular collection of packages (ggplot,dplyr,tidyr,…) that also makes using R easier. So, should you pick thetidyverseoreasystats? Pick both!

Indeed, these two ecosystems have been designed with very different goals in mind. Thetidyversepackages are primarily made to create a new R experience, where data manipulation and exploration is intuitive and consistent. On the other hand,easystatsfocuses more on the final stretch of the analysis: understanding and interpreting your results and reporting them in a manuscript or a report, while following best practices. You can definitely use theeasystatsfunctions in a tidyverseworkflow!

easystats + tidyverse =❤️

Caneasystatsbe useful to advanced users and/or developers?

Yes, definitely!easystatsis built in terms of modules that are general enough to be used inside other packages. For instance, the insightpackage is made to easily implement support for post-processing of pretty much all regression model packages under the sun. We use it in all theeasystatspackages, but it is also used in other non-easystats packages, such as ggstatsplot, modelsummary, ggeffects,and more.

So why not in yours?

Moreover, theeasystatspackages are very lightweight, with a minimal set of dependencies, which again makes it great if you want to rely on them.

Documentation

Websites

Eacheasystatspackage has a dedicated website.

For example, website forparametersis https://easystats.github.io/parameters/.

Blog

In addition to the websites containing documentation for these packages, you can also read posts fromeasystatsblog: https://easystats.github.io/blog/posts/.

Other learning resources

In addition to these websites and blog posts, you can also check out the following presentations and talks to learn more about this ecosystem:

https://easystats.github.io/easystats/articles/resources.html

Dependencies

easystatspackages are designed to be lightweight,i.e.,they don’t have any third-party hard dependencies, other than base-R packages or othereasystatspackages! If you develop R packages, this means that you can safely useeasystatspackages as dependencies in your own packages, without the risk of entering thedependency hell.

library(deepdep)

plot_dependencies("easystats",depth=2,show_stamp=FALSE)

As we can see, the only exception is the {see}package, which is responsible for plotting and creating figures and relies on{ggplot2}, which does have a substantial number of dependencies.

Usage

Total downloads

Total	insight	datawizard	parameters	performance	bayestestR	effectsize	correlation	see	modelbased	report	easystats
24,076,996	7,008,088	4,287,422	2,927,848	2,855,836	2,780,112	2,212,309	748,005	612,868	371,362	204,749	68,397

Trend

Contributing

We are happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. Pull Requests for contributions are encouraged.

Here are some simple ways in which you can contribute (in the increasing order of commitment):

• Read and correct any inconsistencies in the documentation
• Raise issues about bugs or wanted features
• Review code
• Add new functionality

Code of Conduct

Please note that the ‘easystats’ project is released with aContributor Code of Conduct.By contributing to this project, you agree to abide by its terms.