---
title: "Visualizations with statistical details: The 'ggstatsplot' approach"
output:
  rmarkdown::html_vignette:
    toc: true
bibliography: paper.bib
csl: apa.csl
link-citations: yes
vignette: >
  %\VignetteIndexEntry{Visualizations with statistical details: The 'ggstatsplot' approach}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r}
#| label = "setup",
#| include = FALSE

source("setup.R")
```

This vignette can be cited as:

```{r}
#| label = "citation",
#| echo = FALSE,
#| comment = ""
citation("ggstatsplot")
```

# Summary

Graphical displays can reveal problems in a statistical model that might not be
apparent from purely numerical summaries. Such visualizations can also be
helpful for the reader to evaluate the validity of a model if it is reported in
a scholarly publication or report. But, given the onerous costs involved,
researchers often avoid preparing information-rich graphics and exploring
several statistical approaches or tests available. The `{ggstatsplot}` package in
the R programming language [@base2021] provides a one-line syntax to enrich
`{ggplot2}`-based visualizations with the results from statistical analysis
embedded in the visualization itself. In doing so, the package helps researchers
adopt a rigorous, reliable, and robust data exploratory and reporting workflow.

# Statement of Need

In a typical data analysis workflow, data visualization and statistical modeling
are two different phases: visualization informs modeling, and in turn, modeling
can suggest a different visualization method, and so on and so forth
[@wickham2016r]. The central idea of `{ggstatsplot}` is simple: combine these two
phases into one in the form of an informative graphic with statistical details.

Before discussing benefits of this approach, we will show an example (Figure
1).

```{r}
#| label = "iris",
#| fig.width = 8,
#| fig.height = 5,
#| error = TRUE,
#| fig.cap = "Example plot from the `{ggstatsplot}` package illustrating its philosophy of
#| juxtaposing informative visualizations with details from statistical analysis. To see all
#| supported plots and statistical analyses, see the package website:
#| \\url{https://indrajeetpatil.github.io/ggstatsplot/}"


ggbetweenstats(iris, Species, Sepal.Length)
```

As can be seen, with a single line of code, the function produces details about
descriptive statistics, inferential statistics, effect size estimate and its
uncertainty, pairwise comparisons, Bayesian hypothesis testing, Bayesian
posterior estimate and its uncertainty. Moreover, these details are juxtaposed
with informative and well-labeled visualizations. The defaults are designed to
follow best practices in both data visualization [@cleveland1985;
@grant2018data; @healy2018data; @tufte2001; @wilke2019fundamentals] and
(frequentist/Bayesian) statistical reporting [@apa2019; @van2020jasp]. Without
`{ggstatsplot}`, getting these statistical details and customizing a plot would
require significant amount of time and effort. In other words, this package
removes the trade-off often faced by researchers between ease and thoroughness
of data exploration and further cements good data exploration habits.

Internally, data cleaning is carried out using the `tidyverse` [@Wickham2019],
while statistical analysis is carried out via the `{statsExpressions}`
[@Patil2021] and `easystats` [@Ben-Shachar2020; @Lüdecke2020parameters;
@Lüdecke2020performance;
@Lüdecke2019; @Makowski2019; @Makowski2020] packages. All visualizations are
constructed using the grammar of graphics framework [@Wilkinson2012], as
implemented in the `{ggplot2}` package [@Wickham2016].

# Benefits

In summary, the benefits of `{ggstatsplot}`'s approach are the following. It:

a. produces charts displaying both raw data, and numerical plus graphical
   summary indices,

b. avoids errors in and increases reproducibility of statistical reporting,

c. highlights the importance of the effect by providing effect size measures by
   default,

d. provides an easy way to evaluate *absence* of an effect using Bayes factors,

e. encourages researchers and readers to evaluate statistical assumptions of a
model in the context of the underlying data (Figure 2),

f. is easy and simple enough that someone with little to no coding experience
   can use it without making an error and may even encourage beginners to
   programmatically analyze data, instead of using GUI software.

```{r}
#| label = "reporting",
#| echo = FALSE,
#| fig.cap = "Comparing the 'Standard' approach of reporting statistical analysis in a
#| publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an
#| informative graphic. Note that the results described in the 'Standard' approach are about the
#| 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to
#| evaluate the validity of the results. The ideal reporting practice will be a hybrid of these
#| two approaches where the plot contains both the visual and numerical summaries about a
#| statistical model, while the narrative provides interpretative context for the reported
#| statistics."
knitr::include_graphics("../man/figures/stats_reporting_format.png")
```

# Future Scope

This package is an ambitious, ongoing, and long-term project. It currently
supports common statistical tests (parametric, non-parametric, robust, or
Bayesian *t*-test, one-way ANOVA, contingency table analysis, correlation
analysis, meta-analysis, regression analyses, etc.) and corresponding
visualizations (box/violin plot, scatter plot, dot-and-whisker plot, pie chart,
bar chart, etc.). It will continue expanding to support an increasing
collection of statistical analyses and visualizations.

# Licensing and Availability

`{ggstatsplot}` is licensed under the GNU General Public License (v3.0), with all
source code stored at [GitHub](https://github.com/IndrajeetPatil/ggstatsplot/).
In the spirit of honest and open science, requests and suggestions for fixes,
feature updates, as well as general questions and concerns are encouraged via
direct interaction with contributors and developers by filing an
[issue](https://github.com/IndrajeetPatil/ggstatsplot/issues) while respecting
[*Contribution
Guidelines*](https://indrajeetpatil.github.io/ggstatsplot/CONTRIBUTING.html).

# Acknowledgements

I would like to acknowledge the support of Mina Cikara, Fiery Cushman, and Iyad
Rahwan during the development of this project. `{ggstatsplot}` relies heavily on
the [`easystats`](https://github.com/easystats/easystats) ecosystem, a
collaborative project created to facilitate the usage of `R` for statistical
analyses. Thus, I would like to thank the
[members](https://github.com/orgs/easystats/people) of `easystats` as well as
the users. I would additionally like to thank the contributors to `{ggstatsplot}`
for reporting bugs, providing helpful feedback, or helping with enhancements.

# References
