
<!-- README.md is generated from README.Rmd. Please edit that file -->

## `{ggstatsplot}`: `{ggplot2}` Based Plots with Statistical Details

| Status | Usage | Miscellaneous |
|----|----|----|
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## Raison d’être <img src="man/figures/logo.png" alt="ggstatsplot package logo" align="right" width="360" />

> “What is to be sought in designs for the display of information is the
> clear portrayal of complexity. Not the complication of the simple;
> rather … the revelation of the complex.” - Edward R. Tufte

[`{ggstatsplot}`](https://www.indrapatil.com/ggstatsplot/) is an
extension of [`{ggplot2}`](https://github.com/tidyverse/ggplot2) package
for creating graphics with details from statistical tests included in
the information-rich plots themselves. In a typical exploratory data
analysis workflow, data visualization and statistical modeling are two
different phases: visualization informs modeling, and modeling in its
turn can suggest a different visualization method, and so on and so
forth. The central idea of `{ggstatsplot}` is simple: combine these two
phases into one in the form of graphics with statistical details, which
makes data exploration simpler and faster.

## Installation

| Type        | Command                                  |
|:------------|:-----------------------------------------|
| Release     | `install.packages("ggstatsplot")`        |
| Development | `pak::pak("IndrajeetPatil/ggstatsplot")` |

## Citation

If you want to cite this package in a scientific journal or in any other
context, run the following code in your `R` console:

``` r
citation("ggstatsplot")
To cite package 'ggstatsplot' in publications use:

  Patil, I. (2021). Visualizations with statistical details: The
  'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167,
  doi:10.21105/joss.03167

A BibTeX entry for LaTeX users is

  @Article{,
    doi = {10.21105/joss.03167},
    url = {https://doi.org/10.21105/joss.03167},
    year = {2021},
    publisher = {{The Open Journal}},
    volume = {6},
    number = {61},
    pages = {3167},
    author = {Indrajeet Patil},
    title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}},
    journal = {{Journal of Open Source Software}},
  }
```

## Acknowledgments

I would like to thank all the contributors to `{ggstatsplot}` who
pointed out bugs or requested features I hadn’t considered. I would
especially like to thank other package developers (especially Daniel
Lüdecke, Dominique Makowski, Mattan S. Ben-Shachar, Brenton Wiernik,
Patrick Mair, Salvatore Mangiafico, etc.) who have patiently and
diligently answered my relentless questions and supported feature
requests in their projects. I also want to thank Chuck Powell for his
initial contributions to the package.

The hexsticker was generously designed by Sarah Otterstetter (Max Planck
Institute for Human Development, Berlin). This package has also
benefited from the larger `#rstats` community on Twitter, LinkedIn, and
`StackOverflow`.

Thanks are also due to my postdoc advisers (Mina Cikara and Fiery
Cushman at Harvard University; Iyad Rahwan at Max Planck Institute for
Human Development) who patiently supported me spending hundreds (?) of
hours working on this package rather than what I was paid to do. 😁

## Documentation and Examples

To see the detailed documentation for each function in the stable
**CRAN** version of the package, see:

- [Publication](https://joss.theoj.org/papers/10.21105/joss.03167)

- [Presentation](https://www.indrapatil.com/intro-to-ggstatsplot/#/ggstatsplot-informative-statistical-visualizations)

- [Vignettes](https://www.indrapatil.com/ggstatsplot/articles/)

## Summary of available plots

| Function | Plot | Description |
|:---|:---|:---|
| `ggbetweenstats()` | **violin plots** | for comparisons *between* groups/conditions |
| `ggwithinstats()` | **violin plots** | for comparisons *within* groups/conditions |
| `gghistostats()` | **histograms** | for distribution about numeric variable |
| `ggdotplotstats()` | **dot plots/charts** | for distribution about labeled numeric variable |
| `ggscatterstats()` | **scatterplots** | for correlation between two variables |
| `ggcorrmat()` | **correlation matrices** | for correlations between multiple variables |
| `ggpiestats()` | **pie charts** | for categorical data |
| `ggbarstats()` | **bar charts** | for categorical data |
| `ggcoefstats()` | **dot-and-whisker plots** | for regression models and meta-analysis |

In addition to these basic plots, `{ggstatsplot}` also provides
**`grouped_`** versions (see below) that makes it easy to repeat the
same analysis for any grouping variable.

## Summary of types of statistical analyses

The table below summarizes all the different types of analyses currently
supported in this package-

| Functions | Description | Parametric | Non-parametric | Robust | Bayesian |
|:---|:---|:---|:---|:---|:---|
| `ggbetweenstats()` | Between group/condition comparisons | ✅ | ✅ | ✅ | ✅ |
| `ggwithinstats()` | Within group/condition comparisons | ✅ | ✅ | ✅ | ✅ |
| `gghistostats()`, `ggdotplotstats()` | Distribution of a numeric variable | ✅ | ✅ | ✅ | ✅ |
| `ggcorrmat` | Correlation matrix | ✅ | ✅ | ✅ | ✅ |
| `ggscatterstats()` | Correlation between two variables | ✅ | ✅ | ✅ | ✅ |
| `ggpiestats()`, `ggbarstats()` | Association between categorical variables | ✅ | ✅ | ❌ | ✅ |
| `ggpiestats()`, `ggbarstats()` | Equal proportions for categorical variable levels | ✅ | ✅ | ❌ | ✅ |
| `ggcoefstats()` | Regression model coefficients | ✅ | ✅ | ✅ | ✅ |
| `ggcoefstats()` | Random-effects meta-analysis | ✅ | ❌ | ✅ | ✅ |

Summary of Bayesian analysis

| Analysis                     | Hypothesis testing | Estimation |
|:-----------------------------|:-------------------|:-----------|
| (one/two-sample) *t*-test    | ✅                 | ✅         |
| one-way ANOVA                | ✅                 | ✅         |
| correlation                  | ✅                 | ✅         |
| (unpaired) contingency table | ✅                 | ✅         |
| (paired) contingency table   | ✅                 | ❌         |
| random-effects meta-analysis | ✅                 | ✅         |

## Statistical reporting

For **all** statistical tests reported in the plots, the default
template abides by the gold standard for statistical reporting. For
example, here are results from Yuen’s test for trimmed means (robust
*t*-test):

<img src="man/figures/stats_reporting_format.png" alt="Example of statistical reporting format showing Yuen's test results with test statistic, degrees of freedom, p-value, effect size, and confidence interval" align="center" />

## Summary of statistical tests and effect sizes

Statistical analysis is carried out by `{statsExpressions}` package, and
thus a summary table of all the statistical tests currently supported
across various functions can be found in article for that package:
<https://www.indrapatil.com/statsExpressions/articles/stats_details.html>

## Primary functions

### `ggbetweenstats()`

This function creates either a violin plot, a box plot, or a mix of two
for **between**-group or **between**-condition comparisons with results
from statistical tests in the subtitle. The simplest function call looks
like this-

``` r
set.seed(123)

ggbetweenstats(
  data  = iris,
  x     = Species,
  y     = Sepal.Length,
  title = "Distribution of sepal length across Iris species"
)
```

<img src="man/figures/README-ggbetweenstats1-1.png" alt="Violin plot with boxplot overlay showing distribution of sepal length across three Iris species with statistical test results" width="100%" />

**Defaults** return<br>

✅ raw data + distributions <br> ✅ descriptive statistics <br> ✅
inferential statistics <br> ✅ effect size + CIs <br> ✅ pairwise
comparisons <br> ✅ Bayesian hypothesis-testing <br> ✅ Bayesian
estimation <br>

A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, there
is also a `grouped_` variant of this function that makes it easy to
repeat the same operation across a **single** grouping variable:

``` r
set.seed(123)

grouped_ggbetweenstats(
  data             = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                = mpaa,
  y                = length,
  grouping.var     = genre,
  ggsignif.args    = list(textsize = 4, tip_length = 0.01),
  p.adjust.method  = "bonferroni",
  palette          = "ggsci::default_jama",
  plotgrid.args    = list(nrow = 1),
  annotation.args  = list(title = "Differences in movie length by mpaa ratings for different genres")
)
```

<img src="man/figures/README-ggbetweenstats2-1.png" alt="Grouped violin plots comparing movie length by MPAA rating for Action and Comedy genres with statistical annotations" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggbetweenstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggbetweenstats.html>

### `ggwithinstats()`

`ggbetweenstats()` function has an identical twin function
`ggwithinstats()` for repeated measures designs that behaves in the same
fashion with a few minor tweaks introduced to properly visualize the
repeated measures design. As can be seen from an example below, the only
difference between the plot structure is that now the group means are
connected by paths to highlight the fact that these data are paired with
each other.

If your repeated-measures data include an explicit subject identifier,
it is recommended that you pass it via `subject.id`; rows with missing
identifiers are ignored for paired grouping and repeated-measures
statistics.

``` r
set.seed(123)
library(WRS2) ## for data
library(afex) ## to run ANOVA

ggwithinstats(
  data       = WineTasting,
  x          = Wine,
  y          = Taste,
  subject.id = Taster,
  title      = "Wine tasting"
)
```

<img src="man/figures/README-ggwithinstats1-1.png" alt="Within-subjects violin plot showing wine taste ratings by wine type with paired data paths and statistical results" width="100%" />

**Defaults** return<br>

✅ raw data + distributions <br> ✅ descriptive statistics <br> ✅
inferential statistics <br> ✅ effect size + CIs <br> ✅ pairwise
comparisons <br> ✅ Bayesian hypothesis-testing <br> ✅ Bayesian
estimation <br>

As with the `ggbetweenstats()`, this function also has a `grouped_`
variant that makes repeating the same analysis across a single grouping
variable quicker. We will see an example with only repeated
measurements-

``` r
set.seed(123)

grouped_ggwithinstats(
  data            = dplyr::filter(bugs_long, region %in% c("Europe", "North America"), condition %in% c("LDLF", "LDHF")),
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "np",
  xlab            = "Condition",
  ylab            = "Desire to kill an artrhopod",
  grouping.var    = region
)
```

<img src="man/figures/README-ggwithinstats2-1.png" alt="Grouped within-subjects violin plots showing desire to kill arthropods by condition for Europe and North America" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggwithinstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggwithinstats.html>

### `gghistostats()`

To visualize the distribution of a single variable and check if its mean
is significantly different from a specified value with a one-sample
test, `gghistostats()` can be used.

``` r
set.seed(123)

gghistostats(
  data       = ggplot2::msleep,
  x          = awake,
  title      = "Amount of time spent awake",
  test.value = 12,
  binwidth   = 1
)
```

<img src="man/figures/README-gghistostats1-1.png" alt="Histogram showing distribution of time spent awake in mammals with one-sample test results" width="100%" />

**Defaults** return<br>

✅ counts + proportion for bins<br> ✅ descriptive statistics <br> ✅
inferential statistics <br> ✅ effect size + CIs <br> ✅ Bayesian
hypothesis-testing <br> ✅ Bayesian estimation <br>

There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable:

``` r
set.seed(123)

grouped_gghistostats(
  data              = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                 = budget,
  test.value        = 50,
  type              = "nonparametric",
  xlab              = "Movies budget (in million US$)",
  grouping.var      = genre,
  ggtheme           = ggthemes::theme_tufte(),
  ## modify the defaults from `{ggstatsplot}` for each plot
  plotgrid.args     = list(nrow = 1),
  annotation.args   = list(title = "Movies budgets for different genres")
)
```

<img src="man/figures/README-gghistostats2-1.png" alt="Grouped histograms showing movie budget distributions for Action and Comedy genres with statistical tests" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/gghistostats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/gghistostats.html>

### `ggdotplotstats()`

This function is similar to `gghistostats()`, but is intended to be used
when the numeric variable also has a label.

``` r
set.seed(123)

ggdotplotstats(
  data       = dplyr::filter(gapminder::gapminder, continent == "Asia"),
  y          = country,
  x          = lifeExp,
  test.value = 55,
  type       = "robust",
  title      = "Distribution of life expectancy in Asian continent",
  xlab       = "Life expectancy"
)
```

<img src="man/figures/README-ggdotplotstats1-1.png" alt="Dot plot showing life expectancy distribution across Asian countries with robust one-sample test results" width="100%" />

**Defaults** return<br>

✅descriptives (centrality measure + uncertainty + sample size)<br> ✅
inferential statistics <br> ✅ effect size + CIs <br> ✅ Bayesian
hypothesis-testing <br> ✅ Bayesian estimation <br>

As with the rest of the functions in this package, there is also a
`grouped_` variant of this function to facilitate looping the same
operation for all levels of a single grouping variable.

``` r
set.seed(123)

grouped_ggdotplotstats(
  data            = dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6")),
  x               = cty,
  y               = manufacturer,
  type            = "bayes",
  xlab            = "city miles per gallon",
  ylab            = "car manufacturer",
  grouping.var    = cyl,
  test.value      = 15.5,
  point.args      = list(color = "red", size = 5, shape = 13),
  annotation.args = list(title = "Fuel economy data")
)
```

<img src="man/figures/README-ggdotplotstats2-1.png" alt="Grouped dot plots showing city miles per gallon by car manufacturer for 4 and 6 cylinder vehicles" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggdotplotstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggdotplotstats.html>

### `ggscatterstats()`

This function creates a scatterplot with marginal distributions overlaid
on the axes and results from statistical tests in the subtitle:

``` r
ggscatterstats(
  data  = ggplot2::msleep,
  x     = sleep_rem,
  y     = awake,
  xlab  = "REM sleep (in hours)",
  ylab  = "Amount of time spent awake (in hours)",
  title = "Understanding mammalian sleep"
)
```

<img src="man/figures/README-ggscatterstats1-1.png" alt="Scatterplot with marginal distributions showing relationship between REM sleep and time awake in mammals with correlation results" width="100%" />

**Defaults** return<br>

✅ raw data + distributions <br> ✅ marginal distributions <br> ✅
inferential statistics <br> ✅ effect size + CIs <br> ✅ Bayesian
hypothesis-testing <br> ✅ Bayesian estimation <br>

There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable.

``` r
set.seed(123)

grouped_ggscatterstats(
  data             = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  x                = rating,
  y                = length,
  grouping.var     = genre,
  label.var        = title,
  label.expression = length > 200,
  xlab             = "IMDB rating",
  ggtheme          = ggplot2::theme_grey(),
  ggplot.component = list(ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))),
  plotgrid.args    = list(nrow = 1),
  annotation.args  = list(title = "Relationship between movie length and IMDB ratings")
)
```

<img src="man/figures/README-ggscatterstats2-1.png" alt="Grouped scatterplots showing IMDB rating vs movie length for Action and Comedy genres with correlation annotations" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggscatterstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggscatterstats.html>

### `ggcorrmat`

`ggcorrmat` makes a correlalogram (a matrix of correlation coefficients)
with minimal amount of code. Just sticking to the defaults itself
produces publication-ready correlation matrices. But, for the sake of
exploring the available options, let’s change some of the defaults. For
example, multiple aesthetics-related arguments can be modified to change
the appearance of the correlation matrix.

``` r
set.seed(123)

## as a default this function outputs a correlation matrix plot
ggcorrmat(
  data     = ggplot2::msleep,
  colors   = c("#B2182B", "white", "#4D4D4D"),
  title    = "Correlalogram for mammals sleep dataset",
  subtitle = "sleep units: hours; weight units: kilograms"
)
```

<img src="man/figures/README-ggcorrmat1-1.png" alt="Correlation matrix heatmap for mammals sleep dataset showing pairwise correlations with significance indicators" width="100%" />

**Defaults** return<br>

✅ effect size + significance<br> ✅ careful handling of `NA`s

If there are `NA`s present in the selected variables, the legend will
display minimum, median, and maximum number of pairs used for
correlation tests.

There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable:

``` r
set.seed(123)

grouped_ggcorrmat(
  data            = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
  type            = "robust",
  colors          = c("#cbac43", "white", "#550000"),
  grouping.var    = genre,
  p.adjust.method = "fdr",
  matrix.type     = "lower"
)
```

<img src="man/figures/README-ggcorrmat2-1.png" alt="Grouped correlation matrices for Action and Comedy movie genres showing robust correlations" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggcorrmat.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggcorrmat.html>

### `ggpiestats()`

This function creates a pie chart for categorical or nominal variables
with results from contingency table analysis (Pearson’s chi-squared test
for between-subjects design and McNemar’s chi-squared test for
within-subjects design) included in the subtitle of the plot. If only
one categorical variable is entered, results from one-sample proportion
test (i.e., a chi-squared goodness of fit test) will be displayed as a
subtitle.

To study an interaction between two categorical variables:

``` r
set.seed(123)

ggpiestats(
  data         = mtcars,
  x            = am,
  y            = cyl,
  palette      = "wesanderson::Royal1",
  title        = "Dataset: Motor Trend Car Road Tests",
  legend.title = "Transmission"
)
```

<img src="man/figures/README-ggpiestats1-1.png" alt="Pie charts showing transmission type distribution across cylinder groups in mtcars data with contingency table analysis" width="100%" />

**Defaults** return<br>

✅ descriptives (frequency + %s) <br> ✅ inferential statistics <br> ✅
effect size + CIs <br> ✅ Goodness-of-fit tests <br> ✅ Bayesian
hypothesis-testing <br> ✅ Bayesian estimation <br>

There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable.
Following example is a case where the theoretical question is about
proportions for different levels of a single nominal variable:

``` r
set.seed(123)

grouped_ggpiestats(
  data         = mtcars,
  x            = cyl,
  grouping.var = am,
  label.repel  = TRUE,
  palette      = "ggsci::default_ucscgb"
)
```

<img src="man/figures/README-ggpiestats2-1.png" alt="Grouped pie charts showing cylinder distribution for automatic and manual transmission vehicles" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggpiestats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggpiestats.html>

### `ggbarstats()`

In case you are not a fan of pie charts (for very good reasons), you can
alternatively use `ggbarstats()` function which has a similar
syntax—including support for one-sample goodness-of-fit tests.

To study an interaction between two categorical variables:

``` r
set.seed(123)
library(ggplot2)

ggbarstats(
  data             = movies_long,
  x                = mpaa,
  y                = genre,
  title            = "MPAA Ratings by Genre",
  xlab             = "movie genre",
  legend.title     = "MPAA rating",
  ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
  palette          = "RColorBrewer::Set2"
)
```

<img src="man/figures/README-ggbarstats1-1.png" alt="Stacked bar chart showing MPAA ratings distribution by movie genre with chi-squared test results" width="100%" />

**Defaults** return<br>

✅ descriptives (frequency + %s) <br> ✅ inferential statistics <br> ✅
effect size + CIs <br> ✅ Goodness-of-fit tests <br> ✅ Bayesian
hypothesis-testing <br> ✅ Bayesian estimation <br>

There is also a `grouped_` variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable.
Following example is a case where the theoretical question is about
proportions for different levels of a single nominal variable:

``` r
set.seed(123)

grouped_ggbarstats(
  data         = mtcars,
  x            = cyl,
  grouping.var = am,
  label.repel  = TRUE,
  palette      = "ggsci::default_ucscgb"
)
```

<img src="man/figures/README-ggbarstats2-1.png" alt="Grouped bar charts showing cylinder distribution for automatic and manual transmission vehicles" width="100%" />

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggbarstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggbarstats.html>

### `ggcoefstats()`

The function `ggcoefstats()` generates **dot-and-whisker plots** for
regression models. The tidy data frames are prepared using
`parameters::model_parameters()`. Additionally, if available, the model
summary indices are also extracted from
`performance::model_performance()`.

``` r
set.seed(123)

## model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)

ggcoefstats(mod)
```

<img src="man/figures/README-ggcoefstats1-1.png" alt="Dot-and-whisker plot showing regression coefficients for mpg model with confidence intervals" width="100%" />

**Defaults** return<br>

✅ inferential statistics <br> ✅ estimate + CIs <br> ✅ model summary
(AIC and BIC) <br>

Details about underlying functions used to create graphics and
statistical tests carried out can be found in the function
documentation:
<https://www.indrapatil.com/ggstatsplot/reference/ggcoefstats.html>

For more, also read the following vignette:
<https://www.indrapatil.com/ggstatsplot/articles/web_only/ggcoefstats.html>

### Extracting expressions and data frames with statistical details

`{ggstatsplot}` also offers a convenience function to extract data
frames with statistical details that are used to create expressions
displayed in `{ggstatsplot}` plots.

``` r
set.seed(123)

p <- ggbetweenstats(mtcars, cyl, mpg)

# extracting expression present in the subtitle
extract_subtitle(p)
#> list(italic("F")["Welch"](2, 18.03) == "31.62", italic(p) == 
#>     "1.27e-06", widehat(omega["p"]^2) == "0.74", CI["95%"] ~ 
#>     "[" * "0.53", "1.00" * "]", italic("n")["obs"] == "32")

# extracting expression present in the caption
extract_caption(p)
#> list(log[e] * (BF["01"]) == "-14.92", widehat(italic(R^"2"))["Bayesian"]^"posterior" == 
#>     "0.71", CI["95%"]^HDI ~ "[" * "0.57", "0.79" * "]", italic("r")["Cauchy"]^"JZS" == 
#>     "0.71")

# a list of tibbles containing statistical analysis summaries
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 14
#>   statistic    df df.error    p.value
#>       <dbl> <dbl>    <dbl>      <dbl>
#> 1      31.6     2     18.0 0.00000127
#>   method                                                   effectsize estimate
#>   <chr>                                                    <chr>         <dbl>
#> 1 One-way analysis of means (not assuming equal variances) Omega2        0.744
#>   conf.level conf.low conf.high conf.method conf.distribution n.obs expression
#>        <dbl>    <dbl>     <dbl> <chr>       <chr>             <int> <list>    
#> 1       0.95    0.531         1 ncp         F                    32 <language>
#> 
#> $caption_data
#> # A tibble: 6 × 17
#>   term     pd prior.distribution prior.location prior.scale     bf10
#>   <chr> <dbl> <chr>                       <dbl>       <dbl>    <dbl>
#> 1 mu    1     cauchy                          0       0.707 3008850.
#> 2 cyl-4 1     cauchy                          0       0.707 3008850.
#> 3 cyl-6 0.780 cauchy                          0       0.707 3008850.
#> 4 cyl-8 1     cauchy                          0       0.707 3008850.
#> 5 sig2  1     cauchy                          0       0.707 3008850.
#> 6 g_cyl 1     cauchy                          0       0.707 3008850.
#>   method                          log_e_bf10 effectsize         estimate std.dev
#>   <chr>                                <dbl> <chr>                 <dbl>   <dbl>
#> 1 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 2 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 3 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 4 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 5 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#> 6 Bayes factors for linear models       14.9 Bayesian R-squared    0.714  0.0503
#>   conf.level conf.low conf.high conf.method n.obs expression
#>        <dbl>    <dbl>     <dbl> <chr>       <int> <list>    
#> 1       0.95    0.574     0.788 HDI            32 <language>
#> 2       0.95    0.574     0.788 HDI            32 <language>
#> 3       0.95    0.574     0.788 HDI            32 <language>
#> 4       0.95    0.574     0.788 HDI            32 <language>
#> 5       0.95    0.574     0.788 HDI            32 <language>
#> 6       0.95    0.574     0.788 HDI            32 <language>
#> 
#> $pairwise_comparisons_data
#> # A tibble: 3 × 9
#>   group1 group2 statistic   p.value alternative distribution p.adjust.method
#>   <chr>  <chr>      <dbl>     <dbl> <chr>       <chr>        <chr>          
#> 1 4      6          -6.67 0.00110   two.sided   q            Holm           
#> 2 4      8         -10.7  0.0000140 two.sided   q            Holm           
#> 3 6      8          -7.48 0.000257  two.sided   q            Holm           
#>   test         expression
#>   <chr>        <list>    
#> 1 Games-Howell <language>
#> 2 Games-Howell <language>
#> 3 Games-Howell <language>
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"
```

Note that all of this analysis is carried out by `{statsExpressions}`
package: <https://www.indrapatil.com/statsExpressions/>

### Using `{ggstatsplot}` statistical details with custom plots

Sometimes you may not like the default plots produced by
`{ggstatsplot}`. In such cases, you can use other **custom** plots (from
`{ggplot2}` or other plotting packages) and still use `{ggstatsplot}`
functions to display results from relevant statistical test.

For example, in the following chunk, we will create our own plot using
`{ggplot2}` package, and use `{ggstatsplot}` function for extracting
expression:

``` r
## loading the needed libraries
set.seed(123)
library(ggplot2)

## using `{ggstatsplot}` to get expression with statistical results
stats_results <- ggbetweenstats(morley, Expt, Speed) |> extract_subtitle()

## creating a custom plot of our choosing
ggplot(morley, aes(x = as.factor(Expt), y = Speed)) +
  geom_boxplot() +
  labs(
    title = "Michelson-Morley experiments",
    subtitle = stats_results,
    x = "Speed of light",
    y = "Experiment number"
  )
```

<img src="man/figures/README-customplot-1.png" alt="Custom boxplot of Michelson-Morley experiment data with ggstatsplot-generated statistical subtitle" width="100%" />

## Summary of benefits of using `{ggstatsplot}`

- No need to use scores of packages for statistical analysis (e.g., one
  to get stats, one to get effect sizes, another to get Bayes Factors,
  and yet another to get pairwise comparisons, etc.).

- Minimal amount of code needed for all functions (typically only
  `data`, `x`, and `y`), which minimizes chances of error and makes for
  tidy scripts.

- Conveniently toggle between statistical approaches.

- Truly makes your figures worth a thousand words.

- No need to copy-paste results to the text editor (MS-Word, e.g.).

- Disembodied figures stand on their own and are easy to evaluate for
  the reader.

- More breathing room for theoretical discussion and other text.

- No need to worry about updating figures and statistical details
  separately.

## Misconceptions about `{ggstatsplot}`

This package is…

❌ an alternative to learning `{ggplot2}`<br> ✅ (The better you know
`{ggplot2}`, the more you can modify the defaults to your liking.)

❌ meant to be used in talks/presentations<br> ✅ (Default plots can be
too complicated for effectively communicating results in
time-constrained presentation settings, e.g. conference talks.)

❌ the only game in town<br> ✅ (GUI software alternatives:
[JASP](https://jasp-stats.org/) and [jamovi](https://www.jamovi.org/)).

## Extensions

In case you use the GUI software [`jamovi`](https://www.jamovi.org/),
you can install a module called
[`jjstatsplot`](https://github.com/sbalci/jjstatsplot), which is a
wrapper around `{ggstatsplot}`.

## Contributing

I’m happy to receive bug reports, suggestions, questions, and (most of
all) contributions to fix problems and add features. I personally prefer
using the `GitHub` issues system over trying to reach out to me in other
ways (personal e-mail, Twitter, etc.). 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](https://www.indrapatil.com/ggstatsplot/)
- Raise issues about bugs or wanted features
- Review code
- Add new functionality (in the form of new plotting functions or
  helpers for preparing subtitles)

Please note that this project is released with a [Contributor Code of
Conduct](https://www.contributor-covenant.org/version/3/0/code_of_conduct/).
By participating in this project you agree to abide by its terms.
