README.Rmd

---
output: 
  github_document:
    pandoc_args: --webtex=https://chart.apis.google.com/chart?cht=tx&chl=
---

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

```{r, echo = FALSE}
# show me all columns
options(tibble.width = Inf)

knitr::opts_chunk$set(
  collapse = TRUE,
  dpi = 300,
  warning = FALSE,
  message = FALSE,
  out.width = "100%",
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

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

Package | Status | Usage | GitHub | References
----------------- | ----------------- | ----------------- | ----------------- | -----------------
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# Raison d'être <img src="man/figures/logo.png" 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://indrajeetpatil.github.io/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.

# Summary of available plots

It, therefore, produces a limited kinds of plots for the supported analyses:

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
`ggpiestats`  | **pie charts** | for categorical data 
`ggbarstats`  | **bar charts** | for categorical data 
`ggscatterstats` | **scatterplots** | for correlations between two variables
`ggcorrmat`  | **correlation matrices** | for correlations between multiple variables
`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

Currently, it supports only the most common types of statistical tests:
**parametric**, **nonparametric**, **robust**, and **bayesian** versions of
**t-test**/**anova**, **correlation** analyses, **contingency table** analysis,
**meta-analysis**, and **regression** analyses.

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

Functions | Description | Parametric | Non-parametric | Robust | Bayes Factor
------- | ------------------ | ---- | ----- | ----| ----- 
`ggbetweenstats` | Between group/condition comparisons | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggwithinstats` | Within group/condition comparisons | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`gghistostats`, `ggdotplotstats` | Distribution of a numeric variable | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggcorrmat` | Correlation matrix | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggscatterstats` | Correlation between two variables | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggpiestats`, `ggbarstats` | Association between categorical variables | <font color="green">Yes</font> | `NA` | `NA` | <font color="green">Yes</font>
`ggpiestats`, `ggbarstats` | Equal proportions for categorical variable levels | <font color="green">Yes</font> | `NA` | `NA` | <font color="green">Yes</font>
`ggcoefstats` | Regression model coefficients | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggcoefstats` | Random-effects meta-analysis | <font color="green">Yes</font> | <font color="red">No</font> | <font color="green">Yes</font> | <font color="green">Yes</font>

# Statistical reporting

For **all** statistical tests reported in the plots, the default template abides
by the [APA](https://my.ilstu.edu/~jhkahn/apastats.html) 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" align="center" />

# Summary of statistical tests and effect sizes

Here is a summary table of all the statistical tests currently supported across
various functions:
<https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html>

# Installation

To get the latest, stable `CRAN` release:

```{r installationCRAN, eval = FALSE}
install.packages("ggstatsplot")
```

*Note*:

  - If you are on a linux machine, you will need to have OpenGL libraries
    installed (specifically, `libx11`, `mesa` and Mesa OpenGL Utility library -
    `glu`) for the dependency package `rgl` to work.

  - Additionally, `ggstatsplot` depends on `Rrmpfr`, which requires an OS
    library that might not be installed by default on TSD mpfr-devel:
    <https://rmpfr.r-forge.r-project.org/>

You can get the **development** version of the package from `GitHub`. To see what
new changes (and bug fixes) have been made to the package since the last release
on `CRAN`, you can check the detailed log of changes here:
<https://indrajeetpatil.github.io/ggstatsplot/news/index.html>

If you are in hurry and want to reduce the time of installation,
prefer-

```{r installation1, eval = FALSE}
# needed package to download from GitHub repo
install.packages("remotes")

# downloading the package from GitHub (needs `remotes` package to be installed)
remotes::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = FALSE, # assumes you have already installed needed packages
  quick = TRUE # skips docs, demos, and vignettes
)
```

If time is not a constraint-

```{r installation2, eval = FALSE}
remotes::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = TRUE, # installs packages which ggstatsplot depends on
  upgrade_dependencies = TRUE # updates any out of date dependencies
)
```

# 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}
citation("ggstatsplot")
```

There is currently a publication in preparation corresponding to this package
and the citation will be updated once it's published.

# Documentation and Examples

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

  - README:
    <https://CRAN.R-project.org/package=ggstatsplot/readme/README.html>
    
  - Presentation:
    <https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1>
    
  - Vignettes: <https://CRAN.R-project.org/package=ggstatsplot/vignettes/additional.html>

To see the documentation relevant for the **development** version of the package,
see the dedicated website for `ggstatplot`, which is updated after every new
commit: <https://indrajeetpatil.github.io/ggstatsplot/>.

# Primary functions

Here are examples of the main functions currently supported in `ggstatsplot`.

**Note**: If you are reading this on `GitHub` repository, the documentation below
is for the **development** version of the package. So you may see some features
available here that are not currently present in the stable version of this
package on **CRAN**. For documentation relevant for the `CRAN` version, see:
<https://CRAN.R-project.org/package=ggstatsplot/readme/README.html>

## `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 ggbetweenstats1}
# for reproducibility
set.seed(123)
library(ggstatsplot)

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

Note that this function returns object of class `ggplot` and thus can be further
modified using `ggplot2` functions.

A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, this time we
will use a grouping variable that has only two levels. The function will
automatically switch from carrying out an ANOVA analysis to a *t*-test.

The `type` (of test) argument
also accepts the following abbreviations: `"p"` (for *parametric*) or `"np"`
(for *nonparametric*) or `"r"` (for *robust*) or `"bf"` (for *Bayes Factor*).
Additionally, the type of plot to be displayed can also be modified (`"box"`,
`"violin"`, or `"boxviolin"`).

A number of other arguments can be specified to make this plot even more
informative or change some of the default options. 

```{r ggbetweenstats2}
# for reproducibility
set.seed(123)
library(ggplot2)

# plot
ggstatsplot::ggbetweenstats(
  data = ToothGrowth,
  x = supp,
  y = len,
  notch = TRUE, # show notched box plot
  mean.ci = TRUE, # whether to display confidence interval for means
  k = 3, # number of decimal places for statistical results
  outlier.tagging = TRUE, # whether outliers need to be tagged
  outlier.label = dose, # variable to be used for the outlier tag
  xlab = "Supplement type", # label for the x-axis variable
  ylab = "Tooth length", # label for the y-axis variable
  title = "The Effect of Vitamin C on Tooth Growth", # title text for the plot
  ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
  ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
  package = "wesanderson", # package from which color palette is to be taken
  palette = "Darjeeling1" # choosing a different color palette
)
```

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 ggbetweenstats3, fig.height = 14, fig.width = 12}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggbetweenstats(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  x = mpaa,
  y = length,
  grouping.var = genre, # grouping variable
  ggsignif.args = list(textsize = 4, tip_length = 0.01),
  p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
  # adding new components to `ggstatsplot` default
  ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
  k = 3,
  title.prefix = "Movie genre",
  caption = substitute(paste(italic("Source"), ": IMDb (Internet Movie Database)")),
  palette = "default_jama",
  package = "ggsci",
  plotgrid.args = list(nrow = 2),
  title.text = "Differences in movie length by mpaa ratings for different genres"
)
```

### Summary of tests

Following (between-subjects) tests are carried out for each type of analyses-

Type | No. of groups | Test
----------- | --- | -------------------------
Parametric | > 2 | Fisher's or Welch's one-way ANOVA
Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA
Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means
Bayes Factor | > 2 | Fisher's ANOVA
Parametric | 2 | Student's or Welch's *t*-test
Non-parametric | 2 | Mann-Whitney *U* test
Robust | 2 |  Yuen's test for trimmed means
Bayes Factor | 2 | Student's *t*-test

The omnibus effect in one-way ANOVA design can also be followed up with more
focal pairwise comparison tests. Here is a summary of multiple pairwise
comparison tests supported in *ggbetweenstats*-

Type | Equal variance? | Test | *p*-value adjustment? 
----------- | --- | ------------------------- | ---
Parametric | No | Games-Howell test | <font color="green">Yes</font>  
Parametric | Yes | Student's *t*-test | <font color="green">Yes</font>  
Non-parametric | No | Dunn test | <font color="green">Yes</font>  
Robust | No | Yuen's trimmed means test | <font color="green">Yes</font>  
Bayes Factor | `NA` | Student's *t*-test | `NA`

For more, see the `ggbetweenstats` vignette:
<https://indrajeetpatil.github.io/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.

```{r ggwithinstats1, fig.width = 8, fig.height = 6}
# for reproducibility and data
set.seed(123)
library(WRS2)

# plot
ggstatsplot::ggwithinstats(
  data = WineTasting,
  x = Wine,
  y = Taste,
  title = "Wine tasting",
  caption = "Data source: `WRS2` R package",
  ggtheme = ggthemes::theme_fivethirtyeight(),
  ggstatsplot.layer = FALSE,
  messages = FALSE
)
```

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 ggwithinstats2, fig.height = 6, fig.width = 14}
# common setup
set.seed(123)

# plot
ggstatsplot::grouped_ggwithinstats(
  data = dplyr::filter(
    .data = ggstatsplot::bugs_long,
    region %in% c("Europe", "North America"),
    condition %in% c("LDLF", "LDHF")
  ),
  x = condition,
  y = desire,
  xlab = "Condition",
  ylab = "Desire to kill an artrhopod",
  grouping.var = region,
  outlier.tagging = TRUE,
  outlier.label = education,
  ggtheme = hrbrthemes::theme_ipsum_tw(),
  ggstatsplot.layer = FALSE,
  messages = FALSE
)
```

### Summary of tests

Following (within-subjects) tests are carried out for each type of analyses-

Type | No. of groups | Test
----------- | --- | -------------------------
Parametric | > 2 | One-way repeated measures ANOVA
Non-parametric | > 2 | Friedman's rank sum test
Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means
Bayes Factor | > 2 | One-way repeated measures ANOVA
Parametric | 2 | Student's *t*-test
Non-parametric | 2 | Wilcoxon signed-rank test
Robust | 2 | Yuen's test on trimmed means for dependent samples
Bayes Factor | 2 | Student's *t*-test

The omnibus effect in one-way ANOVA design can also be followed up with more
focal pairwise comparison tests. Here is a summary of multiple pairwise
comparison tests supported in *ggwithinstats*-

Type | Test | *p*-value adjustment? 
----------- | ---------------------------- | ---
Parametric | Student's *t*-test | <font color="green">Yes</font>  
Non-parametric | Durbin-Conover test | <font color="green">Yes</font>  
Robust | Yuen's trimmed means test | <font color="green">Yes</font>  
Bayes Factor | Student's *t*-test | `NA`

For more, see the `ggwithinstats` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html>

## `ggscatterstats`

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

```{r ggscatterstats1}
ggstatsplot::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",
  messages = FALSE
)
```

The available marginal distributions are-

  - histograms
  - boxplots
  - density
  - violin
  - densigram (density + histogram)

Number of other arguments can be specified to modify this basic plot-

```{r ggscatterstats2, fig.width=8}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggscatterstats(
  data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
  x = budget,
  y = rating,
  type = "robust", # type of test that needs to be run
  xlab = "Movie budget (in million/ US$)", # label for x axis
  ylab = "IMDB rating", # label for y axis
  label.var = "title", # variable for labeling data points
  label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
  title = "Movie budget and IMDB rating (action)", # title text for the plot
  caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
  ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
  ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
  marginal.type = "density", # type of marginal distribution to be displayed
  xfill = "pink", # color fill for x-axis marginal distribution
  yfill = "#009E73", # color fill for y-axis marginal distribution
  centrality.parameter = "median", # central tendency lines to be displayed
  messages = FALSE # turn off messages and notes
)
```

Additionally, there is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable. Also,
note that, as opposed to the other functions, this function does not return a
`ggplot` object and any modification you want to make can be made in advance
using `ggplot.component` argument (available for all functions, but especially
useful for this particular function):

```{r ggscatterstats3, fig.height = 12, fig.width = 14}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggscatterstats(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  x = rating,
  y = length,
  grouping.var = genre, # grouping variable
  label.var = title,
  label.expression = length > 200,
  xfill = "#E69F00",
  yfill = "#8b3058",
  xlab = "IMDB rating",
  title.prefix = "Movie genre",
  ggtheme = ggplot2::theme_grey(),
  ggplot.component = list(
    ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
  ),
  plotgrid.args = list(nrow = 2),
  title.text = "Relationship between movie length by IMDB ratings for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses. Additionally, the
correlation coefficients (and their confidence intervals) are used as effect
sizes-

Type | Test | CI?
----------- | ------------------------- | ---
Parametric | Pearson's correlation coefficient | <font color="green">Yes<font>
Non-parametric | Spearman's rank correlation coefficient | <font color="green">Yes<font>
Robust | Percentage bend correlation coefficient | <font color="green">Yes<font>
Bayes Factor | Pearson's correlation coefficient | <font color="green">Yes<font>

For more, see the `ggscatterstats` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.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 ggpiestats1, fig.height=4, fig.width=8}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = mtcars,
  x = am,
  y = cyl,
  title = "Dataset: Motor Trend Car Road Tests", # title for the plot
  legend.title = "Transmission", # title for the legend
  caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine")),
  messages = FALSE
)
```

In case of repeated measures designs, setting `paired = TRUE` will produce
results from McNemar's chi-squared test-

```{r ggpiestats2, fig.height=4}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = data.frame(
    "before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
    "after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
    counts = c(794, 150, 86, 570),
    check.names = FALSE
  ),
  x = before,
  y = after,
  counts = counts,
  title = "Survey results before and after the intervention",
  label = "both",
  paired = TRUE, # within-subjects design
  package = "wesanderson",
  palette = "Royal1"
)
```

Additionally, 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 ggpiestats3, fig.height = 10, fig.width = 10}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggpiestats(
  data = ggstatsplot::movies_long,
  x = genre,
  grouping.var = mpaa, # grouping variable
  title.prefix = "Movie genre", # prefix for the faceted title
  label.repel = TRUE, # repel labels (helpful for overlapping labels)
  package = "ggsci", # package from which color palette is to be taken
  palette = "default_ucscgb", # choosing a different color palette
  title.text = "Composition of MPAA ratings for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses-

Type of data | Design | Test
----------- | ------------ | -------------------------
Unpaired | $n \times p$ contingency table | Pearson's $\chi^{2}$ test
Paired | $n \times p$ contingency table | McNemar's $\chi^{2}$ test
Frequency | $n \times 1$ contingency table | Goodness of fit ($\chi^{2}$)

Following effect sizes (and confidence intervals/CI) are available for each type
of test-

Type | Effect size | CI?
--------------- | ------------------------- | --- 
Pearson's chi-squared test | Cramér's *V* | <font color="green">Yes<font>
McNemar's test | Cohen's *g*  | <font color="green">Yes<font>
Goodness of fit | Cramér's *V*  | <font color="green">Yes<font>

For more, see the `ggpiestats` vignette:
<https://indrajeetpatil.github.io/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-

```{r ggbarstats1, fig.height = 8, fig.width = 10}
# for reproducibility
set.seed(123)
library(ggplot2)

# plot
ggstatsplot::ggbarstats(
  data = ggstatsplot::movies_long,
  x = mpaa,
  y = genre,
  sampling.plan = "jointMulti",
  title = "MPAA Ratings by Genre",
  xlab = "movie genre",
  legend.title = "MPAA rating",
  ggtheme = hrbrthemes::theme_ipsum_pub(),
  ggplot.component = list(scale_x_discrete(guide = guide_axis(n.dodge = 2))),
  palette = "Set2",
  messages = FALSE
)
```

And, needless to say, there is also a `grouped_` variant of this function-

```{r ggbarstats2, fig.height = 12, fig.width = 10}
# setup
set.seed(123)

# smaller dataset
df <-
  dplyr::filter(
    .data = forcats::gss_cat,
    race %in% c("Black", "White"),
    relig %in% c("Protestant", "Catholic", "None"),
    !partyid %in% c("No answer", "Don't know", "Other party")
  )

# plot
ggstatsplot::grouped_ggbarstats(
  data = df,
  x = relig,
  y = partyid,
  grouping.var = race,
  title.prefix = "Race",
  xlab = "Party affiliation",
  package = "wesanderson",
  palette = "Darjeeling2",
  ggtheme = ggthemes::theme_tufte(base_size = 12),
  ggstatsplot.layer = FALSE,
  title.text = "Race, religion, and political affiliation",
  plotgrid.args = list(nrow = 2)
)
```

### Summary of tests

This is identical to the `ggpiestats` function summary of tests.

## `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 gghistostats1}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::gghistostats(
  data = iris, # dataframe from which variable is to be taken
  x = Sepal.Length, # numeric variable whose distribution is of interest
  title = "Distribution of Iris sepal length", # title for the plot
  caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
  bar.measure = "both",
  test.value = 5, # default value is 0
  test.value.line = TRUE, # display a vertical line at test value
  centrality.parameter = "mean", # which measure of central tendency is to be plotted
  centrality.line.args = list(color = "darkred"), # aesthetics for central tendency line
  binwidth = 0.10, # binwidth value (experiment)
  ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
  ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
```

As can be seen from the plot, Bayes Factor can be attached (`bf.message = TRUE`)
to assess evidence in favor of the null hypothesis.

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 gghistostats2, fig.height = 10, fig.width = 10}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_gghistostats(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  x = budget,
  xlab = "Movies budget (in million US$)",
  type = "robust", # use robust location measure
  grouping.var = genre, # grouping variable
  normal.curve = TRUE, # superimpose a normal distribution curve
  normal.curve.args = list(color = "red", size = 1),
  title.prefix = "Movie genre",
  ggtheme = ggthemes::theme_tufte(),
  ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
    ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
  ),
  plotgrid.args = list(nrow = 2),
  title.text = "Movies budgets for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses-

Type | Test
----------- | -------------------------
Parametric | One-sample Student's *t*-test
Non-parametric | One-sample Wilcoxon test 
Robust | One-sample percentile bootstrap
Bayes Factor | One-sample Student's *t*-test

Following effect sizes (and confidence intervals/CI) are available for each type
of test-

Type | Effect size | CI?
------------ | ----------------------- | --- 
Parametric | Cohen's *d*, Hedge's *g* (central-and noncentral-*t*) | <font color="green">Yes<font>
Non-parametric | *r* | <font color="green">Yes<font>
Robust | robust location measure | <font color="green">Yes<font>
Bayes Factor | <font color="green">Yes<font> | <font color="green">Yes<font>

For more, including information about the variant of this function
`grouped_gghistostats`, see the `gghistostats` vignette:
<https://indrajeetpatil.github.io/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 ggdotplotstats1, fig.height = 10, fig.width = 8}
# for reproducibility
set.seed(123)

# plot
ggdotplotstats(
  data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
  y = country,
  x = lifeExp,
  test.value = 55,
  test.value.line = TRUE,
  centrality.parameter = "median",
  centrality.k = 0,
  title = "Distribution of life expectancy in Asian continent",
  xlab = "Life expectancy",
  caption = substitute(
    paste(
      italic("Source"),
      ": Gapminder dataset from https://www.gapminder.org/"
    )
  )
)
```

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 ggdotplotstats2, fig.height = 8, fig.width = 12}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggdotplotstats(
  data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
  x = cty,
  y = manufacturer,
  xlab = "city miles per gallon",
  ylab = "car manufacturer",
  type = "nonparametric", # non-parametric test
  grouping.var = cyl, # grouping variable
  test.value = 15.5,
  test.value.line = TRUE,
  title.prefix = "cylinder count",
  point.args = list(color = "red", size = 5, shape = 13),
  title.text = "Fuel economy data"
)
```

### Summary of tests

This is identical to summary of tests for `gghistostats`.

## `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 ggcorrmat1}
# for reproducibility
set.seed(123)

# as a default this function outputs a correlation matrix plot
ggstatsplot::ggcorrmat(
  data = ggplot2::msleep,
  type = "robust", # correlation method
  p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
  cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
  cor.vars.names = c(
    "REM sleep", # variable names
    "time awake",
    "brain weight",
    "body weight"
  ),
  matrix.type = "upper", # type of visualization matrix
  colors = c("#B2182B", "white", "#4D4D4D"),
  title = "Correlalogram for mammals sleep dataset",
  subtitle = "sleep units: hours; weight units: kilograms",
  caption = "Source: `ggplot2` R package"
)
```

Two things to note:

  - 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.

  - If `cor.vars` are not specified, all numeric variables will be used.

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

```{r ggcorrmat2, fig.height = 10, fig.width = 10}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggcorrmat(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  cor.vars = length:votes,
  colors = c("#cbac43", "white", "#550000"),
  grouping.var = genre, # grouping variable
  k = 3L, # number of digits after decimal point
  title.prefix = "Movie genre",
  plotgrid.args = list(nrow = 2)
)
```

You can also get a dataframe containing all relevant details from the
statistical tests:

```{r ggcorrmat3}
# setup
set.seed(123)

# dataframe in long format
ggcorrmat(
  data = ggplot2::msleep,
  type = "bayes",
  output = "dataframe"
)
```

### Summary of tests

Following tests are carried out for each type of analyses. Additionally, the
correlation coefficients (and their confidence intervals) are used as effect
sizes-

Type | Test | CI?
----------- | ------------------------- | ---
Parametric | Pearson's correlation coefficient | <font color="green">Yes<font>
Non-parametric | Spearman's rank correlation coefficient | <font color="green">Yes<font>
Robust | Percentage bend correlation coefficient | <font color="green">Yes<font>
Bayes Factor | Pearson's correlation coefficient | <font color="green">Yes<font>

For examples and more information, see the `ggcorrmat` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html>

## `ggcoefstats`

The function `ggstatsplot::ggcoefstats` generates **dot-and-whisker plots** for
regression models saved in a tidy data frame. The tidy dataframes are prepared
using the following packages: `broom`, `broom.mixed`, and `parameters`.
Additionally, if available, the model summary indices are also extracted from
the following packages: `broom`, `broom.mixed`, and `performance`.

Although the statistical models displayed in the plot may differ based on the
class of models being investigated, there are few aspects of the plot that will
be invariant across models:

  - The dot-whisker plot contains a dot representing the **estimate** and their
    **confidence intervals** (`95%` is the default). The estimate can either be
    effect sizes (for tests that depend on the `F` statistic) or regression
    coefficients (for tests with `t` and `z` statistic), etc. The function will,
    by default, display a helpful `x`-axis label that should clear up what
    estimates are being displayed. The confidence intervals can sometimes be
    asymmetric if bootstrapping was used.
  
  - The caption will always contain diagnostic information, if available, about
    models that can be useful for model selection: The smaller the Akaike's
    Information Criterion (**AIC**) and the Bayesian Information Criterion
    (**BIC**) values, the "better" the model is.

  - The output of this function will be a `ggplot2` object and, thus, it can be
    further modified (e.g., change themes, etc.) with `ggplot2` functions.

```{r ggcoefstats1, fig.height = 5, fig.width = 6}
# for reproducibility
set.seed(123)

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

# plot
ggstatsplot::ggcoefstats(mod)
```

This default plot can be further modified to one's liking with additional
arguments (also, let's use a different model now):

```{r ggcoefstats2, fig.height = 6, fig.width = 8}
# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggcoefstats(
  x = MASS::rlm(formula = mpg ~ am * cyl, data = mtcars),
  point.args = list(color = "red", size = 3, shape = 15),
  vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
  stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
  title = "Car performance predicted by transmission & cylinder count",
  subtitle = "Source: 1974 Motor Trend US magazine",
  ggtheme = hrbrthemes::theme_ipsum_ps(),
  ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
  ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
  ggplot2::labs(x = "regression coefficient", y = NULL)
```

Most of the regression models that are supported in the underlying packages are
also supported by `ggcoefstats`. For example-

`aareg`, `anova`, `aov`, `aovlist`, `Arima`, `BFBayesFactor`, `bayesx`,
`bayesGARCH`, `BBmm`, `BBreg`, `bcplm`, `betamfx`, `betaor`, `bglmerMod`,
`bife`, `bigglm`, `biglm`, `blavaan`, `bmlm`, `blmerMod`, `bracl`, `brglm2`,
`brmsfit`, `brmultinom`, `btergm`, `cch`, `cgam`, `cgamm`, `cglm`, `clm`,
`clm2`, `clmm`, `clmm2`, `coeftest`, `complmrob`, `confusionMatrix`, `coxme`,
`coxph`, `coxph.penal`, `cpglm`, `cpglmm`, `crch`, `crq`, `DirichReg`, `drc`,
`emmGrid`, `epi.2by2`, `ergm`, `feis`, `felm`, `fitdistr`, `flexsurvreg`, `gam`,
`Gam`, `gamlss`, `garch`, `geeglm`, `glmc`, `glmerMod`, `glmmTMB`, `gls`,
`glht`, `glm`, `glmm`, `glmmadmb`, `glmmPQL`, `glmRob`, `glmrob`, `glmx`, `gmm`,
`hurdle`, `ivreg`, `iv_robust`, `lavaan`, `lm`, `lm.beta`, `lmerMod`,
`lmerModLmerTest`, `lmodel2`, `lmRob`, `lmrob`, `lm_robust`, `logitmfx`,
`logitor`, `logitsf`, `LORgee`, `lqm`, `lqmm`, `lrm`, `manova`, `maov`,
`margins`, `mcmc`, `mcmc.list`, `MCMCglmm`, `mclogit`, `mmclogit`, `mediate`,
`metafor`, `metaplus`, `mixor`, `mjoint`, `mle2`, `mlm`, `multinom`, `negbin`,
`negbinmfx`, `negbinirr`, `nlmerMod`, `nlrq`, `nlreg`, `nls`, `orcutt`, `orm`,
`plm`, `poissonmfx`, `poissonirr`, `polr`, `ridgelm`, `rjags`, `rlm`,
`rlmerMod`, `robmixglm`, `rq`, `rqss`, `semLm`, `semLme`, `slm`, `speedglm`,
`speedlm`, `stanfit`, `stanreg`, `survreg`, `svyglm`, `svyolr`, `svyglm`,
`tobit`, `truncreg`, `vgam`, `vglm`, `wbgee`, `wblm`, `zcpglm`, `zeroinfl`, etc.

Although not shown here, this function can also be used to carry out both
frequentist and Bayesian random-effects meta-analysis.

For a more exhaustive account of this function, see the associated vignette-
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html>

## `combine_plots`

The full power of `ggstatsplot` can be leveraged with a functional programming
package like [`purrr`](https://purrr.tidyverse.org/) that replaces `for` loops
with code that is both more succinct and easier to read and, therefore, `purrr`
should be preferrred `r emo::ji("heart_eyes_cat")`. (Another old school option
to do this effectively is using the `plyr` package.)

In such cases, `ggstatsplot` contains a helper function `combine_plots` to
combine multiple plots, which can be useful for combining a list of plots
produced with `purrr`. This is a wrapper around `cowplot::plot_grid` and lets
you combine multiple plots and add a combination of title, caption, and
annotation texts with suitable defaults.

For examples (both with `plyr` and `purrr`), see the associated vignette-
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/combine_plots.html>

## 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 plot (*ridgeplot*) using
`ggridges` package and use `ggstatsplot` function for extracting results.

```{r ridgeplot, fig.height = 10, fig.width = 7}
# loading the needed libraries
set.seed(123)
library(ggridges)
library(ggplot2)
library(ggstatsplot)

# using `ggstatsplot` to get call with statistical results
stats_results <-
  ggstatsplot::ggbetweenstats(
    data = morley,
    x = Expt,
    y = Speed,
    output = "subtitle"
  )

# using `ggridges` to create plot
ggplot(morley, aes(x = Speed, y = as.factor(Expt), fill = as.factor(Expt))) +
  geom_density_ridges(
    jittered_points = TRUE,
    quantile_lines = TRUE,
    scale = 0.9,
    alpha = 0.7,
    vline_size = 1,
    vline_color = "red",
    point_size = 0.4,
    point_alpha = 1,
    position = position_raincloud(adjust_vlines = TRUE)
  ) + # adding annotations
  labs(
    title = "Michelson-Morley experiments",
    subtitle = stats_results,
    x = "Speed of light",
    y = "Experiment number"
  ) + # remove the legend
  theme(legend.position = "none")
```

# Usage and syntax simplicity

As seen from these examples, `ggstatsplot` relies on non-standard evaluation
(NSE) - implemented via `rlang` - i.e., rather than looking at the values of
arguments (`x`, `y`), it instead looks at their expressions. Therefore, the
syntax is simpler and follows the following principles-

  - When a given function depends on variables in a dataframe, `data` argument
    must always be specified.
    
  - The `$` operator cannot be used to specify variables in a dataframe.
  
  - All functions accept both string (`x = "var1"`) and symbol (`x = var1`)
    arguments.

These set principles combined with the fact that almost all functions produce
publication-ready plots that require very few arguments if one finds the
aesthetic and statistical defaults satisfying make the syntax much less
cognitively demanding and easy to remember/reconstruct.

<img src='man/figures/arguments.png' align="center"/>

<br>

Most functions share a `type` (of test) argument that is helpful to specify the
type of statistical analysis:

  - `"p"` (for **parametric**)
  - `"np"` (for **non-parametric**)
  - `"r"` (for **robust**)
  - `"bf"` (for **Bayes Factor**)
  
All relevant functions in `ggstatsplot` have a `output` argument which can be
used to not only return plots (which is the default), but also to return a
`subtitle` or `caption`, which are objects of type `call` and can be used to
display statistical details in conjunction with a custom plot and at a custom
location in the plot.

Additionally, all functions share the `ggtheme` and `palette` arguments that can
be used to specify your favorite `ggplot` theme and color palette.

# `ggstatsverse`: Components of `ggstatsplot`

To make the maintenance and development of `ggstatsplot` more manageable, it is
being broken into smaller pieces. Currently, the package internally relies on
the following packages that manage different aspects of statistical analyses:

<img src="man/figures/ggstatsverse.jpg" />

## `statsExpressions`

The `statsExpressions` package forms the statistical backend that processes data
and creates expressions containing results from statistical tests and are by
default displayed in as plot **subtitle**.

For more exhaustive documentation for this package, see:
<https://indrajeetpatil.github.io/statsExpressions/>

## `tidyBF`

The `tidyBF` package forms the backend that processes data and creates
expressions containing results from Bayes Factor tests and are by default
displayed in as plot **caption**.

For more exhaustive documentation for this package, see:
<https://indrajeetpatil.github.io/tidyBF/>

## `pairwiseComparisons`

The `pairwiseComparisons` package forms the pairwise comparison backend for
creating results that are used to display *post hoc* multiple comparisons
displayed in `ggbetweenstats` and `ggwithinstats` functions. 

For more exhaustive documentation for this package, see:
<https://indrajeetpatil.github.io/pairwiseComparisons/>

## `ipmisc`

The `ipmisc` package contains some of the data wrangling/cleaning functions and
a few other miscellaneous functions.

For more exhaustive documentation for this package, see:
<https://indrajeetpatil.github.io/ipmisc/>

# 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
Chuck Powell for his initial contributions to the package. I also want to thank
other package maintainers (like Daniel Lüdecke, Dominique Makowski, Patrick
Mair, Salvatore Mangiafico, etc.) who have patiently and diligently answered my
relentless number of questions and added feature requests I wanted.

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 and StackOverflow.

Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at
Harvard; 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. `r emo::ji("smile")`

# Code coverage

As the code stands right now, here is the code coverage for all primary
functions involved:
<https://codecov.io/gh/IndrajeetPatil/ggstatsplot/tree/master/R>

# 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://indrajeetpatil.github.io/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://github.com/IndrajeetPatil/ggstatsplot/blob/master/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.

# Session Information

For reproducibility purposes, the details about the session information in which
this document was rendered, see-
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html>