### Basic Plot with ggbetweenstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/paper/JOSS files/paper.md

Generates a plot comparing groups with embedded statistical details. Requires the palmerpenguins dataset and the ggstatsplot package. Note: The example shows an error indicating the function is not found, suggesting a setup or installation issue.

```r
library(palmerpenguins) # for 'penguins' dataset


ggbetweenstats(penguins, species, body_mass_g)
#> Error in ggbetweenstats(penguins, species, body_mass_g): could not find function "ggbetweenstats"
```

--------------------------------

### Install ggstatsplot Development Version

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Install the latest development version of the ggstatsplot package using the pak package manager.

```r
pak::pak("IndrajeetPatil/ggstatsplot")
```

--------------------------------

### Install ggstatsplot from CRAN

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use this command to install the stable release version of the ggstatsplot package from CRAN.

```r
install.packages("ggstatsplot")
```

--------------------------------

### Custom ggplot with ggstatsplot Statistics (Scatterplot)

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Apply the same approach as the boxplot example to create a custom scatterplot. Extract statistical expressions from `ggscatterstats` using `extract_subtitle()` and embed them in a `ggplot2` scatterplot with custom aesthetics.

```r
corr_stats <- ggscatterstats(iris, Sepal.Length, Petal.Length) |> extract_subtitle()

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Sepal vs Petal Length",
    subtitle = corr_stats
  ) +
  theme_classic()
```

--------------------------------

### Get Citation for ggstatsplot

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Run this R code to retrieve the citation information for the ggstatsplot package, including a BibTeX entry for LaTeX users.

```r
citation("ggstatsplot")
```

--------------------------------

### Create Pie Chart with Contingency Table Analysis

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use ggpiestats to create a pie chart for categorical variables, including results from Pearson's chi-squared test or McNemar's chi-squared test in the subtitle. If only one variable is provided, a one-sample proportion test result is displayed.

```r
set.seed(123)

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

--------------------------------

### Check Palette Sufficiency with .is_palette_sufficient

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/utils.md

Verifies if a given color palette has enough colors for the required number of groups. If insufficient, it provides an error message with guidance on selecting an alternative palette.

```R
.is_palette_sufficient("ggthemes::gdoc", 30L)
```

--------------------------------

### Create Histogram with One-Sample Test using gghistostats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use gghistostats to visualize the distribution of a single variable and perform a one-sample test against a specified value. Requires the ggplot2 package for data.

```r
set.seed(123)

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

--------------------------------

### Create Pie Charts with Contingency Table Analysis using ggpiestats

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Generates pie charts for categorical data with chi-squared or goodness-of-fit test results. Supports one-way, two-way, and Bayesian analyses, as well as grouped pie charts.

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

# One-sample goodness-of-fit test
ggpiestats(
  data = mtcars,
  x    = vs,
  title = "Engine type distribution"
)
```

```r
# Two-way association test (chi-squared)
ggpiestats(
  data         = mtcars,
  x            = am,
  y            = cyl,
  palette      = "wesanderson::Royal1",
  title        = "Dataset: Motor Trend Car Road Tests",
  legend.title = "Transmission"
)
```

```r
# Bayesian contingency analysis
ggpiestats(
  data = mtcars,
  x    = vs,
  y    = cyl,
  type = "bayes",
  title = "Bayesian contingency table analysis"
)
```

```r
# Using pre-aggregated data with counts
ggpiestats(
  data   = as.data.frame(Titanic),
  x      = Survived,
  y      = Class,
  counts = Freq,    # Specify count column
  label  = "both"   # Show both counts and percentages
)
```

```r
# Grouped pie charts
grouped_ggpiestats(
  data         = mtcars,
  x            = cyl,
  grouping.var = am,
  label.repel  = TRUE,    # Prevent label overlap
  palette      = "ggsci::default_ucscgb",
  annotation.args = list(title = "Cylinder distribution by transmission")
)
```

--------------------------------

### Create Coefficient Plots for Regression Models using ggcoefstats

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Generates dot-and-whisker plots for regression model coefficients, displaying estimates with confidence intervals. Automatically extracts model information and fit statistics.

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

# Linear model coefficient plot
mod <- lm(formula = mpg ~ am * cyl, data = mtcars)
ggcoefstats(mod)
```

```r
# Exclude intercept and show only significant terms
ggcoefstats(
  mod,
  exclude.intercept = TRUE,
  only.significant  = TRUE,
  title = "Significant predictors of MPG"
)
```

```r
# ANOVA model (shows F-statistic with effect sizes)
ggcoefstats(
  aov(mpg ~ cyl * am, data = mtcars),
  effectsize.type = "omega"    # Options: "eta", "omega", "epsilon"
)
```

```r
# Using a tidy data frame directly (model-free)
df_estimates <- data.frame(
  term     = c("Age", "Education", "Experience"),
  estimate = c(0.5, -0.2, 1.1),
  std.error = c(0.1, 0.15, 0.2),
  conf.low  = c(0.3, -0.49, 0.71),
  conf.high = c(0.7, 0.09, 1.49),
  statistic = c(5.0, -1.33, 5.5),
  p.value   = c(0.001, 0.18, 0.0001)
)

ggcoefstats(
  df_estimates,
  statistic = "t",
  stats.label.color = c("firebrick", "grey50", "forestgreen")
)
```

```r
# Meta-analysis with regression estimates
ggcoefstats(
  df_estimates,
  meta.analytic.effect = TRUE,
  meta.type = "parametric",    # Options: "parametric", "robust", "bayes"
  bf.message = TRUE
)
```

```r
# Mixed-effects model (requires lme4 package)
# library(lme4)
# ggcoefstats(
#   lmer(Reaction ~ Days + (Days | Subject), sleepstudy),
#   effects = "fixed"
# )
```

--------------------------------

### Basic Correlation Matrix with ggcorrmat

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Generates a basic correlation matrix heatmap with specified colors and titles. Uses a dataset of mammalian sleep data.

```r
ggcorrmat(
  data     = ggplot2::msleep,
  colors   = c("#B2182B", "white", "#4D4D4D"),
  title    = "Correlalogram for mammals sleep dataset",
  subtitle = "sleep units: hours; weight units: kilograms"
)
```

--------------------------------

### Visualize Regression Model Coefficients

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use ggcoefstats to visualize regression model coefficients with confidence intervals and summary statistics. Requires a fitted model object.

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

ggcoefstats(mod)
```

--------------------------------

### Create Grouped Histograms with Statistical Tests using grouped_gghistostats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use grouped_gghistostats to visualize distributions and perform statistical tests for a variable across different groups. Filters data using dplyr and applies custom themes.

```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")
)
```

--------------------------------

### Create Violin Plot with ggbetweenstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates a violin plot for between-group comparisons of Sepal.Length across Iris species. Requires the iris dataset.

```r
set.seed(123)

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

--------------------------------

### gghistostats: Distribution Analysis with One-Sample Tests

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Use gghistostats to create histograms for distribution analysis, including one-sample tests comparing the mean to a specified value. It displays counts and proportions on dual y-axes and marks the centrality measure.

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

# Basic histogram with one-sample t-test
gghistostats(
  data       = ggplot2::msleep,
  x          = awake,
  title      = "Amount of time spent awake",
  test.value = 12,      # Test if mean differs from 12
  binwidth   = 1
)
```

--------------------------------

### Create Grouped Pie Charts with Statistical Analysis

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use grouped_ggpiestats to create multiple pie charts for different levels of a grouping variable, facilitating the analysis of proportions for a single nominal variable across groups.

```r
set.seed(123)

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

--------------------------------

### Create Bar Chart with Contingency Table Analysis

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use ggbarstats to create a bar chart for categorical variables, similar to ggpiestats, but with a bar chart visualization. It also includes results from contingency table analysis in the subtitle.

```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"
)
```

--------------------------------

### Create Bar Charts with Contingency Table Analysis using ggbarstats

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Generates stacked bar charts for contingency table analysis, offering a clearer visual comparison of proportions across groups. Supports one-way, two-way, and grouped analyses.

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

# Two-way association test with bar chart
ggbarstats(
  data             = movies_long,
  x                = mpaa,
  y                = genre,
  title            = "MPAA Ratings by Genre",
  xlab             = "Movie genre",
  legend.title     = "MPAA rating",
  palette          = "RColorBrewer::Set2"
)
```

```r
# One-sample goodness-of-fit test
ggbarstats(
  data  = mtcars,
  x     = cyl,
  label = "both",
  title = "Cylinder distribution in mtcars"
)
```

```r
# Grouped bar chart analysis
grouped_ggbarstats(
  data         = mtcars,
  x            = cyl,
  grouping.var = am,
  label.repel  = TRUE,
  palette      = "ggsci::default_ucscgb",
  annotation.args = list(title = "Engine cylinders by transmission type")
)
```

--------------------------------

### Partial Correlation Matrix with Bayesian Method

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Generates a partial correlation matrix using the Bayesian method. Selects specific variables and displays the matrix.

```r
ggcorrmat(
  data    = mtcars,
  cor.vars = c(mpg, disp, hp, wt, qsec),
  partial = TRUE,
  type    = "bayes",
  title   = "Partial correlation matrix"
)
```

--------------------------------

### Use .grouped_list with Non-Syntactic Group Names

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/utils.md

Demonstrates how to use the .grouped_list function when the grouping variable has a non-syntactic name. Ensure the grouping variable is correctly referenced using backticks.

```R
str(.grouped_list(rename(sleep, `my non-syntactic name` = group), grouping.var = `my non-syntactic name`))
```

--------------------------------

### Scatterplot with Customized Marginal Histograms

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Creates a scatterplot with customized marginal histograms, specifying colors, bin counts, and break points for the axes.

```r
ggscatterstats(
  data                  = mtcars,
  x                     = wt,
  y                     = mpg,
  xsidehistogram.args   = list(fill = "#009E73", color = "black", binwidth = 0.5),
  ysidehistogram.args   = list(fill = "#D55E00", color = "black", bins = 15),
  xsidehistogram.scale  = list(breaks = seq(0, 15, 5)),
  ysidehistogram.scale  = list(breaks = seq(0, 15, 5))
)
```

--------------------------------

### Basic Scatterplot with Pearson Correlation

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Creates a scatterplot with marginal histograms and Pearson correlation analysis. Uses a dataset of mammalian sleep data.

```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"
)
```

--------------------------------

### ggbetweenstats: Between-Group Comparisons

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Use ggbetweenstats for comparing groups with violin/box plots. It automatically selects appropriate tests (t-test, ANOVA) and displays results. Customize tests, pairwise comparisons, and add Bayes Factors.

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

# Basic between-group comparison (automatically runs Welch's ANOVA)
ggbetweenstats(
  data  = iris,
  x     = Species,
  y     = Sepal.Length,
  title = "Distribution of sepal length across Iris species"
)
```

```r
ggbetweenstats(
  data            = mtcars,
  x               = am,
  y               = mpg,
  type            = "robust",          # Options: "parametric", "nonparametric", "robust", "bayes"
  pairwise.display = "significant",    # Options: "significant", "non-significant", "all", "none"
  p.adjust.method = "bonferroni",
  bf.message      = TRUE,              # Show Bayes Factor in caption
  centrality.plotting = TRUE,          # Show mean/median point
  xlab            = "Transmission (0 = automatic, 1 = manual)",
  ylab            = "Miles per gallon"
)
```

--------------------------------

### Create Within-Subjects Plot with ggwithinstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates a within-subjects plot for comparing Taste ratings across different Wines, specifying the subject identifier. Requires WineTasting dataset and WRS2/afex libraries.

```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"
)
```

--------------------------------

### Extract stats from ggcoefstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/extract-stats.md

Shows how to extract statistical information from a ggcoefstats plot, which visualizes regression coefficients. Subtitle and caption are typically NULL for this plot type.

```R
p7 <- ggcoefstats(lm(wt ~ mpg, mtcars))
list(length(extract_stats(p7)), extract_subtitle(p7), extract_caption(p7))
```

--------------------------------

### Create Grouped Bar Charts with Statistical Analysis

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use grouped_ggbarstats to create multiple bar charts for different levels of a grouping variable, enabling the analysis of proportions for a single nominal variable across groups using bar charts.

```r
set.seed(123)

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

--------------------------------

### Create Grouped Violin Plots with grouped_ggbetweenstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates grouped violin plots for comparing movie length by MPAA ratings across different genres. Uses dplyr for filtering and specifies annotation and significance arguments.

```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")
)
```

--------------------------------

### Generate Dot-and-Whisker Plots for Regression Models

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

The ggcoefstats function generates dot-and-whisker plots for regression models, utilizing tidy data frames prepared by parameters::model_parameters() and model performance indices from performance::model_performance().

```r
set.seed(123)


```

--------------------------------

### Extracting Statistical Results for Custom Plots in R

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Load necessary libraries and extract statistical results using ggbetweenstats to be used as a subtitle in a custom ggplot. Ensure the data and aesthetic mappings are correctly specified.

```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"
  )
```

--------------------------------

### Correlation Matrix with Selected Variables and Robust Correlation

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Creates a correlation matrix heatmap for selected variables using robust correlation. Adjusts p-values using the Holm method and sets a significance level.

```r
ggcorrmat(
  data            = iris,
  cor.vars        = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
  type            = "robust",
  matrix.type     = "lower",
  p.adjust.method = "holm",
  sig.level       = 0.01
)
```

--------------------------------

### Extract stats from ggscatterstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/extract-stats.md

Demonstrates extracting statistical details from a ggscatterstats plot. Note that the caption might be NULL for certain plot configurations.

```R
p2 <- ggscatterstats(mtcars, wt, mpg, marginal = FALSE, type = "r")
list(length(extract_stats(p2)), extract_subtitle(p2), extract_caption(p2))
```

--------------------------------

### Create Grouped Dot Plots with Bayesian Test using grouped_ggdotplotstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use grouped_ggdotplotstats to create grouped dot plots with Bayesian tests, visualizing a numeric variable across categories. Filters data using dplyr and customizes point aesthetics.

```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")
)
```

--------------------------------

### Create Grouped Scatterplot with Marginal Distributions using grouped_ggscatterstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Creates grouped scatterplots with marginal distributions and statistical tests, repeating the operation across a single grouping variable. Ideal for comparing relationships between variables across different categories.

```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")
)
```

--------------------------------

### Grouped Histogram Analysis with grouped_gghistostats

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Analyzes and plots grouped histograms with a specified test value and grouping variable. Customizes theme and plot grid layout.

```r
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(),
  plotgrid.args     = list(nrow = 1),
  annotation.args   = list(title = "Movies budgets for different genres")
)
```

--------------------------------

### Extract stats from ggcorrmat

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/extract-stats.md

Shows how to extract statistical information from a ggcorrmat plot. For this plot type, both subtitle and caption are typically NULL.

```R
p3 <- ggcorrmat(iris)
list(length(extract_stats(p3)), extract_subtitle(p3), extract_caption(p3))
```

--------------------------------

### Create Dot Plot with Robust One-Sample Test using ggdotplotstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Use ggdotplotstats to visualize the distribution of a numeric variable against a categorical label and perform a robust one-sample test. Filters data using dplyr.

```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"
)
```

--------------------------------

### Create Grouped Correlation Matrix with grouped_ggcorrmat

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates grouped correlation matrices, allowing for the visualization of correlation patterns across different categories. This is useful for comparing correlation structures between subsets of data defined by a 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,
  matrix.type  = "lower"
)
```

--------------------------------

### Extract stats from ggbarstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/extract-stats.md

Demonstrates extracting statistical data from a ggbarstats plot, including Pearson's chi-squared test and Bayesian analysis.

```R
p6 <- ggbarstats(mtcars, cyl, am)
list(length(extract_stats(p6)), extract_subtitle(p6), extract_caption(p6))
```

--------------------------------

### Create Grouped Within-Subjects Plot with grouped_ggwithinstats

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates grouped within-subjects plots comparing desire to kill arthropods by condition for different regions. Specifies subject ID, plot type, and labels.

```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
)
```

--------------------------------

### Basic Dot Plot with ggdotplotstats

Source: https://context7.com/indrajeetpatil/ggstatsplot/llms.txt

Creates a basic dot plot with a robust one-sample test for a numeric variable against a categorical variable. Filters data for a specific continent.

```r
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"
)
```

--------------------------------

### Create Correlation Matrix with ggcorrmat

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/README.md

Generates a publication-ready correlation matrix (correlalogram) with minimal code. This function is useful for quickly visualizing pairwise correlations between variables in a dataset, including effect sizes and significance.

```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"
)
```

--------------------------------

### Extract stats from ggbetweenstats (F-test)

Source: https://github.com/indrajeetpatil/ggstatsplot/blob/main/tests/testthat/_snaps/extract-stats.md

Extracts statistical details from a ggbetweenstats plot, specifically showing F-test results when comparing multiple groups.

```R
p4 <- ggbetweenstats(mtcars, cyl, mpg)
list(length(extract_stats(p4)), extract_subtitle(p4), extract_caption(p4))
```