### Github Action setup

Source: https://github.com/kassambara/rstatix/blob/master/cran-comments.md

Example of setting up a Github Action for checking the package.

```R
usethis::use_github_action("check-standard")
```

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

### Install rstatix

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Install the latest developmental version from GitHub or from CRAN.

```R
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/rstatix")
```

```R
install.packages("rstatix")
```

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

### Example

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/prop_test.html

Example of comparing an observed proportion to an expected proportion.

```r
# Comparing an observed proportion to an expected proportion
```

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

### Adding a New Statistical Test - Test file example

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Example R code for writing unit tests for a new statistical test function.

```R
test_that("new_test works with two groups", {
  result <- mtcars %>% new_test(mpg ~ am)
  expect_s3_class(result, "rstatix_test")
  expect_true("p" %in% colnames(result))
})
```

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

### Example Usage

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/eta_squared.html

An example demonstrating how to compute ANOVA and then calculate eta-squared and partial eta-squared.

```R
# Data preparation
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Compute ANOVA
res.aov <- aov(len ~ supp*dose, data = df)
summary(res.aov)
#>             Df Sum Sq Mean Sq F value   Pr(>F)    
#> supp         1  205.4   205.4  15.572 0.000231 ***
#> dose         2 2426.4  1213.2  92.000  < 2e-16 ***
#> supp:dose    2  108.3    54.2   4.107 0.021860 *  
#> Residuals   54  712.1    13.2                     
#> --- 
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Effect size
eta_squared(res.aov)
#>       supp       dose  supp:dose 
#> 0.05948365 0.70286419 0.03137672 
partial_eta_squared(res.aov)
#>      supp      dose supp:dose 
#> 0.2238254 0.7731092 0.1320279 
```

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

### Building and Documentation

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Commands for generating documentation, building the package tarball, and installing the package locally using devtools.

```r
# Generate documentation from roxygen2 comments
devtools::document()

# Build package tarball
devtools::build()
# Or: R CMD build .

# Install package locally
devtools::install()
```

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

### Example 1: Basic Usage

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/kruskal_effsize.html

An example demonstrating the basic usage of kruskal_effsize with the ToothGrowth dataset.

```R
# Load data
#::::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth

# Kruskal-wallis rank sum test
#:::::::::::::::::::::::::::::::::::::::::
df %>% kruskal_effsize(len ~ dose)
#> # A tibble: 1 × 5
#>   .y.       n effsize method  magnitude
#> * <chr> <int>   <dbl> <chr>   <ord>    
#> 1 len      60   0.678 eta2[H] large    
```

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

### Correlation tests

Source: https://github.com/kassambara/rstatix/blob/master/README.md

Examples of correlation tests, including pairwise and against all variables.

```r
# Data preparation
mydata <- mtcars %>% 
  select(mpg, disp, hp, drat, wt, qsec)
head(mydata, 3)
#>                mpg disp  hp drat    wt  qsec
#> Mazda RX4     21.0  160 110 3.90 2.620 16.46
#> Mazda RX4 Wag 21.0  160 110 3.90 2.875 17.02
#> Datsun 710    22.8  108  93 3.85 2.320 18.61

# Correlation test between two variables
mydata %>% cor_test(wt, mpg, method = "pearson")
#> # A tibble: 1 × 8
#>   var1  var2    cor statistic        p conf.low conf.high method 
#>   <chr> <chr> <dbl>     <dbl>    <dbl>    <dbl>     <dbl> <chr>  
#> 1 wt    mpg   -0.87     -9.56 1.29e-10   -0.934    -0.744 Pearson

# Correlation of one variable against all
mydata %>% cor_test(mpg, method = "pearson")
#> # A tibble: 5 × 8
#>   var1  var2    cor statistic        p conf.low conf.high method 
#>   <chr> <chr> <dbl>     <dbl>    <dbl>    <dbl>     <dbl> <chr>  
#> 1 mpg   disp  -0.85     -8.75 9.38e-10  -0.923     -0.708 Pearson
#> 2 mpg   hp    -0.78     -6.74 1.79e- 7  -0.885     -0.586 Pearson
#> 3 mpg   drat   0.68      5.10 1.78e- 5   0.436      0.832 Pearson
#> 4 mpg   wt    -0.87     -9.56 1.29e-10  -0.934     -0.744 Pearson
#> 5 mpg   qsec   0.42      2.53 1.71e- 2   0.0820     0.670 Pearson
```

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

### Example 1: Select column using standard evaluation

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/df_arrange.html

This example demonstrates how to select columns for arrangement using standard evaluation with the 'vars' argument.

```R
df <- head(ToothGrowth)
df %>% df_arrange(vars = c("dose", "len"))
```

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

### Roxygen2 Structure - Example one-sample test

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Example of a one-sample t-test using roxygen2 documentation format.

```R
# One-sample test
df %>% t_test(len ~ 1, mu = 0)
```

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

### Formula Interface Example 1

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Example of using the t_test function with a formula interface for a two-sample test.

```r
# outcome ~ group
df %>% t_test(len ~ supp)
```

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

### Simple Test Example

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/dunn_test.html

An example demonstrating a simple Dunn's test on the ToothGrowth dataset.

```R
ToothGrowth %>% dunn_test(len ~ dose)
#> # A tibble: 3 × 9
#>   .y.   group1 group2    n1    n2 statistic        p    p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl>    <dbl>    <dbl> <chr>       
#> 1 len   0.5    1         20    20      3.55 3.78e- 4 7.56e- 4 ***         
#> 2 len   0.5    2         20    20      6.36 1.98e-10 5.95e-10 ****        
#> 3 len   1      2         20    20      2.81 4.99e- 3 4.99e- 3 **          
```

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

### Remove bracket

Source: https://github.com/kassambara/rstatix/blob/master/README.md

Example of removing brackets in p-value annotations.

```r
ggboxplot(df, x = "dose", y = "len", ylim = c(0, 40)) +
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", 
    y.position = c(29, 35),
    remove.bracket = TRUE
    )
```

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

### Formula Interface Example 2

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Example of using the t_test function with a formula interface for a one-sample test comparing to a mu value.

```r
# One-sample test (compare to mu)
df %>% t_test(len ~ 1, mu = 0)
```

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

### Example Usage

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/emmeans_test.html

An example demonstrating how to use the emmeans_test function for pairwise comparisons on the ToothGrowth dataset.

```r
# Data preparation
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Pairwise comparisons
res <- df %>%
 group_by(supp) %>%
 emmeans_test(len ~ dose, p.adjust.method = "bonferroni")
res
#> # A tibble: 6 × 10
#>   supp  term  .y.   group1 group2    df statistic        p    p.adj p.adj.signif
#> * <fct> <chr> <chr> <chr>  <chr>  <dbl>     <dbl>    <dbl>    <dbl> <chr>       
#> 1 OJ    dose  len   0.5    1         54     -5.83 3.18e- 7 9.53e- 7 ****        
#> 2 OJ    dose  len   0.5    2         54     -7.90 1.43e-10 4.29e-10 ****        
#> 3 OJ    dose  len   1      2         54     -2.07 4.34e- 2 1.30e- 1 ns          
#> 4 VC    dose  len   0.5    1         54     -5.41 1.46e- 6 4.39e- 6 ****        
#> 5 VC    dose  len   0.5    2         54    -11.2  1.13e-15 3.39e-15 ****        
#> 6 VC    dose  len   1      2         54     -5.77 3.98e- 7 1.19e- 6 ****        

# Display estimated marginal means
attr(res, "emmeans")
#> # A tibble: 6 × 8
#>   supp  dose  emmean    se    df conf.low conf.high method      
#>   <fct> <fct>  <dbl> <dbl> <dbl>    <dbl>     <dbl> <chr>       
#> 1 OJ    0.5    13.2   1.15    54    10.9       15.5 Emmeans test
```

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

### Example usage of freq_table.

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/freq_table.html

Example of how to use the freq_table function with the ToothGrowth dataset.

```R
data("ToothGrowth")
ToothGrowth %>% freq_table(supp, dose)
#> # A tibble: 6 × 4
#>   supp   dose     n  prop
#>   <fct> <dbl> <int> <dbl>
#> 1 OJ      0.5    10  33.3
#> 2 OJ      1      10  33.3
#> 3 OJ      2      10  33.3
#> 4 VC      0.5    10  33.3
#> 5 VC      1      10  33.3
#> 6 VC      2      10  33.3
```

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

### No mode

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_mode.html

Example demonstrating a case where there is no distinct mode.

```R
x <- c(1, 2, 3, 4, 5)
get_mode(x)
```

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

### Basic grouped t-test example

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Demonstrates a grouped t-test on a data frame.

```R
df %>%
  group_by(dose) %>%
  t_test(len ~ supp)
```

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

### Adding a New Statistical Test - R script example

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Example R code for creating a new statistical test function within the rstatix package.

```R
'@include utilities.R utilities_two_sample_test.R
NULL

'@export
new_test <- function(data, formula, ...) {
  args <- as.list(environment())
  params <- args %>%
    remove_null_items() %>%
    add_item(method = "base.r.function")

  # Determine test type
  number.of.groups <- guess_number_of_groups(data, group)
  test.func <- if(number.of.groups > 2) pairwise_two_sample_test else two_sample_test

  do.call(test.func, params) %>%
    set_attrs(args = args) %>%
    add_class(c("rstatix_test", "new_test"))
}
```

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

### Example 2: Select column using non-standard evaluation

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/df_arrange.html

This example shows how to select columns for arrangement using non-standard evaluation, including sorting a column in descending order.

```R
df <- head(ToothGrowth)
df %>% df_arrange(dose, desc(len))
```

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

### Compute only mean and sd

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_summary_stats.html

Example of computing only the mean and standard deviation for a variable.

```R
ToothGrowth %>% get_summary_stats(len, type = "mean_sd")
#> # A tibble: 1 × 4
#>   variable     n  mean    sd
#>   <fct>    <dbl> <dbl> <dbl>
#> 1 len         60  18.8  7.65
```

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

### Example: Comparing two proportions

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/fisher_test.html

An example demonstrating how to use the fisher_test function to compare proportions of smokers between two groups.

```R
# Comparing two proportions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data: frequencies of smokers between two groups
xtab <- as.table(rbind(c(490, 10), c(400, 100)))
dimnames(xtab) <- list(
  group = c("grp1", "grp2"),
  smoker = c("yes", "no")
)
xtab
#>       smoker
#> group  yes  no
#>   grp1 490  10
#>   grp2 400 100
# compare the proportion of smokers
fisher_test(xtab, detailed = TRUE)
#> # A tibble: 1 × 8
#>       n estimate        p conf.low conf.high method              alter…¹ p.sig…²
#> * <dbl>    <dbl>    <dbl>    <dbl>     <dbl> <chr>               <chr>   <chr>
#> 1  1000     12.2 8.77e-22     6.27      26.6 Fisher's Exact test two.si… ****   
```

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

### Five number summary statistics

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_summary_stats.html

Example of computing the five-number summary statistics for a variable.

```R
ToothGrowth %>% get_summary_stats(len, type = "five_number")
#> # A tibble: 1 × 7
#>   variable     n   min   max    q1 median    q3
#>   <fct>    <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1 len         60   4.2  33.9  13.1   19.2  25.3
```

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

### Example usage of friedman_effsize

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/friedman_effsize.html

An example demonstrating how to use the friedman_effsize function with the ToothGrowth dataset.

```R
# Load data
#::::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth %>%
    filter(supp == "VC") %>%
    mutate(id = rep(1:10, 3))
head(df)
#>    len supp dose id
#> 1  4.2   VC  0.5  1
#> 2 11.5   VC  0.5  2
#> 3  7.3   VC  0.5  3
#> 4  5.8   VC  0.5  4
#> 5  6.4   VC  0.5  5
#> 6 10.0   VC  0.5  6

# Friedman test effect size
#:::::::::::::::::::::::::::::::::::::::::
df %>% friedman_effsize(len ~ dose | id)
#> # A tibble: 1 × 5
#>   .y.       n effsize method    magnitude
#> * <chr> <int>   <dbl> <chr>     <ord>    
#> 1 len      10       1 Kendall W large    

```

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

### Bimodal

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_mode.html

Example demonstrating a bimodal case where there are two modes.

```R
x <- c(1:5, 6, 6, 7, 8, 9, 9, 10)
get_mode(x)
```

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

### Full summary statistics

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_summary_stats.html

Example of computing full summary statistics for a single variable in a dataset.

```R
data("ToothGrowth")
ToothGrowth %>% get_summary_stats(len)
#> # A tibble: 1 × 13
#>   variable     n   min   max median    q1    q3   iqr   mad  mean    sd    se
#>   <fct>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 len         60   4.2  33.9   19.2  13.1  25.3  12.2  9.04  18.8  7.65 0.988
#> # … with 1 more variable: ci <dbl>
```

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

### Get pairwise comparison labels

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_test_label.html

Example of getting a label for pairwise comparisons from a T test.

```R
get_pwc_label(ttest, type = "text")
```

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

### Get test label for Wilcoxon test

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_test_label.html

Example of getting a formatted string for a Wilcoxon test result.

```R
get_test_label(wilcox, detailed = TRUE, type = "text")
```

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

### Loading packages

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Load the rstatix and ggpubr packages.

```R
library(rstatix)
library(ggpubr)  # For easy data-visualization
```

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

### Get test label for T test

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_test_label.html

Example of getting formatted strings for a T test result, showing multiple outputs.

```R
ttest <- df %>% t_test(len ~ dose)
get_test_label(ttest, detailed = TRUE, type = "text")
```

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

### Comparisons against all (basemean)

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_comparisons.html

Illustrates how to compare each group level against all other groups (basemean).

```R
ToothGrowth %>%
  get_comparisons("dose", ref.group = "all")
#> $V1
#> [1] "all" "0.5"
#> 
#> $V2
#> [1] "all" "1"  
#> 
#> $V3
#> [1] "all" "2"  
```

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

### Non standard evaluation

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/df_get_var_names.html

Example demonstrating non-standard evaluation for selecting variables.

```R
ToothGrowth %>%
 df_get_var_names(dose, len)
#> [1] "dose" "len"
```

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

### Standard evaluation

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/df_get_var_names.html

Example demonstrating standard evaluation for selecting variables using the 'vars' argument.

```R
ToothGrowth %>%
 df_get_var_names(vars = c("len", "dose"))
#> [1] "len"  "dose"
```

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

### Documentation Website

Source: https://github.com/kassambara/rstatix/blob/master/CLAUDE.md

Commands for building and previewing the pkgdown documentation website locally.

```r
# Build pkgdown site (output in docs/)
pkgdown::build_site()

# Preview locally
pkgdown::preview_site()

# Build single reference page
pkgdown::build_reference()
```

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

### Examples

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/multinom_test.html

Examples of using the multinom_test function.

```r
# Data
tulip <- c(red = 81, yellow = 50, white = 27)

# Question 1: are the color equally common ?
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# this is a test of homogeneity
res <- multinom_test(tulip)
res
#> # A tibble: 1 × 2
#>             p p.signif
#> *       <dbl> <chr>   
#> 1 0.000000711 ****    

attr(res, "descriptives")
#> # A tibble: 3 × 3
#>   group  observed expected
#>   <chr>     <dbl>    <dbl>
#> 1 red          81     52.7
#> 2 yellow       50     52.7
#> 3 white        27     52.7

# Pairwise comparisons between groups
pairwise_binom_test(tulip, p.adjust.method = "bonferroni")
#> # A tibble: 3 × 9
#>   group1 group2     n estimate conf.low conf.high           p      p.adj p.adj…¹
#> * <chr>  <chr>  <dbl>    <dbl>    <dbl>     <dbl>       <dbl>      <dbl> <chr>  
#> 1 red    yellow   131    0.618    0.529     0.702 0.00851        2.55e-2 *
#> 2 red    white    108    0.75     0.657     0.828 0.000000191    5.72e-7 ****   
#> 3 yellow white     77    0.649    0.532     0.755 0.0117         3.5 e-2 *
#> # … with abbreviated variable name ¹​p.adj.signif


# Question 2: comparing observed to expected proportions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# this is a goodness-of-fit test
expected.p <- c(red = 0.5, yellow = 0.33, white = 0.17)
res <- multinom_test(tulip, expected.p)
res
#> # A tibble: 1 × 2
#>       p p.signif
#> * <dbl> <chr>   
#> 1 0.942 ns      
attr(res, "descriptives")
#> # A tibble: 3 × 3
#>   group  observed expected
#>   <chr>     <dbl>    <dbl>
#> 1 red          81     79  
#> 2 yellow       50     52.1
#> 3 white        27     26.9

# Pairwise comparisons against a given probabilities
pairwise_binom_test_against_p(tulip, expected.p)
#> # A tibble: 3 × 10
#>   group  observed expected     n estimate conf.low conf.high     p p.adj p.adj…¹
#> * <chr>     <dbl>    <dbl> <dbl>    <dbl>    <dbl>     <dbl> <dbl> <dbl> <chr>  
#> 1 red          81     79     158    0.513    0.432     0.593 0.811     1 ns     
#> 2 yellow       50     52.1   158    0.316    0.245     0.395 0.800     1 ns     
#> 3 white        27     26.9   158    0.171    0.116     0.239 1         1 ns     
#> # … with abbreviated variable name ¹​p.adj.signif

```

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

### All possible pairwise comparisons

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_comparisons.html

Demonstrates how to get all possible pairwise comparisons between groups using the get_comparisons function.

```R
ToothGrowth %>%
  get_comparisons("dose")
#> $V1
#> [1] "0.5" "1"  
#> 
#> $V2
#> [1] "0.5" "2"  
#> 
#> $V3
#> [1] "1" "2"
```

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

### Prepare demo data for t-test

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Prepare the ToothGrowth dataset for a t-test by converting dose to a factor.

```R
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df)
```

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

### Comparisons against reference groups

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_comparisons.html

Shows how to perform comparisons against a specified reference group.

```R
ToothGrowth %>%
  get_comparisons("dose", ref.group = "0.5")
#> $V1
#> [1] "0.5" "1"  
#> 
#> $V2
#> [1] "0.5" "2"  
```

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

### Example 2: Grouped Data

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/kruskal_effsize.html

An example showing how to use kruskal_effsize with grouped data.

```R
# Grouped data
df %>%
  group_by(supp) %>%
  kruskal_effsize(len ~ dose)
#> # A tibble: 2 × 6
#>   supp  .y.       n effsize method  magnitude
#> * <fct> <chr> <int>   <dbl> <chr>   <ord>    
#> 1 OJ    len      30   0.611 eta2[H] large    
#> 2 VC    len      30   0.855 eta2[H] large    
```

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

### Formula Examples

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/anova_test.html

Examples of supported formula structures for different ANOVA types.

```R
# Between-Ss ANOVA (independent measures ANOVA): y ~ b1*b2
# Within-Ss ANOVA (repeated measures ANOVA): y ~ w1*w2 + Error(id/(w1*w2))
# Mixed ANOVA: y ~ b1*b2*w1 + Error(id/w1)
```

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

### Sample n rows by group

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/sample_n_by.html

This example demonstrates how to sample n rows by group using the sample_n_by function.

```R
ToothGrowth %>% sample_n_by(dose, supp, size = 2)
#> # A tibble: 12 × 3
#>      len supp   dose
#>    <dbl> <fct> <dbl>
#>  1  14.5 OJ      0.5
#>  2   8.2 OJ      0.5
#>  3   5.2 VC      0.5
#>  4   7   VC      0.5
#>  5  27.3 OJ      1  
#>  6  25.8 OJ      1  
#>  7  22.5 VC      1  
#>  8  13.6 VC      1  
#>  9  24.8 OJ      2  
#> 10  30.9 OJ      2  
#> 11  23.3 VC      2  
#> 12  18.5 VC      2  
```

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

### Compute correlation matrix

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Compute correlation matrix.

```R
# Compute correlation matrix
cor.mat <- mydata %>% cor_mat()
cor.mat
```

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

### ANOVA Table

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Compute and display ANOVA table for linear models.

```R
#.my.model <- lm(yield ~ block + N*P*K, npk)
anova_test(.my.model)
```

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

### Use model as arguments

Source: https://github.com/kassambara/rstatix/blob/master/README.md

Performing ANOVA test using a linear model object as input.

```r
# Use model as arguments
#:::::::::::::::::::::::::::::::::::::::::
.my.model <- lm(yield ~ block + N*P*K, npk)
anova_test(.my.model)
#> ANOVA Table (type II tests)
#> 
#>   Effect DFn DFd      F     p p<.05   ges
#> 1  block   4  12  4.959 0.014     * 0.623
#> 2      N   1  12 12.259 0.004     * 0.505
#> 3      P   1  12  0.544 0.475       0.043
#> 4      K   1  12  6.166 0.029     * 0.339
#> 5    N:P   1  12  1.378 0.263       0.103
#> 6    N:K   1  12  2.146 0.169       0.152
#> 7    P:K   1  12  0.031 0.863       0.003
#> 8  N:P:K   0  12     NA    NA  <NA>    NA
```

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

### Show the significance levels

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Show the significance levels of the correlation matrix.

```R
# Show the significance levels
cor.mat %>% cor_get_pval()
```

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

### Summary statistics of some selected variables

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Calculate common summary statistics for selected variables in the iris dataset.

```R
# Summary statistics of some selected variables
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>%
  get_summary_stats(Sepal.Length, Sepal.Width, type = "common")
```

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

### T-test

Source: https://github.com/kassambara/rstatix/blob/master/README.md

Performing a t-test and visualizing the results with p-value annotations and a horizontal mean line.

```r
# T-test
stat.test <- df %>% t_test(len ~ dose, ref.group = "all")
stat.test
#> # A tibble: 3 × 10
#>   .y.   group1 group2    n1    n2 statistic    df         p   p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>     <dbl>   <dbl> <chr>       
#> 1 len   all    0.5       60    20     5.82   56.4   2.90e-7 8.70e-7 ****        
#> 2 len   all    1         60    20    -0.660  57.5   5.12e-1 5.12e-1 ns          
#> 3 len   all    2         60    20    -5.61   66.5   4.25e-7 8.70e-7 ****
# Box plot with horizontal mean line
ggboxplot(df, x = "dose", y = "len") +
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", 
    y.position = 35,
    remove.bracket = TRUE
    ) +
  geom_hline(yintercept = mean(df$len), linetype = 2)
```

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

### Example Usage

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/cor_mark_significant.html

An example demonstrating how to use cor_mark_significant on the mtcars dataset after calculating a correlation matrix.

```R
mtcars %>%
  select(mpg, disp, hp, drat, wt, qsec) %>%
  cor_mat() %>%
  cor_mark_significant()
#>   rowname       mpg      disp        hp      drat    wt qsec
#> 1     mpg                                                   
#> 2    disp -0.85****                                         
#> 3      hp -0.78****  0.79****                               
#> 4    drat  0.68**** -0.71****   -0.45**                     
#> 5      wt -0.87****  0.89****  0.66**** -0.71****           
#> 6    qsec     0.42*    -0.43* -0.71****     0.091 -0.17     

```

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

### Example usage of box_m

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/box_m.html

An example demonstrating how to use the box_m function with the iris dataset.

```R
data(iris)
box_m(iris[, -5], iris[, 5])
#> # A tibble: 1 × 4
#>   statistic  p.value parameter method                                           
#>       <dbl>    <dbl>     <dbl> <chr>                                            
#> 1      141. 3.35e-20        20 Box's M-test for Homogeneity of Covariance Matri…

```

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

### Grouped Data Example

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/dunn_test.html

An example demonstrating Dunn's test on grouped data from the ToothGrowth dataset.

```R
ToothGrowth %>%
  group_by(supp) %>%
  dunn_test(len ~ dose)
#> # A tibble: 6 × 10
#>   supp  .y.   group1 group2    n1    n2 statistic           p      p.adj p.adj…¹
#> * <fct> <chr> <chr>  <chr>  <int> <int>     <dbl>       <dbl>      <dbl> <chr>  
#> 1 OJ    len   0.5    1         10    10      2.83 0.00459     0.00918    **     
#> 2 OJ    len   0.5    2         10    10      4.22 0.0000245   0.0000734  ****   
#> 3 OJ    len   1      2         10    10      1.39 0.166       0.166      ns     
#> 4 VC    len   0.5    1         10    10      2.62 0.00887     0.0177     *      
#> 5 VC    len   0.5    2         10    10      5.01 0.000000557 0.00000167 ****   
#> 6 VC    len   1      2         10    10      2.39 0.0169      0.0177     *      
#> # … with abbreviated variable name ¹​p.adj.signif
```

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

### Grouped summary statistics

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Calculate mean and standard deviation for Sepal.Length, grouped by Species.

```R
# Grouped data
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>%
  group_by(Species) %>%
  get_summary_stats(Sepal.Length, type = "mean_sd")
```

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

### Load data

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/factorial_design.html

This snippet demonstrates how to load the ToothGrowth dataset and view its initial rows.

```R
data("ToothGrowth")
df <- ToothGrowth
head(df)
```

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

### Compute full summary statistics but show only selected statistics

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_summary_stats.html

Example of computing full summary statistics but filtering the output to show only mean, sd, median, and iqr.

```R
ToothGrowth %>%
    get_summary_stats(len, show = c("mean", "sd", "median", "iqr"))
#> # A tibble: 1 × 6
#>   variable     n  mean    sd median   iqr
#>   <fct>    <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1 len         60  18.8  7.65   19.2  12.2
```

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

### Select Columns in a Data Frame

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/df_select.html

This example demonstrates how to select columns from a data frame using the df_select function. It shows both standard evaluation using the 'vars' argument and non-standard evaluation by directly passing column names.

```R
df <- head(ToothGrowth)
df
#>    len supp dose
#> 1  4.2   VC  0.5
#> 2 11.5   VC  0.5
#> 3  7.3   VC  0.5
#> 4  5.8   VC  0.5
#> 5  6.4   VC  0.5
#> 6 10.0   VC  0.5

# Select column using standard evaluation
df %>% df_select(vars = c("dose", "len"))
#>   dose  len
#> 1  0.5  4.2
#> 2  0.5 11.5
#> 3  0.5  7.3
#> 4  0.5  5.8
#> 5  0.5  6.4
#> 6  0.5 10.0

# Select column using non-standard evaluation
df %>% df_select(dose, len)
#>   dose  len
#> 1  0.5  4.2
#> 2  0.5 11.5
#> 3  0.5  7.3
#> 4  0.5  5.8
#> 5  0.5  6.4
#> 6  0.5 10.0
```

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

### Display both SSn and SSd using detailed = TRUE

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/anova_summary.html

Show generalized eta squared using effect.size = "ges".

```r
# Display both SSn and SSd using detailed = TRUE
# Show generalized eta squared using effect.size = "ges"
anova_summary(res.anova, detailed = TRUE, effect.size = "ges")
#>      Effect      SSn     SSd DFn DFd      F        p p<.05   ges
#> 1      dose 2426.434 712.106   2  54 92.000 4.05e-18     * 0.773
#> 2      supp  205.350 712.106   1  54 15.572 2.31e-04     * 0.224
#> 3 dose:supp  108.319 712.106   2  54  4.107 2.20e-02     * 0.132
```

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

### Summary statistics for the whole data frame

Source: https://github.com/kassambara/rstatix/blob/master/docs/index.html

Calculate common summary statistics for all variables in the iris dataset.

```R
# Whole data frame
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
iris %>% get_summary_stats(type = "common")
```

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

### Vector

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/make_clean_names.html

Example of using make_clean_names on a character vector.

```R
make_clean_names(c("a and b", "a-and-b"))
#> [1] "a.and.b" "a.and.b"
make_clean_names(1:10)
#>  [1] "X1"  "X2"  "X3"  "X4"  "X5"  "X6"  "X7"  "X8"  "X9"  "X10"
```

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

### Data frame

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/make_clean_names.html

Example of using make_clean_names on a data frame.

```R
df <- data.frame(
`a and b` = 1:4,
`c and d` = 5:8,
 check.names = FALSE
)
df
#>   a and b c and d
#> 1       1       5
#> 2       2       6
#> 3       3       7
#> 4       4       8
make_clean_names(df)
#>   a.and.b c.and.d
#> 1       1       5
#> 2       2       6
#> 3       3       7
#> 4       4       8
```

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

### convert_as_factor, set_ref_level, reorder_levels

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/factors.html

Demonstrates the usage of convert_as_factor to convert variables to factors, set_ref_level to change the reference level, and reorder_levels to change the order of factor levels.

```R
df <- tibble(
      group = c("a", "a", "b", "b", "c", "c"),
      time = c("t1", "t2", "t1", "t2", "t1", "t2"),
      value = c(5, 6, 1, 3, 4, 5)
    )
df

```

```R
result <- df %>% convert_as_factor(group, time)
result

```

```R
levels(result$group)

```

```R
result <- result %>%
      set_ref_level("group", ref = "c")
levels(result$group)

```

```R
result <- result %>%
      reorder_levels("group", order = c("b", "c", "a"))
levels(result$group)

```

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

### Robust summary statistics

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/get_summary_stats.html

Example of computing robust summary statistics for a variable.

```R
ToothGrowth %>% get_summary_stats(len, type = "robust")
#> # A tibble: 1 × 4
#>   variable     n median   iqr
#>   <fct>    <dbl>  <dbl> <dbl>
#> 1 len         60   19.2  12.2
```

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

### R base function t.test() usage

Source: https://github.com/kassambara/rstatix/blob/master/docs/reference/doo.html

Shows how to use the base R 't.test' function with 'doo' when it's not pipe-friendly.

```R
comparisons <- ToothGrowth %>%
   group_by(dose) %>%
   doo(~t.test(len ~ supp, data =.))
```