### Install and Load ggpubr and rstatix

Source: https://github.com/kassambara/ggpubr/wiki/Adding-Adjusted-P-values-to-a-GGPlot

Installs the ggpubr and rstatix packages from GitHub and loads them into the R session. Ensure devtools is installed.

```r
# Install required package
devtools::install_github("kassambara/ggpubr")
devtools::install_github("kassambara/rstatix")

# Load required package
library(ggpubr)
library(rstatix)
```

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

### Install ggpubr from GitHub

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

Install the latest development version of ggpubr directly from GitHub using the devtools package. Ensure devtools is installed first.

```r
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
```

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

### Insert Reprex Example

Source: https://github.com/kassambara/ggpubr/blob/master/ISSUE_TEMPLATE.md

Placeholder for inserting a minimal reproducible example (reprex) when reporting issues or seeking help.

```r
# insert reprex here
```

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

### Install and Load ggpubr Package

Source: https://context7.com/kassambara/ggpubr/llms.txt

Install the ggpubr package from CRAN or GitHub and load it into the R session.

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

```r
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
```

```r
library(ggpubr)
```

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

### Install ggpubr from CRAN

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

Use this command to install the ggpubr package from the Comprehensive R Archive Network (CRAN).

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

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

### Get Session Information

Source: https://github.com/kassambara/ggpubr/blob/master/ISSUE_TEMPLATE.md

Command to retrieve detailed session information, including package versions, which is useful for debugging and reproducibility.

```r
# please paste here the result of
devtools::session_info()
```

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

### Combine ggscatter with Publication Theme

Source: https://context7.com/kassambara/ggpubr/llms.txt

Integrate ggpubr plots with publication themes by passing a theme object to the ggtheme argument. This example uses ggscatter with theme_pubr.

```r
ggscatter(mtcars, x = "wt", y = "mpg",
          color = "cyl",
          add = "reg.line",
          ggtheme = theme_pubr(border = TRUE,
                               legend = "bottom"))
```

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

### Get table dimensions with tab_ncol and tab_nrow

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Provides `tab_ncol()` and `tab_nrow()` functions to retrieve the number of columns and rows, respectively, from a `ggtexttable` object.

```R
tab_ncol(tt)
```

```R
tab_nrow(tt)
```

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

### Apply Classic Theme with Axis Lines

Source: https://context7.com/kassambara/ggpubr/llms.txt

Apply theme_classic2() to get a classic ggplot theme that includes visible axis lines, suitable for certain publication styles.

```r
p + theme_classic2()
```

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

### Load ggpubr and create sample data

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

Loads the ggpubr package and creates a sample data frame 'wdata' for demonstrating plotting functions. Includes setting a random seed for reproducibility.

```r
library(ggpubr)
#> Loading required package: ggplot2
# Create some data format
# :::::::::::::::::::::::::::::::::::::::::::::::::::
set.seed(1234)
wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)
```

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

### Load sample data for box plots

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

Loads the built-in 'ToothGrowth' dataset and displays the first few rows to prepare for creating box plots.

```r
# Load data
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)
```

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

### Create a density plot with mean lines and rug

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

Generates a density plot for the 'weight' variable, colored and filled by 'sex'. Adds mean lines and a rug plot. Requires the 'wdata' data frame.

```r
# Density plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom palette
ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))
```

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

### Prepare ToothGrowth Data

Source: https://github.com/kassambara/ggpubr/wiki/Adding-Adjusted-P-values-to-a-GGPlot

Prepares the ToothGrowth dataset by converting the 'dose' column to a factor. This is a common preprocessing step before analysis.

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

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

### Create Violin Plots with ggviolin

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generates violin plots showing kernel probability density with optional internal box plots or summary statistics. Requires the ToothGrowth dataset.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth

# Violin plot with internal box plot
ggviolin(df, x = "dose", y = "len",
         fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot",                     # Add boxplot inside
         add.params = list(fill = "white"))   # White fill for boxplot

# Violin with mean_se and jitter points
ggviolin(df, x = "dose", y = "len",
         color = "dose",
         palette = "npg",                     # Nature Publishing Group palette
         add = c("jitter", "mean_se"),        # Multiple additions
         error.plot = "crossbar",             # Crossbar for error
         trim = FALSE,                        # Don't trim violin tails
         width = 0.8)

# Add significance comparisons
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot", add.params = list(fill = "white")) +
  stat_compare_means(comparisons = my_comparisons, label = "p.signif") +
  stat_compare_means(label.y = 50)
```

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

### Create Density Plots with ggdensity

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generates density plots with optional mean/median lines and marginal rug marks. Uses generated sample data for weight and sex.

```r
library(ggpubr)

# Create sample data
set.seed(1234)
wdata <- data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)

# Density plot with mean lines and marginal rug
ggdensity(wdata, x = "weight",
          add = "mean",                        # Add vertical mean line
          rug = TRUE,                          # Add marginal rug
          color = "sex",                       # Color by group
          fill = "sex",                        # Fill by group
          palette = c("#00AFBB", "#E7B800"),   # Custom colors
          alpha = 0.5)                         # Transparency

# Faceted density plot
ggdensity(wdata, x = "weight",
          add = "median",
          rug = TRUE,
          fill = "sex",
          palette = "jco",
          facet.by = "sex",                    # Facet by sex
          panel.labs = list(sex = c("Female", "Male")))
```

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

### Create Histograms with Density Overlay using gghistogram

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generates histograms with optional density curves, mean/median lines, and marginal rug marks. Requires the ggpubr library and a data frame.

```r
library(ggpubr)

set.seed(1234)
wdata <- data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)

# Histogram with density overlay
gghistogram(wdata, x = "weight",
            add = "mean",                      # Add mean line
            rug = TRUE,                        # Marginal rug
            fill = "sex",
            palette = c("#00AFBB", "#E7B800"),
            add_density = TRUE,                # Overlay density curve
            bins = 30)                         # Number of bins
```

```r
# Stacked histogram
gghistogram(wdata, x = "weight",
            fill = "sex",
            palette = "Paired",
            position = "stack",                # Stacked bars
            alpha = 0.8,
            add = "median",
            add.params = list(linetype = "dashed"))
```

```r
# Weighted histogram
gghistogram(iris, x = "Sepal.Length",
            weight = "Petal.Length",           # Weight by another variable
            fill = "Species",
            palette = "jco")
```

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

### Configure ggpubr to handle non-standard column names

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Set the global option `ggpubr.parse.aes` to `FALSE` to prevent `create_aes()` from parsing its input, allowing ggpubr to handle non-standard column names (e.g., 'A-A') without parsing.

```R
options(ggpubr.parse.aes = FALSE)
```

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

### Create a histogram with mean lines and rug

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

Generates a histogram for the 'weight' variable, colored and filled by 'sex'. Adds mean lines and a rug plot. Requires the 'wdata' data frame.

```r
# Histogram plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom color palette
gghistogram(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))
```

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

### Create aesthetic mappings with create_aes

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

The `create_aes()` function simplifies the creation of aesthetic mappings from a list, making programming with ggplot2 easier. It now defaults to parsing input and supports spaces in column names.

```R
create_aes(list(x = "Sepal.Length", y = "Sepal.Width", color = "Species"))
```

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

### Apply Basic Publication Theme

Source: https://context7.com/kassambara/ggpubr/llms.txt

Apply the basic publication-ready theme to any ggplot object using theme_pubr(). This theme provides clean aesthetics suitable for publications.

```r
library(ggpubr)
library(ggplot2)

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(aes(color = factor(cyl)))

# Basic publication theme
p + theme_pubr()
```

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

### Add summary statistics arguments in ggsummarystats

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Introduces `digits` and `table.font.size` arguments to `ggsummarystats()` for customizing the decimal places and text size of the summary table.

```R
ggsummarystats(data, x = "dose", y = "len", color = "supp",
                 palette = c("#00AFBB", "#E7B800"),
                 digits = 3, table.font.size = 8)
```

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

### Load and prepare data for bar plots

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

Loads the built-in 'mtcars' dataset into the 'dfm' data frame, preparing it for bar plot creation.

```r
# Load data
data("mtcars")
dfm <- mtcars
```

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

### Apply Clean Publication Theme

Source: https://context7.com/kassambara/ggpubr/llms.txt

Use theme_pubclean() for a minimalist publication theme that removes most non-essential elements, focusing on axis lines.

```r
p + theme_pubclean()
```

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

### ggline: Simple Line Plot

Source: https://context7.com/kassambara/ggpubr/llms.txt

Create a basic line plot using ggline with specified x and y aesthetics.

```r
library(ggpubr)

# Simple line plot
df <- data.frame(dose = c("D0.5", "D1", "D2"),
                 len = c(4.2, 10, 29.5))
ggline(df, x = "dose", y = "len")
```

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

### Create Scatter Plots with ggscatter

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generates scatter plots with options for regression lines, confidence intervals, correlation coefficients, and group ellipses. Requires the ggpubr library and a data frame.

```r
library(ggpubr)
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)

# Scatter plot with regression line and correlation coefficient
ggscatter(df, x = "wt", y = "mpg",
          add = "reg.line",                    # Add regression line
          add.params = list(color = "blue", fill = "lightgray"),
          conf.int = TRUE,                     # Confidence interval
          cor.coef = TRUE,                     # Show correlation coefficient
          cor.coeff.args = list(method = "pearson",
                                label.x = 3,
                                label.sep = "\n"))
```

```r
# Scatter with groups, ellipses, and mean points
ggscatter(df, x = "wt", y = "mpg",
          color = "cyl",
          shape = "cyl",
          palette = c("#00AFBB", "#E7B800", "#FC4E07"),
          ellipse = TRUE,                      # Group ellipses
          ellipse.type = "confidence",         # Confidence ellipse
          ellipse.level = 0.95,
          mean.point = TRUE,                   # Show group mean
          star.plot = TRUE)                    # Star plot from centroid
```

```r
# Scatter with text labels
df$name <- rownames(df)
ggscatter(df, x = "wt", y = "mpg",
          color = "cyl",
          palette = "jco",
          label = "name",                      # Label points
          repel = TRUE,                        # Avoid label overlap
          label.select = list(top.up = 5, top.down = 5))  # Select labels
```

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

### Compute median and quartiles with median_q1q3

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

The `median_q1q3()` function computes the sample median along with the 25th and 75th percentiles. It is a wrapper around `median_hilow_()` with `ci = 0.5`.

```R
median_q1q3(data$len)
```

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

### Configure hide.ns argument in stat_pvalue_manual

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

The `hide.ns` argument in `stat_pvalue_manual()` can now accept logical values (TRUE/FALSE) or character values ('p' or 'p.adj') to filter non-significant results based on p-value or adjusted p-values.

```R
stat_pvalue_manual(label = "p.adj", hide.ns = "p.adj")
```

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

### Create Box Plots with ggboxplot

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generates box plots with jittered points, error bars, and statistical comparisons. Requires the ToothGrowth dataset.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth

# Basic box plot with jitter points colored by group
p <- ggboxplot(df, x = "dose", y = "len",
               color = "dose",
               palette = c("#00AFBB", "#E7B800", "#FC4E07"),
               add = "jitter",           # Add jittered points
               shape = "dose")           # Shape by group

# Add pairwise comparisons with p-values
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
p + stat_compare_means(comparisons = my_comparisons) +
    stat_compare_means(label.y = 50)     # Global ANOVA p-value

# Box plot with notch and error bars
ggboxplot(df, x = "dose", y = "len",
          fill = "dose",
          palette = "jco",              # Journal of Clinical Oncology palette
          notch = TRUE,                 # Notched box plot
          bxp.errorbar = TRUE,          # Add error bars
          bxp.errorbar.width = 0.2,
          add = "mean_sd")              # Add mean and standard deviation
```

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

### Prepare Data for Plotting

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

Converts the 'cyl' variable to a factor and adds a 'name' column from row names. Inspects the first few rows of the modified data frame.

```r
dfm$cyl <- as.factor(dfm$cyl)
dfm$name <- rownames(dfm)
head(dfm[, c("name", "wt", "mpg", "cyl")])
```

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

### Apply Publication Theme with Custom Legend and Labels

Source: https://context7.com/kassambara/ggpubr/llms.txt

Customize the publication theme by specifying legend position, base font size, and rotating x-axis labels using theme_pubr().

```r
p + theme_pubr(legend = "right",
               base_size = 14,                  # Base font size
               x.text.angle = 45)               # Rotate x-axis labels
```

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

### Add borders to ggtexttable elements

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Utilize `tab_add_border()`, `tbody_add_border()`, and `thead_add_border()` to add borders to the entire table, table body, or table head, respectively.

```R
tab_add_border()
```

```R
tbody_add_border()
```

```R
thead_add_border()
```

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

### Export Plots to PNG with Custom Resolution

Source: https://context7.com/kassambara/ggpubr/llms.txt

Export arranged plots to PNG format using ggexport. Customize filename, width, height, and resolution (dpi) for high-quality output.

```r
ggarrange(bxp, dp, ncol = 2) %>%
  ggexport(filename = "figure.png",
           width = 1600,                        # Width in pixels
           height = 800,                        # Height in pixels
           res = 300)                           # Resolution (dpi)
```

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

### Perform Statistical Comparisons with stat_compare_means

Source: https://context7.com/kassambara/ggpubr/llms.txt

Adds statistical comparisons (t-test, Wilcoxon, ANOVA, Kruskal-Wallis) to plots with bracket annotations. Requires ggpubr and data.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth

# Two group comparison
p <- ggboxplot(df, x = "supp", y = "len",
               color = "supp", palette = "npg",
               add = "jitter")
p + stat_compare_means()                       # Default: Wilcoxon test
p + stat_compare_means(method = "t.test")      # t-test
```

```r
# Paired samples
ggpaired(df, x = "supp", y = "len",
         color = "supp",
         line.color = "gray", line.size = 0.4,
         palette = "npg") +
  stat_compare_means(paired = TRUE)
```

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

### Compute median and confidence intervals with median_hilow_

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

The `median_hilow_()` function computes the sample median and specified outer quantiles for confidence limits. By default, it uses the 2.5th and 97.5th percentiles (ci = 0.95).

```R
median_hilow_(data$len, conf.level = 0.95)
```

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

### Create a box plot with jittered points and custom colors

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

Generates a box plot of 'len' by 'dose', with points jittered. Colors the boxes and points by 'dose' using a custom palette. Requires the 'df' data frame.

```r
# Box plots with jittered points
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
p <- ggboxplot(df, x = "dose", y = "len",
               color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
               add = "jitter", shape = "dose")
p
```

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

### Cleveland's Dot Plot with Colored Y-Text and Dashed Grids

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

Creates a Cleveland's dot plot with y-axis text colored by group using 'y.text.col = TRUE'. Applies a dashed grid theme using 'theme_cleveland()'.

```r
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           rotate = TRUE,                                # Rotate vertically
           dot.size = 2,                                 # Large dot size
           y.text.col = TRUE,                            # Color y text by groups
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+ 
  theme_cleveland()                                      # Add dashed grids
```

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

### Add titles and footnotes to ggtexttable

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Use `tab_add_title()` and `tab_add_footnote()` to incorporate titles and footnotes into `ggtexttable` objects.

```R
tab_add_title(title = "Table Title")
```

```R
tab_add_footnote(footnote = "Table footnote")
```

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

### Lollipop Chart Colored by Group

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

Generates a lollipop chart colored by a grouping variable. Requires the 'ggpubr' package and a data frame. Customize colors with 'palette' and sort with 'sorting'.

```r
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "ascending",                        # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           ggtheme = theme_pubr()                        # ggplot2 theme
           )
```

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

### Create a violin plot with embedded box plots and p-values

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

Generates a violin plot of 'len' by 'dose', filled by 'dose', with embedded box plots. Adds significance levels for pairwise comparisons and a global p-value. Requires the 'df' data frame and 'my_comparisons'.

```r

# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill color
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot", add.params = list(fill = "white"))+
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
  stat_compare_means(label.y = 50)                                      # Add global the p-value
```

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

### ggbarplot: Simple Bar Plot with Data Labels

Source: https://context7.com/kassambara/ggpubr/llms.txt

Create a simple bar plot using ggbarplot. Set 'label = TRUE' to display data values on the bars and 'lab.pos = "out"' to place them outside.

```r
library(ggpubr)

# Simple bar plot with data labels
df <- data.frame(dose = c("D0.5", "D1", "D2"),
                 len = c(4.2, 10, 29.5))
ggbarplot(df, x = "dose", y = "len",
          fill = "dose",
          palette = "jco",
          label = TRUE,                         # Show values as labels
          lab.pos = "out")                      # Labels outside bars
```

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

### Export Plots to PDF

Source: https://context7.com/kassambara/ggpubr/llms.txt

Export single or arranged plots to PDF format using ggexport. Specify filename, width, and height for the output file.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Create plots
bxp <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette = "jco")
dp <- ggdotplot(df, x = "dose", y = "len", color = "dose", palette = "jco")
dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco")

# Export single plot to PDF
ggexport(bxp, filename = "boxplot.pdf")

# Export arranged plots to PDF
ggarrange(bxp, dp, dens, ncol = 2) %>%
  ggexport(filename = "combined_plots.pdf",
           width = 10, height = 8)
```

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

### Arrange Plots with Custom Widths

Source: https://context7.com/kassambara/ggpubr/llms.txt

Use ggarrange to arrange multiple plots, specifying custom widths for each plot to control their relative sizes. Align plots horizontally and vertically.

```r
ggarrange(bxp, dens,
          ncol = 2,
          widths = c(2, 1),                     # First plot twice as wide
          align = "hv")                         # Align horizontally and vertically
```

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

### ggboxplot: Multiple Comparisons with Brackets

Source: https://context7.com/kassambara/ggpubr/llms.txt

Use stat_compare_means with the 'comparisons' argument for pairwise comparisons and 'label.y' to position brackets. A separate call to stat_compare_means can add a global p-value.

```r
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
ggboxplot(df, x = "dose", y = "len",
          color = "dose", palette = "npg") + 
  stat_compare_means(comparisons = my_comparisons,   # Pairwise comparisons
                     label.y = c(29, 35, 40)) +      # Bracket positions
  stat_compare_means(label.y = 45)                   # Global p-value
```

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

### Apply Publication Theme with Panel Border

Source: https://context7.com/kassambara/ggpubr/llms.txt

Enhance the publication theme by adding a panel border using theme_pubr(border = TRUE). This clearly delineates the plot area.

```r
p + theme_pubr(border = TRUE)
```

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

### Rotated Lollipop Chart with Labels and Customizations

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

Creates a rotated lollipop chart with descending sort order, larger dot size, and displays mpg values as labels. Adjust label font properties using 'font.label'.

```r
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = TRUE,                                # Rotate vertically
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9,
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )
```

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

### ggarrange: Arrangement with Publication Labels

Source: https://context7.com/kassambara/ggpubr/llms.txt

Add publication-style labels (A, B, C) to arranged plots using the 'labels' argument. Customize label appearance with 'font.label'.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Create individual plots
bxp <- ggboxplot(df, x = "dose", y = "len",
                 color = "dose", palette = "jco")
dp <- ggdotplot(df, x = "dose", y = "len",
                color = "dose", palette = "jco")
dens <- ggdensity(df, x = "len",
                  fill = "dose", palette = "jco")

# With labels for publication
ggarrange(bxp, dp, dens,
          labels = c("A", "B", "C"),            # Panel labels
          ncol = 2, nrow = 2,
          font.label = list(size = 14,
                           color = "black",
                           face = "bold"),
          common.legend = TRUE,
          legend = "right")
```

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

### Align x-axis tick labels with 90-degree rotation

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Ensures correct alignment of x-axis tick labels with ticks when text rotation is set to 90 degrees by internally setting `vjust = 0.5`.

```R
ggboxplot(my_data, x = "dose", y = "len",
            color = "supp", palette = c("#00AFBB", "#E7B800"),
            order = c("D0", "D20", "D40"),
            ylab = "Len", xlab = "Dose") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
```

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

### ggline: Multi-group Line Plot

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generate a multi-group line plot by mapping grouping variables to 'linetype', 'shape', and 'color' aesthetics. Use 'palette' to specify colors.

```r
library(ggpubr)

# Multi-group line plot
df2 <- data.frame(
  supp = rep(c("VC", "OJ"), each = 3),
  dose = rep(c("D0.5", "D1", "D2"), 2),
  len = c(6.8, 15, 33, 4.2, 10, 29.5)
)
ggline(df2, x = "dose", y = "len",
       linetype = "supp",
       shape = "supp",
       color = "supp",
       palette = c("#00AFBB", "#E7B800"))
```

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

### ggboxplot: Compare Against Reference Group

Source: https://context7.com/kassambara/ggpubr/llms.txt

Use stat_compare_means with 'method = "t.test"' and 'ref.group' to compare all groups against a specified reference group. 'anova' can be used for overall comparison.

```r
ggboxplot(df, x = "dose", y = "len",
          color = "dose", palette = "npg") + 
  stat_compare_means(method = "anova", label.y = 40) + 
  stat_compare_means(aes(label = after_stat(p.signif)),
                     method = "t.test",
                     ref.group = "0.5")              # Reference group
```

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

### Add crossout functionality to ggtexttable cells

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Introduces `tab_cell_crossout()` to cross out specific cells within a `ggtexttable`.

```R
tab_cell_crossout(text = "crossout", i = 1, j = 1)
```

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

### Perform t-test on ToothGrowth Data

Source: https://github.com/kassambara/ggpubr/wiki/Adding-Adjusted-P-values-to-a-GGPlot

Performs independent samples t-tests on the ToothGrowth dataset, grouped by 'supp'. It calculates the t-test for 'len' across different 'dose' levels.

```r
# Statistical tests
stat.test <- df %>%
  group_by(supp) %>%
  t_test(len ~ dose)
stat.test
```

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

### Create Box Plot with P-values

Source: https://github.com/kassambara/ggpubr/wiki/Adding-Adjusted-P-values-to-a-GGPlot

Generates a faceted box plot of 'len' by 'dose', separated by 'supp', and adds adjusted p-values from a statistical test. Requires the 'stat.test' object with y-positions.

```r
# Box plots with p-values
stat.test <- stat.test %>%
  add_y_position()
bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") 
bxp + stat_pvalue_manual(stat.test, label = "p.adj", tip.length = 0.01)
```

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

### Facet Plots with Custom Panel Labels

Source: https://context7.com/kassambara/ggpubr/llms.txt

Customize the labels for faceted plots using the panel.labs argument. This allows for more descriptive or user-friendly panel titles. Set short.panel.labs to FALSE for full labels.

```r
ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "npg",
          facet.by = "dose",
          panel.labs = list(dose = c("Low", "Medium", "High")),
          short.panel.labs = FALSE)
```

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

### Facet Plots by Two Variables

Source: https://context7.com/kassambara/ggpubr/llms.txt

Generate faceted plots based on two variables by providing a vector of variable names to the facet.by argument. This creates a grid of panels.

```r
data("mtcars")
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$gear <- as.factor(mtcars$gear)

ggboxplot(mtcars, x = "cyl", y = "mpg",
          fill = "cyl", palette = "jco",
          facet.by = c("gear"),
          add = "jitter")
```

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

### ggboxplot: P-value Formatting Styles

Source: https://context7.com/kassambara/ggpubr/llms.txt

Customize p-value display using 'p.format.style' (e.g., 'apa') and 'p.digits' for decimal places. Use 'label = "p.format.signif"' to show p-values with significance stars.

```r
ggboxplot(df, x = "dose", y = "len", color = "dose") + 
  stat_compare_means(comparisons = my_comparisons,
                     p.format.style = "apa",         # APA style
                     p.digits = 3,                   # 3 decimal places
                     label = "p.format.signif")      # Show p-value with stars
```

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

### Add vertical lines to ggtexttable columns

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Use `tab_add_vline()` to add vertical lines or separators to the right or left of specified columns in a `ggtexttable`.

```R
tab_add_vline(where = "right", col = 1)
```

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

### Facet Plots by Single Variable

Source: https://context7.com/kassambara/ggpubr/llms.txt

Create faceted plots by specifying a single variable in the facet.by argument of ggboxplot. This generates panels for each level of the specified variable.

```r
library(ggpubr)
data("ToothGrowth")

# Facet by single variable
ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "npg",
          add = "jitter",
          facet.by = "dose")                    # Facet by dose
```

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

### Export Plots to TIFF for Publication

Source: https://context7.com/kassambara/ggpubr/llms.txt

Export plots to TIFF format using ggexport, suitable for publications. Specify dimensions and resolution for print quality.

```r
ggexport(bxp, filename = "figure.tiff",
         width = 2400, height = 2400,
         res = 300)
```

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

### Use ColorBrewer Palettes

Source: https://context7.com/kassambara/ggpubr/llms.txt

Apply a ColorBrewer palette, such as 'RdBu' (Red-Blue diverging), to a ggboxplot. This is useful for creating plots with specific color gradients.

```r
ggboxplot(ToothGrowth, x = "dose", y = "len",
          fill = "dose",
          palette = "RdBu")                     # Red-Blue diverging
```

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

### Update ggdensity and gghistogram for ggplot2 v3.4.0

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Replaces dot-dot notation (`..density..`, `..count..`) with `after_stat(density)` and `after_stat(count)` in `ggdensity()` and `gghistogram()` for compatibility with ggplot2 3.4.0.

```R
ggdensity(data, x = "x", fill = "Stage", palette = c("#00AFBB", "#E7B800")) +
    scale_x_continuous(breaks = seq(0, 10, by = 2))
```

```R
gghistogram(data, x = "x", fill = "Stage", palette = c("#00AFBB", "#E7B800"))
```

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

### Create Ordered Bar Plot

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

Generates an ordered bar plot using ggbarplot, with bars filled by the 'cyl' grouping variable. Bars are sorted in descending order globally, not by group.

```r
ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
```

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

### ggline: Line Plot with Jitter Points

Source: https://context7.com/kassambara/ggpubr/llms.txt

Combine error bars with jittered points on a line plot using 'add = c("mean_se", "jitter")'. Use 'error.plot = "pointrange"' for point range error bars and 'position = position_dodge(0.2)' to prevent overlap.

```r
library(ggpubr)

# Line plot with jitter points
ggline(ToothGrowth, x = "dose", y = "len",
       color = "supp",
       add = c("mean_se", "jitter"),            # Error bars + jitter
       palette = c("#00AFBB", "#E7B800"),
       error.plot = "pointrange",               # Point range error bars
       position = position_dodge(0.2))          # Dodge overlapping points
```

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

### Calculate MPG Z-score and Group

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

Calculates the z-score for the 'mpg' variable and creates a grouping factor 'mpg_grp' based on whether the z-score is positive or negative. Inspects the resulting data frame.

```r
# Calculate the z-score of the mpg data
dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
                     levels = c("low", "high"))
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
```

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

### Use Custom Color Vectors

Source: https://context7.com/kassambara/ggpubr/llms.txt

Define and apply a custom vector of colors to a ggboxplot. This allows for precise control over plot aesthetics when predefined palettes are insufficient.

```r
ggboxplot(ToothGrowth, x = "dose", y = "len",
          fill = "dose",
          palette = c("#E64B35", "#4DBBD5", "#00A087"))
```

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

### Use Nature Publishing Group Color Palette

Source: https://context7.com/kassambara/ggpubr/llms.txt

Apply the 'npg' color palette from ggsci, mimicking Nature Publishing Group's color scheme, to a ggboxplot. This ensures consistent and professional aesthetics.

```r
library(ggpubr)
data("ToothGrowth")

# Using journal palettes
ggboxplot(ToothGrowth, x = "dose", y = "len",
          fill = "dose",
          palette = "npg")                      # Nature palette
```

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

### ggarrange: Basic Arrangement

Source: https://context7.com/kassambara/ggpubr/llms.txt

Combine multiple ggplot objects into a single figure using ggarrange. Specify the number of columns and rows for the layout.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Create individual plots
bxp <- ggboxplot(df, x = "dose", y = "len",
                 color = "dose", palette = "jco")
dp <- ggdotplot(df, x = "dose", y = "len",
                color = "dose", palette = "jco")
dens <- ggdensity(df, x = "len",
                  fill = "dose", palette = "jco")

# Basic arrangement
ggarrange(bxp, dp, dens, ncol = 2, nrow = 2)
```

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

### Add horizontal lines to ggtexttable rows

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Use `tab_add_hline()` to add horizontal lines or separators at the top or bottom of specified rows in a `ggtexttable`.

```R
tab_add_hline(where = "bottom", row = 1)
```

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

### ggarrange: Arrangement with Common Legend

Source: https://context7.com/kassambara/ggpubr/llms.txt

Share a common legend among plots in an arrangement by setting 'common.legend = TRUE'. Specify the legend position using the 'legend' argument.

```r
library(ggpubr)
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Create individual plots
bxp <- ggboxplot(df, x = "dose", y = "len",
                 color = "dose", palette = "jco")
dp <- ggdotplot(df, x = "dose", y = "len",
                color = "dose", palette = "jco")

# With common legend
ggarrange(bxp, dp,
          common.legend = TRUE,                 # Share legend
          legend = "bottom")                    # Legend position
```

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

### ggline: Line Plot with Mean and Error Bars

Source: https://context7.com/kassambara/ggpubr/llms.txt

Add mean and standard error to a line plot using 'add = "mean_se"'. This is useful for visualizing summary statistics from raw data.

```r
library(ggpubr)

# Line plot with mean and error bars from raw data
data("ToothGrowth")
ggline(ToothGrowth, x = "dose", y = "len",
       color = "supp",
       add = "mean_se",                         # Mean + standard error
       palette = c("#00AFBB", "#E7B800"))
```

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

### Format P-values for Display

Source: https://github.com/kassambara/ggpubr/wiki/Adding-Adjusted-P-values-to-a-GGPlot

Formats the adjusted p-values in the 'stat.test' object for cleaner display on plots. It removes leading zeros and sets accuracy.

```r
# Format-p-values
stat.test <- stat.test %>%
  p_format(p.adj, leading.zero = FALSE, accuracy = 0.0001)
bxp + stat_pvalue_manual(stat.test, label = "p.adj", tip.length = 0.01)
```

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

### ggboxplot with single numeric vector

Source: https://github.com/kassambara/ggpubr/blob/master/NEWS.md

Demonstrates the usage of ggboxplot with a single numeric vector. This functionality was introduced in ggpubr version 0.1.1.

```r
ggboxplot(iris$Sepal.Length)
```

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

### Create Ordered Bar Plot Sorted by Groups

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

Generates an ordered bar plot using ggbarplot, with bars filled by the 'cyl' grouping variable. Bars are sorted in ascending order within each group.

```r
ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in dscending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
```