ggpubr

https://r-packages.io/packages/ggpubr
Admin
ggpubr is an R package that provides a simple way to create publication-ready ggplot2...
Tokens:100,507
Snippets:802
Trust Score:6.5
Update:3 months ago
Show doc for...
Tokens:
Raw
### Install and use png package for background image

Source: https://r-packages.io/packages/ggpubr/background_image

Demonstrates how to install the png package, read a PNG image file using system.file, and then use it as a background for a ggplot2 plot with the background_image function. Requires the ggpubr and ggplot2 packages.

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

# Import the image
img.file <- system.file(file.path("images", "background-image.png"),
                       package = "ggpubr")
img <- png::readPNG(img.file)

# Plot with background image
ggplot(iris, aes(Species, Sepal.Length))+
 background_image(img)+
 geom_boxplot(aes(fill = Species), color = "white")+
 fill_palette("jco")
```

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

### Set Ticks by Step and Starting Point

Source: https://r-packages.io/packages/ggpubr/get_breaks

Shows how to set axis ticks using a specific step and starting point, along with axis limits, for a ggplot.

```r
# Set ticks according to a specific step, starting from 0
p + scale_x_continuous(
  breaks = get_breaks(by = 1.5, from = 0),
  limits =  c(0, 6)
) +
 scale_y_continuous(
  breaks = get_breaks(by = 10, from = 0),
  limits = c(0, 40)
  )
```

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

### Apply Blank Table Theme

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Creates a table with a blank theme, useful for starting with a clean slate or when only borders are needed.

```R
df <- head(iris)

# Blank theme
ggtexttable(df, rows = NULL, theme = ttheme("blank"))
```

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

### Data Preparation for Boxplot Example

Source: https://r-packages.io/packages/ggpubr/ggadjust_pvalue

Prepares the ToothGrowth dataset by converting the 'dose' column to a factor and adding a random grouping variable. This data is used for subsequent boxplot examples.

```R
# Data preparation
#:::::::::::::::::::::::::::::::::::::::
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
df$group <- factor(rep(c("grp1", "grp2"), 30))
head(df, 3)
```

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

### Add Mean and Standard Deviation to Violin Plot

Source: https://r-packages.io/packages/ggpubr/add_summary

This example demonstrates how to add mean and standard deviation summary statistics to a basic violin plot created using ggviolin. Ensure the 'ggpubr' package is installed and loaded.

```R
# Basic violin plot
p <- ggviolin(ToothGrowth, x = "dose", y = "len", add = "none")
p

# Add mean_sd
add_summary(p, "mean_sd")
```

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

### Data Preparation for ggsummarystats

Source: https://r-packages.io/packages/ggpubr/ggsummarystats

Prepares the ToothGrowth dataset by converting dose to a factor and adding a random QC column. This data is used in subsequent examples.

```R
# Data preparation
#::::::::::::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add random QC column
set.seed(123)
qc <- rep(c("pass", "fail"), 30)
df$qc <- as.factor(sample(qc, 60))
# Inspect the data
head(df)
```

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

### ggpubr yscale log2 example

Source: https://r-packages.io/packages/ggpubr/axis_scale

Demonstrates how to apply a log2 scale to the y-axis of a scatter plot using the yscale function with label formatting enabled.

```r
# Basic scatter plots
data(cars)
p <- ggscatter(cars, x = "speed", y = "dist")
p

# Set log scale
p + yscale("log2", .format = TRUE)
```

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

### Data Preparation for Friedman Test

Source: https://r-packages.io/packages/ggpubr/stat_friedman_test

Prepares the ToothGrowth dataset by converting 'dose' and 'id' to factors, and adding a random grouping variable 'group'. This setup is necessary for demonstrating grouped statistical tests.

```R
df <- ToothGrowth
df$dose <- as.factor(df$dose)
df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3)))
# Add a random grouping variable
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len+2, df$len)
df$len <- ifelse(df$group == "grp3", df$len+7, df$len)
head(df, 3)
```

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

### Export ggplot to PNG

Source: https://r-packages.io/packages/ggpubr/ggexport

Example demonstrating how to export a grid of plots to a PNG file using ggexport. Requires ggpubr and magrittr packages.

```r
require("magrittr")
# Load data
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Box plot
bxp <- ggboxplot(df, x = "dose", y = "len",
    color = "dose", palette = "jco")
# Dot plot
dp <- ggdotplot(df, x = "dose", y = "len",
    color = "dose", palette = "jco")
# Density plot
dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco")

# Export to png
ggarrange(bxp, dp, dens, ncol = 2) %>%
  ggexport(filename = "test.png")
```

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

### Export ggplot to PDF

Source: https://r-packages.io/packages/ggpubr/ggexport

Example demonstrating how to export a grid of plots to a PDF file using ggexport. Requires ggpubr and magrittr packages.

```r
require("magrittr")
# Load data
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)

# Box plot
bxp <- ggboxplot(df, x = "dose", y = "len",
    color = "dose", palette = "jco")
# Dot plot
dp <- ggdotplot(df, x = "dose", y = "len",
    color = "dose", palette = "jco")
# Density plot
dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco")

# Export to pdf
ggarrange(bxp, dp, dens, ncol = 2) %>%
  ggexport(filename = "test.pdf")
```

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

### Basic Plotting Example

Source: https://r-packages.io/packages/ggpubr/theme_pubr

A basic ggplot2 scatter plot using mtcars dataset, serving as a base for theme application.

```r
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
   geom_point(aes(color = gear))
```

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

### Generate Breaks for a Variable

Source: https://r-packages.io/packages/ggpubr/get_breaks

Example of generating a specified number of breaks for a numeric variable.

```r
# Generate 5 breaks for a variable x
get_breaks(n = 5)(x = 1:100)
```

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

### Customizing Facet Labels and Appearance

Source: https://r-packages.io/packages/ggpubr/facet

Shows how to customize facet labels and panel backgrounds using the facet function. This example disables short panel labels and sets a custom background fill and color for the panels.

```R
facet(p + theme_bw(), facet.by = "supp",
  short.panel.labs = FALSE,   # Allow long labels in panels
  panel.labs.background = list(fill = "steelblue", color = "steelblue")
)
```

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

### Pairwise mean comparison example

Source: https://r-packages.io/packages/ggpubr/compare_means

Automatically performs pairwise comparisons when the grouping variable has more than two levels. Uses the ToothGrowth dataset.

```r
# pairwise comparisons
#::::::::::::::::::::::::::::::::::::::::
# As dose contains more thant two levels ==>
# pairwise test is automatically performed.
compare_means(len ~ dose, df)
```

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

### Data Preparation for ANOVA Test

Source: https://r-packages.io/packages/ggpubr/stat_welch_anova_test

Prepares the ToothGrowth dataset by converting 'dose' to a factor, adding a random grouping variable, and adjusting 'len' based on the group. This setup is used for demonstrating ANOVA tests.

```R
df <- ToothGrowth
df$dose <- as.factor(df$dose)
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len+2, df$len)
df$len <- ifelse(df$group == "grp3", df$len+7, df$len)
head(df, 3)
```

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

### Calculate Descriptive Statistics by Groups

Source: https://r-packages.io/packages/ggpubr/desc_statby

This example demonstrates how to use desc_statby to compute descriptive statistics for the 'len' variable in the ToothGrowth dataset, grouped by 'dose' and 'supp'. The head of the resulting data frame is displayed.

```R
# Load data
data("ToothGrowth")

# Descriptive statistics
res <- desc_statby(ToothGrowth, measure.var = "len",
   grps = c("dose", "supp"))
head(res[, 1:10])
```

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

### Facet by Species

Source: https://r-packages.io/packages/ggpubr/stat_overlay_normal_density

This example demonstrates how to create faceted density plots for sepal length in the iris dataset, with a red dashed normal density curve overlaid on each facet. The `facet.by = "Species"` argument creates separate plots for each species.

```r
# Facet
ggdensity(iris, "Sepal.Length", facet.by = "Species") + 
 stat_overlay_normal_density(color = "red", linetype = "dashed")
```

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

### One-sample t-test example

Source: https://r-packages.io/packages/ggpubr/compare_means

Performs a one-sample t-test to compare the mean of a variable to a specified value (mu). Requires the ToothGrowth dataset.

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

# One-sample test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ 1, df, mu = 0)
```

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

### Overlay Normal Density Plot

Source: https://r-packages.io/packages/ggpubr/stat_overlay_normal_density

This example demonstrates how to overlay a normal density plot with a dashed red line to the density distribution of 'mpg' from the mtcars dataset. It also shows how to set x-axis limits.

```r
# Simple density plot
data("mtcars")
ggdensity(mtcars, x = "mpg", fill = "red") + 
  scale_x_continuous(limits = c(-1, 50)) + 
  stat_overlay_normal_density(color = "red", linetype = "dashed")
```

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

### Color by Groups

Source: https://r-packages.io/packages/ggpubr/stat_overlay_normal_density

This example shows how to overlay normal density plots for different species in the iris dataset, coloring each density curve according to the species. It uses `aes(color = Species)` to map the color aesthetic to the 'Species' variable.

```r
# Color by groups
data(iris)
ggdensity(iris, "Sepal.Length", color = "Species") + 
 stat_overlay_normal_density(aes(color = Species), linetype = "dashed")
```

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

### Data Preparation for Kruskal-Wallis Test

Source: https://r-packages.io/packages/ggpubr/stat_kruskal_test

Prepares the ToothGrowth dataset by converting 'dose' to a factor and adding a random grouping variable. This setup is necessary for demonstrating the Kruskal-Wallis test on grouped data.

```R
# Data preparation
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len+2, df$len)
df$len <- ifelse(df$group == "grp3", df$len+7, df$len)
head(df, 3)
```

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

### Add Median as Points to Density Plot

Source: https://r-packages.io/packages/ggpubr/stat_central_tendency

Shows how to display the median of 'Sepal.Length' as points on a density plot using the iris dataset. This example customizes the geom to 'point' and adjusts its size.

```r
# Use geom = "point" for central tendency
data(iris)
ggdensity(iris, "Sepal.Length", color = "Species") +
  stat_central_tendency(
     aes(color = Species), type = "median",
     geom = "point", size = 4
     )
```

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

### Style Multiple Cells in ggtexttable

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Applies font and background styles to a range of cells. This example styles column 3 from row 2 to the last row with italic font and white color, and a dark blue background.

```R
tab <- ggtexttable(df, rows = NULL, theme = ttheme("classic"))
tab %>%
 table_cell_bg(row = 2:tab_nrow(tab), column = 3, fill = "darkblue") %>%
 table_cell_font(row = 2:tab_nrow(tab), column = 3, face = "italic", color = "white")
```

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

### Simple Aes Creation with create_aes

Source: https://r-packages.io/packages/ggpubr/create_aes

Demonstrates the basic usage of create_aes to map 'Sepal.Length' to the x-axis and 'Petal.Length' to the y-axis.

```r
# Simple aes creation
create_aes(list(x = "Sepal.Length", y = "Petal.Length" ))
```

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

### Create a Scatter Plot with Transparent Theme

Source: https://r-packages.io/packages/ggpubr/theme_transparent

Demonstrates how to create a scatter plot using ggscatter and then apply the theme_transparent to it.

```r
# Create a scatter plot
sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
               color = "Species", palette = "jco",
               size = 3, alpha = 0.6)
sp

# Transparent theme
sp + theme_transparent()
```

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

### Create ggtexttable with Minimal Theme

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Use the 'minimal' theme for a clean and simple table appearance. The 'rows = NULL' argument indicates that default row names should not be displayed.

```R
ggtexttable(df, rows = NULL, theme = ttheme("minimal"))
```

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

### Visualize Summary Table

Source: https://r-packages.io/packages/ggpubr/ggsummarystats

Creates a summary table visualization using ggsummarytable, displaying 'n', 'median', and 'iqr' statistics against 'dose'. Uses a black and white theme.

```R
# Visualize summary table
ggsummarytable(
  summary.stats, x = "dose", y = c("n", "median", "iqr"),
  ggtheme = theme_bw()
)
```

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

### Get Number of Rows with tab_nrow

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Returns the number of rows in a ggtexttable object.

```r
tab_nrow(tab)
```

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

### Get Number of Columns with tab_ncol

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Returns the number of columns in a ggtexttable object.

```r
tab_ncol(tab)
```

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

### Parse Expression for Aes Mapping

Source: https://r-packages.io/packages/ggpubr/create_aes

Shows how to use create_aes with `parse = TRUE` to map log-transformed variables to the x and y axes.

```r
# Parse an expression
x <- "log2(Sepal.Length)"
y <- "log2(Petal.Length)"
create_aes(list(x = x, y = y ), parse = TRUE)
```

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

### Create ggtexttable with mBlue Theme

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Applies the 'mBlue' theme for a blue-toned table. Ensure the 'df' object is properly defined before use.

```R
ggtexttable(df, rows = NULL, theme = ttheme("mBlue"))
```

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

### Generate Breaks with a Specific Step

Source: https://r-packages.io/packages/ggpubr/get_breaks

Example of generating breaks with a defined step between ticks for a numeric variable.

```r
# Generate breaks using an increasing step
get_breaks(by = 10)(x = 1:100)
```

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

### Specify Multiple Brackets Manually

Source: https://r-packages.io/packages/ggpubr/geom_bracket

Demonstrates how to specify multiple brackets by providing vectors for xmin, xmax, y.position, and label.

```R
# Specify multiple brackets manually
ggboxplot(df, x = "dose", y = "len") +
  geom_bracket(
    xmin = c("0.5", "1"), xmax = c("1", "2"),
    y.position = c(30, 35), label = c("***", "**"),
    tip.length = 0.01
  )
```

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

### Apply Light Table Theme

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Generates a table using the 'light' theme, providing a clean and minimalist appearance.

```R
df <- head(iris)

# light theme
ggtexttable(df, rows = NULL, theme = ttheme("light"))
```

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

### Change Comparison Method (Independent Groups)

Source: https://r-packages.io/packages/ggpubr/stat_compare_means

Modifies the statistical test used for comparison in a ggboxplot. This example uses the t.test method.

```R
p + stat_compare_means(method = "t.test")
```

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

### Grouped mean comparison example

Source: https://r-packages.io/packages/ggpubr/compare_means

Compares means of a variable between groups, further stratified by another grouping variable. Uses the ToothGrowth dataset.

```r
# Compare supp levels after grouping the data by "dose"
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df, group.by = "dose")
```

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

### Create Sample Data for ECDF Plot

Source: https://r-packages.io/packages/ggpubr/ggecdf

Generates a sample data frame named 'wdata' with 'sex' and 'weight' columns, suitable for demonstrating ECDF plotting with ggpubr.

```r
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)
```

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

### Create Basic Density Plot with Mean Line and Rug

Source: https://r-packages.io/packages/ggpubr/ggdensity

Generates a basic density plot for the 'weight' variable, adding a mean line and a marginal rug plot. Requires the ggdensity function and sample data.

```R
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)

# Basic density plot
 # Add mean line and marginal rug
ggdensity(wdata, x = "weight", fill = "lightgray",
   add = "mean", rug = TRUE)
```

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

### Balloon Plot for Stretched Table

Source: https://r-packages.io/packages/ggpubr/ggballoonplot

Visualizes a stretched contingency table where size and fill are mapped to a 'Value' column. Guides for size can be hidden.

```R
# Streched contingency table
#:::::::::::::::::::::::::::::::::::::::::::::::::::::::::

# Create an Example Data Frame Containing Car x Color data
carnames <- c("bmw","renault","mercedes","seat")
carcolors <- c("red","white","silver","green")
datavals <- round(rnorm(16, mean=100, sd=60),1)
car_data <- data.frame(Car = rep(carnames,4),
                   Color = rep(carcolors, c(4,4,4,4) ),
                   Value=datavals )

car_data

ggballoonplot(car_data, x = "Car", y = "Color",
              size = "Value", fill = "Value") +   scale_fill_gradientn(colors = my_cols) +
  guides(size = FALSE)
```

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

### Create and Draw a Customized Text Grob

Source: https://r-packages.io/packages/ggpubr/text_grob

Demonstrates how to create a multi-line text grob with specified formatting (italic, steelblue color) and then draw it using as_ggplot. This is useful for adding formatted text annotations to plots.

```R
text <- paste("iris data set gives the measurements in cm",
             "of the variables sepal length and width",
             "and petal length and width, respectively,",
             "for 50 flowers from each of 3 species of iris.",
             "The species are Iris setosa, versicolor, and virginica.", sep = "\n")

# Create a text grob
tgrob <- text_grob(text, face = "italic", color = "steelblue")
# Draw the text
as_ggplot(tgrob)
```

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

### ANOVA and Kruskal-Wallis test examples

Source: https://r-packages.io/packages/ggpubr/compare_means

Performs one-way ANOVA or Kruskal-Wallis tests for comparing means across multiple groups. Uses the ToothGrowth dataset.

```r
# Anova and kruskal.test
#::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ dose, df, method = "anova")
compare_means(len ~ dose, df, method = "kruskal.test")
```

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

### Two-samples unpaired t-test example

Source: https://r-packages.io/packages/ggpubr/compare_means

Performs an unpaired t-test to compare the means of a variable between two groups defined by a factor. Uses the ToothGrowth dataset.

```r
# Two-samples unpaired test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df)
```

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

### Basic ggstripchart with Mean and SE

Source: https://r-packages.io/packages/ggpubr/ggstripchart

Generates a basic strip plot showing the length of tooth growth for different doses, with mean and standard error added. The shape of the points is varied by dose.

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

ggstripchart(df, x = "dose", y = "len",
   shape = "dose", size = 3,
   add = "mean_se")
```

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

### Basic Faceting with ggpubr

Source: https://r-packages.io/packages/ggpubr/facet

Demonstrates the basic usage of the facet function to create a multi-panel plot faceted by a single grouping variable.

```R
p <- ggboxplot(ToothGrowth, x = "dose", y = "len",
      color = "supp")
print(p)

facet(p, facet.by = "supp")
```

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

### Scatter Plot with Polygon Convex Hull

Source: https://r-packages.io/packages/ggpubr/stat_chull

Creates a scatter plot similar to the previous example but fills the convex hulls with color and transparency, using a polygon geometry.

```R
ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ 
 stat_chull(aes(color = cyl, fill = cyl), alpha = 0.1, geom = "polygon")
```

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

### Annotating an Arranged ggplot Figure

Source: https://r-packages.io/packages/ggpubr/annotate_figure

Example demonstrating how to use annotate_figure to add various text annotations and a figure label to an arranged ggplot. Requires ggpubr and ggplot2.

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

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

# Arrange and annotate
# ::::::::::::::::::::::::::::::::::::::::::::::::::
figure <- ggarrange(bxp, dp, dens, ncol = 2, nrow = 2)
annotate_figure(figure,
               top = text_grob("Visualizing Tooth Growth", color = "red", face = "bold", size = 14),
               bottom = text_grob("Data source: \n ToothGrowth data set", color = "blue",
                                  hjust = 1, x = 1, face = "italic", size = 10),
               left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),
               right = text_grob(bquote("Superscript: ("*kg~NH[3]~ha^-1~yr^-1*")"), rot = 90),
               fig.lab = "Figure 1", fig.lab.face = "bold"
)
```

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

### Display allowed global options

Source: https://r-packages.io/packages/ggpubr/ggpubr_options

Call the ggpubr_options() function without any arguments to display all available global options.

```r
ggpubr_options()
```

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

### Two-samples paired t-test example

Source: https://r-packages.io/packages/ggpubr/compare_means

Performs a paired t-test to compare the means of a variable between two related groups. Requires the 'paired = TRUE' argument. Uses the ToothGrowth dataset.

```r
# Two-samples paired test
#:::::::::::::::::::::::::::::::::::::::::
compare_means(len ~ supp, df, paired = TRUE)
```

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

### Basic ECDF Plot

Source: https://r-packages.io/packages/ggpubr/ggecdf

Generates a basic Empirical Cumulative Density Function (ECDF) plot using the 'weight' variable from the 'wdata' data frame. This serves as a starting point for further customization.

```r
ggecdf(wdata, x = "weight")
```

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

### Data Preparation for ANOVA Tests

Source: https://r-packages.io/packages/ggpubr/stat_anova_test

Prepares the ToothGrowth dataset by converting 'dose' to a factor, adding individual IDs, and creating a random grouping variable. It also modifies the 'len' variable based on the group for demonstration purposes.

```R
# Data preparation
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add individuals id
df$id <- rep(1:10, 6)
# Add a random grouping variable
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len+2, df$len)
df$len <- ifelse(df$group == "grp3", df$len+7, df$len)
head(df, 3)
```

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

### Transform arrangeGrob output to ggplot

Source: https://r-packages.io/packages/ggpubr/as_ggplot

Demonstrates how to use as_ggplot to convert a gtable object created by arrangeGrob into a ggplot object for further use. Requires the gridExtra package.

```r
library(gridExtra)
bxp <- ggboxplot(iris, x = "Species", y = "Sepal.Length")
vp <- ggviolin(iris, x = "Species", y = "Sepal.Length",
              add = "mean_sd")

gt <- arrangeGrob(bxp, vp, ncol = 2)

as_ggplot(gt)
```

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

### get_breaks

Source: https://r-packages.io/packages/ggpubr/get_breaks

Creates breaks for numeric axes to be used in scale_x_continuous() and scale_y_continuous(). It allows users to specify the number of breaks (n) or the step between breaks (by), and optionally define the starting (from) and ending (to) values for the breaks.

```APIDOC
## get_breaks

### Description
Creates breaks for numeric axes to be used in the functions `scale_x_continuous()` and `scale_y_continuous()`. It allows control over the number of x and y ticks by specifying the option `n`, or by controlling the axis breaks by specifying a step between ticks using `by`.

### Function Signature
```r
get_breaks(n = NULL, by = NULL, from = NULL, to = NULL)
```

### Arguments
* `n` (numeric): The desired number of breaks.
* `by` (numeric): The step size between breaks. For example, if `by = 5`, a tick mark is shown on every 5 units.
* `from` (numeric): The starting value for the breaks. Defaults to 0 for positive variables.
* `to` (numeric): The end value for the breaks, typically corresponding to the maximum limit of the axis.

### Returns
A break function that can be applied to ggplot scales.

### Examples
```r
# Generate 5 breaks for a variable x
get_breaks(n = 5)(x = 1:100)

# Generate breaks using an increasing step
get_breaks(by = 10)(x = 1:100)

# Combine with ggplot scale_xx functions
library(ggplot2)

# Create a basic plot
p <- ggscatter(mtcars, x = "wt", y = "mpg")
p

# Increase the number of ticks
p + scale_x_continuous(breaks = get_breaks(n = 10)) + scale_y_continuous(breaks = get_breaks(n = 10))

# Set ticks according to a specific step, starting from 0
p + scale_x_continuous(
  breaks = get_breaks(by = 1.5, from = 0),
  limits =  c(0, 6)
) + scale_y_continuous(
  breaks = get_breaks(by = 10, from = 0),
  limits = c(0, 40)
)
```
```

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

### Apply Classic Table Theme

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Renders a table with the 'classic' theme, which typically includes borders and standard formatting.

```R
df <- head(iris)

# classic theme
ggtexttable(df, rows = NULL, theme = ttheme("classic"))
```

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

### Custom Text Justification in Table Cells

Source: https://r-packages.io/packages/ggpubr/ggtexttable

Applies custom text justification to table cells using tbody_style. This example demonstrates left justification for the first column and right justification for the rest.

```R
df <- head(iris)

table_theme <- ttheme(
  tbody.style = tbody_style(
   hjust = as.vector(matrix(c(0, 1, 1, 1, 1), ncol = 5, nrow = nrow(df), byrow = TRUE)),
   x = as.vector(matrix(c(.1, .9, .9,.9, .9), ncol = 5, nrow = nrow(df), byrow = TRUE))
 )
)
ggtexttable(df, rows = NULL, theme = table_theme)
```

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

### ggstripchart with Multiple Groups (Shape and Color)

Source: https://r-packages.io/packages/ggpubr/ggstripchart

Generates a strip plot visualizing tooth growth, with both point shape and color differentiated by the 'supp' group. A specific color palette is applied.

```R
ggstripchart(df, "dose", "len", shape = "supp",
  color = "supp", palette = c("#00AFBB", "#E7B800"))
```

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

### Compute Statistical Tests and Add P-values

Source: https://r-packages.io/packages/ggpubr/geom_bracket

Shows how to compute statistical tests (e.g., t-test) using compare_means and then add the significance levels as labels to the brackets.

```R
# Compute statistical tests and add p-values
stat.test <- compare_means(len ~ dose, ToothGrowth, method = "t.test")
ggboxplot(df, x = "dose", y = "len") +
  geom_bracket(
    aes(xmin = group1, xmax = group2, label = signif(p, 2)),
    data = stat.test, y.position = 35
  )
```

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

### Add Median by Group to Density Plot

Source: https://r-packages.io/packages/ggpubr/stat_central_tendency

Illustrates adding a dashed line for the median of 'Sepal.Length', colored by 'Species', to a density plot of the iris dataset. This example shows how to map aesthetics to grouping variables.

```r
# Color by groups
data(iris)
ggdensity(iris, "Sepal.Length", color = "Species") +
  stat_central_tendency(aes(color = Species), type = "median", linetype = 2)
```

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

### Customize Point Shape Display with Theme

Source: https://r-packages.io/packages/ggpubr/show_point_shapes

Apply a minimal theme to the plot generated by show_point_shapes() for a cleaner appearance. This demonstrates basic customization of the plot output.

```r
show_point_shapes() +
 theme_minimal()
```

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

### Execute ggplot2 geom_point with geom_exec

Source: https://r-packages.io/packages/ggpubr/geom_exec

Demonstrates how to use geom_exec to call the geom_point function from ggplot2. This example maps 'mpg' to the x-axis, 'wt' to the y-axis, and 'cyl' to both size and color, using the mtcars dataset.

```R
ggplot() + geom_exec(geom_point, data = mtcars,
    x = "mpg", y = "wt", size = "cyl", color = "cyl")
```

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

### Customize Bracket Tip Length

Source: https://r-packages.io/packages/ggpubr/geom_bracket

Shows how to customize the tip length of the bracket, controlling the length of the small lines at each end of the bracket.

```R
ggboxplot(df, x = "dose", y = "len") +
  geom_bracket(
    xmin = "0.5", xmax = "1", y.position = 30,
    label = "t-test, p < 0.05", tip.length = c(0.2, 0.02)
  )
```

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

### Plot Confidence Ellipses

Source: https://r-packages.io/packages/ggpubr/stat_conf_ellipse

This snippet shows the default arguments for the stat_conf_ellipse function, which is used to plot confidence ellipses around data barycenters. It requires no specific setup beyond loading the ggpubr package.

```r
stat_conf_ellipse(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  level = 0.95,
  npoint = 100,
  bary = TRUE,
  ...
)
```

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

### Basic Pipe Operator Usage

Source: https://r-packages.io/packages/ggpubr/pipe

Demonstrates the fundamental syntax of the pipe operator, chaining a left-hand side (lhs) to a right-hand side (rhs).

```R
__
lhs %>% rhs

```

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

### Basic background_image function call

Source: https://r-packages.io/packages/ggpubr/background_image

This snippet shows the basic syntax for calling the background_image function with a raster image object.

```R
background_image(raster.img)
```

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

### Draw Group Mean Points with ggscatter

Source: https://r-packages.io/packages/ggpubr/stat_mean

This example demonstrates how to use stat_mean to draw group mean points on a scatter plot created with ggscatter. It requires loading the mtcars dataset and converting the 'cyl' column to a factor.

```r
# Load data
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)

# Scatter plot with ellipses and group mean points
ggscatter(df, x = "wt", y = "mpg",
   color = "cyl", shape = "cyl", ellipse = TRUE)+
 stat_mean(aes(color = cyl, shape = cyl), size = 4)
```

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

### Using Plotmath Expression for Labels

Source: https://r-packages.io/packages/ggpubr/geom_bracket

Illustrates using plotmath expressions for labels, allowing for mathematical notation like italicized p-values.

```R
#Using plotmath expression
ggboxplot(df, x = "dose", y = "len") +
 geom_bracket(
   xmin = "0.5", xmax = "1", y.position = 30,
   label = "list(~italic(p)<=0.001)", type = "expression",
   tip.length = c(0.2, 0.02)
 )
```

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

### Load and Prepare Data for ggtext

Source: https://r-packages.io/packages/ggpubr/ggtext

Loads the mtcars dataset and prepares it for use with the ggtext function by converting 'cyl' to a factor and adding a 'name' column for labels.

```r
# Load data
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)
df$name <- rownames(df)
head(df[, c("wt", "mpg", "cyl")], 3)
```

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

### ggdotchart with Multiple Groups and Segments

Source: https://r-packages.io/packages/ggpubr/ggdotchart

Creates a dot plot for data with multiple groups, adding segments to connect related points. This example demonstrates using different color schemes and themes, and adjusting point size and segment appearance.

```r
# Plot with multiple groups
# +++++++++++++++++++++
# Create some data
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))
print(df2)

ggdotchart(df2, x = "dose", y = "len",
          color = "supp", size = 3,
          add = "segment",
          add.params = list(color = "lightgray", size = 1.5),
          position = position_dodge(0.3),
          palette = "jco",
          ggtheme = theme_pubclean()
)

```
Tokens:
Raw